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OpenLink Community Blog
A Collection of blogs by OpenLink Staff
kidehen@openlinksw.com
kidehen@openlinksw.com
2009-11-23T13:01:34Z
Virtuoso Universal Server 05.12.3041
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RDF Geography With Virtuoso
http://www.openlinksw.com/blog/vdb/blog/?date=2009-11-11#1588
2009-11-11T17:17:27Z
2009-11-11T16:47:12.000007-05:00
<p>We have just added a geometry <a href="http://dbpedia.org/resource/Data" id="link-id0x22a7df78">data</a> type and corresponding <a href="http://dbpedia.org/resource/R-tree" id="link-id0x132dfbe0">R</a>-tree index to <a href="http://virtuoso.openlinksw.com" id="link-id0x1e41e1b0">Virtuoso</a>. This follows the general scheme of <a href="http://dbpedia.org/resource/SQL" id="link-id0x14568960">SQL</a>/MM, as is implemented by <a href="http://dbpedia.org/resource/PostGIS" id="link-id0x141653b0">PostGIS</a> and many others. We have all the engine-side stuff, including optimizer support for geometry cardinality sampling and good execution plans for combinations of spatial and other joins. We have however not yet implemented all the different geometry types and library function support for them, like shortest distance between two arbitrary shapes.</p> <p>The geometry support is for both SQL and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x11563f40">SPARQL</a>. On the SQL side, it works with the ISO/IEC 13249 SQL/MM API; with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x2209a3a8">RDF</a>, a geometry can occur as the object of a quad. If the object is a typed-literal of the <code>virtrdf:Geometry</code> type, it gets indexed in a geometry index over all geometries in quads; no special declarations are needed. After this, SQL MM predicates and functions can be used with SPARQL, like this:</p> <blockquote> <pre><code> PREFIX geo: <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x21051a00">http</a>://www.w3.org/2003/01/geo/wgs84_pos#> SELECT ?class COUNT (*) WHERE { ?m geo:geometry ?geo . ?m a ?class . FILTER ( <bif:st_intersects> ( ?geo, <bif:st_point> (0, 52), 100 ) ) } GROUP BY ?class ORDER BY DESC 2 </code> </pre></blockquote> <p>This returns the counts of objects of each class occurring within 100 km of (0, 52), a point near London.</p> <p>For any data set with <a href="http://dbpedia.org/resource/World_Geodetic_System" id="link-id0x20050048">WGS 84</a> <code>geo:long</code> and <code>geo:lat</code> values, a simple SQL function makes a point geometry for each such coordinate pair and adds it as the <code>geo:geometry</code> property of the subject with the long/lat. This then enables fast spatial access to arbitrary location data in RDF.</p> <p>Right now, we hardly see any geometries other than points in RDF data, even though there are some efforts for vocabularies for more complex entities. As these get adopted we will support them.</p> <p>For scalability, we tried the implementation with <a href="http://www.openstreetmap.org/" id="link-id0x100e0188">OpenStreetMap</a>'s 350 million or so points. The geometry implementation partitions well over a cluster, similarly to a full text index, i.e., every server has its slice of the geometries, partitioned by the geometry object's key, thus not by range of coordinates or such. Like this, the items are evenly spread even though the coordinate distribution is highly uneven.</p> <p>We can do spatial joins like â</p> <blockquote> <pre><code> SELECT ?s ( <sql:num_or_null> (?p) ) COUNT (*) WHERE { ?s <http://<a href="http://dbpedia.org/resource/DBpedia" id="link-id0x25465c68">dbpedia</a>.org/ontology/populationTotal> ?p . FILTER ( <sql:num_or_null> (?p) > 1000000 ) . ?s geo:geometry ?geo . FILTER ( <bif:st_intersects> ( ?pt, ?geo, 5 ) ) . ?xx geo:geometry ?pt } GROUP BY ?s ( <sql:num_or_null> (?p) ) ORDER BY DESC 3 LIMIT 20 </code> </pre></blockquote> <p>This takes the DBpedia subjects that have a population over 1 million and a geometry. We then count all the geometries within 5 km of the point location of the first geometry. With DBpedia (about 5 million points), <a href="http://www.geonames.org/" id="link-id0x230328d0">GeoNames</a> (7 million points), and OpenStreetMap (350 million points), we get the result:</p> <blockquote> <pre><code>http://dbpedia.org/resource/Munich 1356594 117280 http://dbpedia.org/resource/London 7355400 81486 http://dbpedia.org/resource/Davao_City 1363337 58640 http://dbpedia.org/resource/Belo_Horizonte 2412937 58640 http://dbpedia.org/resource/Chengde 3610000 58640 http://dbpedia.org/resource/Hamburg 1769117 51664 http://dbpedia.org/resource/San_Diego%2C_California 1266731 47685 http://dbpedia.org/resource/Bursa 1562828 47685 http://dbpedia.org/resource/Port-au-Prince 1082800 47685 http://dbpedia.org/resource/Oakland_County%2C_Michigan 1194156 45636 http://dbpedia.org/resource/Sana%27a 1747627 40923 http://dbpedia.org/resource/Milan 1303437 40923 http://dbpedia.org/resource/Campinas 1059420 40923 http://dbpedia.org/resource/Hohhot 2580000 40923 http://dbpedia.org/resource/Brussels 1031215 40923 http://dbpedia.org/resource/Bogra_District 2988567 40923 http://dbpedia.org/resource/Cort%C3%A9s_Department 1202510 40923 http://dbpedia.org/resource/Berlin 3416300 35668 http://dbpedia.org/resource/New_York_City 8274527 30810 http://dbpedia.org/resource/Los_Angeles%2C_California 3849378 25614<br /> 20 Rows. -- 1733 msec.<br /> Cluster 8 nodes, 1 s. 358 m/s 1596 KB/s 664% <a href="http://dbpedia.org/resource/Central_processing_unit" id="link-id0x139250c8">cpu</a> 2% read 16% clw threads 1r 0w 0i buffers 1124351 0 d 0 w 0 pfs </code></pre></blockquote> <p>This takes 1.7 seconds on a Virtuoso Cluster configured with 8 processes on a single dual-Xeon 5520 box, running at about 664% CPU with warm <a href="http://dbpedia.org/resource/Cache" id="link-id0x216d1070">cache</a>. Fair enough for a first crack, this can obviously be optimized further. Still, the geo part of the processing is already as good as instantaneous.</p> <p>We will shortly have the geography features installed on DBpedia and the other data sets we host. As these come online we will show more demo queries.</p> <p>For more about SQL/MM, you can look to a couple of PDFs:</p> <ul> <li> <a href="http://www.fer.hr/_download/repository/SQLMM_Spatial-_The_Standard_to_Manage_Spatial_Data_in_Relational_Database_Systems.pdf" id="link-id133775f0">SQL/MM Spatial: The Standard to Manage Spatial Data in Relational Database Systems</a> by Knut Stolze</li> <li> <a href="http://www.sigmod.org/record/issues/0112/standards.pdf" id="link-id1433c5e0">SQL Multimedia and Application Packages (SQL/MM)</a> by Jim Melton and Andrew Eisenberg</li> </ul>
RDF Geography With Virtuoso
http://www.openlinksw.com/weblog/oerling/?date=2009-11-11#1587
2009-11-11T17:17:27Z
2009-11-11T17:07:03.000002-05:00
<p>We have just added a geometry <a href="http://dbpedia.org/resource/Data" id="link-id0x1c4085f8">data</a> type and corresponding <a href="http://dbpedia.org/resource/R-tree" id="link-id0x1c2ea830">R</a>-tree index to <a href="http://virtuoso.openlinksw.com" id="link-id0x201556b0">Virtuoso</a>. This follows the general scheme of <a href="http://dbpedia.org/resource/SQL" id="link-id0x20152fc0">SQL</a>/MM, as is implemented by <a href="http://dbpedia.org/resource/PostGIS" id="link-id0x1c1a7610">PostGIS</a> and many others. We have all the engine-side stuff, including optimizer support for geometry cardinality sampling and good execution plans for combinations of spatial and other joins. We have however not yet implemented all the different geometry types and library function support for them, like shortest distance between two arbitrary shapes.</p> <p>The geometry support is for both SQL and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1c0fe6f8">SPARQL</a>. On the SQL side, it works with the ISO/IEC 13249 SQL/MM API; with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x20d637e8">RDF</a>, a geometry can occur as the object of a quad. If the object is a typed-literal of the <code>virtrdf:Geometry</code> type, it gets indexed in a geometry index over all geometries in quads; no special declarations are needed. After this, SQL MM predicates and functions can be used with SPARQL, like this:</p> <blockquote> <pre><code> PREFIX geo: <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1c4f4b50">http</a>://www.w3.org/2003/01/geo/wgs84_pos#> SELECT ?class COUNT (*) WHERE { ?m geo:geometry ?geo . ?m a ?class . FILTER ( <bif:st_intersects> ( ?geo, <bif:st_point> (0, 52), 100 ) ) } GROUP BY ?class ORDER BY DESC 2 </code> </pre></blockquote> <p>This returns the counts of objects of each class occurring within 100 km of (0, 52), a point near London.</p> <p>For any data set with <a href="http://dbpedia.org/resource/World_Geodetic_System" id="link-id0x1fa3a1d0">WGS 84</a> <code>geo:long</code> and <code>geo:lat</code> values, a simple SQL function makes a point geometry for each such coordinate pair and adds it as the <code>geo:geometry</code> property of the subject with the long/lat. This then enables fast spatial access to arbitrary location data in RDF.</p> <p>Right now, we hardly see any geometries other than points in RDF data, even though there are some efforts for vocabularies for more complex entities. As these get adopted we will support them.</p> <p>For scalability, we tried the implementation with <a href="http://www.openstreetmap.org/" id="link-id0x1c4207d0">OpenStreetMap</a>'s 350 million or so points. The geometry implementation partitions well over a cluster, similarly to a full text index, i.e., every server has its slice of the geometries, partitioned by the geometry object's key, thus not by range of coordinates or such. Like this, the items are evenly spread even though the coordinate distribution is highly uneven.</p> <p>We can do spatial joins like â</p> <blockquote> <pre><code> SELECT ?s ( <sql:num_or_null> (?p) ) COUNT (*) WHERE { ?s <http://<a href="http://dbpedia.org/resource/DBpedia" id="link-id0x10da9b08">dbpedia</a>.org/ontology/populationTotal> ?p . FILTER ( <sql:num_or_null> (?p) > 1000000 ) . ?s geo:geometry ?geo . FILTER ( <bif:st_intersects> ( ?pt, ?geo, 5 ) ) . ?xx geo:geometry ?pt } GROUP BY ?s ( <sql:num_or_null> (?p) ) ORDER BY DESC 3 LIMIT 20 </code> </pre></blockquote> <p>This takes the DBpedia subjects that have a population over 1 million and a geometry. We then count all the geometries within 5 km of the point location of the first geometry. With DBpedia (about 5 million points), <a href="http://www.geonames.org/" id="link-id0x21af78d0">GeoNames</a> (7 million points), and OpenStreetMap (350 million points), we get the result:</p> <blockquote> <pre><code>http://dbpedia.org/resource/Munich 1356594 117280 http://dbpedia.org/resource/London 7355400 81486 http://dbpedia.org/resource/Davao_City 1363337 58640 http://dbpedia.org/resource/Belo_Horizonte 2412937 58640 http://dbpedia.org/resource/Chengde 3610000 58640 http://dbpedia.org/resource/Hamburg 1769117 51664 http://dbpedia.org/resource/San_Diego%2C_California 1266731 47685 http://dbpedia.org/resource/Bursa 1562828 47685 http://dbpedia.org/resource/Port-au-Prince 1082800 47685 http://dbpedia.org/resource/Oakland_County%2C_Michigan 1194156 45636 http://dbpedia.org/resource/Sana%27a 1747627 40923 http://dbpedia.org/resource/Milan 1303437 40923 http://dbpedia.org/resource/Campinas 1059420 40923 http://dbpedia.org/resource/Hohhot 2580000 40923 http://dbpedia.org/resource/Brussels 1031215 40923 http://dbpedia.org/resource/Bogra_District 2988567 40923 http://dbpedia.org/resource/Cort%C3%A9s_Department 1202510 40923 http://dbpedia.org/resource/Berlin 3416300 35668 http://dbpedia.org/resource/New_York_City 8274527 30810 http://dbpedia.org/resource/Los_Angeles%2C_California 3849378 25614<br /> 20 Rows. -- 1733 msec.<br /> Cluster 8 nodes, 1 s. 358 m/s 1596 KB/s 664% <a href="http://dbpedia.org/resource/Central_processing_unit" id="link-id0x1c4406a0">cpu</a> 2% read 16% clw threads 1r 0w 0i buffers 1124351 0 d 0 w 0 pfs </code></pre></blockquote> <p>This takes 1.7 seconds on a Virtuoso Cluster configured with 8 processes on a single dual-Xeon 5520 box, running at about 664% CPU with warm <a href="http://dbpedia.org/resource/Cache" id="link-id0x1c420158">cache</a>. Fair enough for a first crack, this can obviously be optimized further. Still, the geo part of the processing is already as good as instantaneous.</p> <p>We will shortly have the geography features installed on DBpedia and the other data sets we host. As these come online we will show more demo queries.</p> <p>For more about SQL/MM, you can look to a couple of PDFs:</p> <ul> <li> <a href="http://www.fer.hr/_download/repository/SQLMM_Spatial-_The_Standard_to_Manage_Spatial_Data_in_Relational_Database_Systems.pdf" id="link-id133775f0">SQL/MM Spatial: The Standard to Manage Spatial Data in Relational Database Systems</a> by Knut Stolze</li> <li> <a href="http://www.sigmod.org/record/issues/0112/standards.pdf" id="link-id1433c5e0">SQL Multimedia and Application Packages (SQL/MM)</a> by Jim Melton and Andrew Eisenberg</li> </ul>
European Commission and the Data Overflow
http://www.openlinksw.com/blog/vdb/blog/?date=2009-10-27#1586
2009-10-27T18:29:51Z
2009-10-27T14:57:31-04:00
<p>The European Commission recently circulated a questionnaire to selected experts on what could be done for the future of big <a href="http://dbpedia.org/resource/Data" id="link-id0x43bae00">data</a>.</p> <p>Since the <a href="http://cordis.europa.eu/fp7/ict/content-knowledge/consultation_en.html" id="link-id1191c0f8">questionnaire is public</a>, I am publishing my answers below.</p> <ol type="1" start="1"> <li> <p> <b>Data and data types</b> </p> <ol type="a" start="1"> <li> <p> <b>What volumes of data are we dealing with today? What is the growth rate? Where can we expect to be in 2015? </b> </p> <p>Private data warehouses of corporations have more than doubled yearly for the past years; hundreds of TB is not exceptional. This will continue. The real shift is in structured data being published in increasing quantities with a minimum level of integrate-ability through use of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x5c7add0">RDF</a> and <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x5c7adb8">linked data</a> principles. There are rewards for use of standard vocabularies and identifiers through search engines recognizing such data. There is convergence around <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x5c7ada0">DBpedia</a> identifiers for real-world entities, e.g., most things that would be in the news.</p> <p>This also means that internal data processes and silos may be enriched with this content. There is consequent pressure for accommodating more diversity of data, with more flexible <a href="http://dbpedia.org/resource/Database_schema" id="link-id0x7d87a88">schema</a>.</p> <p>Ultimately, all content presently stored in RDBs and presented in public accessible dynamic web pages will end up on the web of linked data. Examples are product catalogs, price lists, event schedules and the like.</p> <p>The volume of the well known linked data sets is around 10 billion statements. With the above mentioned trends, growth by two or three orders of magnitude by 2015 seems reasonable, This is so especially if explicit semantics are extracted from the document web and if there is some further progress in the precision/recall of such extraction.</p> <p>Relevant sections of this mass of data are a potential addition to any present or future analytics application.</p> <p>Since arbitrary analytics over the database which is the web cannot be economically provided by a centralized search engine, a cloud model may be used for on-demand selection of relevant data and mixing it with private data. This will drive database innovation for the next years even more than the continued classical warehouse growth.</p> <p>Science data is another driver of the data overflow. For example, faster gene sequencing, more accurate measurements in high energy physics, better imaging, and remote sensing will produce large volumes of data. This data has highly regular structure but labeling this data with source and lineage calls for a flexible, schema-last, self-describing model, such as RDF and linked data. Data and <a href="http://dbpedia.org/resource/Metadata" id="link-id0x7a3fb40">metadata</a> should travel together but may have different data models.</p> <p>By and large, the metadata of science data will be another stream to the web of linked data, at least to the degree it is publicly accessible. Restricted circles can and likely will implement similar ideas.</p> </li> <li> <p> <b>What types of data can we deal with intelligently due to their inherent structure (geospatial, temporal, social or <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x5a48058">knowledge</a> graphs, 3D, sensor streams...)?</b> </p> <p>All the above types should be supported inside one DBMS so as to allow efficient querying combining conditions on all these types of data, e.g., <i>photos of sunsets taken last summer in Ibiza, with over 20 megapixels, by people I know.</i> </p> <p>Note that the test for being a sunset is an operation on the image blob that should be taken to the data; the images cannot be economically transferred.</p> <p>Interleaving of all database functions and types becomes increasingly important.</p> </li> </ol> </li> <li> <p> <b>Industries, communities</b> </p> <ol type="a" start="1"> <li> <p> <b>Who is producing these data and why? Could they do it better? How?</b> </p> <p>Right now, projects such as <a href="http://www.bio2rdf.org/" id="link-id0x2a29de8">Bio2RDF</a>, <a href="http://neurocommons.org/page/Main_Page" id="link-id0x7ddaed0">Neurocommons</a>, and DBPedia produce this data. The processes are in place and are reasonable. Incremental improvement is to be expected. These processes, along with the <a href="http://www.w3.org/DesignIssues/LinkedData.html" id="link-id0xbab4dfd0">linked data meme</a> generally taking off, drive demand for better <a href="http://dbpedia.org/resource/Natural_language_processing" id="link-id0x51f4e0">NLP</a> (<a href="http://dbpedia.org/resource/Natural_language_processing" id="link-id0x51a1b48">Natural Language Processing</a>), e.g., <a href="http://dbpedia.org/resource/Entity" id="link-id0x956680">entity</a> and relationship extraction, especially extraction that can produce instance data in given ontologies (e.g., events) using common identifiers (e.g., DBPedia URIs).</p> <p>Mapping of RDBs to RDF is possible, and a W3C working group is developing standards for this. The required baseline level has been reached; the rest is a matter of automating deployment. Within the enterprise, there are advantages to be gained for <a href="http://dbpedia.org/resource/Information" id="link-id0x7da9e80">information</a> integration; e.g., all entities in the CRM space can be integrated with all email and support tickets through giving everything a <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x71673f8">URI</a>. Some of this information may even be published on an <a href="http://dbpedia.org/resource/Extranet" id="link-id0x9aa6e0">extranet</a> for self-service and web-service interfaces. This has been done at small scales and the rest is a matter of spreading adoption and lowering the entry barrier. Incremental progress will take place, eventually resulting in qualitatively better integration along the value chain when adoption is sufficiently widespread.</p> </li> <li> <p> <b>Who is consuming these data and why? Could they do it better? How?</b> </p> <p>Consumers are various. The greatest need is for tools that summarize complex data and allow getting a bird's eye view of what data is in the first instance available. Consuming the data is hindered by the user not even necessarily knowing what data there is. This is somewhat new, as traditionally the business analyst did know the schema of the warehouse and was proficient with <a href="http://dbpedia.org/resource/SQL" id="link-id0x7f7b148">SQL</a> report generators and statistics packages.</p> <p>Where Web 2.0 made the <i>citizen journalist</i>, the web of linked data will make the <i>citizen analyst</i>. For this to happen, with benefits for individuals, enterprises, and governments alike, more work in user interfaces, knowledge discovery, and query composition will be useful. We may envision a "meshup economy" where data is plentiful, but the unit of value and exchange is the smart report that crystallizes actionable value from this ocean.</p> </li> <li> <p> <b>What industrial sectors in Europe could become more competitive if they became much better at managing data?</b> </p> <p>Any sector could benefit. Early adopters are seen in the biomedical field and to an extent in media. </p> </li> <li> <p> <b>Is the regulation landscape imposing constraints (privacy, compliance ...) that don't have today good tool support?</b> </p> <p>The regulation landscape drives database demand through data retention requirements and the like.</p> <p>With data integration, especially with privacy-sensitive data (as in medicine), there are issues of whether one dares put otherwise-shareable information online. Regulation is needed to protect individuals, but integration should still be possible for science.</p> <p>For this, we see a need for progress in applying policy-based approaches (e.g., row level security) to relatively schema-last data such as RDF. This is possible but needs some more work. Also, creating on-the-fly-anonymizing views on data might help.</p> <p>More research is needed for reconciling the need for security with the advantages of broad-based <i>ad hoc</i> integration. Ideally, data should be intelligent, aware of its origins and classification and cautious of whom it interacts with, all of this supported under the covers so that the user could ask anything but the data might refuse to answer or might restrict answers according to the user's profile. This is a tall order and implementing something of the sort is an open question.</p> </li> <li> <p> <b>What are the main practical problem identified for individuals and organizations? Please give examples and tell us about the main obstacles and barriers.</b> </p> <p>We have come across the following:</p> <ul> <li>Knowing that the data exists in the first place.</li> <li>If the data is found, figuring out the provenance, units and precision of measurement, identifiers, and the like.</li> <li>Compatible subject matter but incompatible representation: For example, one has numbers on a map with different maps for different points in time; another has time series of instrument data with geo-location for the instrument. It is only to be expected that the time interval between measurements is not the same. So there is need for a lot of one-off programming to align data.</li> </ul> <p>Other problems have to do with sheer volume, i.e., transfer of data even in a local area network is too slow, let alone over a wide area network. Computation needs to go to the data, and databases need to support this.</p> </li> </ol> </li> <li> <p> <b>Services, software stacks, protocols, standards, benchmarks</b> </p> <ol type="a" start="1"> <li> <p> <b>What combinations of components are needed to deal with these problems?</b> </p> <p>Recent times have seen a proliferation of special purpose databases. Since the data needs of the future are about combining data with maximum agility and minimum performance hit, there is need to gather the currently-separate functionality into an integrated system with sufficient flexibility. We see some of this in integration of map-reduce and scale-out databases. The former antagonists have become partners. Vertica, <a href="http://dbpedia.org/resource/Greenplum" id="link-id0x7a94e70">Greenplum</a>, and OpenLink <a href="http://virtuoso.openlinksw.com" id="link-id0x2ab2868">Virtuoso</a> are example of DBMS featuring work in this direction.</p> <p>Interoperability and at least <i>de facto</i> standards in ways of doing this will emerge.</p> </li> <li> <p> <b>What data exchange and processing mechanisms will be needed to work across platforms and programming languages?</b> </p> <p> <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x78a0458">HTTP</a>, <a href="http://dbpedia.org/resource/XML" id="link-id0x7ff2360">XML</a>, and RDF are in fact very verbose, yet these are the formats and models that have uptake. Thus, these will continue to be used even though one might think binary formats to be more efficient.</p> <p>There are of course science data set standards that are more compressed and these will continue, hopefully adding a practice of rich metadata in RDF.</p> <p>For internals of systems, MPI and TCP/IP with proprietary optimized wire formats will continue. Inter-system communication will likely continue to be HTTP, XML, and RDF as appropriate.</p> </li> <li> <p> <b>What data environments are today so wastefully messy that they would benefit from the development of standards?</b> </p> <p>RDF and <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x5643d70">OWL</a> are not messy but they could use some more performance; we are working on this. <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x152ab18">SPARQL</a> is finally acquiring the capabilities of a serious query language, so things are slowly coming together.</p> <p>Community process for developing application domain specific vocabularies works quite well, even though one could argue it is <i>ad hoc</i> and not up to what a modeling purist might wish.</p> <p>Top-down imposition of standards has a mixed history, with long and expensive development and sometimes no or little uptake, consider some WS* standards for example.</p> </li> <li> <p> <b>What kind of performance is expected or required of these systems? Who will measure it reliably? How?</b> </p> <p>Relational databases have a history of substantial investment in <a href="http://dbpedia.org/resource/Program_optimization" id="link-id0xecc100">optimization</a> and some of them are very good for what they do, e.g., the newer generation of analytics databases.</p> <p>The very large schema-last, no-SQL, sometimes eventually consistent key-value stores have a somewhat shorter history but do fill a real need.</p> <p>These trends will merge: Extreme scale, schema-last, complex queries, even more complex inference, custom code for in-database machine learning and other bulk processing.</p> <p>We find RDF augmented with some binary types at this crossroads. This point of the design space will have to provide performance roughly on the level of today's best relational solution for workloads that fit the relational model. The added cost of schema-last and inference must come down. We are working on this. Research work such as carried out with <a href="http://dbpedia.org/resource/MonetDB" id="link-id0x7ae2890">MonetDB</a> gives clues as to how these aims can be reached.</p> <p>The separation of query language and inference is artificial. After the concepts are mature, these functions will merge and execute close to the data; there are clear evolutionary pressures in this direction.</p> <p>Benchmarks are key. Some gain can be had even from repurposing standard relational benchmarks like <a href="http://www.tpc.org/" id="link-id0x71eb528">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id0x5e16a40">H</a>. But the TPC-H rules do not allow official reporting of such.</p> <p>Development of benchmarks for RDF, complex queries, and inference is needed. A bold challenge to the community, it should be rooted in real-life integration needs and involve high heterogeneity. A key-value store benchmark might also be conceived. A transaction benchmark like TPC-<a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0x78562d0">C</a> might be the basis, maybe augmented with massive user-generated content like reviews and blogs.</p> <p>If benchmarks exist and are not too easy nor inaccessibly difficult nor too expensive to run â think of the high end TPC-C results â then TPC-style rules and processes would be quite adequate. The threshold to publish should be lowered: Everybody runs the TPC workloads internally but few publish.</p> <p>Some EC initiative for benchmarking could make sense, similar to the TREC initiative of the US government. Industry should be consulted for the specific content; possibly the answers to the present questionnaire can provide an approximate direction.</p> <p>Benchmarks should be run by software vendors on their own systems, tuned by themselves. But there should be a process of disclosure and auditing; the TPC rules give an example. Compliance should not be too expensive or time consuming. Some community development for automating these things would be a worthwhile target for EC funding.</p> </li> </ol> </li> <li> <p> <b>Usability and training</b> </p> <ol type="a" start="1"> <li> <p> <b>How difficult will it be for a developer of average competence to deploy components whose core is based on rather deep computer science? Do we all need to understand Monads and Continuations? What can be done to make it ever easier?</b> </p> <p>In the database world, huge advances in technology have taken place behind a relatively simple and stable interface: SQL. For the linked data <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x7761e50">web</a>, the same will take place behind SPARQL.</p> <p>Beyond these, for example, programming with MPI with good utilization of a cluster platform for an arbitrary algorithm, is quite difficult. The casual amateur is hereby warned.</p> <p>There is no single solution. For automatic parallelization, since explicit, programmatic parallelization of things with MPI for example is very unscalable in terms of required skill, we should favor declarative and/or functional approaches.</p> <p>Developing a debugger and explanation engine for rule-based and description-logics-based inference would be an idea.</p> <p>For procedural workloads, things like Erlang may be good in cases and are not overly difficult in principle, especially if there are good debugging facilities.</p> <p>For shipping functions in a cluster or cloud, the <a href="http://www.eecs.berkeley.edu/Research/Projects/Data/105733.html" id="link-id0x5494b0">BOOM</a> (<a href="http://www.eecs.berkeley.edu/Research/Projects/Data/105733.html" id="link-id0x7f1f148">Berkeley Orders Of Magnitude</a>) approach or logic programming with explicit specification of compute location seem promising, surely more flexible than map-reduce. The question is whether a <a href="http://dbpedia.org/resource/PHP" id="link-id0x5c758c8">PHP</a> developer can be made to do logic programming.</p> <p>This bridge will be crossed only with actual need and even then reluctantly. We may look at the Web 2.0 practice of sharding <a href="http://dbpedia.org/resource/MySQL" id="link-id0x432f868">MySQL</a>, inconvenient as this may be, for an example. There is inertia and thus re-architecting is a constant process that is generally in reaction to facts, <i>post hoc</i>, often a point solution. One could argue that planning ahead would be smarter but by and large the world does not work so.</p> <p>One part of the answer is an infinitely-scalable SQL database that expands and shrinks in the clouds, with the usual semantics, maybe optional eventual consistency and built-in map reduce. If such a thing is inexpensive enough and syntax-level-compatible with present installed base, many developers do not have to learn very much more.</p> <p>This is maybe good for the bread-and-butter IT, but European competitiveness should not rest on this. Therefore we wish to go for bold new application types for which the client-server database application is not the model. Data-centric languages like BOOM, if they can be made very efficient and have good debugging support, are attractive there. These do require more intellectual investment but that is not a problem since the less-inquisitive part of the developer community is served by the first part of the answer.</p> </li> <li> <p> <b>How is a developer of average skills going to learn about these new advanced tools? How can we plan for excellent documentation and training, community mentoring, exchange of good practices, etc... across all EU countries?</b> </p> <p>For the most part, developers do not learn things for the sake of learning. When they have learned something and it is adequate, they stay with it for the most part and are even reluctant to engage in cross-camps interaction. The research world is often similarly insular. A new inflection in the application landscape is needed to drive learning. This inflection is provided by the <a href="https://wiki.mozilla.org/Labs/Ubiquity" id="link-id0x7f051c8">ubiquity</a> of mobile devices, sensor data, explicit semantics, NLP concept extraction, web of linked data, and such factors.</p> <p>RDFa is a good example of a new technique piggybacking on something everybody uses, namely HTML. These new things should, within possibility, be deployed in the usual technology stack, <a href="http://en.wikipedia.org/wiki/LAMP_%28software_bundle%29" id="link-id0x77151e0">LAMP</a> or Java. Of course these do not have to be LAMP or Java or HTML or HTTP themselves but they must manifest through these.</p> <p>A lot of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x7940cd0">semantic web</a> potential can be realized within the client-server database application model, thus no fundamental re-architecting, just some new data types and queries.</p> <p>For data- or processing-intensive tasks, an on-demand hookup to cloud-based servers with Erlang and/or BOOM for programming model would be easy enough to learn and utilize.</p> <p>The question is one of providing challenges. Addressing actual challenges with these techniques will lead to maturity, documentation, examples, and training. With virtual, Europe-wide distributed teams a reality in many places, Europe-wide dissemination is no longer insurmountable.</p> <p>As the data overflow proceeds, its victims will multiply and create demand for solutions. The EC could here encourage research project use cases gaining an extended life past the end of research projects, possibly being maintained and multiplied and spun off.</p> <p>If such things could be mutated into self-sustaining service businesses with pay-per-use revenue, say through a cloud SaaS business model, still primarily leveraging an open source technology stack, we could have self-propagating and self-supporting models for exploiting advanced IT. This would create interest, and interest would drive training and dissemination.</p> <p>The problem is creating the pull.</p> </li> </ol> </li> <li> <p> <b>Challenges</b> </p> <ol type="a" start="1"> <li> <p> <b>What should be, in this domain, the equivalent of the Netflix challenge, Ansari X Prize, <a href="http://dbpedia.org/resource/Google" id="link-id0x7e72f40">Google</a> Lunar X Prize, etc. ... ?</b> </p> <p>The EC itself no doubt suffers from data overflow in one function or another. Unless security/secrecy prohibits, simply publishing a large data set and a description of what operations should be done on it would be a start. The more real the data, the better â reality is consistently more complex and surprising than imagination. Since many interesting problems touch on fraud detection and law enforcement, there may be some security obstacles for using these application domains as subject matters of open challenges.</p> <p>Once there is a good benchmark, as discussed above, there can be some prize money allocated for the winners, specially if the race is tight.</p> <p>The Semantic Web Challenge and the Billion Triples Challenge exist and are useful as such, but do not seem to have any huge impact.</p> <p>The incentives should be sufficient and part of the expenses arising from running for such challenges could be funded. Otherwise investing in existing business development will be more interesting to industry. Some industry participation seems necessary; we would wish academia and industry to work closer. Also, having industry supply the baseline guarantees that academia actually does further the state of the art. This is not always certain.</p> <p>If challenges are based on actual problems, whether of the EC, its member governments, or private entities, and winning the challenge may lead to a contract for supplying an actual solution, these will naturally become more interesting for consortia involving integrators, specialist software vendors, and academia. Such a model would build actual capacity to deploy leading edge technologies in production, which is sorely needed.</p> </li> <li> <p> <b>What should one do to set up such a challenge, administer, and monitor it?</b> </p> <p>The EC should probably circulate a call for actual problem scenarios involving big data. If the matter of the overflow is as dire as represented, cases should be easy to find. A few should be selected and then anonymized if needed.</p> <p>The party with the use case would benefit by having hopefully the best work on it. The contestants would benefit from having real world needs guide R&D. The EC would not have to do very much, except possibly use some money for funding the best proposals. The winner would possibly get a large account and related sales and service income. The contestants would have to be teams possibly involving many organizations; for example, development and first-line services and support could come from different companies along a systems integrator model such as is widely used in the US.</p> <p>There may be a good benchmark at the time, possibly resulting from FP7 itself. In such a case, the EC could offer a prize for winners. Details would have to be worked out case by case. Such a challenge could be repeated a few times, as benchmark-driven progress in databases or TREC for example have taken some years to reach a point of slowdown in progress.</p> <p>Administrating such an activity should not be prohibitive, as most of the expertise can be found with the stakeholders.</p> </li> </ol> </li> </ol>
European Commission and the Data Overflow
http://www.openlinksw.com/weblog/oerling/?date=2009-10-27#1585
2009-10-27T18:29:51Z
2009-10-27T14:57:28.000002-04:00
<p>The European Commission recently circulated a questionnaire to selected experts on what could be done for the future of big <a href="http://dbpedia.org/resource/Data" id="link-id0x79cfe58">data</a>.</p> <p>Since the <a href="http://cordis.europa.eu/fp7/ict/content-knowledge/consultation_en.html" id="link-id1191c0f8">questionnaire is public</a>, I am publishing my answers below.</p> <ol type="1" start="1"> <li> <p> <b>Data and data types</b> </p> <ol type="a" start="1"> <li> <p> <b>What volumes of data are we dealing with today? What is the growth rate? Where can we expect to be in 2015? </b> </p> <p>Private data warehouses of corporations have more than doubled yearly for the past years; hundreds of TB is not exceptional. This will continue. The real shift is in structured data being published in increasing quantities with a minimum level of integrate-ability through use of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x7d7e7a0">RDF</a> and <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x7f2a788">linked data</a> principles. There are rewards for use of standard vocabularies and identifiers through search engines recognizing such data. There is convergence around <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x7dfbca8">DBpedia</a> identifiers for real-world entities, e.g., most things that would be in the news.</p> <p>This also means that internal data processes and silos may be enriched with this content. There is consequent pressure for accommodating more diversity of data, with more flexible <a href="http://dbpedia.org/resource/Database_schema" id="link-id0x7babaf8">schema</a>.</p> <p>Ultimately, all content presently stored in RDBs and presented in public accessible dynamic web pages will end up on the web of linked data. Examples are product catalogs, price lists, event schedules and the like.</p> <p>The volume of the well known linked data sets is around 10 billion statements. With the above mentioned trends, growth by two or three orders of magnitude by 2015 seems reasonable, This is so especially if explicit semantics are extracted from the document web and if there is some further progress in the precision/recall of such extraction.</p> <p>Relevant sections of this mass of data are a potential addition to any present or future analytics application.</p> <p>Since arbitrary analytics over the database which is the web cannot be economically provided by a centralized search engine, a cloud model may be used for on-demand selection of relevant data and mixing it with private data. This will drive database innovation for the next years even more than the continued classical warehouse growth.</p> <p>Science data is another driver of the data overflow. For example, faster gene sequencing, more accurate measurements in high energy physics, better imaging, and remote sensing will produce large volumes of data. This data has highly regular structure but labeling this data with source and lineage calls for a flexible, schema-last, self-describing model, such as RDF and linked data. Data and <a href="http://dbpedia.org/resource/Metadata" id="link-id0x96ce60">metadata</a> should travel together but may have different data models.</p> <p>By and large, the metadata of science data will be another stream to the web of linked data, at least to the degree it is publicly accessible. Restricted circles can and likely will implement similar ideas.</p> </li> <li> <p> <b>What types of data can we deal with intelligently due to their inherent structure (geospatial, temporal, social or <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x7e8e248">knowledge</a> graphs, 3D, sensor streams...)?</b> </p> <p>All the above types should be supported inside one DBMS so as to allow efficient querying combining conditions on all these types of data, e.g., <i>photos of sunsets taken last summer in Ibiza, with over 20 megapixels, by people I know.</i> </p> <p>Note that the test for being a sunset is an operation on the image blob that should be taken to the data; the images cannot be economically transferred.</p> <p>Interleaving of all database functions and types becomes increasingly important.</p> </li> </ol> </li> <li> <p> <b>Industries, communities</b> </p> <ol type="a" start="1"> <li> <p> <b>Who is producing these data and why? Could they do it better? How?</b> </p> <p>Right now, projects such as <a href="http://www.bio2rdf.org/" id="link-id0x43bd098">Bio2RDF</a>, <a href="http://neurocommons.org/page/Main_Page" id="link-id0x5c074b0">Neurocommons</a>, and DBPedia produce this data. The processes are in place and are reasonable. Incremental improvement is to be expected. These processes, along with the <a href="http://www.w3.org/DesignIssues/LinkedData.html" id="link-id0x72131d0">linked data meme</a> generally taking off, drive demand for better <a href="http://dbpedia.org/resource/Natural_language_processing" id="link-id0x71e7798">NLP</a> (<a href="http://dbpedia.org/resource/Natural_language_processing" id="link-id0x7e0e2f0">Natural Language Processing</a>), e.g., <a href="http://dbpedia.org/resource/Entity" id="link-id0x71ab500">entity</a> and relationship extraction, especially extraction that can produce instance data in given ontologies (e.g., events) using common identifiers (e.g., DBPedia URIs).</p> <p>Mapping of RDBs to RDF is possible, and a W3C working group is developing standards for this. The required baseline level has been reached; the rest is a matter of automating deployment. Within the enterprise, there are advantages to be gained for <a href="http://dbpedia.org/resource/Information" id="link-id0x7a8e9a8">information</a> integration; e.g., all entities in the CRM space can be integrated with all email and support tickets through giving everything a <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x599f630">URI</a>. Some of this information may even be published on an <a href="http://dbpedia.org/resource/Extranet" id="link-id0x2a28f98">extranet</a> for self-service and web-service interfaces. This has been done at small scales and the rest is a matter of spreading adoption and lowering the entry barrier. Incremental progress will take place, eventually resulting in qualitatively better integration along the value chain when adoption is sufficiently widespread.</p> </li> <li> <p> <b>Who is consuming these data and why? Could they do it better? How?</b> </p> <p>Consumers are various. The greatest need is for tools that summarize complex data and allow getting a bird's eye view of what data is in the first instance available. Consuming the data is hindered by the user not even necessarily knowing what data there is. This is somewhat new, as traditionally the business analyst did know the schema of the warehouse and was proficient with <a href="http://dbpedia.org/resource/SQL" id="link-id0x5999558">SQL</a> report generators and statistics packages.</p> <p>Where Web 2.0 made the <i>citizen journalist</i>, the web of linked data will make the <i>citizen analyst</i>. For this to happen, with benefits for individuals, enterprises, and governments alike, more work in user interfaces, knowledge discovery, and query composition will be useful. We may envision a "meshup economy" where data is plentiful, but the unit of value and exchange is the smart report that crystallizes actionable value from this ocean.</p> </li> <li> <p> <b>What industrial sectors in Europe could become more competitive if they became much better at managing data?</b> </p> <p>Any sector could benefit. Early adopters are seen in the biomedical field and to an extent in media. </p> </li> <li> <p> <b>Is the regulation landscape imposing constraints (privacy, compliance ...) that don't have today good tool support?</b> </p> <p>The regulation landscape drives database demand through data retention requirements and the like.</p> <p>With data integration, especially with privacy-sensitive data (as in medicine), there are issues of whether one dares put otherwise-shareable information online. Regulation is needed to protect individuals, but integration should still be possible for science.</p> <p>For this, we see a need for progress in applying policy-based approaches (e.g., row level security) to relatively schema-last data such as RDF. This is possible but needs some more work. Also, creating on-the-fly-anonymizing views on data might help.</p> <p>More research is needed for reconciling the need for security with the advantages of broad-based <i>ad hoc</i> integration. Ideally, data should be intelligent, aware of its origins and classification and cautious of whom it interacts with, all of this supported under the covers so that the user could ask anything but the data might refuse to answer or might restrict answers according to the user's profile. This is a tall order and implementing something of the sort is an open question.</p> </li> <li> <p> <b>What are the main practical problem identified for individuals and organizations? Please give examples and tell us about the main obstacles and barriers.</b> </p> <p>We have come across the following:</p> <ul> <li>Knowing that the data exists in the first place.</li> <li>If the data is found, figuring out the provenance, units and precision of measurement, identifiers, and the like.</li> <li>Compatible subject matter but incompatible representation: For example, one has numbers on a map with different maps for different points in time; another has time series of instrument data with geo-location for the instrument. It is only to be expected that the time interval between measurements is not the same. So there is need for a lot of one-off programming to align data.</li> </ul> <p>Other problems have to do with sheer volume, i.e., transfer of data even in a local area network is too slow, let alone over a wide area network. Computation needs to go to the data, and databases need to support this.</p> </li> </ol> </li> <li> <p> <b>Services, software stacks, protocols, standards, benchmarks</b> </p> <ol type="a" start="1"> <li> <p> <b>What combinations of components are needed to deal with these problems?</b> </p> <p>Recent times have seen a proliferation of special purpose databases. Since the data needs of the future are about combining data with maximum agility and minimum performance hit, there is need to gather the currently-separate functionality into an integrated system with sufficient flexibility. We see some of this in integration of map-reduce and scale-out databases. The former antagonists have become partners. Vertica, <a href="http://dbpedia.org/resource/Greenplum" id="link-id0x45ecfa0">Greenplum</a>, and OpenLink <a href="http://virtuoso.openlinksw.com" id="link-id0x7f73fc8">Virtuoso</a> are example of DBMS featuring work in this direction.</p> <p>Interoperability and at least <i>de facto</i> standards in ways of doing this will emerge.</p> </li> <li> <p> <b>What data exchange and processing mechanisms will be needed to work across platforms and programming languages?</b> </p> <p> <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x776a1a0">HTTP</a>, <a href="http://dbpedia.org/resource/XML" id="link-id0x2a4e8d0">XML</a>, and RDF are in fact very verbose, yet these are the formats and models that have uptake. Thus, these will continue to be used even though one might think binary formats to be more efficient.</p> <p>There are of course science data set standards that are more compressed and these will continue, hopefully adding a practice of rich metadata in RDF.</p> <p>For internals of systems, MPI and TCP/IP with proprietary optimized wire formats will continue. Inter-system communication will likely continue to be HTTP, XML, and RDF as appropriate.</p> </li> <li> <p> <b>What data environments are today so wastefully messy that they would benefit from the development of standards?</b> </p> <p>RDF and <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x2a35960">OWL</a> are not messy but they could use some more performance; we are working on this. <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x12362e8">SPARQL</a> is finally acquiring the capabilities of a serious query language, so things are slowly coming together.</p> <p>Community process for developing application domain specific vocabularies works quite well, even though one could argue it is <i>ad hoc</i> and not up to what a modeling purist might wish.</p> <p>Top-down imposition of standards has a mixed history, with long and expensive development and sometimes no or little uptake, consider some WS* standards for example.</p> </li> <li> <p> <b>What kind of performance is expected or required of these systems? Who will measure it reliably? How?</b> </p> <p>Relational databases have a history of substantial investment in <a href="http://dbpedia.org/resource/Program_optimization" id="link-id0x7b2d7c8">optimization</a> and some of them are very good for what they do, e.g., the newer generation of analytics databases.</p> <p>The very large schema-last, no-SQL, sometimes eventually consistent key-value stores have a somewhat shorter history but do fill a real need.</p> <p>These trends will merge: Extreme scale, schema-last, complex queries, even more complex inference, custom code for in-database machine learning and other bulk processing.</p> <p>We find RDF augmented with some binary types at this crossroads. This point of the design space will have to provide performance roughly on the level of today's best relational solution for workloads that fit the relational model. The added cost of schema-last and inference must come down. We are working on this. Research work such as carried out with <a href="http://dbpedia.org/resource/MonetDB" id="link-id0x794ee48">MonetDB</a> gives clues as to how these aims can be reached.</p> <p>The separation of query language and inference is artificial. After the concepts are mature, these functions will merge and execute close to the data; there are clear evolutionary pressures in this direction.</p> <p>Benchmarks are key. Some gain can be had even from repurposing standard relational benchmarks like <a href="http://www.tpc.org/" id="link-id0x7d45c58">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id0x45b0198">H</a>. But the TPC-H rules do not allow official reporting of such.</p> <p>Development of benchmarks for RDF, complex queries, and inference is needed. A bold challenge to the community, it should be rooted in real-life integration needs and involve high heterogeneity. A key-value store benchmark might also be conceived. A transaction benchmark like TPC-<a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0x7e32178">C</a> might be the basis, maybe augmented with massive user-generated content like reviews and blogs.</p> <p>If benchmarks exist and are not too easy nor inaccessibly difficult nor too expensive to run â think of the high end TPC-C results â then TPC-style rules and processes would be quite adequate. The threshold to publish should be lowered: Everybody runs the TPC workloads internally but few publish.</p> <p>Some EC initiative for benchmarking could make sense, similar to the TREC initiative of the US government. Industry should be consulted for the specific content; possibly the answers to the present questionnaire can provide an approximate direction.</p> <p>Benchmarks should be run by software vendors on their own systems, tuned by themselves. But there should be a process of disclosure and auditing; the TPC rules give an example. Compliance should not be too expensive or time consuming. Some community development for automating these things would be a worthwhile target for EC funding.</p> </li> </ol> </li> <li> <p> <b>Usability and training</b> </p> <ol type="a" start="1"> <li> <p> <b>How difficult will it be for a developer of average competence to deploy components whose core is based on rather deep computer science? Do we all need to understand Monads and Continuations? What can be done to make it ever easier?</b> </p> <p>In the database world, huge advances in technology have taken place behind a relatively simple and stable interface: SQL. For the linked data <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x7e01618">web</a>, the same will take place behind SPARQL.</p> <p>Beyond these, for example, programming with MPI with good utilization of a cluster platform for an arbitrary algorithm, is quite difficult. The casual amateur is hereby warned.</p> <p>There is no single solution. For automatic parallelization, since explicit, programmatic parallelization of things with MPI for example is very unscalable in terms of required skill, we should favor declarative and/or functional approaches.</p> <p>Developing a debugger and explanation engine for rule-based and description-logics-based inference would be an idea.</p> <p>For procedural workloads, things like Erlang may be good in cases and are not overly difficult in principle, especially if there are good debugging facilities.</p> <p>For shipping functions in a cluster or cloud, the <a href="http://www.eecs.berkeley.edu/Research/Projects/Data/105733.html" id="link-id0x43665a8">BOOM</a> (<a href="http://www.eecs.berkeley.edu/Research/Projects/Data/105733.html" id="link-id0x7718f00">Berkeley Orders Of Magnitude</a>) approach or logic programming with explicit specification of compute location seem promising, surely more flexible than map-reduce. The question is whether a <a href="http://dbpedia.org/resource/PHP" id="link-id0x7d64f68">PHP</a> developer can be made to do logic programming.</p> <p>This bridge will be crossed only with actual need and even then reluctantly. We may look at the Web 2.0 practice of sharding <a href="http://dbpedia.org/resource/MySQL" id="link-id0xbab1ae98">MySQL</a>, inconvenient as this may be, for an example. There is inertia and thus re-architecting is a constant process that is generally in reaction to facts, <i>post hoc</i>, often a point solution. One could argue that planning ahead would be smarter but by and large the world does not work so.</p> <p>One part of the answer is an infinitely-scalable SQL database that expands and shrinks in the clouds, with the usual semantics, maybe optional eventual consistency and built-in map reduce. If such a thing is inexpensive enough and syntax-level-compatible with present installed base, many developers do not have to learn very much more.</p> <p>This is maybe good for the bread-and-butter IT, but European competitiveness should not rest on this. Therefore we wish to go for bold new application types for which the client-server database application is not the model. Data-centric languages like BOOM, if they can be made very efficient and have good debugging support, are attractive there. These do require more intellectual investment but that is not a problem since the less-inquisitive part of the developer community is served by the first part of the answer.</p> </li> <li> <p> <b>How is a developer of average skills going to learn about these new advanced tools? How can we plan for excellent documentation and training, community mentoring, exchange of good practices, etc... across all EU countries?</b> </p> <p>For the most part, developers do not learn things for the sake of learning. When they have learned something and it is adequate, they stay with it for the most part and are even reluctant to engage in cross-camps interaction. The research world is often similarly insular. A new inflection in the application landscape is needed to drive learning. This inflection is provided by the <a href="https://wiki.mozilla.org/Labs/Ubiquity" id="link-id0x770df38">ubiquity</a> of mobile devices, sensor data, explicit semantics, NLP concept extraction, web of linked data, and such factors.</p> <p>RDFa is a good example of a new technique piggybacking on something everybody uses, namely HTML. These new things should, within possibility, be deployed in the usual technology stack, <a href="http://en.wikipedia.org/wiki/LAMP_%28software_bundle%29" id="link-id0x55596a8">LAMP</a> or Java. Of course these do not have to be LAMP or Java or HTML or HTTP themselves but they must manifest through these.</p> <p>A lot of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x3d5378">semantic web</a> potential can be realized within the client-server database application model, thus no fundamental re-architecting, just some new data types and queries.</p> <p>For data- or processing-intensive tasks, an on-demand hookup to cloud-based servers with Erlang and/or BOOM for programming model would be easy enough to learn and utilize.</p> <p>The question is one of providing challenges. Addressing actual challenges with these techniques will lead to maturity, documentation, examples, and training. With virtual, Europe-wide distributed teams a reality in many places, Europe-wide dissemination is no longer insurmountable.</p> <p>As the data overflow proceeds, its victims will multiply and create demand for solutions. The EC could here encourage research project use cases gaining an extended life past the end of research projects, possibly being maintained and multiplied and spun off.</p> <p>If such things could be mutated into self-sustaining service businesses with pay-per-use revenue, say through a cloud SaaS business model, still primarily leveraging an open source technology stack, we could have self-propagating and self-supporting models for exploiting advanced IT. This would create interest, and interest would drive training and dissemination.</p> <p>The problem is creating the pull.</p> </li> </ol> </li> <li> <p> <b>Challenges</b> </p> <ol type="a" start="1"> <li> <p> <b>What should be, in this domain, the equivalent of the Netflix challenge, Ansari X Prize, <a href="http://dbpedia.org/resource/Google" id="link-id0x6a6c2b0">Google</a> Lunar X Prize, etc. ... ?</b> </p> <p>The EC itself no doubt suffers from data overflow in one function or another. Unless security/secrecy prohibits, simply publishing a large data set and a description of what operations should be done on it would be a start. The more real the data, the better â reality is consistently more complex and surprising than imagination. Since many interesting problems touch on fraud detection and law enforcement, there may be some security obstacles for using these application domains as subject matters of open challenges.</p> <p>Once there is a good benchmark, as discussed above, there can be some prize money allocated for the winners, specially if the race is tight.</p> <p>The Semantic Web Challenge and the Billion Triples Challenge exist and are useful as such, but do not seem to have any huge impact.</p> <p>The incentives should be sufficient and part of the expenses arising from running for such challenges could be funded. Otherwise investing in existing business development will be more interesting to industry. Some industry participation seems necessary; we would wish academia and industry to work closer. Also, having industry supply the baseline guarantees that academia actually does further the state of the art. This is not always certain.</p> <p>If challenges are based on actual problems, whether of the EC, its member governments, or private entities, and winning the challenge may lead to a contract for supplying an actual solution, these will naturally become more interesting for consortia involving integrators, specialist software vendors, and academia. Such a model would build actual capacity to deploy leading edge technologies in production, which is sorely needed.</p> </li> <li> <p> <b>What should one do to set up such a challenge, administer, and monitor it?</b> </p> <p>The EC should probably circulate a call for actual problem scenarios involving big data. If the matter of the overflow is as dire as represented, cases should be easy to find. A few should be selected and then anonymized if needed.</p> <p>The party with the use case would benefit by having hopefully the best work on it. The contestants would benefit from having real world needs guide R&D. The EC would not have to do very much, except possibly use some money for funding the best proposals. The winner would possibly get a large account and related sales and service income. The contestants would have to be teams possibly involving many organizations; for example, development and first-line services and support could come from different companies along a systems integrator model such as is widely used in the US.</p> <p>There may be a good benchmark at the time, possibly resulting from FP7 itself. In such a case, the EC could offer a prize for winners. Details would have to be worked out case by case. Such a challenge could be repeated a few times, as benchmark-driven progress in databases or TREC for example have taken some years to reach a point of slowdown in progress.</p> <p>Administrating such an activity should not be prohibitive, as most of the expertise can be found with the stakeholders.</p> </li> </ol> </li> </ol>
Provenance and Reification in Virtuoso
http://www.openlinksw.com/blog/vdb/blog/?date=2009-09-01#1573
2009-09-01T14:44:08Z
2009-09-01T11:20:46.000006-04:00
<p>These days, <a href="http://dbpedia.org/resource/Data" id="link-id0x37019c8">data</a> provenance is a big topic across the board, ranging from the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x53c3620">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x4aa3848">web</a>, to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x385aff0">RDF</a> in general, to any kind of data integration, with or without RDF. Especially with scientific data we encounter the need for metadata and provenance, repeatability of experiments, etc. Data without context is worthless, yet the producers of said data do not always have a model or budget for metadata. And if they do, the approach is often a proprietary relational schema with web services in front.</p> <p>RDF and linked data principles could evidently be a great help. This is a large topic that goes into the culture of doing science and will deserve a more extensive treatment down the road.</p> <p>For now, I will talk about possible ways of dealing with provenance annotations in <a href="http://virtuoso.openlinksw.com" id="link-id0x51c4da0">Virtuoso</a> at a fairly technical level.</p> <p>If data comes many-triples-at-a-time from some source (e.g., library catalogue, user of a social network), then it is often easiest to put the data from each source/user into its own graph. Annotations can then be made on the graph. The graph IRI will simply occur as the subject of a triple in the same or some other graph. For example, all such annotations could go into a special annotations graph.</p> <p>On the query side, having lots of distinct graphs does not have to be a problem if the index scheme is the right one, i.e., the 4 index scheme <a href="http://docs.openlinksw.com/virtuoso/rdfperformancetuning.html#rdfperfindexes" id="link-id142a0798">discussed in the Virtuoso documentation</a>. If the query does not specify a graph, then triples in any graph will be considered when evaluating the query.</p> <p>One could write queries like â</p> <blockquote> <code><pre>SELECT ?pub WHERE { GRAPH ?g { ?person foaf:knows ?contact } ?contact foaf:name "Alice" . ?g xx:has_publisher ?pub }</pre> </code> </blockquote> <p>This would return the publishers of graphs that assert that somebody knows Alice.</p> <p>Of course, the <a href="http://www.w3.org/TR/2004/REC-rdf-primer-20040210/#reification" id="link-id14fa9488">RDF reification vocabulary</a> can be used as-is to say things about single triples. It is however very inefficient and is not supported by any specific optimization. Further, reification does not seem to get used very much; thus there is no great pressure to specially optimize it.</p> <p>If we have to say things about specific triples and this occurs frequently (i.e., for more than 10% or so of the triples), then modifying the quad table becomes an option. For all its inefficiency, the RDF reification vocabulary is applicable if reification is a rarity.</p> <p>Virtuoso's <code>RDF_QUAD</code> table can be altered to have more columns. The problem with this is that space usage is increased and the RDF loading and query functions will not know about the columns. A <a href="http://dbpedia.org/resource/SQL" id="link-id0x4784bf0">SQL</a> update statement can be used to set values for these additional columns if one knows the <code>G,S,P,O</code>. </p> <p>Suppose we annotated each quad with the user who inserted it and a timestamp. These would be columns in the <code>RDF_QUAD</code> table. The next choice would be whether these were primary key parts or dependent parts. If primary key parts, these would be non-<code>NULL</code> and would occur on every index. The same quad would exist for each distinct user and time this quad had been inserted. For loading functions to work, these columns would need a default. In practice, we think that having such metadata as a dependent part is more likely, so that <code>G,S,P,O</code> are the unique identifier of the quad. Whether one would then include these columns on indices other than the primary key would depend on how frequently they were accessed.</p> <p>In <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x4a8a7c0">SPARQL</a>, one could use an extension syntax like â</p> <blockquote> <code><pre>SELECT * WHERE { ?person foaf:knows ?connection OPTION ( time ?ts ) . ?connection foaf:name "Alice" . FILTER ( ?ts > "2009-08-08"^^xsd:datetime ) }</pre> </code> </blockquote> <p>This would return everybody who knows Alice since a date more recent than 2009-08-08. This presupposes that the quad table has been extended with a datetime column.</p> <p>The <code>OPTION (time ?ts)</code> syntax is not presently supported but we can easily add something of the sort if there is user demand for it. In practice, this would be an extension mechanism enabling one to access extension columns of <code>RDF_QUAD</code> via a column <code>?variable</code> syntax in the <code>OPTION</code> clause.</p> <p>If quad metadata were not for every quad but still relatively frequent, another possibility would be making a separate table with a key of <code>GSPO</code> and a dependent part of <code>R</code>, where <code>R</code> would be the reification <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x49e6108">URI</a> of the quad. Reification statements would then be made with <code>R</code> as a subject. This would be more compact than the reification vocabulary and would not modify the <code>RDF_QUAD</code> table. The syntax for referring to this could be something like â</p> <blockquote> <code><pre>SELECT * WHERE { ?person foaf:knows ?contact OPTION ( reify ?r ) . ?r xx:assertion_time ?ts . ?contact foaf:name "Alice" . FILTER ( ?ts > "2008-8-8"^^xsd:datetime ) }</pre> </code> </blockquote> <p>We could even recognize the reification vocabulary and convert it into the reify option if this were really necessary. But since it is so unwieldy I don't think there would be huge demand. Who knows? You tell us.</p>
Provenance and Reification in Virtuoso
http://www.openlinksw.com/weblog/oerling/?date=2009-09-01#1572
2009-09-01T14:44:08Z
2009-09-01T11:20:44-04:00
<p>These days, <a href="http://dbpedia.org/resource/Data" id="link-id0x4a44870">data</a> provenance is a big topic across the board, ranging from the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x4e10e60">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x4738350">web</a>, to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1fe33310">RDF</a> in general, to any kind of data integration, with or without RDF. Especially with scientific data we encounter the need for metadata and provenance, repeatability of experiments, etc. Data without context is worthless, yet the producers of said data do not always have a model or budget for metadata. And if they do, the approach is often a proprietary relational schema with web services in front.</p> <p>RDF and linked data principles could evidently be a great help. This is a large topic that goes into the culture of doing science and will deserve a more extensive treatment down the road.</p> <p>For now, I will talk about possible ways of dealing with provenance annotations in <a href="http://virtuoso.openlinksw.com" id="link-id0x36581e8">Virtuoso</a> at a fairly technical level.</p> <p>If data comes many-triples-at-a-time from some source (e.g., library catalogue, user of a social network), then it is often easiest to put the data from each source/user into its own graph. Annotations can then be made on the graph. The graph IRI will simply occur as the subject of a triple in the same or some other graph. For example, all such annotations could go into a special annotations graph.</p> <p>On the query side, having lots of distinct graphs does not have to be a problem if the index scheme is the right one, i.e., the 4 index scheme <a href="http://docs.openlinksw.com/virtuoso/rdfperformancetuning.html#rdfperfindexes" id="link-id142a0798">discussed in the Virtuoso documentation</a>. If the query does not specify a graph, then triples in any graph will be considered when evaluating the query.</p> <p>One could write queries like â</p> <blockquote> <code><pre>SELECT ?pub WHERE { GRAPH ?g { ?person foaf:knows ?contact } ?contact foaf:name "Alice" . ?g xx:has_publisher ?pub }</pre> </code> </blockquote> <p>This would return the publishers of graphs that assert that somebody knows Alice.</p> <p>Of course, the <a href="http://www.w3.org/TR/2004/REC-rdf-primer-20040210/#reification" id="link-id14fa9488">RDF reification vocabulary</a> can be used as-is to say things about single triples. It is however very inefficient and is not supported by any specific optimization. Further, reification does not seem to get used very much; thus there is no great pressure to specially optimize it.</p> <p>If we have to say things about specific triples and this occurs frequently (i.e., for more than 10% or so of the triples), then modifying the quad table becomes an option. For all its inefficiency, the RDF reification vocabulary is applicable if reification is a rarity.</p> <p>Virtuoso's <code>RDF_QUAD</code> table can be altered to have more columns. The problem with this is that space usage is increased and the RDF loading and query functions will not know about the columns. A <a href="http://dbpedia.org/resource/SQL" id="link-id0x4b1d938">SQL</a> update statement can be used to set values for these additional columns if one knows the <code>G,S,P,O</code>. </p> <p>Suppose we annotated each quad with the user who inserted it and a timestamp. These would be columns in the <code>RDF_QUAD</code> table. The next choice would be whether these were primary key parts or dependent parts. If primary key parts, these would be non-<code>NULL</code> and would occur on every index. The same quad would exist for each distinct user and time this quad had been inserted. For loading functions to work, these columns would need a default. In practice, we think that having such metadata as a dependent part is more likely, so that <code>G,S,P,O</code> are the unique identifier of the quad. Whether one would then include these columns on indices other than the primary key would depend on how frequently they were accessed.</p> <p>In <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x472afb0">SPARQL</a>, one could use an extension syntax like â</p> <blockquote> <code><pre>SELECT * WHERE { ?person foaf:knows ?connection OPTION ( time ?ts ) . ?connection foaf:name "Alice" . FILTER ( ?ts > "2009-08-08"^^xsd:datetime ) }</pre> </code> </blockquote> <p>This would return everybody who knows Alice since a date more recent than 2009-08-08. This presupposes that the quad table has been extended with a datetime column.</p> <p>The <code>OPTION (time ?ts)</code> syntax is not presently supported but we can easily add something of the sort if there is user demand for it. In practice, this would be an extension mechanism enabling one to access extension columns of <code>RDF_QUAD</code> via a column <code>?variable</code> syntax in the <code>OPTION</code> clause.</p> <p>If quad metadata were not for every quad but still relatively frequent, another possibility would be making a separate table with a key of <code>GSPO</code> and a dependent part of <code>R</code>, where <code>R</code> would be the reification <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x365b190">URI</a> of the quad. Reification statements would then be made with <code>R</code> as a subject. This would be more compact than the reification vocabulary and would not modify the <code>RDF_QUAD</code> table. The syntax for referring to this could be something like â</p> <blockquote> <code><pre>SELECT * WHERE { ?person foaf:knows ?contact OPTION ( reify ?r ) . ?r xx:assertion_time ?ts . ?contact foaf:name "Alice" . FILTER ( ?ts > "2008-8-8"^^xsd:datetime ) }</pre> </code> </blockquote> <p>We could even recognize the reification vocabulary and convert it into the reify option if this were really necessary. But since it is so unwieldy I don't think there would be huge demand. Who knows? You tell us.</p>
More On Parallel RDF/Text Query Evaluation
http://www.openlinksw.com/blog/vdb/blog/?date=2009-08-19#1571
2009-08-19T17:28:50Z
2009-08-19T14:00:32.000006-04:00
<p>We have received some more questions about <a href="http://virtuoso.openlinksw.com" id="link-id0x15ca9a30">Virtuoso</a>'s parallel query evaluation model.</p> <p>In answer, we will here explain how we do search engine style processing by writing <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1574c560">SPARQL</a>. There is no need for custom procedural code because the query optimizer does all the partitioning and the equivalent of map reduce.</p> <p>The point is that what used to require programming can often be done in a generic query language. The technical detail is that the implementation must be smart enough with respect to parallelizing queries for this to be of practical benefit. But by combining these two things, we are a step closer to the web being the database.</p> <p>I will here show how we do some joins combining full text, <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x15949970">RDF</a> conditions, and aggregates and <code>ORDER BY</code>. The sample task is finding the top 20 entities with New York in some attribute value. Then we specify the search further by only taking actors associated with New York. The results are returned in the order of a composite of <a href="http://dbpedia.org/resource/Entity" id="link-id0x213bf310">entity</a> rank and text match score.</p> <p>The basic query is:</p> <blockquote> <code><pre> SELECT ( <a href="http://dbpedia.org/resource/SQL" id="link-id0x23632230">sql</a>:s_sum_page ( <sql:vector_agg> ( <bif:vector> ( ?c1 , ?sm ) ), bif:vector ( 'new', 'york' ) ) ) AS ?res WHERE { { SELECT ( <SHORT_OR_LONG::>(?s1) ) AS ?c1 ( <sql:S_SUM> ( <SHORT_OR_LONG::IRI_RANK> ( ?s1 ) , <SHORT_OR_LONG::> ( ?s1textp ) , <SHORT_OR_LONG::> ( ?o1 ) , ?sc ) ) AS ?sm WHERE { ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) } ORDER BY DESC ( <sql:sum_rank> (( <sql:S_SUM> ( <SHORT_OR_LONG::IRI_RANK> ( ?s1 ) , <SHORT_OR_LONG::> ( ?s1textp ) , <SHORT_OR_LONG::> ( ?o1 ) , ?sc ) )) ) LIMIT 20 } } </pre> </code> </blockquote> <p>This takes some explaining. The basic part is</p> <blockquote> <code><pre>{ ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) }</pre> </code> </blockquote> <p>This just makes tuples where <code>?s1</code> is the object, <code>?s1textp</code> the property, and <code>?o1</code> the literal which contains "New York". For a single <code>?s1</code>, there can of course be many properties which all contain "New York".</p> <p>The rest of the query gathers all the "New York" containing properties of an entity into a single aggregate, and then gets the entity ranks of all such entities.</p> <p>After this, the aggregates are sorted by a sum of the entity rank and a combined text score calculated based on the individual text match scores between "New York" and the strings containing "New York". The text hit score is higher if the words repeat often and in close proximity.</p> <p>The <code>s_sum</code> function is a user-defined aggregate which takes 4 arguments: The rank of the subject of the triple; the predicate of the triple containing the text; the object of the triple containing the text; and the text match score.</p> <p>These are grouped by the subject of the triple. After this, these are sorted by <code>sum_score</code> of the aggregate constructed with <code>s_sum</code>. The <code>sum_score</code> is a SQL function combining the entity rank with the text scores of the different literals.</p> <p>This executes as one would expect: All partitions make a text index lookup, retrieving the object of the triple. The text index entries of an object are stored in the same partition as the object. But the entity rank is a property of the subject and is partitioned by the subject. Also the <code>GROUP BY</code> is by the subject. Thus the <a href="http://dbpedia.org/resource/Data" id="link-id0x15da01b8">data</a> is produced from all partitions, then streamed into the receiving partitions, determined by the subject. This partition can then get the score and group the matches by the subject. Since all these partial aggregates are partitioned by the subject, there is no need to merge them; thus, the top <code>k</code> sort can be done for each partition separately. Finally, the top 20 of each partition are merged into the global top 20. This is then passed to a final function <code>s_sum_page</code> that turns this all into an <a href="http://dbpedia.org/resource/XML" id="link-id0x15d59fc8">XML</a> fragment that can be processed with XSLT for inclusion on a web page.</p> <p>This differs from the text search engine in that the query pipeline can contain arbitrary cross-partition joins. Also, the string "New York" is a common label that occurs in many distinct entities. Thus one text match, to one document, in the case the containing only the string "New York" will get many entities, likely all from different partitions.</p> <p>So, if we only want actors with a mention of "New York", we need to get the inner part of the query as:</p> <blockquote> <code><pre>{ ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) . ?s1 a <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x15befb10">http</a>://<a href="http://umbel.org/about/" id="link-id0x15c92330">umbel</a>.org/umbel/sc/Actor> }</pre> </code> </blockquote> <p>Whether an entity is an actor can be checked in the same partition as the rank of the entity. Thus the query plan gets this check right before getting the rank. This is natural since there is no point in getting the rank of something that is not an actor.</p> <p>The <code><short_or_long::sql:func></code> notation means that we call <code>func</code>, which is a SQL stored procedure with the arguments in their internal form. Thus, if a variable bound to an IRI is passed, the <code>short_or_long</code> specifies that it is passed as its internal ID and is not converted into its text form. This is essential, since there is no point getting the text of half a million IRIs when only 20 at most will be shown in the end.</p> <p>Now, when we run this on a collection of 4.5 billion triples of <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x153772e8">linked data</a>, once we have the working set, we can get the top 20 "New York" occurrences, with text summaries and all, in just 1.1s, with 12 of 16 cores busy. (The hardware is two boxes with two quad-core Xeon 5345 each.)</p> <p>If we run this query in two parallel sessions, we get both results in 1.9s, with 14 of 16 cores busy. This gets about 200K "New York" strings, which becomes about 400K entities with New York somewhere, for which a rank then has to be retrieved. After this, all the possibly-many occurrences of New York in the title, text, and other properties of the entity are aggregated together, resolving into some 220K groups. These are then sorted. This is internally over 1.5 million random lookups and some 40MB of traffic between processes. Restricting the type of the entity to actor drops the execution time of one query to 0.8s because there are then fewer ranks to retrieve and less data to aggregate and sort.</p> <p>By adding partitions and cores, we scale horizontally, as evaluating the query involves almost no central control, even though data are swapped between partitions. There is some flow control to avoid constructing overly-large intermediate results but generally partitions run independently and asynchronously. In the above case, there is just one fence at the point where all aggregates are complete, so that they can be sorted; otherwise, all is asynchronous.</p> <p>Doing <code>JOINs</code> between partitions and partitioned <code>GROUP BY</code>/<code>ORDER BY</code> is pretty regular database stuff. Applying this to RDF is a most natural thing.</p> <p>If we do not parallelize the user-defined aggregate for grouping all the "New York" occurrences, the query takes 8s instead of 1.1s. If we could not put SQL procedures as user-defined aggregates to be parallelized with the query, we'd have to either bring all the data to a central point before the top k, which would destroy performance, or we would have to do procedures with explicit parallel procedure calls which is hard to write, surely too hard for <i>ad hoc</i> queries.</p> <a href="http://bit.ly/4jAVHC" id="link-id114d58f0">Results of live execution</a> may not be complete on initial load, as this link includes a "Virtuoso Anytime" timeout of 10 seconds. Running against a cold cache, these results may take much longer to return; a warm cache will deliver response times along the lines of those discussed above. <p>Engineering matters. If we wish to commoditize queries on a lot of data, such intelligence in the DBMS is necessary; it is very unscalable to require people to do procedural code or give query parallelization hints. If you need to optimize a workload of 10 different transactions, this is of course possible and even desirable, but for the infinity of all search or analysis, this will not happen.</p>
More On Parallel RDF/Text Query Evaluation
http://www.openlinksw.com/weblog/oerling/?date=2009-08-19#1570
2009-08-19T17:28:50Z
2009-08-19T14:00:29.000002-04:00
<p>We have received some more questions about <a href="http://virtuoso.openlinksw.com" id="link-id0x266cd288">Virtuoso</a>'s parallel query evaluation model.</p> <p>In answer, we will here explain how we do search engine style processing by writing <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x23c628b8">SPARQL</a>. There is no need for custom procedural code because the query optimizer does all the partitioning and the equivalent of map reduce.</p> <p>The point is that what used to require programming can often be done in a generic query language. The technical detail is that the implementation must be smart enough with respect to parallelizing queries for this to be of practical benefit. But by combining these two things, we are a step closer to the web being the database.</p> <p>I will here show how we do some joins combining full text, <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x22ff08b0">RDF</a> conditions, and aggregates and <code>ORDER BY</code>. The sample task is finding the top 20 entities with New York in some attribute value. Then we specify the search further by only taking actors associated with New York. The results are returned in the order of a composite of <a href="http://dbpedia.org/resource/Entity" id="link-id0x22da5258">entity</a> rank and text match score.</p> <p>The basic query is:</p> <blockquote> <code><pre> SELECT ( <a href="http://dbpedia.org/resource/SQL" id="link-id0x237a6530">sql</a>:s_sum_page ( <sql:vector_agg> ( <bif:vector> ( ?c1 , ?sm ) ), bif:vector ( 'new', 'york' ) ) ) AS ?res WHERE { { SELECT ( <SHORT_OR_LONG::>(?s1) ) AS ?c1 ( <sql:S_SUM> ( <SHORT_OR_LONG::IRI_RANK> ( ?s1 ) , <SHORT_OR_LONG::> ( ?s1textp ) , <SHORT_OR_LONG::> ( ?o1 ) , ?sc ) ) AS ?sm WHERE { ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) } ORDER BY DESC ( <sql:sum_rank> (( <sql:S_SUM> ( <SHORT_OR_LONG::IRI_RANK> ( ?s1 ) , <SHORT_OR_LONG::> ( ?s1textp ) , <SHORT_OR_LONG::> ( ?o1 ) , ?sc ) )) ) LIMIT 20 } } </pre> </code> </blockquote> <p>This takes some explaining. The basic part is</p> <blockquote> <code><pre>{ ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) }</pre> </code> </blockquote> <p>This just makes tuples where <code>?s1</code> is the object, <code>?s1textp</code> the property, and <code>?o1</code> the literal which contains "New York". For a single <code>?s1</code>, there can of course be many properties which all contain "New York".</p> <p>The rest of the query gathers all the "New York" containing properties of an entity into a single aggregate, and then gets the entity ranks of all such entities.</p> <p>After this, the aggregates are sorted by a sum of the entity rank and a combined text score calculated based on the individual text match scores between "New York" and the strings containing "New York". The text hit score is higher if the words repeat often and in close proximity.</p> <p>The <code>s_sum</code> function is a user-defined aggregate which takes 4 arguments: The rank of the subject of the triple; the predicate of the triple containing the text; the object of the triple containing the text; and the text match score.</p> <p>These are grouped by the subject of the triple. After this, these are sorted by <code>sum_score</code> of the aggregate constructed with <code>s_sum</code>. The <code>sum_score</code> is a SQL function combining the entity rank with the text scores of the different literals.</p> <p>This executes as one would expect: All partitions make a text index lookup, retrieving the object of the triple. The text index entries of an object are stored in the same partition as the object. But the entity rank is a property of the subject and is partitioned by the subject. Also the <code>GROUP BY</code> is by the subject. Thus the <a href="http://dbpedia.org/resource/Data" id="link-id0x24381030">data</a> is produced from all partitions, then streamed into the receiving partitions, determined by the subject. This partition can then get the score and group the matches by the subject. Since all these partial aggregates are partitioned by the subject, there is no need to merge them; thus, the top <code>k</code> sort can be done for each partition separately. Finally, the top 20 of each partition are merged into the global top 20. This is then passed to a final function <code>s_sum_page</code> that turns this all into an <a href="http://dbpedia.org/resource/XML" id="link-id0x2363d6c0">XML</a> fragment that can be processed with XSLT for inclusion on a web page.</p> <p>This differs from the text search engine in that the query pipeline can contain arbitrary cross-partition joins. Also, the string "New York" is a common label that occurs in many distinct entities. Thus one text match, to one document, in the case the containing only the string "New York" will get many entities, likely all from different partitions.</p> <p>So, if we only want actors with a mention of "New York", we need to get the inner part of the query as:</p> <blockquote> <code><pre>{ ?s1 ?s1textp ?o1 . ?o1 bif:contains "new AND york" OPTION ( SCORE ?sc ) . ?s1 a <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x237110b8">http</a>://<a href="http://umbel.org/about/" id="link-id0x2318e198">umbel</a>.org/umbel/sc/Actor> }</pre> </code> </blockquote> <p>Whether an entity is an actor can be checked in the same partition as the rank of the entity. Thus the query plan gets this check right before getting the rank. This is natural since there is no point in getting the rank of something that is not an actor.</p> <p>The <code><short_or_long::sql:func></code> notation means that we call <code>func</code>, which is a SQL stored procedure with the arguments in their internal form. Thus, if a variable bound to an IRI is passed, the <code>short_or_long</code> specifies that it is passed as its internal ID and is not converted into its text form. This is essential, since there is no point getting the text of half a million IRIs when only 20 at most will be shown in the end.</p> <p>Now, when we run this on a collection of 4.5 billion triples of <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x24381160">linked data</a>, once we have the working set, we can get the top 20 "New York" occurrences, with text summaries and all, in just 1.1s, with 12 of 16 cores busy. (The hardware is two boxes with two quad-core Xeon 5345 each.)</p> <p>If we run this query in two parallel sessions, we get both results in 1.9s, with 14 of 16 cores busy. This gets about 200K "New York" strings, which becomes about 400K entities with New York somewhere, for which a rank then has to be retrieved. After this, all the possibly-many occurrences of New York in the title, text, and other properties of the entity are aggregated together, resolving into some 220K groups. These are then sorted. This is internally over 1.5 million random lookups and some 40MB of traffic between processes. Restricting the type of the entity to actor drops the execution time of one query to 0.8s because there are then fewer ranks to retrieve and less data to aggregate and sort.</p> <p>By adding partitions and cores, we scale horizontally, as evaluating the query involves almost no central control, even though data are swapped between partitions. There is some flow control to avoid constructing overly-large intermediate results but generally partitions run independently and asynchronously. In the above case, there is just one fence at the point where all aggregates are complete, so that they can be sorted; otherwise, all is asynchronous.</p> <p>Doing <code>JOINs</code> between partitions and partitioned <code>GROUP BY</code>/<code>ORDER BY</code> is pretty regular database stuff. Applying this to RDF is a most natural thing.</p> <p>If we do not parallelize the user-defined aggregate for grouping all the "New York" occurrences, the query takes 8s instead of 1.1s. If we could not put SQL procedures as user-defined aggregates to be parallelized with the query, we'd have to either bring all the data to a central point before the top k, which would destroy performance, or we would have to do procedures with explicit parallel procedure calls which is hard to write, surely too hard for <i>ad hoc</i> queries.</p> <a href="http://bit.ly/4jAVHC" id="link-id114d58f0">Results of live execution</a> may not be complete on initial load, as this link includes a "Virtuoso Anytime" timeout of 10 seconds. Running against a cold cache, these results may take much longer to return; a warm cache will deliver response times along the lines of those discussed above. <p>Engineering matters. If we wish to commoditize queries on a lot of data, such intelligence in the DBMS is necessary; it is very unscalable to require people to do procedural code or give query parallelization hints. If you need to optimize a workload of 10 different transactions, this is of course possible and even desirable, but for the infinity of all search or analysis, this will not happen.</p>
Updated hardware improves LUBM 8000 load rate in Virtuoso 6
http://www.openlinksw.com/blog/vdb/blog/?date=2009-08-14#1569
2009-08-14T19:01:30Z
2009-08-15T15:27:29-04:00
<p>We repeated the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1562" id="link-id173d3068">earlier LUBM 8000 experiment</a> on a newer machine, with 2 x Xeon 5520 and 72G 1333MHz memory, and once again with the 2 machines as a networked cluster. Otherwise the settings were the same.</p> <p>The load rate is now 160,739 triples-per-second.</p> <table> <tr> <th></th> <td> </td> <th align="center"><a href="http://virtuoso.openlinksw.com" id="link-id0x240daf38">Virtuoso</a> 6 <br /> (previous run)</th> <td> </td> <th align="center">Virtuoso 6 <br /> (new run)</th> <td> </td> <th align="center">Virtuoso 6 <br /> (newest run)</th> </tr> <tr> <td align="left">blades</td> <td> </td> <td align="center">1 </td> <td> </td> <td align="center">1 </td> <td> </td> <td align="center">2</td> </tr> <tr> <td align="left">processors</td> <td> </td> <td align="center">2 x Xeon 5410</td> <td> </td> <td align="center">2 x Xeon 5520</td> <td> </td> <td align="center"> 2 x Xeon 5520 <br />+ <br />2 x Xeon 5410 <br />with 1x1GigE <br />interconnect </td> </tr> <tr> <td align="left">memory</td> <td> </td> <td align="center"> 16G 667 MHz</td> <td> </td> <td align="center">72G 1333 MHz</td> <td> </td> <td align="center">72G 1333 MHz <br />+ <br /> 16G 667 MHz <br /> respectively</td> </tr> <tr> <td align="left">reported load rate<br />triples-per-second</td> <td> </td> <td align="center"> 110,532 </td> <td> </td> <td align="center"> 160,739 </td> <td> </td> <td align="center"> 214,188 </td> </tr> </table> <p>Again, if others talk about loading LUBM, so must we. Otherwise, this metric is rather uninteresting.</p>
Updated hardware improves LUBM 8000 load rate in Virtuoso 6
http://www.openlinksw.com/weblog/oerling/?date=2009-08-14#1568
2009-08-14T19:01:30Z
2009-08-15T15:27:25-04:00
<p>We repeated the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1562" id="link-id173d3068">earlier LUBM 8000 experiment</a> on a newer machine, with 2 x Xeon 5520 and 72G 1333MHz memory, and once again with the 2 machines as a networked cluster. Otherwise the settings were the same.</p> <p>The load rate is now 160,739 triples-per-second.</p> <table> <tr> <th></th> <td> </td> <th align="center"><a href="http://virtuoso.openlinksw.com" id="link-id0x199b9740">Virtuoso</a> 6 <br /> (previous run)</th> <td> </td> <th align="center">Virtuoso 6 <br /> (new run)</th> <td> </td> <th align="center">Virtuoso 6 <br /> (newest run)</th> </tr> <tr> <td align="left">blades</td> <td> </td> <td align="center">1 </td> <td> </td> <td align="center">1 </td> <td> </td> <td align="center">2</td> </tr> <tr> <td align="left">processors</td> <td> </td> <td align="center">2 x Xeon 5410</td> <td> </td> <td align="center">2 x Xeon 5520</td> <td> </td> <td align="center"> 2 x Xeon 5520 <br />+ <br />2 x Xeon 5410 <br />with 1x1GigE <br />interconnect </td> </tr> <tr> <td align="left">memory</td> <td> </td> <td align="center"> 16G 667 MHz</td> <td> </td> <td align="center">72G 1333 MHz</td> <td> </td> <td align="center">72G 1333 MHz <br />+ <br /> 16G 667 MHz <br /> respectively</td> </tr> <tr> <td align="left">reported load rate<br />triples-per-second</td> <td> </td> <td align="center"> 110,532 </td> <td> </td> <td align="center"> 160,739 </td> <td> </td> <td align="center"> 214,188 </td> </tr> </table> <p>Again, if others talk about loading LUBM, so must we. Otherwise, this metric is rather uninteresting.</p>
Single Virtuoso host loads 110,500 triples-per-second on LUBM 8000
http://www.openlinksw.com/blog/vdb/blog/?date=2009-06-29#1563
2009-06-29T16:12:34Z
2009-08-15T16:06:45.000001-04:00
<p>LUBM load speed still seems to be a metric that is quoted in comparisons of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id142df6e8">RDF</a> stores. Consequently, we too measured the load time of LUBM 8000, 1,068-million triples, on the newest <a href="http://virtuoso.openlinksw.com" id="link-id1389dfa0">Virtuoso</a>.</p> <p>The real time for the load was 161m 3s. The rate was 110,532 triples-per-second. The hardware was one machine with 2 x Xeon 5410 (quad core, 2.33 GHz) and 16G 667 MHz RAM. The software was Virtuoso 6 Cluster, configured into 8 partitions (processes) — one partition per CPU core. Each partition had its database striped over 6 disks total; the 6 disks on the system were shared between the 8 database processes.</p> <p>The load was done on 8 streams, one per server process. At the beginning of the load, the CPU usage was 740% with no disk; at the end, it was around 700% with 25% disk wait. 100% counts here for one CPU core or one disk being constantly busy.</p> <p>The RDF store was configured with the default two indices over quads, these being GSPO and OGPS. Text indexing of literals was not enabled. No materialization of entailed triples was made.</p> <p>We think that LUBM loading is not a realistic benchmark for the world but since other people publish such numbers, so do we.</p>
Single Virtuoso host loads 110,500 triples-per-second on LUBM 8000
http://www.openlinksw.com/weblog/oerling/?date=2009-06-29#1562
2009-06-29T16:12:34Z
2009-08-15T16:06:42.000001-04:00
<p>LUBM load speed still seems to be a metric that is quoted in comparisons of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id142df6e8">RDF</a> stores. Consequently, we too measured the load time of LUBM 8000, 1,068-million triples, on the newest <a href="http://virtuoso.openlinksw.com" id="link-id1389dfa0">Virtuoso</a>.</p> <p>The real time for the load was 161m 3s. The rate was 110,532 triples-per-second. The hardware was one machine with 2 x Xeon 5410 (quad core, 2.33 GHz) and 16G 667 MHz RAM. The software was Virtuoso 6 Cluster, configured into 8 partitions (processes) — one partition per CPU core. Each partition had its database striped over 6 disks total; the 6 disks on the system were shared between the 8 database processes.</p> <p>The load was done on 8 streams, one per server process. At the beginning of the load, the CPU usage was 740% with no disk; at the end, it was around 700% with 25% disk wait. 100% counts here for one CPU core or one disk being constantly busy.</p> <p>The RDF store was configured with the default two indices over quads, these being GSPO and OGPS. Text indexing of literals was not enabled. No materialization of entailed triples was made.</p> <p>We think that LUBM loading is not a realistic benchmark for the world but since other people publish such numbers, so do we.</p>
Comparing Virtuoso Performance on Different Processors
http://www.openlinksw.com/blog/vdb/blog/?date=2009-05-28#1558
2009-05-28T14:54:59Z
2009-05-28T11:15:41.000006-04:00
<p>Over the years we have run <a href="http://virtuoso.openlinksw.com" id="link-id0x16735e20">Virtuoso</a> on different hardware. We will here give a few figures that help identify the best price point for machines running Virtuoso.</p> <p>Our test is very simple: <i>Load 20 warehouses of <a href="http://dbpedia.org/resource/TPC-C" id="link-id0x16e0dba8">TPC-C</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x14ff4f80">data</a>, and then run one client per warehouse for 10,000 new orders</i>. The way this is set up, disk I/O does not play a role and lock contention between the clients is minimal.</p> <p>The test essentially has 20 server and 20 client threads running the same workload in parallel. The load time gives the single thread number; the 20 clients run gives the multi-threaded number. The test uses about 2-3 GB of data, so all is in RAM but is large enough not to be all in processor cache.</p> <p>All times reported are real times, starting from the start of the first client and ending with the completion of the last client.</p> <p>Do not confuse these results with official TPC-C. The measurement protocols are entirely incomparable.</p> <style type="text/css"> TABLE { background: none; border: none } TH { text-align: center; font-weight: bold } TR.top { background: } TD { text-align: center; border: none } </style> <table align="center" cellspacing="10"> <tr> <th>Test</th> <th>Platform</th> <th>Load<br />(seconds)</th> <th>Run<br />(seconds)</th> <th>GHz / cores / threads</th> </tr> <tr> <td>1</td> <td>Amazon <a href="http://aws.amazon.com/ec2/" id="link-id0x15d68e20">EC2</a> Extra Large<br />(4 virtual cores)</td> <td>340</td> <td>42</td> <td>1.2 GHz? / 4 / 1</td> </tr> <tr> <td>1</td> <td>Amazon EC2 Extra Large<br />(4 virtual cores)</td> <td>305</td> <td>43.3</td> <td>1.2 GHz? / 4 / 1</td> </tr> <tr> <td>2</td> <td>1 x dual-core AMD 5900</td> <td>263</td> <td>58.2</td> <td>2.9 GHz / 2 / 1</td> </tr> <tr> <td>3</td> <td>2 x dual-core Xeon 5130 ("Woodcrest")</td> <td>245</td> <td>35.7</td> <td>2.0 GHz / 4 / 1</td> </tr> <tr> <td>4</td> <td>2 x quad-core Xeon 5410 ("Harpertown")</td> <td>237</td> <td>18.0</td> <td>2.33 GHz / 8 / 1</td> </tr> <tr> <td>5</td> <td>2 x quad-core Xeon 5520 ("Nehalem")</td> <td>162</td> <td>18.3</td> <td>2.26 GHz / 8 / 2</td> </tr> </table> <p>We tried two different EC2 instances to see if there would be variation. The variation was quite small. The tested EC2 instances costs 20 US cents per hour. The AMD dual-core costs 550 US dollars with 8G. The 3 Xeon configurations are Supermicro boards with 667MHz memory for the Xeon 5130 ("Woodcrest") and Xeon 5410 ("Harpertown"), and 800MHz memory for the Nehalem. The Xeon systems cost between 4000 and 7000 US dollars, with 5000 for a configuration with 2 x Xeon 5520 ("Nehalem"), 72 GB RAM, and 8 x 500 GB SATA disks.</p> <p> <i>Caveat: Due to slow memory (we could not get faster within available time), the results for the Nehalem do not take full advantage of its principal edge over the previous generation, i.e., memory subsystem. We'll see another time with faster memories.</i> </p> <p>The operating systems were various 64 bit Linux distributions.</p> <p>We did some further measurements comparing Harpertown and Nehalem processors. The Nehalem chip was a bit faster for a slightly lower clock but we did not see any of the twofold and greater differences advertised by Intel.</p> <p>We tried some <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1460b688">RDF</a> operations on the two last systems:</p> <table align="center" cellspacing="10"> <tr> <th>operation</th> <th> Harpertown</th> <th>Nehalem</th> </tr> <tr> <th>Build text index for <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x16a94590">DBpedia</a></th> <td>1080s</td> <td>770s</td> </tr> <tr> <th><a href="http://dbpedia.org/resource/Entity" id="link-id0xc37f380">Entity</a> Rank iteration</th> <td>263s</td> <td>251s</td> </tr> </table> <p>Then we tried to see if the core multithreading of Nehalem could be seen anywhere. To this effect, we ran the Fibonacci function in <a href="http://dbpedia.org/resource/SQL" id="link-id0x15842a20">SQL</a> to serve as an example of an all in-cache integer operation. 16 concurrent operations took exactly twice as long as 8 concurrent ones, as expected.</p> <p>For something that used memory, we took a count of RDF quads on two different indices, getting the same count. The database was a cluster setup with one process per core, so a count involved one thread per core. The counts in series took 5.02s and in parallel they took 4.27s.</p> <p>Then we took a more memory intensive piece that read the RDF quads table in the order of one index and for each row checked that there is the equal row on another, differently-partitioned index. This is a cross-partition join. One of the indices is read sequentially and the other at random. The throughput can be reported as random-lookups-per-second. The data was English DBpedia, about 140M triples. One such query takes a couple of minutes with a 650% CPU utilization. Running multiple such queries should show effects of core multithreading since we expect frequent cache misses.</p> <ol> <li>On the host OS of the Nehalem system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td>503</td> <td>906,413</td> </tr> <tr> <th>2 queries</th> <td>1263</td> <td>1,578,585</td> </tr> <tr> <th>3 queries</th> <td>1204</td> <td>1,566,849</td> </tr> </table> </li> <li>In a VM under Xen, on the Nehalem system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td>652</td> <td>799,293</td> </tr> <tr> <th>2 queries</th> <td>1266</td> <td>1,486,710</td> </tr> <tr> <th>3 queries</th> <td>1222</td> <td>1,484,093</td> </tr> </table> </li> <li> On the host OS of the Harpertown system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td> 648 </td> <td> 1,041,448 </td> </tr> <tr> <th>2 queries</th> <td> 708 </td> <td> 1,124,866 </td> </tr> </table> </li> </ol> <p>The CPU percentages are as reported by the OS: user + system CPU divided by real time.</p> <p>So, Nehalem is in general somewhat faster, around 20-30%, than Harpertown. The effect of core multithreading can be noticed but is not huge, another 20% or so for situations with more threads than cores. The join where Harpertown did better could be attributed to its larger cache â 12 MB vs 8 MB.</p> <p>We see that Xen has a measurable but not prohibitive overhead; count a little under 10% for everything, also tasks with no I/O. The VM was set up to have all CPU for the test and the queries did not do disk I/O.</p> <p>The executables were compiled with <code>gcc</code> with default settings. Specifying <code>-march=nocona</code> (Core 2 target) dropped the cross-partition join time mentioned above from 128s to 122s on Harpertown. We did not try this on Nehalem but presume the effect would be the same, since the out-of-order unit is not much different. We did not do anything about process-to-memory affinity on Nehalem, which is a non-uniform architecture. We would expect this to increase performance since we have many equal size processes with even load.</p> <p>The mainstay of the Nehalem value proposition is a better memory subsystem. Since the unit we got was at 800 MHz memory, we did not see any great improvement. So if you buy Nehalem, you should make sure it is with 1333 MHz memory, else the best case will not be over 50% over a 667 MHz Core 2-based Xeon.</p> <p>Nehalem remains a better deal for us because of more memory per board. One Nehalem box with 72 GB costs less than two Harpertown boxes with 32 GB and offers almost the same performance. Having a lot of memory in a small space is key. With faster memory, it might even outperform two Harpertown boxes, but this remains to be seen.</p> <p>If space were not a constraint, we could make a cluster of 12 small workstations for the price of our largest system and get still more memory and more processor power per unit of memory. The Nehalem box was almost 4x faster than the AMD box but then it has 9x the memory, so the CPU to memory ratio might be better with the smaller boxes.</p>
Comparing Virtuoso Performance on Different Processors
http://www.openlinksw.com/weblog/oerling/?date=2009-05-28#1557
2009-05-28T14:54:59Z
2009-05-28T11:15:39-04:00
<p>Over the years we have run <a href="http://virtuoso.openlinksw.com" id="link-id0xd420b90">Virtuoso</a> on different hardware. We will here give a few figures that help identify the best price point for machines running Virtuoso.</p> <p>Our test is very simple: <i>Load 20 warehouses of <a href="http://dbpedia.org/resource/TPC-C" id="link-id0xdaaec90">TPC-C</a> <a href="http://dbpedia.org/resource/Data" id="link-id0xca1b7e0">data</a>, and then run one client per warehouse for 10,000 new orders</i>. The way this is set up, disk I/O does not play a role and lock contention between the clients is minimal.</p> <p>The test essentially has 20 server and 20 client threads running the same workload in parallel. The load time gives the single thread number; the 20 clients run gives the multi-threaded number. The test uses about 2-3 GB of data, so all is in RAM but is large enough not to be all in processor cache.</p> <p>All times reported are real times, starting from the start of the first client and ending with the completion of the last client.</p> <p>Do not confuse these results with official TPC-C. The measurement protocols are entirely incomparable.</p> <style type="text/css"> TABLE { background: none; border: none } TH { text-align: center; font-weight: bold } TR.top { background: } TD { text-align: center; border: none } </style> <table align="center" cellspacing="10"> <tr> <th>Test</th> <th>Platform</th> <th>Load<br />(seconds)</th> <th>Run<br />(seconds)</th> <th>GHz / cores / threads</th> </tr> <tr> <td>1</td> <td>Amazon <a href="http://aws.amazon.com/ec2/" id="link-id0xdaab030">EC2</a> Extra Large<br />(4 virtual cores)</td> <td>340</td> <td>42</td> <td>1.2 GHz? / 4 / 1</td> </tr> <tr> <td>1</td> <td>Amazon EC2 Extra Large<br />(4 virtual cores)</td> <td>305</td> <td>43.3</td> <td>1.2 GHz? / 4 / 1</td> </tr> <tr> <td>2</td> <td>1 x dual-core AMD 5900</td> <td>263</td> <td>58.2</td> <td>2.9 GHz / 2 / 1</td> </tr> <tr> <td>3</td> <td>2 x dual-core Xeon 5130 ("Woodcrest")</td> <td>245</td> <td>35.7</td> <td>2.0 GHz / 4 / 1</td> </tr> <tr> <td>4</td> <td>2 x quad-core Xeon 5410 ("Harpertown")</td> <td>237</td> <td>18.0</td> <td>2.33 GHz / 8 / 1</td> </tr> <tr> <td>5</td> <td>2 x quad-core Xeon 5520 ("Nehalem")</td> <td>162</td> <td>18.3</td> <td>2.26 GHz / 8 / 2</td> </tr> </table> <p>We tried two different EC2 instances to see if there would be variation. The variation was quite small. The tested EC2 instances costs 20 US cents per hour. The AMD dual-core costs 550 US dollars with 8G. The 3 Xeon configurations are Supermicro boards with 667MHz memory for the Xeon 5130 ("Woodcrest") and Xeon 5410 ("Harpertown"), and 800MHz memory for the Nehalem. The Xeon systems cost between 4000 and 7000 US dollars, with 5000 for a configuration with 2 x Xeon 5520 ("Nehalem"), 72 GB RAM, and 8 x 500 GB SATA disks.</p> <p> <i>Caveat: Due to slow memory (we could not get faster within available time), the results for the Nehalem do not take full advantage of its principal edge over the previous generation, i.e., memory subsystem. We'll see another time with faster memories.</i> </p> <p>The operating systems were various 64 bit Linux distributions.</p> <p>We did some further measurements comparing Harpertown and Nehalem processors. The Nehalem chip was a bit faster for a slightly lower clock but we did not see any of the twofold and greater differences advertised by Intel.</p> <p>We tried some <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xce85438">RDF</a> operations on the two last systems:</p> <table align="center" cellspacing="10"> <tr> <th>operation</th> <th> Harpertown</th> <th>Nehalem</th> </tr> <tr> <th>Build text index for <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xab826a8">DBpedia</a></th> <td>1080s</td> <td>770s</td> </tr> <tr> <th><a href="http://dbpedia.org/resource/Entity" id="link-id0xcbb9938">Entity</a> Rank iteration</th> <td>263s</td> <td>251s</td> </tr> </table> <p>Then we tried to see if the core multithreading of Nehalem could be seen anywhere. To this effect, we ran the Fibonacci function in <a href="http://dbpedia.org/resource/SQL" id="link-id0xcd62218">SQL</a> to serve as an example of an all in-cache integer operation. 16 concurrent operations took exactly twice as long as 8 concurrent ones, as expected.</p> <p>For something that used memory, we took a count of RDF quads on two different indices, getting the same count. The database was a cluster setup with one process per core, so a count involved one thread per core. The counts in series took 5.02s and in parallel they took 4.27s.</p> <p>Then we took a more memory intensive piece that read the RDF quads table in the order of one index and for each row checked that there is the equal row on another, differently-partitioned index. This is a cross-partition join. One of the indices is read sequentially and the other at random. The throughput can be reported as random-lookups-per-second. The data was English DBpedia, about 140M triples. One such query takes a couple of minutes with a 650% CPU utilization. Running multiple such queries should show effects of core multithreading since we expect frequent cache misses.</p> <ol> <li>On the host OS of the Nehalem system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td>503</td> <td>906,413</td> </tr> <tr> <th>2 queries</th> <td>1263</td> <td>1,578,585</td> </tr> <tr> <th>3 queries</th> <td>1204</td> <td>1,566,849</td> </tr> </table> </li> <li>In a VM under Xen, on the Nehalem system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td>652</td> <td>799,293</td> </tr> <tr> <th>2 queries</th> <td>1266</td> <td>1,486,710</td> </tr> <tr> <th>3 queries</th> <td>1222</td> <td>1,484,093</td> </tr> </table> </li> <li> On the host OS of the Harpertown system â <table align="center" cellspacing="10"> <tr> <th>n</th> <th>cpu%</th> <th>rows per second</th> </tr> <tr> <th>1 query</th> <td> 648 </td> <td> 1,041,448 </td> </tr> <tr> <th>2 queries</th> <td> 708 </td> <td> 1,124,866 </td> </tr> </table> </li> </ol> <p>The CPU percentages are as reported by the OS: user + system CPU divided by real time.</p> <p>So, Nehalem is in general somewhat faster, around 20-30%, than Harpertown. The effect of core multithreading can be noticed but is not huge, another 20% or so for situations with more threads than cores. The join where Harpertown did better could be attributed to its larger cache â 12 MB vs 8 MB.</p> <p>We see that Xen has a measurable but not prohibitive overhead; count a little under 10% for everything, also tasks with no I/O. The VM was set up to have all CPU for the test and the queries did not do disk I/O.</p> <p>The executables were compiled with <code>gcc</code> with default settings. Specifying <code>-march=nocona</code> (Core 2 target) dropped the cross-partition join time mentioned above from 128s to 122s on Harpertown. We did not try this on Nehalem but presume the effect would be the same, since the out-of-order unit is not much different. We did not do anything about process-to-memory affinity on Nehalem, which is a non-uniform architecture. We would expect this to increase performance since we have many equal size processes with even load.</p> <p>The mainstay of the Nehalem value proposition is a better memory subsystem. Since the unit we got was at 800 MHz memory, we did not see any great improvement. So if you buy Nehalem, you should make sure it is with 1333 MHz memory, else the best case will not be over 50% over a 667 MHz Core 2-based Xeon.</p> <p>Nehalem remains a better deal for us because of more memory per board. One Nehalem box with 72 GB costs less than two Harpertown boxes with 32 GB and offers almost the same performance. Having a lot of memory in a small space is key. With faster memory, it might even outperform two Harpertown boxes, but this remains to be seen.</p> <p>If space were not a constraint, we could make a cluster of 12 small workstations for the price of our largest system and get still more memory and more processor power per unit of memory. The Nehalem box was almost 4x faster than the AMD box but then it has 9x the memory, so the CPU to memory ratio might be better with the smaller boxes.</p>
Short Recap of Virtuoso Basics (#3 of 5)
http://www.openlinksw.com/blog/vdb/blog/?date=2009-04-30#1552
2009-04-30T15:49:53Z
2009-04-30T12:11:45-04:00
<p>(Third of five posts related to the <a href="http://www2009.org/" id="link-id0x14b582b8">WWW 2009</a> conference, held the week of April 20, 2009.) </p> <p>There are some points that came up in conversation at WWW 2009 that I will reiterate here. We find there is still some lack of clarity in the product image, so I will here condense it.</p> <p> <a href="http://virtuoso.openlinksw.com" id="link-id0x14bf48b8">Virtuoso</a> is a DBMS. We pitch it primarily to the <a href="http://dbpedia.org/resource/Data" id="link-id0x16bc4490">data</a> web space because this is where we see the emerging frontier. Virtuoso does both <a href="http://dbpedia.org/resource/SQL" id="link-id0x1223dc30">SQL</a> and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x170eec88">SPARQL</a> and can do both at large scale and high performance. The popular perception of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x15a05fc0">RDF</a> and Relational models as mutually exclusive and antagonistic poles is based on the poor scalability of early RDF implementations. What we do is to have all the RDF specifics, like IRIs and typed literals as native SQL types, and to have a cost based optimizer that knows about this all.</p> <p>If you want application-specific data structures as opposed to a schema-agnostic quad-store model (triple + graph-name), then Virtuoso can give you this too. <a href="http://docs.openlinksw.com/virtuoso/rdfsparqlintegrationmiddleware.html#rdfviews" id="link-id14ddc7c8">Rendering application specific data structures as RDF</a> applies equally to relational data in non-Virtuoso databases because Virtuoso SQL can <a href="http://docs.openlinksw.com/virtuoso/qsvdbsrv.html" id="link-id14aaea70">federate tables from heterogenous DBMS</a>.</p> <p>On top of this, there is a <a href="http://docs.openlinksw.com/virtuoso/qswebserver.html" id="link-id16fcde60">web server built in</a>, so that no extra server is needed for web services, web pages, and the like.</p> <p>Installation is simple, just one exe and one config file. There is a huge amount of code in <a href="http://docs.openlinksw.com/virtuoso/installation.html" id="link-id16767b40">installers</a> â application code and test suites and such â but none of this is needed when you deploy. Scale goes from a 25MB memory footprint on the desktop to hundreds of gigabytes of RAM and endless terabytes of disk on shared-nothing clusters.</p> <p>Clusters (coming in Release 6) and SQL federation are <a href="http://download.openlinksw.com/download/product_matrix.vsp?p=l_os&c=39&df=16" id="link-id16722550">commercial only</a>; the rest can be had <a href="http://sourceforge.net/project/showfiles.php?group_id=161622" id="link-id131080a8">under GPL</a>.</p> <p>To condense further:</p> <ul> <li>Scalable Delivery of <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1060ad98">Linked Data</a> </li> <li>SPARQL and SQL <ul> <li>Arbitrary RDF Data + Relational</li> <li>Also From 3rd Party <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x16bbce60">RDBMS</a> </li> </ul> </li> <li>Easy Deployment </li> <li>Standard Interfaces <ul> <li> <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x12e284d8">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xb5e1400">JDBC</a>, OLE DB, <a href="http://dbpedia.org/resource/ADO.NET" id="link-id0x15a55db8">ADO</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x16beb070">NET</a>, XMLA</li> <li> <a href="http://jena.sourceforge.net/" id="link-id0x122b5008">Jena</a>, <a href="http://sourceforge.net/projects/sesame/" id="link-id0x148d4078">Sesame</a>, etc.</li> <li>All Web Protocols </li> </ul> </li> </ul>
Short Recap of Virtuoso Basics (#3 of 5)
http://www.openlinksw.com/weblog/oerling/?date=2009-04-30#1550
2009-04-30T15:49:53Z
2009-04-30T12:11:43.000001-04:00
<p>(Third of five posts related to the <a href="http://www2009.org/" id="link-id0x1081fe40">WWW 2009</a> conference, held the week of April 20, 2009.) </p> <p>There are some points that came up in conversation at WWW 2009 that I will reiterate here. We find there is still some lack of clarity in the product image, so I will here condense it.</p> <p> <a href="http://virtuoso.openlinksw.com" id="link-id0xd0e85f0">Virtuoso</a> is a DBMS. We pitch it primarily to the <a href="http://dbpedia.org/resource/Data" id="link-id0x14a294d8">data</a> web space because this is where we see the emerging frontier. Virtuoso does both <a href="http://dbpedia.org/resource/SQL" id="link-id0x108042f8">SQL</a> and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x10889878">SPARQL</a> and can do both at large scale and high performance. The popular perception of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x107d3b40">RDF</a> and Relational models as mutually exclusive and antagonistic poles is based on the poor scalability of early RDF implementations. What we do is to have all the RDF specifics, like IRIs and typed literals as native SQL types, and to have a cost based optimizer that knows about this all.</p> <p>If you want application-specific data structures as opposed to a schema-agnostic quad-store model (triple + graph-name), then Virtuoso can give you this too. <a href="http://docs.openlinksw.com/virtuoso/rdfsparqlintegrationmiddleware.html#rdfviews" id="link-id14ddc7c8">Rendering application specific data structures as RDF</a> applies equally to relational data in non-Virtuoso databases because Virtuoso SQL can <a href="http://docs.openlinksw.com/virtuoso/qsvdbsrv.html" id="link-id14aaea70">federate tables from heterogenous DBMS</a>.</p> <p>On top of this, there is a <a href="http://docs.openlinksw.com/virtuoso/qswebserver.html" id="link-id16fcde60">web server built in</a>, so that no extra server is needed for web services, web pages, and the like.</p> <p>Installation is simple, just one exe and one config file. There is a huge amount of code in <a href="http://docs.openlinksw.com/virtuoso/installation.html" id="link-id16767b40">installers</a> â application code and test suites and such â but none of this is needed when you deploy. Scale goes from a 25MB memory footprint on the desktop to hundreds of gigabytes of RAM and endless terabytes of disk on shared-nothing clusters.</p> <p>Clusters (coming in Release 6) and SQL federation are <a href="http://download.openlinksw.com/download/product_matrix.vsp?p=l_os&c=39&df=16" id="link-id16722550">commercial only</a>; the rest can be had <a href="http://sourceforge.net/project/showfiles.php?group_id=161622" id="link-id131080a8">under GPL</a>.</p> <p>To condense further:</p> <ul> <li>Scalable Delivery of <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x12211da8">Linked Data</a> </li> <li>SPARQL and SQL <ul> <li>Arbitrary RDF Data + Relational</li> <li>Also From 3rd Party <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x168db0e0">RDBMS</a> </li> </ul> </li> <li>Easy Deployment </li> <li>Standard Interfaces <ul> <li> <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x10473bf0">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x12187f58">JDBC</a>, OLE DB, <a href="http://dbpedia.org/resource/ADO.NET" id="link-id0x10354e48">ADO</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x16eeadd0">NET</a>, XMLA</li> <li> <a href="http://jena.sourceforge.net/" id="link-id0x12e3fe08">Jena</a>, <a href="http://sourceforge.net/projects/sesame/" id="link-id0x15e62470">Sesame</a>, etc.</li> <li>All Web Protocols </li> </ul> </li> </ul>
Linked Data at WWW 2009 (#1 of 5)
http://www.openlinksw.com/blog/vdb/blog/?date=2009-04-27#1545
2009-04-27T21:28:11Z
2009-04-28T11:27:57-04:00
<p>(First of five posts related to the <a href="http://www2009.org/" id="link-id0x12d8ed90">WWW 2009</a> conference, held the week of April 20, 2009.)</p> <p>We gave a talk at the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x152bf430">Linked Open Data</a> workshop, <a href="http://events.linkeddata.org/ldow2009/" id="link-id0x191721c8">LDOW 2009</a>, at WWW 2009. I did not go very far into the technical points in the talk, as there was almost no time and the points are rather complex. Instead, I emphasized what new things had become possible with recent developments.</p> <p>The problem we do not cease hearing about is scale. We have solved most of it. There is scale in the schema: Put together, ontologies go over a million classes/properties. Which ones are relevant depends, and the user should have the choice. The instance <a href="http://dbpedia.org/resource/Data" id="link-id0x17c8f998">data</a> is in the tens of billions of triples, much derived from Web 2.0 sources but also much published as <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xd562090">RDF</a>.</p> <p>To make sense of this all, we need quick summaries and search. Without navigation via joins, the value will be limited. Fast joining, counting, grouping, and ranking are key.</p> <p>People will use different terms for the same thing. The issue of identity is philosophical. In order to do reasoning one needs strong identity; a statement like <i>x is a bit like y</i> is not very useful in a database context. Whether any x and y can be considered the same depends on the context. So leave this for query time. The conditions under which two people are considered the same will depend on whether you are doing marketing analysis or law enforcement. A general purpose data store cannot anticipate all the possibilities, so smush on demand, as you go, as has been said many times.</p> <p>Against this backdrop, we offer a solution with which anybody who so chooses can play with big data, whether a search or analytics player.</p> <p>We are going in the direction of more and more ad hoc processing at larger and larger scale. With good query parallelization, we can do big joins without complex programming. No explicit Map Reduce jobs or the like. What was done with special code with special parallel programming models, can now be done in <a href="http://dbpedia.org/resource/SQL" id="link-id0x60bd0c48">SQL</a> and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x13db1ff0">SPARQL</a>.</p> <p>To showcase this, we do <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x10a5dde8">linked data</a> search, browsing, and so on, but are essentially a platform provider.</p> <p>Entry costs into relatively high end databases have dropped significantly. A cluster with 1 TB of RAM sells for $75K or so at today's retail prices and fits under a desk. For intermittent use, the rent for 1TB RAM is $1228 per day on <a href="http://aws.amazon.com/ec2/" id="link-id0xa59039d8">EC2</a>. With this on one side and <a href="http://virtuoso.openlinksw.com" id="link-id0x19f86c10">Virtuoso</a> on the other, a lot that was impractical in the past is now within reach. Like <a href="http://g1o.net/foaf.rdf#me" id="link-id0xa1853af8">Giovanni Tummarello</a> put it for airplanes, the physics are as they were for <a href="http://dbpedia.org/resource/Leonardo_da_Vinci" id="link-id0x12df02e0">da Vinci</a> but materials and engines had to develop a bit before there was commercial potential. So it is also with analytics for everyone.</p> <p>A remark from the audience was that all the stuff being shown, not limited to Virtuoso, was non-standard, having to do with text search, with ranking, with extensions, and was in fact not SPARQL and pure linked data principles. Further, by throwing this all together, one got something overcomplicated, too heavy.</p> <p>I answered as follows, which apparently cannot be repeated too much:</p> <p>First, everybody expects a text search box, and is conditioned to having one. No text search and no ranking is a non-starter. <i>Ceterum censeo</i>, for database, the next generation cannot be less expressive than the previous. All of SQL and then some is where SPARQL must be. The barest minimum is being able to say anything one can say in SQL, and then justify SPARQL by saying that it is better for heterogenous data, schema last, and so on. On top of this, transitivity and rules will not hurt. For now, the current SPARQL working group will at least reach basic SQL parity; the edge will still remain implementation dependent.</p> <p>Another remark was that joining is slow. Depends. Anything involving more complex disk access than linear reading of a blob is generally not good for interactive use. But with adequate memory, and with all hot spots in memory, we do some 3.2 million random-accesses-per-second on 12 cores, with easily 80% platform utilization for a single large query. The high utilization means that times drop as processing gets divided over more partitions.</p> <p>There was a talk about <a href="http://semanticweb.org/wiki/MashQL" id="link-id0x1642a780">MashQL</a> by <a href="http://data.semanticweb.org/person/mustafa-jarrar" id="link-id0x116e5af8">Mustafa Jarrar</a>, concerning an abstraction on top of SPARQL for easy composition of tree-structured queries. The idea was that such queries can be evaluated "on the fly" as they are being composed. As it happens, we already have an <a href="http://dbpedia.org/resource/XML" id="link-id0x11442520">XML</a>-based query abstraction layer incorporated into Virtuoso 6.0's built-in <a href="http://lod.openlinksw.com/fct/facet.vsp" id="link-id0x6a9ebfe0">Faceted Data Browser Service</a>, and the effects are probably quite similar. The most important point here is that by using XML, both of these approaches are interoperable against a Virtuoso back-end. Along similar lines, we did not get to talk to the G Facets people but our message to them is the same: <i>Use the <a href="http://lod.openlinksw.com/fct/facet.vsp" id="link-id0x1676e158">faceted browser service</a> to get vastly higher performance when querying against Linked Data, be it <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x12653418">DBpedia</a> or the <a href="http://dbpedia.org/resource/Entity" id="link-id0x10a61e78">entity</a> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x164150d8">LOD</a> <a href="http://lod.openlinksw.com/" id="link-id0xc5ec918">Cloud</a>. Virtuoso 6.0 (Open Source Edition) "<a href="http://sourceforge.net/project/showfiles.php?group_id=161622&package_id=319652&release_id=677866" id="link-id12159728">TP1</a>" is now publicly available as a Technology Preview (beta).</i> </p> <p>We heard that there is an effort for porting Freebase's Parallax to SPARQL. The same thing applies to this. With a number of different data viewers on top of SPARQL, we come closer to broad-audience linked-data applications. These viewers are still too generic for the end user, though. We fully believe that for both search and transactions, application-domain-specific workflows will stay relevant. But these can be made to a fair degree by specializing generic linked-data-bound controls and gluing them together with some scripting.</p> <p>As said before, the application will interface the user to the vocabulary. The vocabulary development takes the modeling burden from the application and makes for interchangeable experience on the same data. The data in turn is "virtualized" into the database cloud or the local secure server, as the use case may require. </p> <p>For ease of adoption, open competition, and safety from lock-in, the community needs a SPARQL whose usability is not totally dependent on vendor extensions. But we might <i>de facto</i> have that in just a bit, whenever there is a working draft from the SPARQL WG.</p> <p>Another topic that we encounter often is the question of integration (or lack thereof) between communities. For example, database conferences reject <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x12563ea0">semantic web</a> papers and vice versa. Such politics would seem to emerge naturally but are nonetheless detrimental. We really should partner with people who write papers as their principal occupation. We ourselves do software products and use very little time for papers, so some of the bad reviews we have received do make a legitimate point. By rights, we should go for database venues but we cannot have this take too much time. So we are open to partnering for splitting the opportunity cost of multiple submissions.</p> <p>For future work, there is nothing radically new. We continue testing and productization of cluster databases. Just deliver what is in the pipeline. The essential nature of this is adding more and more cases of better and better parallelization in different query situations. The present usage patterns work well for finding bugs and performance bottlenecks. For presentation, our goal is to have third party viewers operate with our platform. We cannot completely leave data browsing and UI to third parties since we must from time to time introduce various unique functionality. Most interaction should however go via third party applications.</p>
Linked Data at WWW 2009 (#1 of 5)
http://www.openlinksw.com/weblog/oerling/?date=2009-04-27#1544
2009-04-27T21:28:11Z
2009-04-28T11:27:50-04:00
<p>(First of five posts related to the <a href="http://www2009.org/" id="link-id0x114c2450">WWW 2009</a> conference, held the week of April 20, 2009.)</p> <p>We gave a talk at the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x166e10f0">Linked Open Data</a> workshop, <a href="http://events.linkeddata.org/ldow2009/" id="link-id0x19c2b1f0">LDOW 2009</a>, at WWW 2009. I did not go very far into the technical points in the talk, as there was almost no time and the points are rather complex. Instead, I emphasized what new things had become possible with recent developments.</p> <p>The problem we do not cease hearing about is scale. We have solved most of it. There is scale in the schema: Put together, ontologies go over a million classes/properties. Which ones are relevant depends, and the user should have the choice. The instance <a href="http://dbpedia.org/resource/Data" id="link-id0x12c65250">data</a> is in the tens of billions of triples, much derived from Web 2.0 sources but also much published as <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x441128e0">RDF</a>.</p> <p>To make sense of this all, we need quick summaries and search. Without navigation via joins, the value will be limited. Fast joining, counting, grouping, and ranking are key.</p> <p>People will use different terms for the same thing. The issue of identity is philosophical. In order to do reasoning one needs strong identity; a statement like <i>x is a bit like y</i> is not very useful in a database context. Whether any x and y can be considered the same depends on the context. So leave this for query time. The conditions under which two people are considered the same will depend on whether you are doing marketing analysis or law enforcement. A general purpose data store cannot anticipate all the possibilities, so smush on demand, as you go, as has been said many times.</p> <p>Against this backdrop, we offer a solution with which anybody who so chooses can play with big data, whether a search or analytics player.</p> <p>We are going in the direction of more and more ad hoc processing at larger and larger scale. With good query parallelization, we can do big joins without complex programming. No explicit Map Reduce jobs or the like. What was done with special code with special parallel programming models, can now be done in <a href="http://dbpedia.org/resource/SQL" id="link-id0x16766eb0">SQL</a> and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1645ddc8">SPARQL</a>.</p> <p>To showcase this, we do <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0xa167e698">linked data</a> search, browsing, and so on, but are essentially a platform provider.</p> <p>Entry costs into relatively high end databases have dropped significantly. A cluster with 1 TB of RAM sells for $75K or so at today's retail prices and fits under a desk. For intermittent use, the rent for 1TB RAM is $1228 per day on <a href="http://aws.amazon.com/ec2/" id="link-id0xa1a67b70">EC2</a>. With this on one side and <a href="http://virtuoso.openlinksw.com" id="link-id0x1622d4e0">Virtuoso</a> on the other, a lot that was impractical in the past is now within reach. Like <a href="http://g1o.net/foaf.rdf#me" id="link-id0x3d5c8b50">Giovanni Tummarello</a> put it for airplanes, the physics are as they were for <a href="http://dbpedia.org/resource/Leonardo_da_Vinci" id="link-id0x198e7cc0">da Vinci</a> but materials and engines had to develop a bit before there was commercial potential. So it is also with analytics for everyone.</p> <p>A remark from the audience was that all the stuff being shown, not limited to Virtuoso, was non-standard, having to do with text search, with ranking, with extensions, and was in fact not SPARQL and pure linked data principles. Further, by throwing this all together, one got something overcomplicated, too heavy.</p> <p>I answered as follows, which apparently cannot be repeated too much:</p> <p>First, everybody expects a text search box, and is conditioned to having one. No text search and no ranking is a non-starter. <i>Ceterum censeo</i>, for database, the next generation cannot be less expressive than the previous. All of SQL and then some is where SPARQL must be. The barest minimum is being able to say anything one can say in SQL, and then justify SPARQL by saying that it is better for heterogenous data, schema last, and so on. On top of this, transitivity and rules will not hurt. For now, the current SPARQL working group will at least reach basic SQL parity; the edge will still remain implementation dependent.</p> <p>Another remark was that joining is slow. Depends. Anything involving more complex disk access than linear reading of a blob is generally not good for interactive use. But with adequate memory, and with all hot spots in memory, we do some 3.2 million random-accesses-per-second on 12 cores, with easily 80% platform utilization for a single large query. The high utilization means that times drop as processing gets divided over more partitions.</p> <p>There was a talk about <a href="http://semanticweb.org/wiki/MashQL" id="link-id0x60bd57b0">MashQL</a> by <a href="http://data.semanticweb.org/person/mustafa-jarrar" id="link-id0xa1fb98d8">Mustafa Jarrar</a>, concerning an abstraction on top of SPARQL for easy composition of tree-structured queries. The idea was that such queries can be evaluated "on the fly" as they are being composed. As it happens, we already have an <a href="http://dbpedia.org/resource/XML" id="link-id0x1923a380">XML</a>-based query abstraction layer incorporated into Virtuoso 6.0's built-in <a href="http://lod.openlinksw.com/fct/facet.vsp" id="link-id0x67712740">Faceted Data Browser Service</a>, and the effects are probably quite similar. The most important point here is that by using XML, both of these approaches are interoperable against a Virtuoso back-end. Along similar lines, we did not get to talk to the G Facets people but our message to them is the same: <i>Use the <a href="http://lod.openlinksw.com/fct/facet.vsp" id="link-id0x70df2798">faceted browser service</a> to get vastly higher performance when querying against Linked Data, be it <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1b3fd608">DBpedia</a> or the <a href="http://dbpedia.org/resource/Entity" id="link-id0x13ecd708">entity</a> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x17f16970">LOD</a> <a href="http://lod.openlinksw.com/" id="link-id0x54334250">Cloud</a>. Virtuoso 6.0 (Open Source Edition) "<a href="http://sourceforge.net/project/showfiles.php?group_id=161622&package_id=319652&release_id=677866" id="link-id12159728">TP1</a>" is now publicly available as a Technology Preview (beta).</i> </p> <p>We heard that there is an effort for porting Freebase's Parallax to SPARQL. The same thing applies to this. With a number of different data viewers on top of SPARQL, we come closer to broad-audience linked-data applications. These viewers are still too generic for the end user, though. We fully believe that for both search and transactions, application-domain-specific workflows will stay relevant. But these can be made to a fair degree by specializing generic linked-data-bound controls and gluing them together with some scripting.</p> <p>As said before, the application will interface the user to the vocabulary. The vocabulary development takes the modeling burden from the application and makes for interchangeable experience on the same data. The data in turn is "virtualized" into the database cloud or the local secure server, as the use case may require. </p> <p>For ease of adoption, open competition, and safety from lock-in, the community needs a SPARQL whose usability is not totally dependent on vendor extensions. But we might <i>de facto</i> have that in just a bit, whenever there is a working draft from the SPARQL WG.</p> <p>Another topic that we encounter often is the question of integration (or lack thereof) between communities. For example, database conferences reject <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x185d6bf8">semantic web</a> papers and vice versa. Such politics would seem to emerge naturally but are nonetheless detrimental. We really should partner with people who write papers as their principal occupation. We ourselves do software products and use very little time for papers, so some of the bad reviews we have received do make a legitimate point. By rights, we should go for database venues but we cannot have this take too much time. So we are open to partnering for splitting the opportunity cost of multiple submissions.</p> <p>For future work, there is nothing radically new. We continue testing and productization of cluster databases. Just deliver what is in the pipeline. The essential nature of this is adding more and more cases of better and better parallelization in different query situations. The present usage patterns work well for finding bugs and performance bottlenecks. For presentation, our goal is to have third party viewers operate with our platform. We cannot completely leave data browsing and UI to third parties since we must from time to time introduce various unique functionality. Most interaction should however go via third party applications.</p>
Web Scale and Fault Tolerance
http://www.openlinksw.com/blog/vdb/blog/?date=2009-04-01#1541
2009-04-01T15:18:06Z
2009-04-01T11:18:54.000012-04:00
<p>One concern about <a href="http://virtuoso.openlinksw.com" id="link-id0x719d2f8">Virtuoso</a> Cluster is fault tolerance. This post talks about the basics of fault tolerance and what we can do with this, from improving resilience and optimizing performance to accommodating bulk loads without impacting interactive response. We will see that this is yet another step towards a 24/7 web-scale <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0xa9a1d8d8">Linked Data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x25201030">Web</a>. We will see how large scale, continuous operation, and redundancy are related.</p> <p>It has been said many times â when things are large enough, failures become frequent. In view of this, basic storage of partitions in multiple copies is built into the Virtuoso cluster from the start. Until now, this feature has not been tested or used very extensively, aside from the trivial case of keeping all schema <a href="http://dbpedia.org/resource/Information" id="link-id0x4548898">information</a> in synchronous replicas on all servers.</p> <h2>Approaches to Fault Tolerance</h2> <p>Fault tolerance has many aspects but it starts with keeping <a href="http://dbpedia.org/resource/Data" id="link-id0x18757400">data</a> in at least two copies. There are shared-disk cluster databases like <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x711c900">Oracle</a> RAC that do not depend on partitioning. With these, as long as the disk image is intact, servers can come and go. The fault tolerance of the disk in turn comes from mirroring done by the disk controller. Raids other than mirrored disk are not really good for databases because of write speed.</p> <p>With shared-nothing setups like Virtuoso, fault tolerance is based on multiple servers keeping the same logical data. The copies are synchronized transaction-by-transaction but are not bit-for-bit identical nor write-by-write synchronous as is the case with mirrored disks.</p> <p>There are asynchronous replication schemes generally based on log shipping, where the replica replays the transaction log of the master copy. The master copy gets the updates, the replica replays them. Both can take queries. These do not guarantee an entirely ACID fail-over but for many applications they come close enough.</p> <p>In a tightly coupled cluster, it is possible to do synchronous, transactional updates on multiple copies without great added cost. Sending the message to two places instead of one does not make much difference since it is the latency that counts. But once we go to wide area networks, this becomes as good as unworkable for any sort of update volume. Thus, wide area replication must in practice be asynchronous.</p> <p>This is a subject for another discussion. For now, the short answer is that wide area log shipping must be adapted to the application's requirements for synchronicity and consistency. Also, exactly what content is shipped and to where depends on the application. Some application-specific logic will likely be involved; more than this one cannot say without a specific context.</p> <h2>Basics of Partition Fail-Over</h2> <p>For now, we will be concerned with redundancy protecting against broken hardware, software slowdown, or crashes inside a single site.</p> <p>The basic idea is simple: Writes go to all copies; reads that must be repeatable or serializable (i.e., locking) go to the first copy; reads that refer to committed state without guarantee of repeatability can be balanced among all copies. When a copy goes offline, nobody needs to know, as long as there is at least one copy online for each partition. The exception in practice is when there are open cursors or such stateful things as aggregations pending on a copy that goes down. Then the query or transaction will abort and the application can retry. This looks like a deadlock to the application.</p> <p>Coming back online is more complicated. This requires establishing that the recovering copy is actually in sync. In practice this requires a short window during which no transactions have uncommitted updates. Sometimes, forcing this can require aborting some transactions, which again looks like a deadlock to the application.</p> <p>When an error is seen, such as a process no longer accepting connections and dropping existing cluster connections, we in practice go via two stages. First, the operations that directly depended on this process are aborted, as well as any computation being done on behalf of the disconnected server. At this stage, attempting to read data from the partition of the failed server will go to another copy but writes will still try to update all copies and will fail if the failed copy continues to be offline. After it is established that the failed copy will stay off for some time, writes may be re-enabled â but now having the failed copy rejoin the cluster will be more complicated, requiring an atomic window to ensure sync, as mentioned earlier.</p> <p>For the DBA, there can be intermittent software crashes where a failed server automatically restarts itself, and there can be prolonged failures where this does not happen. Both are alerts but the first kind can wait. Since a system must essentially run itself, it will wait for some time for the failed server to restart itself. During this window, all reads of the failed partition go to the spare copy and writes give an error. If the spare does not come back up in time, the system will automatically re-enable writes on the spare but now the failed server may no longer rejoin the cluster without a complex sync cycle. This all can happen in well under a minute, faster than a human operator can react. The diagnostics can be done later.</p> <p>If the situation was a hardware failure, recovery consists of taking a spare server and copying the database from the surviving online copy. This done, the spare server can come on line. Copying the database can be done while online and accepting updates but this may take some time, maybe an hour for every 200G of data copied over a network. In principle this could be automated by scripting, but we would normally expect a human DBA to be involved.</p> <p>As a general rule, reacting to the failure goes automatically without disruption of service but bringing the failed copy online will usually require some operator action.</p> <h2>Levels of Tolerance and Performance</h2> <p>The only way to make failures totally invisible is to have all in duplicate and provisioned so that the system never runs at more than half the total capacity. This is often not economical or necessary. This is why we can do better, using the spare capacity for more than standby.</p> <p>Imagine keeping a repository of linked data. Most of the content will come in through periodic bulk replacement of data sets. Some data will come in through pings from applications publishing FOAF and similar. Some data will come through on-demand RDFization of resources.</p> <p>The performance of such a repository essentially depends on having enough memory. Having this memory in duplicate is just added cost. What we can do instead is have all copies store the whole partition but when routing queries, apply range partitioning on top of the basic hash partitioning. If one partition stores IDs 64K - 128K, the next partition 128K - 192K, and so forth, and all partitions are stored in two full copies, we can route reads to the first 32K IDs to the first copy and reads to the second 32K IDs to the second copy. In this way, the copies will keep different working sets. The RAM is used to full advantage.</p> <p>Of course, if there is a failure, then the working set will degrade, but if this is not often and not for long, this can be quite tolerable. The alternate expense is buying twice as much RAM, likely meaning twice as many servers. This workload is memory intensive, thus servers should have the maximum memory they can have without going to parts that are so expensive one gets a new server for the price of doubling memory.</p> <h2>Background Bulk Processing</h2> <p>When loading data, the system is online in principle, but query response can be quite bad. A large <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x19fd9c18">RDF</a> load will involve most memory and queries will miss the cache. The load will further keep most disks busy, so response is not good. This is the case as soon as a server's partition of the database is four times the size of RAM or greater. Whether the work is bulk-load or bulk-delete makes little difference.</p> <p>But if partitions are replicated, we can temporarily split the database so that the first copies serve queries and the second copies do the load. If the copies serving on line activities do some updates also, these updates will be committed on both copies. But the load will be committed on the second copy only. This is fully appropriate as long as the data are different. When the bulk load is done, the second copy of each partition will have the full up to date state, including changes that came in during the bulk load. The online activity can be now redirected to the second copies and the first copies can be overwritten in the background by the second copies, so as to again have all data in duplicate.</p> <p>Failures during such operations are not dangerous. If the copies doing the bulk load fail, the bulk load will have to be restarted. If the front end copies fail, the front end load goes to the copies doing the bulk load. Response times will be bad until the bulk load is stopped, but no data is lost.</p> <p>This technique applies to all data intensive background tasks â calculation of <a href="http://dbpedia.org/resource/Entity" id="link-id0x20b7a568">entity</a> search ranks, data cleansing, consistency checking, and so on. If two copies are needed to keep up with the online load, then data can be kept just as well in three copies instead of two. This method applies to any data-warehouse-style workload which must coexist with online access and occasional low volume updating.</p> <h2>Configurations of Redundancy</h2> <p>Right now, we can declare that two or more server processes in a cluster form a group. All data managed by one member of the group is stored by all others. The members of the group are interchangeable. Thus, if there is four-servers-worth of data, then there will be a minimum of eight servers. Each of these servers will have one server process per core. The first hardware failure will not affect operations. For the second failure, there is a 1/7 chance that it stops the whole system, if it falls on the server whose pair is down. If groups consist of three servers, for a total of 12, the two first failures are guaranteed not to interrupt operations; for the third, there is a 1/10 chance that it will.</p> <p>We note that for big databases, as said before, the RAM cache capacity is the sum of all the servers' RAM when in normal operation.</p> <p>There are other, more dynamic ways of splitting data among servers, so that partitions migrate between servers and spawn extra copies of themselves if not enough copies are online. The Google File System (GFS) does something of this sort at the file system level; Amazon's Dynamo does something similar at the database level. The analogies are not exact, though.</p> <p>If data is partitioned in this manner, for example into 1K slices, each in duplicate, with the rule that the two duplicates will not be on the same physical server, the first failure will not break operations but the second probably will. Without extra logic, there is a probability that the partitions formerly hosted by the failed server have their second copies randomly spread over the remaining servers. This scheme equalizes load better but is less resilient.</p> <h2>Maintenance and Continuity</h2> <p>Databases may benefit from defragmentation, rebalancing of indices, and so on. While these are possible online, by definition they affect the working set and make response times quite bad as soon as the database is significantly larger than RAM. With duplicate copies, the problem is largely solved. Also, software version changes need not involve downtime.</p> <h2>Present Status</h2> <p>The basics of replicated partitions are operational. The items to finalize are about system administration procedures and automatic synchronization of recovering copies. This must be automatic because if it is not, the operator will find a way to forget something or do some steps in the wrong order. This also requires a management view that shows what the different processes are doing and whether something is hung or failing repeatedly. All this is for the recovery part; taking failed partitions offline is easy.</p>
Web Scale and Fault Tolerance
http://www.openlinksw.com/weblog/oerling/?date=2009-04-01#1540
2009-04-01T15:18:06Z
2009-04-01T11:18:51.000014-04:00
<p>One concern about <a href="http://virtuoso.openlinksw.com" id="link-id0x3b82c38">Virtuoso</a> Cluster is fault tolerance. This post talks about the basics of fault tolerance and what we can do with this, from improving resilience and optimizing performance to accommodating bulk loads without impacting interactive response. We will see that this is yet another step towards a 24/7 web-scale <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x22c42e10">Linked Data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x1e4f0b58">Web</a>. We will see how large scale, continuous operation, and redundancy are related.</p> <p>It has been said many times â when things are large enough, failures become frequent. In view of this, basic storage of partitions in multiple copies is built into the Virtuoso cluster from the start. Until now, this feature has not been tested or used very extensively, aside from the trivial case of keeping all schema <a href="http://dbpedia.org/resource/Information" id="link-id0x224401c0">information</a> in synchronous replicas on all servers.</p> <h2>Approaches to Fault Tolerance</h2> <p>Fault tolerance has many aspects but it starts with keeping <a href="http://dbpedia.org/resource/Data" id="link-id0x230b7500">data</a> in at least two copies. There are shared-disk cluster databases like <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0xa9a1d8d8">Oracle</a> RAC that do not depend on partitioning. With these, as long as the disk image is intact, servers can come and go. The fault tolerance of the disk in turn comes from mirroring done by the disk controller. Raids other than mirrored disk are not really good for databases because of write speed.</p> <p>With shared-nothing setups like Virtuoso, fault tolerance is based on multiple servers keeping the same logical data. The copies are synchronized transaction-by-transaction but are not bit-for-bit identical nor write-by-write synchronous as is the case with mirrored disks.</p> <p>There are asynchronous replication schemes generally based on log shipping, where the replica replays the transaction log of the master copy. The master copy gets the updates, the replica replays them. Both can take queries. These do not guarantee an entirely ACID fail-over but for many applications they come close enough.</p> <p>In a tightly coupled cluster, it is possible to do synchronous, transactional updates on multiple copies without great added cost. Sending the message to two places instead of one does not make much difference since it is the latency that counts. But once we go to wide area networks, this becomes as good as unworkable for any sort of update volume. Thus, wide area replication must in practice be asynchronous.</p> <p>This is a subject for another discussion. For now, the short answer is that wide area log shipping must be adapted to the application's requirements for synchronicity and consistency. Also, exactly what content is shipped and to where depends on the application. Some application-specific logic will likely be involved; more than this one cannot say without a specific context.</p> <h2>Basics of Partition Fail-Over</h2> <p>For now, we will be concerned with redundancy protecting against broken hardware, software slowdown, or crashes inside a single site.</p> <p>The basic idea is simple: Writes go to all copies; reads that must be repeatable or serializable (i.e., locking) go to the first copy; reads that refer to committed state without guarantee of repeatability can be balanced among all copies. When a copy goes offline, nobody needs to know, as long as there is at least one copy online for each partition. The exception in practice is when there are open cursors or such stateful things as aggregations pending on a copy that goes down. Then the query or transaction will abort and the application can retry. This looks like a deadlock to the application.</p> <p>Coming back online is more complicated. This requires establishing that the recovering copy is actually in sync. In practice this requires a short window during which no transactions have uncommitted updates. Sometimes, forcing this can require aborting some transactions, which again looks like a deadlock to the application.</p> <p>When an error is seen, such as a process no longer accepting connections and dropping existing cluster connections, we in practice go via two stages. First, the operations that directly depended on this process are aborted, as well as any computation being done on behalf of the disconnected server. At this stage, attempting to read data from the partition of the failed server will go to another copy but writes will still try to update all copies and will fail if the failed copy continues to be offline. After it is established that the failed copy will stay off for some time, writes may be re-enabled â but now having the failed copy rejoin the cluster will be more complicated, requiring an atomic window to ensure sync, as mentioned earlier.</p> <p>For the DBA, there can be intermittent software crashes where a failed server automatically restarts itself, and there can be prolonged failures where this does not happen. Both are alerts but the first kind can wait. Since a system must essentially run itself, it will wait for some time for the failed server to restart itself. During this window, all reads of the failed partition go to the spare copy and writes give an error. If the spare does not come back up in time, the system will automatically re-enable writes on the spare but now the failed server may no longer rejoin the cluster without a complex sync cycle. This all can happen in well under a minute, faster than a human operator can react. The diagnostics can be done later.</p> <p>If the situation was a hardware failure, recovery consists of taking a spare server and copying the database from the surviving online copy. This done, the spare server can come on line. Copying the database can be done while online and accepting updates but this may take some time, maybe an hour for every 200G of data copied over a network. In principle this could be automated by scripting, but we would normally expect a human DBA to be involved.</p> <p>As a general rule, reacting to the failure goes automatically without disruption of service but bringing the failed copy online will usually require some operator action.</p> <h2>Levels of Tolerance and Performance</h2> <p>The only way to make failures totally invisible is to have all in duplicate and provisioned so that the system never runs at more than half the total capacity. This is often not economical or necessary. This is why we can do better, using the spare capacity for more than standby.</p> <p>Imagine keeping a repository of linked data. Most of the content will come in through periodic bulk replacement of data sets. Some data will come in through pings from applications publishing FOAF and similar. Some data will come through on-demand RDFization of resources.</p> <p>The performance of such a repository essentially depends on having enough memory. Having this memory in duplicate is just added cost. What we can do instead is have all copies store the whole partition but when routing queries, apply range partitioning on top of the basic hash partitioning. If one partition stores IDs 64K - 128K, the next partition 128K - 192K, and so forth, and all partitions are stored in two full copies, we can route reads to the first 32K IDs to the first copy and reads to the second 32K IDs to the second copy. In this way, the copies will keep different working sets. The RAM is used to full advantage.</p> <p>Of course, if there is a failure, then the working set will degrade, but if this is not often and not for long, this can be quite tolerable. The alternate expense is buying twice as much RAM, likely meaning twice as many servers. This workload is memory intensive, thus servers should have the maximum memory they can have without going to parts that are so expensive one gets a new server for the price of doubling memory.</p> <h2>Background Bulk Processing</h2> <p>When loading data, the system is online in principle, but query response can be quite bad. A large <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x3c0cfb8">RDF</a> load will involve most memory and queries will miss the cache. The load will further keep most disks busy, so response is not good. This is the case as soon as a server's partition of the database is four times the size of RAM or greater. Whether the work is bulk-load or bulk-delete makes little difference.</p> <p>But if partitions are replicated, we can temporarily split the database so that the first copies serve queries and the second copies do the load. If the copies serving on line activities do some updates also, these updates will be committed on both copies. But the load will be committed on the second copy only. This is fully appropriate as long as the data are different. When the bulk load is done, the second copy of each partition will have the full up to date state, including changes that came in during the bulk load. The online activity can be now redirected to the second copies and the first copies can be overwritten in the background by the second copies, so as to again have all data in duplicate.</p> <p>Failures during such operations are not dangerous. If the copies doing the bulk load fail, the bulk load will have to be restarted. If the front end copies fail, the front end load goes to the copies doing the bulk load. Response times will be bad until the bulk load is stopped, but no data is lost.</p> <p>This technique applies to all data intensive background tasks â calculation of <a href="http://dbpedia.org/resource/Entity" id="link-id0x3b38ac0">entity</a> search ranks, data cleansing, consistency checking, and so on. If two copies are needed to keep up with the online load, then data can be kept just as well in three copies instead of two. This method applies to any data-warehouse-style workload which must coexist with online access and occasional low volume updating.</p> <h2>Configurations of Redundancy</h2> <p>Right now, we can declare that two or more server processes in a cluster form a group. All data managed by one member of the group is stored by all others. The members of the group are interchangeable. Thus, if there is four-servers-worth of data, then there will be a minimum of eight servers. Each of these servers will have one server process per core. The first hardware failure will not affect operations. For the second failure, there is a 1/7 chance that it stops the whole system, if it falls on the server whose pair is down. If groups consist of three servers, for a total of 12, the two first failures are guaranteed not to interrupt operations; for the third, there is a 1/10 chance that it will.</p> <p>We note that for big databases, as said before, the RAM cache capacity is the sum of all the servers' RAM when in normal operation.</p> <p>There are other, more dynamic ways of splitting data among servers, so that partitions migrate between servers and spawn extra copies of themselves if not enough copies are online. The Google File System (GFS) does something of this sort at the file system level; Amazon's Dynamo does something similar at the database level. The analogies are not exact, though.</p> <p>If data is partitioned in this manner, for example into 1K slices, each in duplicate, with the rule that the two duplicates will not be on the same physical server, the first failure will not break operations but the second probably will. Without extra logic, there is a probability that the partitions formerly hosted by the failed server have their second copies randomly spread over the remaining servers. This scheme equalizes load better but is less resilient.</p> <h2>Maintenance and Continuity</h2> <p>Databases may benefit from defragmentation, rebalancing of indices, and so on. While these are possible online, by definition they affect the working set and make response times quite bad as soon as the database is significantly larger than RAM. With duplicate copies, the problem is largely solved. Also, software version changes need not involve downtime.</p> <h2>Present Status</h2> <p>The basics of replicated partitions are operational. The items to finalize are about system administration procedures and automatic synchronization of recovering copies. This must be automatic because if it is not, the operator will find a way to forget something or do some steps in the wrong order. This also requires a management view that shows what the different processes are doing and whether something is hung or failing repeatedly. All this is for the recovery part; taking failed partitions offline is easy.</p>
An Update on Virtuoso Development
http://www.openlinksw.com/blog/vdb/blog/?date=2009-03-05#1529
2009-03-05T10:23:49Z
2009-03-05T09:58:16-05:00
<p>It is time for an update on <a href="http://virtuoso.openlinksw.com" id="link-id0x151e89e8">Virtuoso</a> developments.</p> <p>We continue enhancing our hosting of the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1464e168">Linked Open Data</a> (<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x151e7f38">LOD</a>) cloud at <a href="http://lod.openlinksw.com" id="link-id11ac2448">http://lod.openlinksw.com</a>.</p> <p>We have now added result ranking for both text and URIs. Text hit scores are based on word frequency and proximity; <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x14f9fae0">URI</a> scores are based on link density.</p> <p>We calculate each URI's rank by adding up references and weighing these by the score of the referrer. This is like in web search. Each iteration of the ranking will join every referred to each of its referrers. We do about 1.2 million such joins per second, across partitions, over 2.2 billion triples and 400M distinct subjects without any great optimization, just using <a href="http://dbpedia.org/resource/SQL" id="link-id0xaa36a458">SQL</a> stored procedures and partitioned function calls. This is a sort of SQL map-reduce. We would do over twice as fast if it were all in <a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0x1571e270">C</a> but this is adequate for now. The more interesting bit will be tuning the scoring based on what type of link we have. This is what the web search engines cannot do as well, since document links are untyped.</p> <p>We are moving toward a decent user interface for the LOD hosting, including offering ready-made domain-specific queries, e.g., biomedical.</p> <p>Things like "URI finding with autocomplete" are done and just have to be put online.</p> <p>With <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x14325e08">linked data</a>, there is the whole question of identifier choice. We will have a special page just for this. There we show reference statistics, synonyms declared by <code><a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x638b3900">owl</a>:sameAs</code>, synonyms determined by shared property values, etc. In this way we become a terminology lookup service.</p> <p>Copies of the LOD cluster system are available for evaluators, on a case by case basis. We will make this publicly available on EC2 also in not too long.</p> <p>Otherwise, we continue working on productization, primarily things like reliability and recovery. One exercise is running <a href="http://dbpedia.org/resource/TPC-C" id="link-id0x144d00f0">TPC-C</a> with intentionally stupid partitioning, so that almost all joins and deadlocks are distributed. Then we simulate a cluster interconnect that drops messages now and then, sometimes kill server processes, and still keep full ACID properties. Cloud capable, also in bad weather.</p> <p>The open source release of Virtuoso 6 (no cluster) is basically ready to go, mostly this is a question of logistics.</p> <p>I will talk about these things in greater individual detail next week.</p>
An Update on Virtuoso Development
http://www.openlinksw.com/weblog/oerling/?date=2009-03-05#1528
2009-03-05T10:23:49Z
2009-03-05T09:58:07-05:00
<p>It is time for an update on <a href="http://virtuoso.openlinksw.com" id="link-id0x14b05090">Virtuoso</a> developments.</p> <p>We continue enhancing our hosting of the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x14395f00">Linked Open Data</a> (<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x13e0cf98">LOD</a>) cloud at <a href="http://lod.openlinksw.com" id="link-id11ac2448">http://lod.openlinksw.com</a>.</p> <p>We have now added result ranking for both text and URIs. Text hit scores are based on word frequency and proximity; <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0xa9f5ff20">URI</a> scores are based on link density.</p> <p>We calculate each URI's rank by adding up references and weighing these by the score of the referrer. This is like in web search. Each iteration of the ranking will join every referred to each of its referrers. We do about 1.2 million such joins per second, across partitions, over 2.2 billion triples and 400M distinct subjects without any great optimization, just using <a href="http://dbpedia.org/resource/SQL" id="link-id0x79e77aa0">SQL</a> stored procedures and partitioned function calls. This is a sort of SQL map-reduce. We would do over twice as fast if it were all in <a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0x11a3f3e8">C</a> but this is adequate for now. The more interesting bit will be tuning the scoring based on what type of link we have. This is what the web search engines cannot do as well, since document links are untyped.</p> <p>We are moving toward a decent user interface for the LOD hosting, including offering ready-made domain-specific queries, e.g., biomedical.</p> <p>Things like "URI finding with autocomplete" are done and just have to be put online.</p> <p>With <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x14334a48">linked data</a>, there is the whole question of identifier choice. We will have a special page just for this. There we show reference statistics, synonyms declared by <code><a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x59a757b0">owl</a>:sameAs</code>, synonyms determined by shared property values, etc. In this way we become a terminology lookup service.</p> <p>Copies of the LOD cluster system are available for evaluators, on a case by case basis. We will make this publicly available on EC2 also in not too long.</p> <p>Otherwise, we continue working on productization, primarily things like reliability and recovery. One exercise is running <a href="http://dbpedia.org/resource/TPC-C" id="link-id0x1f6797c0">TPC-C</a> with intentionally stupid partitioning, so that almost all joins and deadlocks are distributed. Then we simulate a cluster interconnect that drops messages now and then, sometimes kill server processes, and still keep full ACID properties. Cloud capable, also in bad weather.</p> <p>The open source release of Virtuoso 6 (no cluster) is basically ready to go, mostly this is a question of logistics.</p> <p>I will talk about these things in greater individual detail next week.</p>
Facets and Large Ontologies of the LOD Cloud
http://www.openlinksw.com/blog/vdb/blog/?date=2009-02-16#1527
2009-02-16T11:21:05Z
2009-02-17T16:24:34.000004-05:00
<p>We have just submitted <a href="http://www.openlinksw.com/weblog/oerling/lodw.pdf" id="link-id13d9bc68">this paper</a> to the WWW09 <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x51c2fd00">Linked Open Data</a> Workshop.</p> <p>The thing is intermittently live with both <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xa16f87a0">Dbpedia</a> on one instance and a <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1a8212d8">LOD</a> Cloud <a href="http://dbpedia.org/resource/Data" id="link-id0xc1097a8">data</a> collection of about 2 billion triples on another. We will give out the links once we have tested a bit more.</p> <p>The present activity is all about testing <a href="http://virtuoso.openlinksw.com" id="link-id0x17d64f90">Virtuoso</a> 6 for release, cluster and otherwise.</p>
Facets and Large Ontologies of the LOD Cloud
http://www.openlinksw.com/weblog/oerling/?date=2009-02-16#1526
2009-02-16T11:21:05Z
2009-02-17T16:24:27.000009-05:00
<p>We have just submitted <a href="http://www.openlinksw.com/weblog/oerling/lodw.pdf" id="link-id13d9bc68">this paper</a> to the WWW09 <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1a2b37a0">Linked Open Data</a> Workshop.</p> <p>The thing is intermittently live with both <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xc0dd578">Dbpedia</a> on one instance and a <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1d721b60">LOD</a> Cloud <a href="http://dbpedia.org/resource/Data" id="link-id0xa141b238">data</a> collection of about 2 billion triples on another. We will give out the links once we have tested a bit more.</p> <p>The present activity is all about testing <a href="http://virtuoso.openlinksw.com" id="link-id0xd69ff70">Virtuoso</a> 6 for release, cluster and otherwise.</p>
Faceted Search: Unlimited Data in Interactive Time
http://www.openlinksw.com/blog/vdb/blog/?date=2009-01-09#1516
2009-01-09T22:03:11Z
2009-01-09T17:15:39-05:00
<p>Why not see the whole world of <a href="http://dbpedia.org/resource/Data" id="link-id0xc3f6b38">data</a> as facets? Well, we'd like to, but there is the feeling that this is not practical.</p> <p>The old problem has been that it is not really practical to pre-compute counts of everything for all possible combinations of search conditions and counting/grouping/sorting. The actual matches take time.</p> <p>Well, neither is in fact necessary. When there are large numbers of items matching the conditions, counting them can take time but then this is the beginning of the search, and the user is not even likely to look very closely at the counts. It is enough to see that there are many of one and few of another. If the user already knows the precise predicate or class to look for, then the top-level faceted view is not even needed. The faceted view for guiding search and precise analytics are two different problems.</p> <p>There are client-side faceted views like Exhibit or our own <a href="http://ode.openlinksw.com/" id="link-id0x1bc1cfe0">ODE</a>. The problem with these is that there are a few orders of magnitude difference between the actual database size and what fits on the user agent. This is compounded by the fact that one does not know what to cache on the user agent because of the open nature of the data web. If this were about a fixed workflow, then a good guess would be possible â but we are talking about the data web, the very soul of serendipity and unexpected discovery.</p> <p>So we made a web service that will do faceted search on arbitrary <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xbb62170">RDF</a>. If it does not get complete results within a timeout, it will return what it has counted so far, using <a href="http://virtuoso.openlinksw.com" id="link-id0xb122b00">Virtuoso</a>'s <a href="http://www.openlinksw.com/weblog/oerling/?id=1494" id="link-id117b0df0"><b>Anytime</b></a> feature. Looking for subjects with some specific combination of properties is however a bit limited, so this will also do <code>JOINs</code>. Many features are one or two <code>JOINs</code> away; take geographical locations or social networks, for example.</p> <p>Yet a faceted search should be point-and-click, and should not involve a full query construction. We put the compromise at starting with full text or property or class, then navigating down properties or classes, to arbitrary depth, tree-wise. At each step, one can see the matching instances or their classes or properties, all with counts, faceted-style.</p> <p>This is good enough for queries like 'what do Harry Potter fans also like' or 'who are the authors of articles tagged <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0xbee32d8">semantic web</a> and machine learning and published in 2008'. For complex grouping, sub-queries, arithmetic or such, one must write the actual query.</p> <p>But one can begin with facets, and then continue refining the query by hand since the service also returns <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xbcc9f38">SPARQL</a> text. We made a small web interface on top of the service with all logic server side. This proves that the web service is usable and that an interface with no AJAX, and no problems with browser interoperability or such, is possible and easy. Also, the problem of syncing between a user-agent-based store and a database is entirely gone.</p> <p>If we are working with a known data structure, the user interface should choose the display by the data type and offer links to related reports. This is all easy to build as web pages or AJAX. We show how the generic interface is done in Virtuoso PL, and you can adapt that or rewrite it in <a href="http://dbpedia.org/resource/PHP" id="link-id0xcdbe268">PHP</a>, Java, JavaScript, or anything else, to accommodate use-case specific navigation needs such as data format.</p> <p>The web service takes an <a href="http://dbpedia.org/resource/XML" id="link-id0xc019c08">XML</a> representation of the search, which is more restricted and easier to process by machine than the SPARQL syntax. The web service returns the results, the SPARQL query it generated, whether the results are complete or not, and some resource use statistics.</p> <p>The source of the PL functions, Web Service and Virtuoso Server Page (HTML UI) will be available as part of Virtuoso 6.0 and higher. A Programmer's Guide will be available as part of the standard Virtuoso Documentation collection, including the Virtuoso Open Source Edition Website.</p>
Faceted Search: Unlimited Data in Interactive Time
http://www.openlinksw.com/weblog/oerling/?date=2009-01-09#1515
2009-01-09T22:03:11Z
2009-01-09T17:15:36-05:00
<p>Why not see the whole world of <a href="http://dbpedia.org/resource/Data" id="link-id0x1a0319c0">data</a> as facets? Well, we'd like to, but there is the feeling that this is not practical.</p> <p>The old problem has been that it is not really practical to pre-compute counts of everything for all possible combinations of search conditions and counting/grouping/sorting. The actual matches take time.</p> <p>Well, neither is in fact necessary. When there are large numbers of items matching the conditions, counting them can take time but then this is the beginning of the search, and the user is not even likely to look very closely at the counts. It is enough to see that there are many of one and few of another. If the user already knows the precise predicate or class to look for, then the top-level faceted view is not even needed. The faceted view for guiding search and precise analytics are two different problems.</p> <p>There are client-side faceted views like Exhibit or our own <a href="http://ode.openlinksw.com/" id="link-id0xc3db130">ODE</a>. The problem with these is that there are a few orders of magnitude difference between the actual database size and what fits on the user agent. This is compounded by the fact that one does not know what to cache on the user agent because of the open nature of the data web. If this were about a fixed workflow, then a good guess would be possible â but we are talking about the data web, the very soul of serendipity and unexpected discovery.</p> <p>So we made a web service that will do faceted search on arbitrary <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xbdbf198">RDF</a>. If it does not get complete results within a timeout, it will return what it has counted so far, using <a href="http://virtuoso.openlinksw.com" id="link-id0x17691878">Virtuoso</a>'s <a href="http://www.openlinksw.com/weblog/oerling/?id=1494" id="link-id117b0df0"><b>Anytime</b></a> feature. Looking for subjects with some specific combination of properties is however a bit limited, so this will also do <code>JOINs</code>. Many features are one or two <code>JOINs</code> away; take geographical locations or social networks, for example.</p> <p>Yet a faceted search should be point-and-click, and should not involve a full query construction. We put the compromise at starting with full text or property or class, then navigating down properties or classes, to arbitrary depth, tree-wise. At each step, one can see the matching instances or their classes or properties, all with counts, faceted-style.</p> <p>This is good enough for queries like 'what do Harry Potter fans also like' or 'who are the authors of articles tagged <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x160eb950">semantic web</a> and machine learning and published in 2008'. For complex grouping, sub-queries, arithmetic or such, one must write the actual query.</p> <p>But one can begin with facets, and then continue refining the query by hand since the service also returns <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x17e82228">SPARQL</a> text. We made a small web interface on top of the service with all logic server side. This proves that the web service is usable and that an interface with no AJAX, and no problems with browser interoperability or such, is possible and easy. Also, the problem of syncing between a user-agent-based store and a database is entirely gone.</p> <p>If we are working with a known data structure, the user interface should choose the display by the data type and offer links to related reports. This is all easy to build as web pages or AJAX. We show how the generic interface is done in Virtuoso PL, and you can adapt that or rewrite it in <a href="http://dbpedia.org/resource/PHP" id="link-id0xbe41d60">PHP</a>, Java, JavaScript, or anything else, to accommodate use-case specific navigation needs such as data format.</p> <p>The web service takes an <a href="http://dbpedia.org/resource/XML" id="link-id0xc2fc358">XML</a> representation of the search, which is more restricted and easier to process by machine than the SPARQL syntax. The web service returns the results, the SPARQL query it generated, whether the results are complete or not, and some resource use statistics.</p> <p>The source of the PL functions, Web Service and Virtuoso Server Page (HTML UI) will be available as part of Virtuoso 6.0 and higher. A Programmer's Guide will be available as part of the standard Virtuoso Documentation collection, including the Virtuoso Open Source Edition Website.</p>
Linked Data & The Year 2009 (updated)
http://www.openlinksw.com/blog/vdb/blog/?date=2009-01-02#1511
2009-01-02T16:17:06Z
2009-01-02T13:26:42.000003-05:00
<p>As is fitting for the season, I will editorialize a bit about what has gone before and what is to come.</p> <p> <a href="http://www.w3.org/People/Berners-Lee/card#i" id="link-id1119f250">Sir Tim</a> said it at WWW08 in <a href="http://www2008.org/" id="link-id0x1dcb93a0">Beijing</a> â <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x13a3efb8">linked data</a> and the linked data <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x13a44cd0">web</a> is the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x10d25788">semantic web</a> and the Web done right.</p> <p>The grail of <i>ad hoc</i> analytics on infinite <a href="http://dbpedia.org/resource/Data" id="link-id0xa201d518">data</a> has lost none of its appeal. We have seen fresh evidence of this in the realm of data warehousing products, as well as storage in general.</p> <p>The benefits of a data model more abstract than the relational are being increasingly appreciated also outside the data web circles. Microsoft's <a href="http://dbpedia.org/resource/Entity" id="link-id0x12fa4e40">Entity</a> Frameworks technology is an example. Agility has been a buzzword for a long time. Everything should be offered in a service based business model and should interoperate and integrate with everything else â business needs first; schema last.</p> <p>Not to forget that when money is tight, reuse of existing assets and paying on a usage basis are naturally emphasized. <a href="http://dbpedia.org/resource/Information" id="link-id0x175b32e8">Information</a>, as the asset it is, is none the less important, on the contrary. But even with information, value should be realized economically, which, among other things, entails not reinventing the wheel.</p> <p>It is against this backdrop that this year will play out.</p> <p>As concerns research, I will <a href="http://www.openlinksw.com/weblog/oerling/?id=1374" id="link-id1151b128">again quote</a> <a href="http://www.ibiblio.org/hhalpin/#" id="link-id141cb740">Harry Halpin</a> at <a href="http://www.eswc2008.org/" id="link-id0x18a8a858">ESWC 2008</a>: "Men will fight in a war, and even lose a war, for what they believe just. And it may come to pass that later, even though the war were lost, the things then fought for will emerge under another name and establish themselves as the prevailing reality" [or words to this effect].</p> <p>Something like the data web, and even the semantic web, will happen. Harry's question was whether this would be the descendant of what is today called semantic web research.</p> <p>I heard in conversation about a project for making a very large metadata store. I also heard that the makers did not particularly insist on this being <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x3c39ed80">RDF</a>-based, though.</p> <p>Why should such a thing be RDF-based? If it is already accepted that there will be <i>ad hoc</i> schema and that queries ought to be able to view the data from all angles, not be limited by having indices one way and not another way, then why not RDF?</p> <p>The justification of RDF is in reusing and linking-to data and terminology out there. Another justification is that by using an RDF store, one is spared a lot of work and tons of compromises which attend making an <a href="http://dbpedia.org/resource/Entity-attribute-value_model" id="link-id0x14a77880">entity</a>-attribute-value (<a href="http://dbpedia.org/resource/Entity-attribute-value_model" id="link-id0x5f978e88">EAV</a>, i.e., triple) store on a generic <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x391bdcd8">RDBMS</a>. The sem-web world has been there, trust me. We came out well because we put all inside the RDBMS, lowest level, which you can't do unless you own the RDBMS. Source access is not enough; you also need the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x138a3a00">knowledge</a>.</p> <p>Technicalities aside, the question is one of proprietary vs. standards-based. This is not only so with software components, where standards have consistently demonstrated benefits, but now also with the data. <a href="http://www.zemanta.com/" id="link-id0x5f92cb38">Zemanta</a> and <a href="http://www.opencalais.com/" id="link-id0x139c3200">OpenCalais</a> serving <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1731dc78">DBpedia</a> URIs are examples. Even in entirely closed applications, there is benefit in reusing open vocabularies and identifiers: One does not need to create a secret language for writing a secret memo.</p> <p>Where data is a carrier of value, its value is enhanced by it being easy to repurpose (i.e., standard vocabularies) and to discover (i.e., data set metadata). As on the web, so on the enterprise <a href="http://dbpedia.org/resource/Intranet" id="link-id0x1324ada8">intranet</a>. In this lies the strength of RDF as opposed to proprietary flexible database schemes. This is a qualitative distinction.</p> <p align="center"> <a href="http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData" id="link-id117178a8"><img src="http://www.openlinksw.com/images/logos/LoDLogo.gif" alt="Linking Open Data project logo" /> </a> <br /> <a href="http://dbpedia.org/resource/In_hoc_signo_vinces" id="link-id115f47e8"><i>In hoc signo vinces.</i> </a> </p> <p>In this light, we welcome the <a href="http://semanticweb.org/wiki/VoiD" id="link-id0x67cf560">voiD</a> (<a href="http://semanticweb.org/wiki/VoiD" id="link-id0x1898c908">VOcabulary of Interlinked Data</a>), which is the first promise of making federatable data discoverable. Now that there is a point of focus for these efforts, the needed expressivity will no doubt accrete around the voiD core.</p> <p>For data as a service, we clearly see the value of open terminologies as prerequisites for service interchangeability, i.e., creating a marketplace. <a href="http://dbpedia.org/resource/XML" id="link-id0x1588d6a8">XML</a> is for the transaction; RDF is for the discovery, query, and analytics. As with databases in general, first there was the transaction; then there was the query. Same here. For monetizing the query, there are models ranging from renting data sets and server capacity in the clouds to hosted services where one pays for processing past a certain quota. For the hosted case, we just removed a major barrier to offering unlimited query against unlimited data when we completed the <a href="http://www.openlinksw.com/weblog/oerling/?id=1374" id="link-id110b8668">Virtuoso Anytime</a> feature. With this, the user gets what is found within a set time, which is already something, and in case of needing more, one can pay for the usage. Of course, we do not forget advertising. When data has explicit semantics, contextuality is better than with keywords.</p> <p>For these visions to materialize on top of the linked data platform, linked data must join the world of data. This means messaging that is geared towards the database public. They know the problem, but the RDF proposition is still not well enough understood for it to connect.</p> <p>For the relational IT world, we offer passage to the data web and its promise of integration through RDF mapping. We are also bringing out new Microsoft Entity <a href="http://dbpedia.org/resource/ADO.NET_Entity_Framework" id="link-id0x13a50fd8">Framework</a> components. This goes in the direction of defining a unified database frontier with RDF and non-RDF entity models side by side.</p> <p>For <a href="http://www.openlinksw.com/dataspace/organization/openlink#this" id="link-id0x1d2ea7f0">OpenLink Software</a>, 2008 was about developing technology for scale, RDF as well as generic relational. We did show a tiny preview with the <a href="http://challenge.semanticweb.org/" id="link-id0x658fbc8">Billion Triples Challenge</a> demo. Now we are set to come out with the real thing, featuring, among other things, faceted search at the billion triple scale. We <a href="http://www.openlinksw.com/blog/kidehen@openlinksw.com/blog/?id=1489" id="link-id150c6090">started offering ready-to-go Virtuoso-hosted linked open data sets</a> on Amazon EC2 in December. Now we continue doing this based on our next-generation server, as well as make Virtuoso 6 Cluster commercially available. Technical specifics are amply discussed on this <a href="http://dbpedia.org/resource/Blog" id="link-id0x1424ec20">blog</a>. There are still some new technology things to be developed this year; first among these are strong <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x14b8ca88">SPARQL</a> federation, and on-the-fly resizing of server clusters. On the research partnerships side, we have an EU grant for working with the OntoWiki project from the University of Leipzig, and we are partners in DERI's <a href="https://lion.deri.ie/" id="link-id115c02f8">LÃon project</a>. These will provide platforms for further demonstrating the "web" in data web, as in web-scale smart databasing.</p> <p>2009 will see change through scale. The things that exist will start interconnecting and there will be emergent value. Deployments will be larger and scale will be readily available through a services model or by installation at one's own facilities. We may see the start of Search becoming Find, like <a href="http://myopenlink.net/dataspace/person/kidehen#this" id="link-id14e43050">Kingsley</a> says, meaning semantics of data guiding search. Entity extraction will multiply data volumes and bring parts of the data web to real time.</p> <p>Exciting 2009 to all.</p>
Linked Data & The Year 2009 (updated)
http://www.openlinksw.com/weblog/oerling/?date=2009-01-02#1510
2009-01-02T16:17:06Z
2009-01-02T13:26:35-05:00
<p>As is fitting for the season, I will editorialize a bit about what has gone before and what is to come.</p> <p> <a href="http://www.w3.org/People/Berners-Lee/card#i" id="link-id1119f250">Sir Tim</a> said it at WWW08 in <a href="http://www2008.org/" id="link-id0x14ab66b0">Beijing</a> â <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x115a4588">linked data</a> and the linked data <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0xa5c678">web</a> is the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x7cbe5540">semantic web</a> and the Web done right.</p> <p>The grail of <i>ad hoc</i> analytics on infinite <a href="http://dbpedia.org/resource/Data" id="link-id0xa4b25428">data</a> has lost none of its appeal. We have seen fresh evidence of this in the realm of data warehousing products, as well as storage in general.</p> <p>The benefits of a data model more abstract than the relational are being increasingly appreciated also outside the data web circles. Microsoft's <a href="http://dbpedia.org/resource/Entity" id="link-id0x1c3c72b0">Entity</a> Frameworks technology is an example. Agility has been a buzzword for a long time. Everything should be offered in a service based business model and should interoperate and integrate with everything else â business needs first; schema last.</p> <p>Not to forget that when money is tight, reuse of existing assets and paying on a usage basis are naturally emphasized. <a href="http://dbpedia.org/resource/Information" id="link-id0xa0743bd8">Information</a>, as the asset it is, is none the less important, on the contrary. But even with information, value should be realized economically, which, among other things, entails not reinventing the wheel.</p> <p>It is against this backdrop that this year will play out.</p> <p>As concerns research, I will <a href="http://www.openlinksw.com/weblog/oerling/?id=1374" id="link-id1151b128">again quote</a> <a href="http://www.ibiblio.org/hhalpin/#" id="link-id141cb740">Harry Halpin</a> at <a href="http://www.eswc2008.org/" id="link-id0x28f68040">ESWC 2008</a>: "Men will fight in a war, and even lose a war, for what they believe just. And it may come to pass that later, even though the war were lost, the things then fought for will emerge under another name and establish themselves as the prevailing reality" [or words to this effect].</p> <p>Something like the data web, and even the semantic web, will happen. Harry's question was whether this would be the descendant of what is today called semantic web research.</p> <p>I heard in conversation about a project for making a very large metadata store. I also heard that the makers did not particularly insist on this being <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x13c8af68">RDF</a>-based, though.</p> <p>Why should such a thing be RDF-based? If it is already accepted that there will be <i>ad hoc</i> schema and that queries ought to be able to view the data from all angles, not be limited by having indices one way and not another way, then why not RDF?</p> <p>The justification of RDF is in reusing and linking-to data and terminology out there. Another justification is that by using an RDF store, one is spared a lot of work and tons of compromises which attend making an <a href="http://dbpedia.org/resource/Entity-attribute-value_model" id="link-id0x1ca17b20">entity</a>-attribute-value (<a href="http://dbpedia.org/resource/Entity-attribute-value_model" id="link-id0x1c9d6050">EAV</a>, i.e., triple) store on a generic <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x557dff0">RDBMS</a>. The sem-web world has been there, trust me. We came out well because we put all inside the RDBMS, lowest level, which you can't do unless you own the RDBMS. Source access is not enough; you also need the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1470c748">knowledge</a>.</p> <p>Technicalities aside, the question is one of proprietary vs. standards-based. This is not only so with software components, where standards have consistently demonstrated benefits, but now also with the data. <a href="http://www.zemanta.com/" id="link-id0x524bea0">Zemanta</a> and <a href="http://www.opencalais.com/" id="link-id0x46132d38">OpenCalais</a> serving <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x13624fb8">DBpedia</a> URIs are examples. Even in entirely closed applications, there is benefit in reusing open vocabularies and identifiers: One does not need to create a secret language for writing a secret memo.</p> <p>Where data is a carrier of value, its value is enhanced by it being easy to repurpose (i.e., standard vocabularies) and to discover (i.e., data set metadata). As on the web, so on the enterprise <a href="http://dbpedia.org/resource/Intranet" id="link-id0xa1392eb8">intranet</a>. In this lies the strength of RDF as opposed to proprietary flexible database schemes. This is a qualitative distinction.</p> <p align="center"> <a href="http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData" id="link-id117178a8"><img src="http://www.openlinksw.com/images/logos/LoDLogo.gif" alt="Linking Open Data project logo" /> </a> <br /> <a href="http://dbpedia.org/resource/In_hoc_signo_vinces" id="link-id115f47e8"><i>In hoc signo vinces.</i> </a> </p> <p>In this light, we welcome the <a href="http://semanticweb.org/wiki/VoiD" id="link-id0x12352cc0">voiD</a> (<a href="http://semanticweb.org/wiki/VoiD" id="link-id0x722c18">VOcabulary of Interlinked Data</a>), which is the first promise of making federatable data discoverable. Now that there is a point of focus for these efforts, the needed expressivity will no doubt accrete around the voiD core.</p> <p>For data as a service, we clearly see the value of open terminologies as prerequisites for service interchangeability, i.e., creating a marketplace. <a href="http://dbpedia.org/resource/XML" id="link-id0x2c21c00">XML</a> is for the transaction; RDF is for the discovery, query, and analytics. As with databases in general, first there was the transaction; then there was the query. Same here. For monetizing the query, there are models ranging from renting data sets and server capacity in the clouds to hosted services where one pays for processing past a certain quota. For the hosted case, we just removed a major barrier to offering unlimited query against unlimited data when we completed the <a href="http://www.openlinksw.com/weblog/oerling/?id=1374" id="link-id110b8668">Virtuoso Anytime</a> feature. With this, the user gets what is found within a set time, which is already something, and in case of needing more, one can pay for the usage. Of course, we do not forget advertising. When data has explicit semantics, contextuality is better than with keywords.</p> <p>For these visions to materialize on top of the linked data platform, linked data must join the world of data. This means messaging that is geared towards the database public. They know the problem, but the RDF proposition is still not well enough understood for it to connect.</p> <p>For the relational IT world, we offer passage to the data web and its promise of integration through RDF mapping. We are also bringing out new Microsoft Entity <a href="http://dbpedia.org/resource/ADO.NET_Entity_Framework" id="link-id0x723080">Framework</a> components. This goes in the direction of defining a unified database frontier with RDF and non-RDF entity models side by side.</p> <p>For <a href="http://www.openlinksw.com/dataspace/organization/openlink#this" id="link-id0x11e1dfc0">OpenLink Software</a>, 2008 was about developing technology for scale, RDF as well as generic relational. We did show a tiny preview with the <a href="http://challenge.semanticweb.org/" id="link-id0x722d08">Billion Triples Challenge</a> demo. Now we are set to come out with the real thing, featuring, among other things, faceted search at the billion triple scale. We <a href="http://www.openlinksw.com/blog/kidehen@openlinksw.com/blog/?id=1489" id="link-id150c6090">started offering ready-to-go Virtuoso-hosted linked open data sets</a> on Amazon EC2 in December. Now we continue doing this based on our next-generation server, as well as make Virtuoso 6 Cluster commercially available. Technical specifics are amply discussed on this <a href="http://dbpedia.org/resource/Blog" id="link-id0x10fc1930">blog</a>. There are still some new technology things to be developed this year; first among these are strong <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x7fd25590">SPARQL</a> federation, and on-the-fly resizing of server clusters. On the research partnerships side, we have an EU grant for working with the OntoWiki project from the University of Leipzig, and we are partners in DERI's <a href="https://lion.deri.ie/" id="link-id115c02f8">LÃon project</a>. These will provide platforms for further demonstrating the "web" in data web, as in web-scale smart databasing.</p> <p>2009 will see change through scale. The things that exist will start interconnecting and there will be emergent value. Deployments will be larger and scale will be readily available through a services model or by installation at one's own facilities. We may see the start of Search becoming Find, like <a href="http://myopenlink.net/dataspace/person/kidehen#this" id="link-id14e43050">Kingsley</a> says, meaning semantics of data guiding search. Entity extraction will multiply data volumes and bring parts of the data web to real time.</p> <p>Exciting 2009 to all.</p>
Virtuoso 6 FAQ directory
http://www.openlinksw.com/blog/vdb/blog/?date=2008-12-18#1507
2008-12-18T15:46:18Z
2008-12-22T14:30:07.000004-05:00
<p>We have received various inquiries on high-end metadata stores. I will here go through some salient questions. The requested features include:</p> <ul> <li>Scaling to trillions of triples</li> <li>Running on clusters of commodity servers</li> <li>Running in federated environments, possibly over wide area networks</li> <li>Built-in inference</li> <li>Transactions</li> <li>Security</li> <li>Support for extra triple level metadata, such as security attributes</li> </ul> <p>Q: What is the storage cost per triple? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#StorageCostPerTriple" id="link-id147f61e8">answer</a> </p> <p>Q: What is the cost to insert a triple? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleInsertionCost" id="link-id112e2488">answer</a> </p> <p>Q: What is the cost to delete a triple? (For the insertion itself, as well as for updating any indices) <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleDeletionCost" id="link-id11728528">answer</a> </p> <p>Q: What is the cost to search on a given property? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PropertySearchCost" id="link-id1586e360">answer</a> </p> <p>Q: What <a href="http://dbpedia.org/resource/Data" id="link-id14688e38">data</a> types are supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedDataTypes" id="link-id1593dbf0">answer</a> </p> <p>Q: What inferencing is supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedInferencing" id="link-id112f3248">answer</a> </p> <p>Q: Is the inferencing dynamic or is an extra step required before inferencing can be used? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#InferencingDynamism" id="link-id1477e2e0">answer</a> </p> <p>Q: Do you support <a href="http://dbpedia.org/resource/Full_text_search" id="link-id1177b198">full text search</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#FullTextSearchSupport" id="link-id1543b170">answer</a> </p> <p>Q: What programming interfaces are supported? Do you support standard <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id14bb69c0">SPARQL protocol</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedProgrammingInterfaces" id="link-id14d4eb18">answer</a> </p> <p>Q: How can data be partitioned across multiple servers? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#MultipleServerDataPartitioning" id="link-id13722e00">answer</a> </p> <p>Q: How many triples can a single server handle? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SingleServerTripleLimits" id="link-id14046e58">answer</a> </p> <p>Q: What is the performance impact of going from the billion to the trillion triples? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PerformanceImpactBillionToTrillion" id="link-id113cfc10">answer</a> </p> <p>Q: Do you support additional metadata for triples, such as timestamps, security tags etc? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleMetadataSupport" id="link-id14c75fa8">answer</a> </p> <p>Q: Should we use <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id11342010">RDF</a> for our large metadata store? What are the alternatives? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#LargeMetadataStoreFormat" id="link-id1478db38">answer</a> </p> <p>Q: How multithreaded is <a href="http://virtuoso.openlinksw.com" id="link-id1651d028">Virtuoso</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#VirtuosoMultiThreading" id="link-id152ad310">answer</a> </p> <p>Q: Can multiple servers run off a single shared disk database? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#MultipleServersOneDiskDatabase" id="link-id14d9d528">answer</a> </p> <p>Q: Can Virtuoso run on a SAN? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#VirtuosoOnSAN" id="link-id111b55d0">answer</a> </p> <p>Q: How does Virtuoso join across partitions? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#CrossPartitionJoins" id="link-id11094db8">answer</a> </p> <p>Q: Does Virtuoso support federated triple stores? If there are multiple <a href="http://dbpedia.org/resource/SPARQL" id="link-id19156b48">SPARQL</a> end points, can Virtuoso be used to do queries joining between these? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#FederatedTripleStoresAndQueries" id="link-id15447ef8">answer</a> </p> <p>Q: How many servers can a cluster contain? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ClusterServerLimit" id="link-id125fe0d0">answer</a> </p> <p>Q: How do I reconfigure a cluster, adding and removing machines, etc? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ClusterReconfiguration" id="link-id1150c448">answer</a> </p> <p>Q: How will Virtuoso handle regional clusters? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#RegionalClustering" id="link-id1596ca48">answer</a> </p> <p>Q: Is there a mechanism for terminating long running queries? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TerminatingLongRunningQueries" id="link-id116bbd60">answer</a> </p> <p>Q: Can the user be asynchronously notified when a long running query terminates? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AsynchNotificationOfQueryTermination" id="link-id15a59a50">answer</a> </p> <p>Q: How many concurrent queries can Virtuoso handle? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ConcurrentQueryLimits" id="link-id110a8c00">answer</a> </p> <p>Q: What is the relative performance of SPARQL queries vs. native relational queries <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#RelativePerformanceSparqlVsSql" id="link-id110914f8">answer</a> </p> <p>Q: Does Virtuoso support property tables? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PropertyTableSupport" id="link-id1581f8c8">answer</a> </p> <p>Q: What performance metrics does Virtuoso offer? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PerformanceMetricSupport" id="link-id14e92300">answer</a> </p> <p>Q: What support do you provide for concurrency/multithreading operation? Is your interface thread-safe? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ConcurrencyAndThreadSafety" id="link-id15964b80">answer</a> </p> <p>Q: What level of ACID properties are supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AcidComplianceLevel" id="link-id11035ac0">answer</a> </p> <p>Q: Do you provide the ability to atomically add a set of triples, where either all are added or none are added? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AtomicTripleInsertion" id="link-id15290e68">answer</a> </p> <p>Q: Do you provide the ability to add a set of triples, respecting the isolation property (so concurrent accessors either see none of the triple values, or all of them)? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#IsolationDuringInsertion" id="link-id15855df0">answer</a> </p> <p>Q: What is the time to start a database, create/open a graph? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#StartupTimes" id="link-id14227f40">answer</a> </p> <p>Q: What sort of security features are built into Virtuoso? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#BuiltInSecurity" id="link-id11927810">answer</a> </p>
Virtuoso 6 FAQ directory
http://www.openlinksw.com/weblog/oerling/?date=2008-12-18#1506
2008-12-18T15:46:18Z
2008-12-22T14:30:00-05:00
<p>We have received various inquiries on high-end metadata stores. I will here go through some salient questions. The requested features include:</p> <ul> <li>Scaling to trillions of triples</li> <li>Running on clusters of commodity servers</li> <li>Running in federated environments, possibly over wide area networks</li> <li>Built-in inference</li> <li>Transactions</li> <li>Security</li> <li>Support for extra triple level metadata, such as security attributes</li> </ul> <p>Q: What is the storage cost per triple? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#StorageCostPerTriple" id="link-id147f61e8">answer</a> </p> <p>Q: What is the cost to insert a triple? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleInsertionCost" id="link-id112e2488">answer</a> </p> <p>Q: What is the cost to delete a triple? (For the insertion itself, as well as for updating any indices) <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleDeletionCost" id="link-id11728528">answer</a> </p> <p>Q: What is the cost to search on a given property? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PropertySearchCost" id="link-id1586e360">answer</a> </p> <p>Q: What <a href="http://dbpedia.org/resource/Data" id="link-id14688e38">data</a> types are supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedDataTypes" id="link-id1593dbf0">answer</a> </p> <p>Q: What inferencing is supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedInferencing" id="link-id112f3248">answer</a> </p> <p>Q: Is the inferencing dynamic or is an extra step required before inferencing can be used? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#InferencingDynamism" id="link-id1477e2e0">answer</a> </p> <p>Q: Do you support <a href="http://dbpedia.org/resource/Full_text_search" id="link-id1177b198">full text search</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#FullTextSearchSupport" id="link-id1543b170">answer</a> </p> <p>Q: What programming interfaces are supported? Do you support standard <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id14bb69c0">SPARQL protocol</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SupportedProgrammingInterfaces" id="link-id14d4eb18">answer</a> </p> <p>Q: How can data be partitioned across multiple servers? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#MultipleServerDataPartitioning" id="link-id13722e00">answer</a> </p> <p>Q: How many triples can a single server handle? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#SingleServerTripleLimits" id="link-id14046e58">answer</a> </p> <p>Q: What is the performance impact of going from the billion to the trillion triples? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PerformanceImpactBillionToTrillion" id="link-id113cfc10">answer</a> </p> <p>Q: Do you support additional metadata for triples, such as timestamps, security tags etc? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TripleMetadataSupport" id="link-id14c75fa8">answer</a> </p> <p>Q: Should we use <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id11342010">RDF</a> for our large metadata store? What are the alternatives? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#LargeMetadataStoreFormat" id="link-id1478db38">answer</a> </p> <p>Q: How multithreaded is <a href="http://virtuoso.openlinksw.com" id="link-id1651d028">Virtuoso</a>? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#VirtuosoMultiThreading" id="link-id152ad310">answer</a> </p> <p>Q: Can multiple servers run off a single shared disk database? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#MultipleServersOneDiskDatabase" id="link-id14d9d528">answer</a> </p> <p>Q: Can Virtuoso run on a SAN? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#VirtuosoOnSAN" id="link-id111b55d0">answer</a> </p> <p>Q: How does Virtuoso join across partitions? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#CrossPartitionJoins" id="link-id11094db8">answer</a> </p> <p>Q: Does Virtuoso support federated triple stores? If there are multiple <a href="http://dbpedia.org/resource/SPARQL" id="link-id19156b48">SPARQL</a> end points, can Virtuoso be used to do queries joining between these? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#FederatedTripleStoresAndQueries" id="link-id15447ef8">answer</a> </p> <p>Q: How many servers can a cluster contain? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ClusterServerLimit" id="link-id125fe0d0">answer</a> </p> <p>Q: How do I reconfigure a cluster, adding and removing machines, etc? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ClusterReconfiguration" id="link-id1150c448">answer</a> </p> <p>Q: How will Virtuoso handle regional clusters? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#RegionalClustering" id="link-id1596ca48">answer</a> </p> <p>Q: Is there a mechanism for terminating long running queries? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#TerminatingLongRunningQueries" id="link-id116bbd60">answer</a> </p> <p>Q: Can the user be asynchronously notified when a long running query terminates? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AsynchNotificationOfQueryTermination" id="link-id15a59a50">answer</a> </p> <p>Q: How many concurrent queries can Virtuoso handle? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ConcurrentQueryLimits" id="link-id110a8c00">answer</a> </p> <p>Q: What is the relative performance of SPARQL queries vs. native relational queries <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#RelativePerformanceSparqlVsSql" id="link-id110914f8">answer</a> </p> <p>Q: Does Virtuoso support property tables? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PropertyTableSupport" id="link-id1581f8c8">answer</a> </p> <p>Q: What performance metrics does Virtuoso offer? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#PerformanceMetricSupport" id="link-id14e92300">answer</a> </p> <p>Q: What support do you provide for concurrency/multithreading operation? Is your interface thread-safe? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#ConcurrencyAndThreadSafety" id="link-id15964b80">answer</a> </p> <p>Q: What level of ACID properties are supported? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AcidComplianceLevel" id="link-id11035ac0">answer</a> </p> <p>Q: Do you provide the ability to atomically add a set of triples, where either all are added or none are added? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#AtomicTripleInsertion" id="link-id15290e68">answer</a> </p> <p>Q: Do you provide the ability to add a set of triples, respecting the isolation property (so concurrent accessors either see none of the triple values, or all of them)? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#IsolationDuringInsertion" id="link-id15855df0">answer</a> </p> <p>Q: What is the time to start a database, create/open a graph? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#StartupTimes" id="link-id14227f40">answer</a> </p> <p>Q: What sort of security features are built into Virtuoso? <a href="http://virtuoso.openlinksw.com/Whitepapers/html/Virt6FAQ.html#BuiltInSecurity" id="link-id11927810">answer</a> </p>
Virtuoso RDF: A Getting Started Guide for the Developer
http://www.openlinksw.com/blog/vdb/blog/?date=2008-12-17#1505
2008-12-17T12:31:34Z
2008-12-17T12:41:27.000006-05:00
<p>It is a long standing promise of mine to dispel the false impression that using <a href="http://virtuoso.openlinksw.com/" id="link-id113506d0">Virtuoso</a> to work with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id115d9528">RDF</a> is complicated.</p> <p>The purpose of this presentation is to show a programmer how to put RDF into Virtuoso and how to query it. This is done programmatically, with no confusing user interfaces.</p> <p>You should have a Virtuoso Open Source tree built and installed. We will look at the LUBM benchmark demo that comes with the package. All you need is a Unix shell. Running the shell under emacs (<code>m-x shell</code>) is the best. But the open source <code>isql</code> utility should have command line editing also. The emacs shell is however convenient for cutting and pasting things between shell and files.</p> <p>To get started, cd into <code>binsrc/tests/lubm</code>.</p> <p>To verify that this works, you can do </p> <blockquote> <pre>./test_server.sh virtuoso-t</pre></blockquote> <p>This will test the server with the LUBM queries. This should report 45 tests passed. After this we will do the tests step-by-step.</p> <h2>Loading the <a href="http://dbpedia.org/resource/Data" id="link-id10f7bd90">Data</a> </h2> <p>The file <code>lubm-load.sql</code> contains the commands for loading the LUBM single university qualification database.</p> <p>The data files themselves are in <code>lubm_8000</code>, 15 files in RDFXML.</p> <p>There is also a little ontology called <code>inf.nt</code>. This declares the subclass and subproperty relations used in the benchmark.</p> <p>So now let's go through this procedure.</p> <p>Start the server:</p> <blockquote> <pre>$ virtuoso-t -f & </pre></blockquote> <p>This starts the server in foreground mode, and puts it in the background of the shell.</p> <p>Now we connect to it with the isql utility.</p> <blockquote> <pre>$ isql 1111 dba dba </pre></blockquote> <p>This gives a <code>SQL></code> prompt. The default username and password are both <code>dba</code>.</p> <p>When a command is <a href="http://dbpedia.org/resource/SQL" id="link-id1176ce70">SQL</a>, it is entered directly. If it is <a href="http://dbpedia.org/resource/SPARQL" id="link-id156df468">SPARQL</a>, it is prefixed with the keyword <code>sparql</code>. This is how all the SQL clients work. Any SQL client, such as any <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id152d0a00">ODBC</a> or <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id157ad6a0">JDBC</a> application, can use SPARQL if the SQL string starts with this keyword.</p> <p>The <code>lubm-load.sql</code> file is quite self-explanatory. It begins with defining an SQL procedure that calls the RDF/XML load function, <code>DB..RDF_LOAD_RDFXML</code>, for each file in a directory.</p> <p>Next it calls this function for the <code>lubm_8000</code> directory under the server's working directory.</p> <blockquote> <pre>sparql CLEAR GRAPH <lubm>; sparql CLEAR GRAPH <inf>; load_lubm ( server_root() || '/lubm_8000/' ); </pre></blockquote> <p>Then it verifies that the right number of triples is found in the <lubm> graph.</p> <blockquote> <pre>sparql SELECT COUNT(*) FROM <lubm> WHERE { ?x ?y ?z } ; </pre></blockquote> <p>The echo commands below this are interpreted by the isql utility, and produce output to show whether the test was passed. They can be ignored for now.</p> <p>Then it adds some implied <code>subOrganizationOf</code> triples. This is part of setting up the LUBM test database.</p> <blockquote> <pre>sparql PREFIX ub: <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#> INSERT INTO GRAPH <lubm> { ?x ub:subOrganizationOf ?z } FROM <lubm> WHERE { ?x ub:subOrganizationOf ?y . ?y ub:subOrganizationOf ?z . }; </pre></blockquote> <p>Then it loads the ontology file, <code>inf.nt</code>, using the Turtle load function, <code>DB.DBA.TTLP</code>. The arguments of the function are the text to load, the default namespace prefix, and the <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id15835550">URI</a> of the target graph.</p> <blockquote> <pre>DB.DBA.TTLP ( file_to_string ( 'inf.nt' ), 'http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl', 'inf' ) ; sparql SELECT COUNT(*) FROM <inf> WHERE { ?x ?y ?z } ; </pre></blockquote> <p>Then we declare that the triples in the <code><inf></code> graph can be used for inference at run time. To enable this, a SPARQL query will declare that it uses the <code>'inft'</code> rule set. Otherwise this has no effect.</p> <blockquote> <pre>rdfs_rule_set ('inft', 'inf'); </pre></blockquote> <p>This is just a log checkpoint to finalize the work and truncate the transaction log. The server would also eventually do this in its own time.</p> <blockquote> <pre>checkpoint; </pre></blockquote> <p>Now we are ready for querying.</p> <h2>Querying the Data</h2> <p>The queries are given in 3 different versions: The first file, <code>lubm.sql</code>, has the queries with most inference open coded as <code>UNIONs</code>. The second file, <code>lubm-inf.sql</code>, has the inference performed at run time using the ontology <a href="http://dbpedia.org/resource/Information" id="link-id1109faf0">information</a> in the <code><inf></code> graph we just loaded. The last, <code>lubm-phys.sql</code>, relies on having the entailed triples physically present in the <code><lubm></code> graph. These entailed triples are inserted by the SPARUL commands in the <code>lubm-cp.sql</code> file.</p> <p>If you wish to run all the commands in a SQL file, you can type <code>load <filename>;</code> (e.g., <code>load lubm-cp.sql;</code>) at the <code>SQL></code> prompt. If you wish to try individual statements, you can paste them to the command line.</p> <p>For example: </p> <blockquote> <pre>SQL> sparql PREFIX ub: <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#> SELECT * FROM <lubm> WHERE { ?x a ub:Publication . ?x ub:publicationAuthor <http://www.Department0.University0.edu/AssistantProfessor0> }; VARCHAR _______________________________________________________________________ http://www.Department0.University0.edu/AssistantProfessor0/Publication0 http://www.Department0.University0.edu/AssistantProfessor0/Publication1 http://www.Department0.University0.edu/AssistantProfessor0/Publication2 http://www.Department0.University0.edu/AssistantProfessor0/Publication3 http://www.Department0.University0.edu/AssistantProfessor0/Publication4 http://www.Department0.University0.edu/AssistantProfessor0/Publication5 6 Rows. -- 4 msec. </pre></blockquote> <p>To stop the server, simply type <code>shutdown;</code> at the <code>SQL></code> prompt.</p> <p>If you wish to use a <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id11384668">SPARQL protocol</a> end point, just enable the HTTP listener. This is done by adding a stanza like â</p> <blockquote> <pre>[HTTPServer] ServerPort = 8421 ServerRoot = . ServerThreads = 2 </pre></blockquote> <p>â to the end of the <code>virtuoso.ini</code> file in the <code>lubm</code> directory. Then shutdown and restart (type <code>shutdown;</code> at the <code>SQL></code> prompt and then <code>virtuoso-t -f &</code> at the shell prompt).</p> <p>Now you can connect to the end point with a web browser. The <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id113d02d8">URL</a> is <code>http://localhost:8421/sparql</code>. Without parameters, this will show a human readable form. With parameters, this will execute SPARQL.</p> <p>We have shown how to load and query RDF with Virtuoso using the most basic SQL tools. Next you can access RDF from, for example, <a href="http://dbpedia.org/resource/PHP" id="link-id142d0ba0">PHP</a>, using the PHP ODBC interface.</p> <p>To see how to use <a href="http://jena.sourceforge.net/" id="link-id117074f0">Jena</a> or <a href="http://sourceforge.net/projects/sesame/" id="link-id1103c9b0">Sesame</a> with Virtuoso, look at <a href="http://docs.openlinksw.com/virtuoso/rdfnativestorageproviders.html" id="link-id15488ce8">Native RDF Storage Providers</a>. To see how RDF data types are supported, see <a href="http://docs.openlinksw.com/virtuoso/VirtuosoDriverJDBC.html#jdbcrdf" id="link-id15784a40">Extension datatype for RDF</a> </p> <p>To work with large volumes of data, you must add memory to the configuration file and use the row-autocommit mode, i.e., do <code>log_enable (2);</code> before the load command. Otherwise Virtuoso will do the entire load as a single transaction, and will run out of rollback space. See <a href="http://docs.openlinksw.com/virtuoso/" id="link-id111410f0">documentation</a> for more.</p>
Virtuoso RDF: A Getting Started Guide for the Developer
http://www.openlinksw.com/weblog/oerling/?date=2008-12-17#1504
2008-12-17T12:31:34Z
2008-12-17T12:41:21.000001-05:00
<p>It is a long standing promise of mine to dispel the false impression that using <a href="http://virtuoso.openlinksw.com/" id="link-id113506d0">Virtuoso</a> to work with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id115d9528">RDF</a> is complicated.</p> <p>The purpose of this presentation is to show a programmer how to put RDF into Virtuoso and how to query it. This is done programmatically, with no confusing user interfaces.</p> <p>You should have a Virtuoso Open Source tree built and installed. We will look at the LUBM benchmark demo that comes with the package. All you need is a Unix shell. Running the shell under emacs (<code>m-x shell</code>) is the best. But the open source <code>isql</code> utility should have command line editing also. The emacs shell is however convenient for cutting and pasting things between shell and files.</p> <p>To get started, cd into <code>binsrc/tests/lubm</code>.</p> <p>To verify that this works, you can do </p> <blockquote> <pre>./test_server.sh virtuoso-t</pre></blockquote> <p>This will test the server with the LUBM queries. This should report 45 tests passed. After this we will do the tests step-by-step.</p> <h2>Loading the <a href="http://dbpedia.org/resource/Data" id="link-id10f7bd90">Data</a> </h2> <p>The file <code>lubm-load.sql</code> contains the commands for loading the LUBM single university qualification database.</p> <p>The data files themselves are in <code>lubm_8000</code>, 15 files in RDFXML.</p> <p>There is also a little ontology called <code>inf.nt</code>. This declares the subclass and subproperty relations used in the benchmark.</p> <p>So now let's go through this procedure.</p> <p>Start the server:</p> <blockquote> <pre>$ virtuoso-t -f & </pre></blockquote> <p>This starts the server in foreground mode, and puts it in the background of the shell.</p> <p>Now we connect to it with the isql utility.</p> <blockquote> <pre>$ isql 1111 dba dba </pre></blockquote> <p>This gives a <code>SQL></code> prompt. The default username and password are both <code>dba</code>.</p> <p>When a command is <a href="http://dbpedia.org/resource/SQL" id="link-id1176ce70">SQL</a>, it is entered directly. If it is <a href="http://dbpedia.org/resource/SPARQL" id="link-id156df468">SPARQL</a>, it is prefixed with the keyword <code>sparql</code>. This is how all the SQL clients work. Any SQL client, such as any <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id152d0a00">ODBC</a> or <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id157ad6a0">JDBC</a> application, can use SPARQL if the SQL string starts with this keyword.</p> <p>The <code>lubm-load.sql</code> file is quite self-explanatory. It begins with defining an SQL procedure that calls the RDF/XML load function, <code>DB..RDF_LOAD_RDFXML</code>, for each file in a directory.</p> <p>Next it calls this function for the <code>lubm_8000</code> directory under the server's working directory.</p> <blockquote> <pre>sparql CLEAR GRAPH <lubm>; sparql CLEAR GRAPH <inf>; load_lubm ( server_root() || '/lubm_8000/' ); </pre></blockquote> <p>Then it verifies that the right number of triples is found in the <lubm> graph.</p> <blockquote> <pre>sparql SELECT COUNT(*) FROM <lubm> WHERE { ?x ?y ?z } ; </pre></blockquote> <p>The echo commands below this are interpreted by the isql utility, and produce output to show whether the test was passed. They can be ignored for now.</p> <p>Then it adds some implied <code>subOrganizationOf</code> triples. This is part of setting up the LUBM test database.</p> <blockquote> <pre>sparql PREFIX ub: <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#> INSERT INTO GRAPH <lubm> { ?x ub:subOrganizationOf ?z } FROM <lubm> WHERE { ?x ub:subOrganizationOf ?y . ?y ub:subOrganizationOf ?z . }; </pre></blockquote> <p>Then it loads the ontology file, <code>inf.nt</code>, using the Turtle load function, <code>DB.DBA.TTLP</code>. The arguments of the function are the text to load, the default namespace prefix, and the <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id15835550">URI</a> of the target graph.</p> <blockquote> <pre>DB.DBA.TTLP ( file_to_string ( 'inf.nt' ), 'http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl', 'inf' ) ; sparql SELECT COUNT(*) FROM <inf> WHERE { ?x ?y ?z } ; </pre></blockquote> <p>Then we declare that the triples in the <code><inf></code> graph can be used for inference at run time. To enable this, a SPARQL query will declare that it uses the <code>'inft'</code> rule set. Otherwise this has no effect.</p> <blockquote> <pre>rdfs_rule_set ('inft', 'inf'); </pre></blockquote> <p>This is just a log checkpoint to finalize the work and truncate the transaction log. The server would also eventually do this in its own time.</p> <blockquote> <pre>checkpoint; </pre></blockquote> <p>Now we are ready for querying.</p> <h2>Querying the Data</h2> <p>The queries are given in 3 different versions: The first file, <code>lubm.sql</code>, has the queries with most inference open coded as <code>UNIONs</code>. The second file, <code>lubm-inf.sql</code>, has the inference performed at run time using the ontology <a href="http://dbpedia.org/resource/Information" id="link-id1109faf0">information</a> in the <code><inf></code> graph we just loaded. The last, <code>lubm-phys.sql</code>, relies on having the entailed triples physically present in the <code><lubm></code> graph. These entailed triples are inserted by the SPARUL commands in the <code>lubm-cp.sql</code> file.</p> <p>If you wish to run all the commands in a SQL file, you can type <code>load <filename>;</code> (e.g., <code>load lubm-cp.sql;</code>) at the <code>SQL></code> prompt. If you wish to try individual statements, you can paste them to the command line.</p> <p>For example: </p> <blockquote> <pre>SQL> sparql PREFIX ub: <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#> SELECT * FROM <lubm> WHERE { ?x a ub:Publication . ?x ub:publicationAuthor <http://www.Department0.University0.edu/AssistantProfessor0> }; VARCHAR _______________________________________________________________________ http://www.Department0.University0.edu/AssistantProfessor0/Publication0 http://www.Department0.University0.edu/AssistantProfessor0/Publication1 http://www.Department0.University0.edu/AssistantProfessor0/Publication2 http://www.Department0.University0.edu/AssistantProfessor0/Publication3 http://www.Department0.University0.edu/AssistantProfessor0/Publication4 http://www.Department0.University0.edu/AssistantProfessor0/Publication5 6 Rows. -- 4 msec. </pre></blockquote> <p>To stop the server, simply type <code>shutdown;</code> at the <code>SQL></code> prompt.</p> <p>If you wish to use a <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id11384668">SPARQL protocol</a> end point, just enable the HTTP listener. This is done by adding a stanza like â</p> <blockquote> <pre>[HTTPServer] ServerPort = 8421 ServerRoot = . ServerThreads = 2 </pre></blockquote> <p>â to the end of the <code>virtuoso.ini</code> file in the <code>lubm</code> directory. Then shutdown and restart (type <code>shutdown;</code> at the <code>SQL></code> prompt and then <code>virtuoso-t -f &</code> at the shell prompt).</p> <p>Now you can connect to the end point with a web browser. The <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id113d02d8">URL</a> is <code>http://localhost:8421/sparql</code>. Without parameters, this will show a human readable form. With parameters, this will execute SPARQL.</p> <p>We have shown how to load and query RDF with Virtuoso using the most basic SQL tools. Next you can access RDF from, for example, <a href="http://dbpedia.org/resource/PHP" id="link-id142d0ba0">PHP</a>, using the PHP ODBC interface.</p> <p>To see how to use <a href="http://jena.sourceforge.net/" id="link-id117074f0">Jena</a> or <a href="http://sourceforge.net/projects/sesame/" id="link-id1103c9b0">Sesame</a> with Virtuoso, look at <a href="http://docs.openlinksw.com/virtuoso/rdfnativestorageproviders.html" id="link-id15488ce8">Native RDF Storage Providers</a>. To see how RDF data types are supported, see <a href="http://docs.openlinksw.com/virtuoso/VirtuosoDriverJDBC.html#jdbcrdf" id="link-id15784a40">Extension datatype for RDF</a> </p> <p>To work with large volumes of data, you must add memory to the configuration file and use the row-autocommit mode, i.e., do <code>log_enable (2);</code> before the load command. Otherwise Virtuoso will do the entire load as a single transaction, and will run out of rollback space. See <a href="http://docs.openlinksw.com/virtuoso/" id="link-id111410f0">documentation</a> for more.</p>
See the Lite: Embeddable/Background Virtuoso starts at 25MB
http://www.openlinksw.com/blog/vdb/blog/?date=2008-12-17#1503
2008-12-17T09:34:12Z
2008-12-17T12:03:49-05:00
<p>We have received many requests for an embeddable-scale <a href="http://virtuoso.openlinksw.com" id="link-id0x1cd69650">Virtuoso</a>. In response to this, we have added a Lite mode, where the initial size of a server process is a tiny fraction of what the initial size would be with default settings. With 2MB of disk cache buffers (ini file setting, <code>NumberOfBuffers = 256</code>), the process size stays under 30MB on 32-bit Linux.</p> <p>The value of this is that one can now have <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1ce89340">RDF</a> and full text indexing on the desktop without running a Java VM or any other memory-intensive software. And of course, all of <a href="http://dbpedia.org/resource/SQL" id="link-id0x1cfc9288">SQL</a> (transactions, stored procedures, etc.) is in the same embeddably-sized container.</p> <p>The Lite executable is a full Virtuoso executable; the Lite mode is controlled by a switch in the configuration file. The executable size is about 10MB for 32-bit Linux. A database created in the Lite mode will be converted into a fully-featured database (tables and indexes are added, among other things) if the server is started with the Lite setting "off"; functionality can be reverted to Lite mode, though it will now consume somewhat more memory, etc.</p> <p>Lite mode offers full SQL and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1c511da8">SPARQL</a>/SPARUL (via SPASQL), but disables all <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1dac1950">HTTP</a>-based services (WebDAV, application hosting, etc.). Clients can still use all typical database access mechanisms (i.e., <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0xb19a488">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1d93ee40">JDBC</a>, OLE-DB, <a href="http://dbpedia.org/resource/ADO.NET" id="link-id0x1ce391c0">ADO</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0xacf1168">NET</a>, and XMLA) to connect, including the <a href="http://jena.sourceforge.net/" id="link-id0xaaf5b58">Jena</a> and <a href="http://sourceforge.net/projects/sesame/" id="link-id0x1b1e4328">Sesame</a> frameworks for RDF. ODBC now offers full support of RDF <a href="http://dbpedia.org/resource/Data" id="link-id0x1cfc9f78">data</a> types for <a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0xa6059d8">C</a>-based clients. A Redland-compatible API also exists, for use with Redland v1.0.8 and later. </p> <p>Especially for embedded use, we now allow restricting the listener to be a Unix socket, which allows client connections only from the localhost.</p> <p>Shipping an embedded Virtuoso is easy. It just takes one executable and one configuration file. Performance is generally comparable to "normal" mode, except that Lite will be somewhat less scalable on multicore systems.</p> <p>The Lite mode will be included in the next Virtuoso 5 Open Source release.</p>
See the Lite: Embeddable/Background Virtuoso starts at 25MB
http://www.openlinksw.com/weblog/oerling/?date=2008-12-17#1502
2008-12-17T09:34:12Z
2008-12-17T12:03:43-05:00
<p>We have received many requests for an embeddable-scale <a href="http://virtuoso.openlinksw.com" id="link-id0xa5aa1b38">Virtuoso</a>. In response to this, we have added a Lite mode, where the initial size of a server process is a tiny fraction of what the initial size would be with default settings. With 2MB of disk cache buffers (ini file setting, <code>NumberOfBuffers = 256</code>), the process size stays under 30MB on 32-bit Linux.</p> <p>The value of this is that one can now have <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1db79ac8">RDF</a> and full text indexing on the desktop without running a Java VM or any other memory-intensive software. And of course, all of <a href="http://dbpedia.org/resource/SQL" id="link-id0xa923298">SQL</a> (transactions, stored procedures, etc.) is in the same embeddably-sized container.</p> <p>The Lite executable is a full Virtuoso executable; the Lite mode is controlled by a switch in the configuration file. The executable size is about 10MB for 32-bit Linux. A database created in the Lite mode will be converted into a fully-featured database (tables and indexes are added, among other things) if the server is started with the Lite setting "off"; functionality can be reverted to Lite mode, though it will now consume somewhat more memory, etc.</p> <p>Lite mode offers full SQL and <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1b388830">SPARQL</a>/SPARUL (via SPASQL), but disables all <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1d56b618">HTTP</a>-based services (WebDAV, application hosting, etc.). Clients can still use all typical database access mechanisms (i.e., <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x1c5abc38">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1dade1f8">JDBC</a>, OLE-DB, <a href="http://dbpedia.org/resource/ADO.NET" id="link-id0x25d8e0f0">ADO</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x1d7a1a28">NET</a>, and XMLA) to connect, including the <a href="http://jena.sourceforge.net/" id="link-id0x1d929b98">Jena</a> and <a href="http://sourceforge.net/projects/sesame/" id="link-id0x1b7a9088">Sesame</a> frameworks for RDF. ODBC now offers full support of RDF <a href="http://dbpedia.org/resource/Data" id="link-id0xaf62aa0">data</a> types for <a href="http://dbpedia.org/resource/C%2B%2B" id="link-id0xa8784b0">C</a>-based clients. A Redland-compatible API also exists, for use with Redland v1.0.8 and later. </p> <p>Especially for embedded use, we now allow restricting the listener to be a Unix socket, which allows client connections only from the localhost.</p> <p>Shipping an embedded Virtuoso is easy. It just takes one executable and one configuration file. Performance is generally comparable to "normal" mode, except that Lite will be somewhat less scalable on multicore systems.</p> <p>The Lite mode will be included in the next Virtuoso 5 Open Source release.</p>
"E Pluribus Unum", or "Inversely Functional Identity", or "Smooshing Without the Stickiness" (re-updated)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-12-16#1499
2008-12-16T14:14:43Z
2008-12-16T15:01:36.000003-05:00
<p>What a terrible word, smooshing... I have understood it to mean that when you have two names for one thing, you give each all the attributes of the other. This smooshes them together, makes them interchangeable.</p> <p>This is complex, so I will begin with the point and the interested may read on for the details and implications. Starting with soon to be released version 6, <a href="http://virtuoso.openlinksw.com" id="link-id15718cb8">Virtuoso</a> allows you to say that two things, if they share a uniquely identifying property, are the same. Examples of uniquely identifying properties would be a book's ISBN number, or a person's social security plus full name. In relational language this is a <i>unique key</i>, and in <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id145ed998">RDF</a> parlance, an <i>inverse functional property</i>.</p> <p>In most systems, such problems are dealt with as a preprocessing step before querying. For example, all the items that are considered the same will get the same properties or at load time all identifiers will be normalized according to some application rules. This is good if the rules are clear and understood. This is so in closed situations, where things tend to have standard identifiers to begin with. But on the open web this is not so clear cut.</p> <p>In this post, we show how to do these things <i>ad hoc</i>, without materializing anything. At the end, we also show how to materialize identity and what the consequences of this are with open web <a href="http://dbpedia.org/resource/Data" id="link-id11726358">data</a>. We use real live web crawls from the <a href="http://challenge.semanticweb.org/" id="link-id14f40448">Billion Triples Challenge</a> data set.</p> <p>On the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id156e2b10">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id1106ce08">web</a>, there are independently arising descriptions of the same thing and thus arises the need to smoosh, if these are to be somehow integrated. But this is only the beginning of the problems.</p> <p>To address these, we have added the option of specifying that some property will be considered inversely functional in a query. This is done at run time and the property does not really have to be inversely functional in the pure sense. <code>foaf:name</code> will do for an example. This simply means that for purposes of the query concerned, two subjects which have at least one <code>foaf:name</code> in common are considered the same. In this way, we can join between FOAF files. With the same database, a query about music preferences might consider having the same name as "same enough," but a query about criminal prosecution would obviously need to be more precise about sameness.</p> <p>Our ontology is defined like this:</p> <blockquote> <pre>-- Populate a named graph with the triples you want to use in query time inferencing<br /> ttlp ( ' @prefix foaf: <xmlns="http" xmlns.com="xmlns.com" foaf="foaf"> </> @prefix owl: <xmlns="http" www.w3.org="www.w3.org" owl="owl"> </> foaf:mbox_sha1sum a owl:InverseFunctionalProperty . foaf:name a owl:InverseFunctionalProperty . ', 'xx', 'b3sifp' );<br /> -- Declare that the graph contains an ontology for use in query time inferencing <br /> rdfs_rule_set ( 'http://example.com/rules/b3sifp#', 'b3sifp' ); </pre></blockquote> <p>Then use it:</p> <blockquote> <pre>sparql DEFINE input:inference "http://example.com/rules/b3sifp#" SELECT DISTINCT ?k ?f1 ?f2 WHERE { ?k foaf:name ?n . ?n bif:contains "'Kjetil Kjernsmo'" . ?k foaf:knows ?f1 . ?f1 foaf:knows ?f2 };<br /> VARCHAR VARCHAR VARCHAR ______________________________________ _______________________________________________ ______________________________<br /> http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/dajobe http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/net_twitter http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/amyvdh http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/pom http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/mattb http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/davorg http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/distobj http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/perigrin .... </pre></blockquote> <p>Without the inference, we get no matches. This is because the data in question has one graph per FOAF file, and blank nodes for persons. No graph references any person outside the ones in the graph. So if somebody is mentioned as known, then without the inference there is no way to get to what that person's FOAF file says, since the same individual will be a different blank node there. The declaration in the context named <code>b3sifp</code> just means that all things with a matching <code>foaf:name</code> or <code>foaf:mbox_sha1sum</code> are the same.</p> <p>Sameness means that two are the same for purposes of <code>DISTINCT</code> or <code>GROUP BY</code>, and if two are the same, then both have the <code>UNION</code> of all of the properties of both.</p> <p>If this were a naive smoosh, then the individuals would have all the same properties but would not be the same for <code>DISTINCT</code>.</p> <p>If we have complex application rules for determining whether individuals are the same, then one can materialize <code>owl:sameAs</code> triples and keep them in a separate graph. In this way, the original data is not contaminated and the materialized volume stays reasonable â nothing like the blow-up of duplicating properties across instances.</p> <p>The pro-smoosh argument is that if every duplicate makes exactly the same statements, then there is no great blow-up. Best and worst cases will always depend on the data. In rough terms, the more <i>ad hoc</i> the use, the less desirable the materialization. If the usage pattern is really set, then a relational-style application-specific representation with identity resolved at load time will perform best. We can do that too, but so can others.</p> <p>The principal point is about agility as concerns the inference. Run time is more agile than materialization, and if the rules change or if different users have different needs, then materialization runs into trouble. When talking web scale, having multiple users is a given; it is very uneconomical to give everybody their own copy, and the likelihood of a user accessing any significant part of the corpus is minimal. Even if the queries were not limited, the user would typically not wait for the answer of a query doing a scan or aggregation over 1 billion <a href="http://dbpedia.org/resource/Blog" id="link-id1156a550">blog</a> posts or something of the sort. So queries will typically be selective. Selective means that they do not access all of the data, hence do not benefit from ready-made materialization for things they do not even look at. </p> <p>The exception is corpus-wide statistics queries. But these will not be done in interactive time anyway, and will not be done very often. Plus, since these do not typically run all in memory, these are disk bound. And when things are disk bound, size matters. Reading extra entailment on the way is just a performance penalty.</p> <p>Enough talk. Time for an experiment. We take the Yahoo and Falcon web crawls from the Billion Triples Challenge set, and do two things with the FOAF data in them:</p> <ol> <li>Resolve identity at insert time. We remove duplicate person URIs, and give the single <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id11317008">URI</a> all the properties of all the duplicate URIs. We expect these to be most often repeats. If a person references another person, we normalize this reference to go to the single URI of the referenced person.</li> <li>Give every duplicate URI of a person all the properties of all the duplicates. If these are the same value, the data should not get much bigger, or so we think.</li> </ol> <p>For the experiment, we will consider two people the same if they have the same <code>foaf:name</code> and are both instances of <code>foaf:Person</code>. This gets some extra hits but should not be statistically significant.</p> <p>The following is a commented <a href="http://dbpedia.org/resource/SQL" id="link-id110945b0">SQL</a> script performing the smoosh. We play with internal IDs of things, thus some of these operations cannot be done in SPARQL alone. We use SPARQL where possible for readability. As the documentation states, <code>iri_to_id</code> converts from the qualified name of an IRI to its ID and <code>id_to_iri</code> does the reverse.</p> <p>We count the triples that enter into the smoosh:</p> <blockquote> <pre>-- the name is an existence because else we'd get several times more due to -- the names occurring in many graphs <br /> sparql SELECT COUNT(*) WHERE { { SELECT DISTINCT ?person WHERE { ?person a foaf:Person } } . FILTER ( bif:exists ( SELECT (1) WHERE { ?person foaf:name ?nn } ) ) . ?person ?p ?o };<br /> -- We get 3284674 </pre></blockquote> <p>We make a few tables for intermediate results.</p> <blockquote> <pre>-- For each distinct name, gather the properties and objects from -- all subjects with this name <br /> CREATE TABLE name_prop ( np_name ANY, np_p IRI_ID_8, np_o ANY, PRIMARY KEY ( np_name, np_p, np_o ) ); ALTER INDEX name_prop ON name_prop PARTITION ( np_name VARCHAR (-1, 0hexffff) );<br /> -- Map from name to canonical IRI used for the name <br /> CREATE TABLE name_iri ( ni_name ANY PRIMARY KEY, ni_s IRI_ID_8 ); ALTER INDEX name_iri ON name_iri PARTITION ( ni_name VARCHAR (-1, 0hexffff) );<br /> -- Map from person IRI to canonical person IRI<br /> CREATE TABLE pref_iri ( i IRI_ID_8, pref IRI_ID_8, PRIMARY KEY ( i ) ); ALTER INDEX pref_iri ON pref_iri PARTITION ( i INT (0hexffff00) );<br /> -- a table for the materialization where all aliases get all properties of every other <br /> CREATE TABLE smoosh_ct ( s IRI_ID_8, p IRI_ID_8, o ANY, PRIMARY KEY ( s, p, o ) ); ALTER INDEX smoosh_ct ON smoosh_ct PARTITION ( s INT (0hexffff00) );<br /> -- disable transaction log and enable row auto-commit. This is necessary, otherwise -- bulk operations are done transactionally and they will run out of rollback space.<br /> LOG_ENABLE (2);<br /> -- Gather all the properties of all persons with a name under that name. -- INSERT SOFT means that duplicates are ignored <br /> INSERT SOFT name_prop SELECT "n", "p", "o" FROM ( sparql DEFINE output:valmode "LONG" SELECT ?n ?p ?o WHERE { ?x a foaf:Person . ?x foaf:name ?n . ?x ?p ?o } ) xx ;<br /> -- Now choose for each name the canonical IRI <br /> INSERT INTO name_iri SELECT np_name, ( SELECT MIN (s) FROM rdf_quad WHERE o = np_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ) AS mini FROM name_prop WHERE np_p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- For each person IRI, map to the canonical IRI of that person <br /> INSERT SOFT pref_iri (i, pref) SELECT s, ni_s FROM name_iri, rdf_quad WHERE o = ni_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- Make a graph where all persons have one iri with all the properties of all aliases -- and where person-to-person refs are canonicalized<br /> INSERT SOFT rdf_quad (g,s,p,o) SELECT IRI_TO_ID ('psmoosh'), ni_s, np_p, COALESCE ( ( SELECT pref FROM pref_iri WHERE i = np_o ), np_o ) FROM name_prop, name_iri WHERE ni_name = np_name OPTION ( loop, quietcast ) ;<br /> -- A little explanation: The properties of names are copied into rdf_quad with the name -- replaced with its canonical IRI. If the object has a canonical IRI, this is used as -- the object, else the object is unmodified. This is the COALESCE with the sub-query.<br /> -- This takes a little time. To check on the progress, take another connection to the -- server and do <br /> STATUS ('cluster');<br /> -- It will return something like -- Cluster 4 nodes, 35 s. 108 m/s 1001 KB/s 75% cpu 186% read 12% clw threads 5r 0w 0i -- buffers 549481 253929 d 8 w 0 pfs<br /> -- Now finalize the state; this makes it permanent. Else the work will be lost on server -- failure, since there was no transaction log <br /> CL_EXEC ('checkpoint');<br /> -- See what we got<br /> sparql SELECT COUNT (*) FROM <psmoosh> WHERE {?s ?p ?o};<br /> -- This is 2253102<br /> -- Now make the copy where all have the properties of all synonyms. This takes so much -- space we do not insert it as RDF quads, but make a special table for it so that we can -- run some statistics. This saves time.<br /> INSERT SOFT smoosh_ct (s, p, o) SELECT s, np_p, np_o FROM name_prop, rdf_quad WHERE o = np_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- as above, INSERT SOFT so as to ignore duplicates <br /> SELECT COUNT (*) FROM smoosh_ct;<br /> -- This is 167360324<br /> -- Find out where the bloat comes from <br /> SELECT TOP 20 COUNT (*), ID_TO_IRI (p) FROM smoosh_ct GROUP BY p ORDER BY 1 DESC; </pre></blockquote> <p>The results are:</p> <blockquote> <pre>54728777 http://www.w3.org/2002/07/owl#sameAs 48543153 http://xmlns.com/foaf/0.1/knows 13930234 http://www.w3.org/2000/01/rdf-schema#seeAlso 12268512 http://xmlns.com/foaf/0.1/interest 11415867 http://xmlns.com/foaf/0.1/nick 6683963 http://xmlns.com/foaf/0.1/weblog 6650093 http://xmlns.com/foaf/0.1/depiction 4231946 http://xmlns.com/foaf/0.1/mbox_sha1sum 4129629 http://xmlns.com/foaf/0.1/homepage 1776555 http://xmlns.com/foaf/0.1/holdsAccount 1219525 http://xmlns.com/foaf/0.1/based_near 305522 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 274965 http://xmlns.com/foaf/0.1/name 155131 http://xmlns.com/foaf/0.1/dateOfBirth 153001 http://xmlns.com/foaf/0.1/img 111130 http://www.w3.org/2001/vcard-rdf/3.0#ADR 52930 http://xmlns.com/foaf/0.1/gender 48517 http://www.w3.org/2004/02/skos/core#subject 45697 http://www.w3.org/2000/01/rdf-schema#label 44860 http://purl.org/vocab/bio/0.1/olb </pre></blockquote> <p>Now compare with the predicate distribution of the smoosh with identities canonicalized </p> <blockquote> <pre>sparql SELECT COUNT (*) ?p FROM <psmoosh> WHERE { ?s ?p ?o } GROUP BY ?p ORDER BY 1 DESC LIMIT 20;</pre></blockquote> <p>Results are:</p> <blockquote> <pre>748311 http://xmlns.com/foaf/0.1/knows 548391 http://xmlns.com/foaf/0.1/interest 140531 http://www.w3.org/2000/01/rdf-schema#seeAlso 105273 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 78497 http://xmlns.com/foaf/0.1/name 48099 http://www.w3.org/2004/02/skos/core#subject 45179 http://xmlns.com/foaf/0.1/depiction 40229 http://www.w3.org/2000/01/rdf-schema#comment 38272 http://www.w3.org/2000/01/rdf-schema#label 37378 http://xmlns.com/foaf/0.1/nick 37186 http://dbpedia.org/property/abstract 34003 http://xmlns.com/foaf/0.1/img 26182 http://xmlns.com/foaf/0.1/homepage 23795 http://www.w3.org/2002/07/owl#sameAs 17651 http://xmlns.com/foaf/0.1/mbox_sha1sum 17430 http://xmlns.com/foaf/0.1/dateOfBirth 15586 http://xmlns.com/foaf/0.1/page 12869 http://dbpedia.org/property/reference 12497 http://xmlns.com/foaf/0.1/weblog 12329 http://blogs.yandex.ru/schema/foaf/school </pre></blockquote> <p>We can drop the <code>owl:sameAs</code> triples from the count, so the bloat is a bit less by that but it still is tens of times larger than the canonicalized copy or the initial state.</p> <p>Now, when we try using the psmoosh graph, we still get different results from the results with the original data. This is because <code>foaf:knows</code> relations to things with no <code>foaf:name</code> are not represented in the smoosh. The exist:</p> <blockquote> <pre>sparql SELECT COUNT (*) WHERE { ?s foaf:knows ?thing . FILTER ( !bif:exists ( SELECT (1) WHERE { ?thing foaf:name ?nn } ) ) };<br /> -- 1393940 </pre></blockquote> <p>So the smoosh graph is not an accurate rendition of the social network. It would have to be smooshed further to be that, since the data in the sample is quite irregular. But we do not go that far here.</p> <p>Finally, we calculate the smoosh blow up factors. We do not include <code>owl:sameAs</code> triples in the counts.</p> <blockquote> <pre>select (167360324 - 54728777) / 3284674.0; 34.290022997716059<br /> select 2229307 / 3284674.0; = 0.678699621332284 </pre></blockquote> <p>So, to get a smoosh that is not really the equivalent of the original, either multiply the original triple count by 34 or 0.68, depending on whether synonyms are collapsed or not.</p> <p>Making the smooshes does not take very long, some minutes for the small one. Inserting the big one would be longer, a couple of hours maybe. It was 33 minutes for filling the <code>smoosh_ct</code> table. The metrics were not with optimal tuning so the performance numbers just serve to show that smooshing takes time. Probably more time than allowable in an interactive situation, no matter how the process is optimized.</p>
"E Pluribus Unum", or "Inversely Functional Identity", or "Smooshing Without the Stickiness" (re-updated)
http://www.openlinksw.com/weblog/oerling/?date=2008-12-16#1498
2008-12-16T14:14:43Z
2008-12-16T15:01:30-05:00
<p>What a terrible word, smooshing... I have understood it to mean that when you have two names for one thing, you give each all the attributes of the other. This smooshes them together, makes them interchangeable.</p> <p>This is complex, so I will begin with the point and the interested may read on for the details and implications. Starting with soon to be released version 6, <a href="http://virtuoso.openlinksw.com" id="link-id15718cb8">Virtuoso</a> allows you to say that two things, if they share a uniquely identifying property, are the same. Examples of uniquely identifying properties would be a book's ISBN number, or a person's social security plus full name. In relational language this is a <i>unique key</i>, and in <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id145ed998">RDF</a> parlance, an <i>inverse functional property</i>.</p> <p>In most systems, such problems are dealt with as a preprocessing step before querying. For example, all the items that are considered the same will get the same properties or at load time all identifiers will be normalized according to some application rules. This is good if the rules are clear and understood. This is so in closed situations, where things tend to have standard identifiers to begin with. But on the open web this is not so clear cut.</p> <p>In this post, we show how to do these things <i>ad hoc</i>, without materializing anything. At the end, we also show how to materialize identity and what the consequences of this are with open web <a href="http://dbpedia.org/resource/Data" id="link-id11726358">data</a>. We use real live web crawls from the <a href="http://challenge.semanticweb.org/" id="link-id14f40448">Billion Triples Challenge</a> data set.</p> <p>On the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id156e2b10">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id1106ce08">web</a>, there are independently arising descriptions of the same thing and thus arises the need to smoosh, if these are to be somehow integrated. But this is only the beginning of the problems.</p> <p>To address these, we have added the option of specifying that some property will be considered inversely functional in a query. This is done at run time and the property does not really have to be inversely functional in the pure sense. <code>foaf:name</code> will do for an example. This simply means that for purposes of the query concerned, two subjects which have at least one <code>foaf:name</code> in common are considered the same. In this way, we can join between FOAF files. With the same database, a query about music preferences might consider having the same name as "same enough," but a query about criminal prosecution would obviously need to be more precise about sameness.</p> <p>Our ontology is defined like this:</p> <blockquote> <pre>-- Populate a named graph with the triples you want to use in query time inferencing<br /> ttlp ( ' @prefix foaf: <xmlns="http" xmlns.com="xmlns.com" foaf="foaf"> </> @prefix owl: <xmlns="http" www.w3.org="www.w3.org" owl="owl"> </> foaf:mbox_sha1sum a owl:InverseFunctionalProperty . foaf:name a owl:InverseFunctionalProperty . ', 'xx', 'b3sifp' );<br /> -- Declare that the graph contains an ontology for use in query time inferencing <br /> rdfs_rule_set ( 'http://example.com/rules/b3sifp#', 'b3sifp' ); </pre></blockquote> <p>Then use it:</p> <blockquote> <pre>sparql DEFINE input:inference "http://example.com/rules/b3sifp#" SELECT DISTINCT ?k ?f1 ?f2 WHERE { ?k foaf:name ?n . ?n bif:contains "'Kjetil Kjernsmo'" . ?k foaf:knows ?f1 . ?f1 foaf:knows ?f2 };<br /> VARCHAR VARCHAR VARCHAR ______________________________________ _______________________________________________ ______________________________<br /> http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/dajobe http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/net_twitter http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/amyvdh http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/pom http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/mattb http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/davorg http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/distobj http://www.kjetil.kjernsmo.net/foaf#me http://norman.walsh.name/knows/who/robin-berjon http://twitter.com/perigrin .... </pre></blockquote> <p>Without the inference, we get no matches. This is because the data in question has one graph per FOAF file, and blank nodes for persons. No graph references any person outside the ones in the graph. So if somebody is mentioned as known, then without the inference there is no way to get to what that person's FOAF file says, since the same individual will be a different blank node there. The declaration in the context named <code>b3sifp</code> just means that all things with a matching <code>foaf:name</code> or <code>foaf:mbox_sha1sum</code> are the same.</p> <p>Sameness means that two are the same for purposes of <code>DISTINCT</code> or <code>GROUP BY</code>, and if two are the same, then both have the <code>UNION</code> of all of the properties of both.</p> <p>If this were a naive smoosh, then the individuals would have all the same properties but would not be the same for <code>DISTINCT</code>.</p> <p>If we have complex application rules for determining whether individuals are the same, then one can materialize <code>owl:sameAs</code> triples and keep them in a separate graph. In this way, the original data is not contaminated and the materialized volume stays reasonable â nothing like the blow-up of duplicating properties across instances.</p> <p>The pro-smoosh argument is that if every duplicate makes exactly the same statements, then there is no great blow-up. Best and worst cases will always depend on the data. In rough terms, the more <i>ad hoc</i> the use, the less desirable the materialization. If the usage pattern is really set, then a relational-style application-specific representation with identity resolved at load time will perform best. We can do that too, but so can others.</p> <p>The principal point is about agility as concerns the inference. Run time is more agile than materialization, and if the rules change or if different users have different needs, then materialization runs into trouble. When talking web scale, having multiple users is a given; it is very uneconomical to give everybody their own copy, and the likelihood of a user accessing any significant part of the corpus is minimal. Even if the queries were not limited, the user would typically not wait for the answer of a query doing a scan or aggregation over 1 billion <a href="http://dbpedia.org/resource/Blog" id="link-id1156a550">blog</a> posts or something of the sort. So queries will typically be selective. Selective means that they do not access all of the data, hence do not benefit from ready-made materialization for things they do not even look at. </p> <p>The exception is corpus-wide statistics queries. But these will not be done in interactive time anyway, and will not be done very often. Plus, since these do not typically run all in memory, these are disk bound. And when things are disk bound, size matters. Reading extra entailment on the way is just a performance penalty.</p> <p>Enough talk. Time for an experiment. We take the Yahoo and Falcon web crawls from the Billion Triples Challenge set, and do two things with the FOAF data in them:</p> <ol> <li>Resolve identity at insert time. We remove duplicate person URIs, and give the single <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id11317008">URI</a> all the properties of all the duplicate URIs. We expect these to be most often repeats. If a person references another person, we normalize this reference to go to the single URI of the referenced person.</li> <li>Give every duplicate URI of a person all the properties of all the duplicates. If these are the same value, the data should not get much bigger, or so we think.</li> </ol> <p>For the experiment, we will consider two people the same if they have the same <code>foaf:name</code> and are both instances of <code>foaf:Person</code>. This gets some extra hits but should not be statistically significant.</p> <p>The following is a commented <a href="http://dbpedia.org/resource/SQL" id="link-id110945b0">SQL</a> script performing the smoosh. We play with internal IDs of things, thus some of these operations cannot be done in SPARQL alone. We use SPARQL where possible for readability. As the documentation states, <code>iri_to_id</code> converts from the qualified name of an IRI to its ID and <code>id_to_iri</code> does the reverse.</p> <p>We count the triples that enter into the smoosh:</p> <blockquote> <pre>-- the name is an existence because else we'd get several times more due to -- the names occurring in many graphs <br /> sparql SELECT COUNT(*) WHERE { { SELECT DISTINCT ?person WHERE { ?person a foaf:Person } } . FILTER ( bif:exists ( SELECT (1) WHERE { ?person foaf:name ?nn } ) ) . ?person ?p ?o };<br /> -- We get 3284674 </pre></blockquote> <p>We make a few tables for intermediate results.</p> <blockquote> <pre>-- For each distinct name, gather the properties and objects from -- all subjects with this name <br /> CREATE TABLE name_prop ( np_name ANY, np_p IRI_ID_8, np_o ANY, PRIMARY KEY ( np_name, np_p, np_o ) ); ALTER INDEX name_prop ON name_prop PARTITION ( np_name VARCHAR (-1, 0hexffff) );<br /> -- Map from name to canonical IRI used for the name <br /> CREATE TABLE name_iri ( ni_name ANY PRIMARY KEY, ni_s IRI_ID_8 ); ALTER INDEX name_iri ON name_iri PARTITION ( ni_name VARCHAR (-1, 0hexffff) );<br /> -- Map from person IRI to canonical person IRI<br /> CREATE TABLE pref_iri ( i IRI_ID_8, pref IRI_ID_8, PRIMARY KEY ( i ) ); ALTER INDEX pref_iri ON pref_iri PARTITION ( i INT (0hexffff00) );<br /> -- a table for the materialization where all aliases get all properties of every other <br /> CREATE TABLE smoosh_ct ( s IRI_ID_8, p IRI_ID_8, o ANY, PRIMARY KEY ( s, p, o ) ); ALTER INDEX smoosh_ct ON smoosh_ct PARTITION ( s INT (0hexffff00) );<br /> -- disable transaction log and enable row auto-commit. This is necessary, otherwise -- bulk operations are done transactionally and they will run out of rollback space.<br /> LOG_ENABLE (2);<br /> -- Gather all the properties of all persons with a name under that name. -- INSERT SOFT means that duplicates are ignored <br /> INSERT SOFT name_prop SELECT "n", "p", "o" FROM ( sparql DEFINE output:valmode "LONG" SELECT ?n ?p ?o WHERE { ?x a foaf:Person . ?x foaf:name ?n . ?x ?p ?o } ) xx ;<br /> -- Now choose for each name the canonical IRI <br /> INSERT INTO name_iri SELECT np_name, ( SELECT MIN (s) FROM rdf_quad WHERE o = np_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ) AS mini FROM name_prop WHERE np_p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- For each person IRI, map to the canonical IRI of that person <br /> INSERT SOFT pref_iri (i, pref) SELECT s, ni_s FROM name_iri, rdf_quad WHERE o = ni_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- Make a graph where all persons have one iri with all the properties of all aliases -- and where person-to-person refs are canonicalized<br /> INSERT SOFT rdf_quad (g,s,p,o) SELECT IRI_TO_ID ('psmoosh'), ni_s, np_p, COALESCE ( ( SELECT pref FROM pref_iri WHERE i = np_o ), np_o ) FROM name_prop, name_iri WHERE ni_name = np_name OPTION ( loop, quietcast ) ;<br /> -- A little explanation: The properties of names are copied into rdf_quad with the name -- replaced with its canonical IRI. If the object has a canonical IRI, this is used as -- the object, else the object is unmodified. This is the COALESCE with the sub-query.<br /> -- This takes a little time. To check on the progress, take another connection to the -- server and do <br /> STATUS ('cluster');<br /> -- It will return something like -- Cluster 4 nodes, 35 s. 108 m/s 1001 KB/s 75% cpu 186% read 12% clw threads 5r 0w 0i -- buffers 549481 253929 d 8 w 0 pfs<br /> -- Now finalize the state; this makes it permanent. Else the work will be lost on server -- failure, since there was no transaction log <br /> CL_EXEC ('checkpoint');<br /> -- See what we got<br /> sparql SELECT COUNT (*) FROM <psmoosh> WHERE {?s ?p ?o};<br /> -- This is 2253102<br /> -- Now make the copy where all have the properties of all synonyms. This takes so much -- space we do not insert it as RDF quads, but make a special table for it so that we can -- run some statistics. This saves time.<br /> INSERT SOFT smoosh_ct (s, p, o) SELECT s, np_p, np_o FROM name_prop, rdf_quad WHERE o = np_name AND p = IRI_TO_ID ('http://xmlns.com/foaf/0.1/name') ;<br /> -- as above, INSERT SOFT so as to ignore duplicates <br /> SELECT COUNT (*) FROM smoosh_ct;<br /> -- This is 167360324<br /> -- Find out where the bloat comes from <br /> SELECT TOP 20 COUNT (*), ID_TO_IRI (p) FROM smoosh_ct GROUP BY p ORDER BY 1 DESC; </pre></blockquote> <p>The results are:</p> <blockquote> <pre>54728777 http://www.w3.org/2002/07/owl#sameAs 48543153 http://xmlns.com/foaf/0.1/knows 13930234 http://www.w3.org/2000/01/rdf-schema#seeAlso 12268512 http://xmlns.com/foaf/0.1/interest 11415867 http://xmlns.com/foaf/0.1/nick 6683963 http://xmlns.com/foaf/0.1/weblog 6650093 http://xmlns.com/foaf/0.1/depiction 4231946 http://xmlns.com/foaf/0.1/mbox_sha1sum 4129629 http://xmlns.com/foaf/0.1/homepage 1776555 http://xmlns.com/foaf/0.1/holdsAccount 1219525 http://xmlns.com/foaf/0.1/based_near 305522 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 274965 http://xmlns.com/foaf/0.1/name 155131 http://xmlns.com/foaf/0.1/dateOfBirth 153001 http://xmlns.com/foaf/0.1/img 111130 http://www.w3.org/2001/vcard-rdf/3.0#ADR 52930 http://xmlns.com/foaf/0.1/gender 48517 http://www.w3.org/2004/02/skos/core#subject 45697 http://www.w3.org/2000/01/rdf-schema#label 44860 http://purl.org/vocab/bio/0.1/olb </pre></blockquote> <p>Now compare with the predicate distribution of the smoosh with identities canonicalized </p> <blockquote> <pre>sparql SELECT COUNT (*) ?p FROM <psmoosh> WHERE { ?s ?p ?o } GROUP BY ?p ORDER BY 1 DESC LIMIT 20;</pre></blockquote> <p>Results are:</p> <blockquote> <pre>748311 http://xmlns.com/foaf/0.1/knows 548391 http://xmlns.com/foaf/0.1/interest 140531 http://www.w3.org/2000/01/rdf-schema#seeAlso 105273 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 78497 http://xmlns.com/foaf/0.1/name 48099 http://www.w3.org/2004/02/skos/core#subject 45179 http://xmlns.com/foaf/0.1/depiction 40229 http://www.w3.org/2000/01/rdf-schema#comment 38272 http://www.w3.org/2000/01/rdf-schema#label 37378 http://xmlns.com/foaf/0.1/nick 37186 http://dbpedia.org/property/abstract 34003 http://xmlns.com/foaf/0.1/img 26182 http://xmlns.com/foaf/0.1/homepage 23795 http://www.w3.org/2002/07/owl#sameAs 17651 http://xmlns.com/foaf/0.1/mbox_sha1sum 17430 http://xmlns.com/foaf/0.1/dateOfBirth 15586 http://xmlns.com/foaf/0.1/page 12869 http://dbpedia.org/property/reference 12497 http://xmlns.com/foaf/0.1/weblog 12329 http://blogs.yandex.ru/schema/foaf/school </pre></blockquote> <p>We can drop the <code>owl:sameAs</code> triples from the count, so the bloat is a bit less by that but it still is tens of times larger than the canonicalized copy or the initial state.</p> <p>Now, when we try using the psmoosh graph, we still get different results from the results with the original data. This is because <code>foaf:knows</code> relations to things with no <code>foaf:name</code> are not represented in the smoosh. The exist:</p> <blockquote> <pre>sparql SELECT COUNT (*) WHERE { ?s foaf:knows ?thing . FILTER ( !bif:exists ( SELECT (1) WHERE { ?thing foaf:name ?nn } ) ) };<br /> -- 1393940 </pre></blockquote> <p>So the smoosh graph is not an accurate rendition of the social network. It would have to be smooshed further to be that, since the data in the sample is quite irregular. But we do not go that far here.</p> <p>Finally, we calculate the smoosh blow up factors. We do not include <code>owl:sameAs</code> triples in the counts.</p> <blockquote> <pre>select (167360324 - 54728777) / 3284674.0; 34.290022997716059<br /> select 2229307 / 3284674.0; = 0.678699621332284 </pre></blockquote> <p>So, to get a smoosh that is not really the equivalent of the original, either multiply the original triple count by 34 or 0.68, depending on whether synonyms are collapsed or not.</p> <p>Making the smooshes does not take very long, some minutes for the small one. Inserting the big one would be longer, a couple of hours maybe. It was 33 minutes for filling the <code>smoosh_ct</code> table. The metrics were not with optimal tuning so the performance numbers just serve to show that smooshing takes time. Probably more time than allowable in an interactive situation, no matter how the process is optimized.</p>
Virtuoso Anytime: No Query Is Too Complex (updated)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-12-11#1495
2008-12-11T16:13:10Z
2008-12-12T10:29:23-05:00
<p>A persistent argument against the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id1199d5f8">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id116f2730">web</a> has been the cost, scalability, and vulnerability of <a href="http://dbpedia.org/resource/SPARQL" id="link-id14e423c0">SPARQL</a> end points, should the linked data web gain serious mass and traffic.</p> <p>As we are on the brink of hosting the whole <a href="http://dbpedia.org/resource/DBpedia" id="link-id1376a8b0">DBpedia</a> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id113c8d20">Linked Open Data</a> cloud in <a href="http://virtuoso.openlinksw.com" id="link-id11425a78">Virtuoso</a> Cluster, we have had to think of what we'll do if, for example, somebody decides to count all the triples in the set.</p> <p>How can we encourage clever use of <a href="http://dbpedia.org/resource/Data" id="link-id116f1210">data</a>, yet not die if somebody, whether through malice, lack of understanding, or simple bad luck, submits impossible queries?</p> <p>Restricting the language is not the way; any language beyond text search can express queries that will take forever to execute. Also, just returning a timeout after the first second (or whatever arbitrary time period) leaves people in the dark and does not produce an impression of responsiveness. So we decided to allow arbitrary queries, and if a quota of time or resources is exceeded, we return partial results and indicate how much processing was done.</p> <p>Here we are looking for the top 10 people whom people claim to know without being known in return, like this:</p> <blockquote> <pre>SQL> sparql SELECT ?celeb, COUNT (*) WHERE { ?claimant foaf:knows ?celeb . FILTER (!bif:exists ( SELECT (1) WHERE { ?celeb foaf:knows ?claimant } ) ) } GROUP BY ?celeb ORDER BY DESC 2 LIMIT 10;<br /> celeb callret-1 VARCHAR VARCHAR ________________________________________ _________<br /> http://twitter.com/BarackObama 252 http://twitter.com/brianshaler 183 http://twitter.com/newmediajim 101 http://twitter.com/HenryRollins 95 http://twitter.com/wilw 81 http://twitter.com/stevegarfield 78 http://twitter.com/cote 66 mailto:adam.westerski@deri.org 66 mailto:michal.zaremba@deri.org 66 http://twitter.com/dsifry 65<br /> *** Error S1TAT: [Virtuoso Driver][Virtuoso Server]RC...: Returning incomplete results, query interrupted by result timeout. Activity: 1R rnd 0R seq 0P disk 1.346KB / 3 messages<br /> SQL> sparql SELECT ?celeb, COUNT (*) WHERE { ?claimant foaf:knows ?celeb . FILTER (!bif:exists ( SELECT (1) WHERE { ?celeb foaf:knows ?claimant } ) ) } GROUP BY ?celeb ORDER BY DESC 2 LIMIT 10;<br /> celeb callret-1 VARCHAR VARCHAR ________________________________________ _________<br /> http://twitter.com/JasonCalacanis 496 http://twitter.com/Twitterrific 466 http://twitter.com/ev 442 http://twitter.com/BarackObama 356 http://twitter.com/laughingsquid 317 http://twitter.com/gruber 294 http://twitter.com/chrispirillo 259 http://twitter.com/ambermacarthur 224 http://twitter.com/t 219 http://twitter.com/johnedwards 188<br /> *** Error S1TAT: [Virtuoso Driver][Virtuoso Server]RC...: Returning incomplete results, query interrupted by result timeout. Activity: 329R rnd 44.6KR seq 342P disk 638.4KB / 46 messages</pre></blockquote> <p>The first query read all data from disk; the second run had the working set from the first and could read some more before time ran out, hence the results were better. But the response time was the same.</p> <p>If one has a query that just loops over consecutive joins, like in basic SPARQL, interrupting the processing after a set time period is simple. But such queries are not very interesting. To give meaningful partial answers with nested aggregation and sub-queries requires some more tricks. The basic idea is to terminate the innermost active sub-query/aggregation at the first timeout, and extend the timeout a bit so that accumulated results get fed to the next aggregation, like from the <code>GROUP BY</code> to the <code>ORDER BY</code>. If this again times out, we continue with the next outer layer. This guarantees that results are delivered if there were any results found for which the query pattern is true. False results are not produced, except in cases where there is comparison with a count and the count is smaller than it would be with the full evaluation.</p> <p>One can also use this as a basis for paid services. The cutoff does not have to be time; it can also be in other units, making it insensitive to concurrent usage and variations of working set.</p> <p>This system will be deployed on our <a href="http://challenge.semanticweb.org/" id="link-id11500a58">Billion Triples Challenge</a> <a href="http://b3s.openlinksw.com/" id="link-id11683120">demo instance</a> in a few days, after some more testing. When Virtuoso 6 ships, all <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id1157a500">LOD</a> Cloud AMIs and OpenLink-hosted LOD Cloud SPARQL endpoints will have this enabled by default. (AMI users will be able to disable the feature, if desired.) The feature works with Virtuoso 6 in both single server and cluster deployment.</p>
Virtuoso Anytime: No Query Is Too Complex (updated)
http://www.openlinksw.com/weblog/oerling/?date=2008-12-11#1494
2008-12-11T16:13:10Z
2008-12-12T10:29:18-05:00
<p>A persistent argument against the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id1199d5f8">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id116f2730">web</a> has been the cost, scalability, and vulnerability of <a href="http://dbpedia.org/resource/SPARQL" id="link-id14e423c0">SPARQL</a> end points, should the linked data web gain serious mass and traffic.</p> <p>As we are on the brink of hosting the whole <a href="http://dbpedia.org/resource/DBpedia" id="link-id1376a8b0">DBpedia</a> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id113c8d20">Linked Open Data</a> cloud in <a href="http://virtuoso.openlinksw.com" id="link-id11425a78">Virtuoso</a> Cluster, we have had to think of what we'll do if, for example, somebody decides to count all the triples in the set.</p> <p>How can we encourage clever use of <a href="http://dbpedia.org/resource/Data" id="link-id116f1210">data</a>, yet not die if somebody, whether through malice, lack of understanding, or simple bad luck, submits impossible queries?</p> <p>Restricting the language is not the way; any language beyond text search can express queries that will take forever to execute. Also, just returning a timeout after the first second (or whatever arbitrary time period) leaves people in the dark and does not produce an impression of responsiveness. So we decided to allow arbitrary queries, and if a quota of time or resources is exceeded, we return partial results and indicate how much processing was done.</p> <p>Here we are looking for the top 10 people whom people claim to know without being known in return, like this:</p> <blockquote> <pre>SQL> sparql SELECT ?celeb, COUNT (*) WHERE { ?claimant foaf:knows ?celeb . FILTER (!bif:exists ( SELECT (1) WHERE { ?celeb foaf:knows ?claimant } ) ) } GROUP BY ?celeb ORDER BY DESC 2 LIMIT 10;<br /> celeb callret-1 VARCHAR VARCHAR ________________________________________ _________<br /> http://twitter.com/BarackObama 252 http://twitter.com/brianshaler 183 http://twitter.com/newmediajim 101 http://twitter.com/HenryRollins 95 http://twitter.com/wilw 81 http://twitter.com/stevegarfield 78 http://twitter.com/cote 66 mailto:adam.westerski@deri.org 66 mailto:michal.zaremba@deri.org 66 http://twitter.com/dsifry 65<br /> *** Error S1TAT: [Virtuoso Driver][Virtuoso Server]RC...: Returning incomplete results, query interrupted by result timeout. Activity: 1R rnd 0R seq 0P disk 1.346KB / 3 messages<br /> SQL> sparql SELECT ?celeb, COUNT (*) WHERE { ?claimant foaf:knows ?celeb . FILTER (!bif:exists ( SELECT (1) WHERE { ?celeb foaf:knows ?claimant } ) ) } GROUP BY ?celeb ORDER BY DESC 2 LIMIT 10;<br /> celeb callret-1 VARCHAR VARCHAR ________________________________________ _________<br /> http://twitter.com/JasonCalacanis 496 http://twitter.com/Twitterrific 466 http://twitter.com/ev 442 http://twitter.com/BarackObama 356 http://twitter.com/laughingsquid 317 http://twitter.com/gruber 294 http://twitter.com/chrispirillo 259 http://twitter.com/ambermacarthur 224 http://twitter.com/t 219 http://twitter.com/johnedwards 188<br /> *** Error S1TAT: [Virtuoso Driver][Virtuoso Server]RC...: Returning incomplete results, query interrupted by result timeout. Activity: 329R rnd 44.6KR seq 342P disk 638.4KB / 46 messages</pre></blockquote> <p>The first query read all data from disk; the second run had the working set from the first and could read some more before time ran out, hence the results were better. But the response time was the same.</p> <p>If one has a query that just loops over consecutive joins, like in basic SPARQL, interrupting the processing after a set time period is simple. But such queries are not very interesting. To give meaningful partial answers with nested aggregation and sub-queries requires some more tricks. The basic idea is to terminate the innermost active sub-query/aggregation at the first timeout, and extend the timeout a bit so that accumulated results get fed to the next aggregation, like from the <code>GROUP BY</code> to the <code>ORDER BY</code>. If this again times out, we continue with the next outer layer. This guarantees that results are delivered if there were any results found for which the query pattern is true. False results are not produced, except in cases where there is comparison with a count and the count is smaller than it would be with the full evaluation.</p> <p>One can also use this as a basis for paid services. The cutoff does not have to be time; it can also be in other units, making it insensitive to concurrent usage and variations of working set.</p> <p>This system will be deployed on our <a href="http://challenge.semanticweb.org/" id="link-id11500a58">Billion Triples Challenge</a> <a href="http://b3s.openlinksw.com/" id="link-id11683120">demo instance</a> in a few days, after some more testing. When Virtuoso 6 ships, all <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id1157a500">LOD</a> Cloud AMIs and OpenLink-hosted LOD Cloud SPARQL endpoints will have this enabled by default. (AMI users will be able to disable the feature, if desired.) The feature works with Virtuoso 6 in both single server and cluster deployment.</p>
An Example of RDF Scalability
http://www.openlinksw.com/blog/vdb/blog/?date=2008-11-27#1488
2008-11-27T11:23:47Z
2008-12-01T12:09:55.000008-05:00
<p>We hear it to exhaustion, where is <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x14e828d8">RDF</a> scalability? We have been suggesting for a while that this is a solved question. I will here give some concrete numbers to back this.</p> <p>The scalability dream is to add hardware and get increased performance in proportion to the power the added component has when measured by itself. A corollary dream is to take scalability effects that are measured in a simple task and see them in a complex task.</p> <p>Below we show how we do 3.3 million random triple lookups per second on two 8 core commodity servers producing complete results, joining across partitions. On a single 4 core server, the figure is about 1 million lookups per second. With a single thread, it is about 250K lookups per second. This is the good case. But even our worse case is quite decent.</p> <p>We took a simple <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x14fef850">SPARQL</a> query, counting how many people say they reciprocally know each other. In the <a href="http://challenge.semanticweb.org/" id="link-id0x1bca04d0">Billion Triples Challenge</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x1be84e88">data</a> set, there are 25M <code>foaf:knows</code> quads of which 92K are reciprocal. <i>Reciprocal</i> here means that when x knows y in some graph, y knows x in the same or any other graph.</p> <pre>SELECT COUNT (*) WHERE { ?p1 foaf:knows ?p2 . ?p2 foaf:knows ?p1 }</pre> <p>There is no guarantee that the triple of <code>x knows y</code> is in the same partition as the triple y knows x. Thus the join is randomly distributed, n partitions to n partitions.</p> <p>We left this out of the Billion Triples Challenge demo because this did not run fast enough for our liking. Since then, we have corrected this.</p> <p>If run on a single thread, this query would be a loop over all the quads with a predicate of <code>foaf:knows</code>, and an inner loop looking for a quad with 3 of 4 fields given (<code>SPO</code>). If we have a partitioned situation, we have a loop over all the <code>foaf:knows</code> quads in each partition, and an inner lookup looking for the reciprocal <code>foaf:knows</code> quad in whatever partition it may be found.</p> <p>We have implemented this with two different message patterns: </p> <ol> <li> <p> <b>Centralized:</b> One process reads all the <code>foaf:knows</code> quads from all processes. Every 50K quads, it sends a batch of reciprocal quad checks to each partition that could contain a reciprocal quad. Each partition keeps the count of found reciprocal quads, and these are gathered and added up at the end.</p> </li> <li> <p> <b>Symmetrical:</b> Each process reads the <code>foaf:knows</code> quads in its partition, and sends a batch of checks to each process that could have the reciprocal <code>foaf:knows</code> quad every 50K quads. At the end, the counts are gathered from all partitions. There is some additional control traffic but we do not go into its details here.</p> </li> </ol> <p>Below is the result measured on 2 machines each with 2 x Xeon 5345 (quad core; total 8 cores), 16G RAM, and each machine running 6 <a href="http://virtuoso.openlinksw.com" id="link-id0x16642a90">Virtuoso</a> instances. The interconnect is dual 1-Gbit ethernet. Numbers are with warm cache.</p> <blockquote> <code>Centralized: 35,543 msec, 728,634 sequential + random lookups per second <br /> Cluster 12 nodes, 35 s. 1072 m/s 39,085 KB/s 316% cpu ... <br /> <br /> Symmetrical: 7706 msec, 3,360,740 sequential + random lookups per second <br /> Cluster 12 nodes, 7 s. 572 m/s 16,983 KB/s 1137% cpu ...</code> </blockquote> <p>The second line is the summary from the cluster status report for the duration of the query. The interesting numbers are the KB/s and the %CPU. The former is the cross-sectional data transfer rate for intra-cluster communication; the latter is the consolidated CPU utilization, where a constantly-busy core counts for 100%. The point to note is that the symmetrical approach takes 4x less real time with under half the data transfer rate. Further, when using multiple machines, the speed of a single interface does not limit the overall throughput as it does in the centralized situation.</p> <p>These figures represent the best and worst cases of distributed <code>JOIN</code>ing. If we have a straight sequence of <code>JOIN</code>s, with single pattern optionals and existences and the order in which results are produced is not significant (i.e., there is aggregation, existence test, or <code>ORDER BY</code>), the symmetrical pattern is applicable. On the other hand, if there are multiple triple pattern optionals, complex sub-queries, <code>DISTINCT</code>s in the middle of the query, or results have to be produced in the order of an index, then the centralized approach must be used at least part of the time.</p> <p>Also, if we must make transitive closures, which can be thought of as an extension of a <code>DISTINCT</code> in a subquery, we must pass the data through a single point before moving the bindings to the next <code>JOIN</code> in the sequence. This happens for example in resolving <code><a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x14e1a160">owl</a>:sameAs</code> at run time. However, the good news is that performance does not fall much below the centralized figure even when there are complex nested structures with intermediate transitive closures, <code>DISTINCT</code>s, complex existence tests, etc., that require passing all intermediate results through a central point. No matter the complexity, it is always possible to vector some tens-of-thousands of variable bindings into a single message exchange. And if there are not that many intermediate results, then single query execution time is not a problem anyhow.</p> <p>For our sample query, we would get still more speed by using a partitioned hash join, filling the hash from the <code>foaf:knows</code> relations and then running the <code>foaf:knows</code> relations through the hash. If the hash size is right, a hash lookup is somewhat better than an index lookup. The problem is that when the hash join is not the right solution, it is an expensive mistake: the best case is good; the worst case is very bad. But if there is no index then hash join is better than nothing. One problem of hash joins is that they make temporary data structures which, if large, will skew the working set. One must be quite sure of the cardinality before it is safe to try a hash join. So we do not do hash joins with RDF, but we do use them sometimes with relational data. </p> <p>These same methods apply to relational data just as well. This does not make generic RDF storage outperform an application-specific relational representation on the same platform, as the latter benefits from all the same optimizations, but in terms of sheer numbers, this makes RDF representation an option where it was not an option before. RDF is all about not needing to design the schema around the queries, and not needing to limit what joins with what else.</p>
An Example of RDF Scalability
http://www.openlinksw.com/weblog/oerling/?date=2008-11-27#1487
2008-11-27T11:23:47Z
2008-12-01T12:09:49.000005-05:00
<p>We hear it to exhaustion, where is <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1eab4128">RDF</a> scalability? We have been suggesting for a while that this is a solved question. I will here give some concrete numbers to back this.</p> <p>The scalability dream is to add hardware and get increased performance in proportion to the power the added component has when measured by itself. A corollary dream is to take scalability effects that are measured in a simple task and see them in a complex task.</p> <p>Below we show how we do 3.3 million random triple lookups per second on two 8 core commodity servers producing complete results, joining across partitions. On a single 4 core server, the figure is about 1 million lookups per second. With a single thread, it is about 250K lookups per second. This is the good case. But even our worse case is quite decent.</p> <p>We took a simple <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x15cb3da8">SPARQL</a> query, counting how many people say they reciprocally know each other. In the <a href="http://challenge.semanticweb.org/" id="link-id0x1bfb7a00">Billion Triples Challenge</a> <a href="http://dbpedia.org/resource/Data" id="link-id0xa57187d8">data</a> set, there are 25M <code>foaf:knows</code> quads of which 92K are reciprocal. <i>Reciprocal</i> here means that when x knows y in some graph, y knows x in the same or any other graph.</p> <pre>SELECT COUNT (*) WHERE { ?p1 foaf:knows ?p2 . ?p2 foaf:knows ?p1 }</pre> <p>There is no guarantee that the triple of <code>x knows y</code> is in the same partition as the triple y knows x. Thus the join is randomly distributed, n partitions to n partitions.</p> <p>We left this out of the Billion Triples Challenge demo because this did not run fast enough for our liking. Since then, we have corrected this.</p> <p>If run on a single thread, this query would be a loop over all the quads with a predicate of <code>foaf:knows</code>, and an inner loop looking for a quad with 3 of 4 fields given (<code>SPO</code>). If we have a partitioned situation, we have a loop over all the <code>foaf:knows</code> quads in each partition, and an inner lookup looking for the reciprocal <code>foaf:knows</code> quad in whatever partition it may be found.</p> <p>We have implemented this with two different message patterns: </p> <ol> <li> <p> <b>Centralized:</b> One process reads all the <code>foaf:knows</code> quads from all processes. Every 50K quads, it sends a batch of reciprocal quad checks to each partition that could contain a reciprocal quad. Each partition keeps the count of found reciprocal quads, and these are gathered and added up at the end.</p> </li> <li> <p> <b>Symmetrical:</b> Each process reads the <code>foaf:knows</code> quads in its partition, and sends a batch of checks to each process that could have the reciprocal <code>foaf:knows</code> quad every 50K quads. At the end, the counts are gathered from all partitions. There is some additional control traffic but we do not go into its details here.</p> </li> </ol> <p>Below is the result measured on 2 machines each with 2 x Xeon 5345 (quad core; total 8 cores), 16G RAM, and each machine running 6 <a href="http://virtuoso.openlinksw.com" id="link-id0x1c0c94a8">Virtuoso</a> instances. The interconnect is dual 1-Gbit ethernet. Numbers are with warm cache.</p> <blockquote> <code>Centralized: 35,543 msec, 728,634 sequential + random lookups per second <br /> Cluster 12 nodes, 35 s. 1072 m/s 39,085 KB/s 316% cpu ... <br /> <br /> Symmetrical: 7706 msec, 3,360,740 sequential + random lookups per second <br /> Cluster 12 nodes, 7 s. 572 m/s 16,983 KB/s 1137% cpu ...</code> </blockquote> <p>The second line is the summary from the cluster status report for the duration of the query. The interesting numbers are the KB/s and the %CPU. The former is the cross-sectional data transfer rate for intra-cluster communication; the latter is the consolidated CPU utilization, where a constantly-busy core counts for 100%. The point to note is that the symmetrical approach takes 4x less real time with under half the data transfer rate. Further, when using multiple machines, the speed of a single interface does not limit the overall throughput as it does in the centralized situation.</p> <p>These figures represent the best and worst cases of distributed <code>JOIN</code>ing. If we have a straight sequence of <code>JOIN</code>s, with single pattern optionals and existences and the order in which results are produced is not significant (i.e., there is aggregation, existence test, or <code>ORDER BY</code>), the symmetrical pattern is applicable. On the other hand, if there are multiple triple pattern optionals, complex sub-queries, <code>DISTINCT</code>s in the middle of the query, or results have to be produced in the order of an index, then the centralized approach must be used at least part of the time.</p> <p>Also, if we must make transitive closures, which can be thought of as an extension of a <code>DISTINCT</code> in a subquery, we must pass the data through a single point before moving the bindings to the next <code>JOIN</code> in the sequence. This happens for example in resolving <code><a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x28005280">owl</a>:sameAs</code> at run time. However, the good news is that performance does not fall much below the centralized figure even when there are complex nested structures with intermediate transitive closures, <code>DISTINCT</code>s, complex existence tests, etc., that require passing all intermediate results through a central point. No matter the complexity, it is always possible to vector some tens-of-thousands of variable bindings into a single message exchange. And if there are not that many intermediate results, then single query execution time is not a problem anyhow.</p> <p>For our sample query, we would get still more speed by using a partitioned hash join, filling the hash from the <code>foaf:knows</code> relations and then running the <code>foaf:knows</code> relations through the hash. If the hash size is right, a hash lookup is somewhat better than an index lookup. The problem is that when the hash join is not the right solution, it is an expensive mistake: the best case is good; the worst case is very bad. But if there is no index then hash join is better than nothing. One problem of hash joins is that they make temporary data structures which, if large, will skew the working set. One must be quite sure of the cardinality before it is safe to try a hash join. So we do not do hash joins with RDF, but we do use them sometimes with relational data. </p> <p>These same methods apply to relational data just as well. This does not make generic RDF storage outperform an application-specific relational representation on the same platform, as the latter benefits from all the same optimizations, but in terms of sheer numbers, this makes RDF representation an option where it was not an option before. RDF is all about not needing to design the schema around the queries, and not needing to limit what joins with what else.</p>
Virtuoso Vs. MySQL: Setting the Berlin Record Straight (update 2)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-11-20#1485
2008-11-20T11:06:11Z
2008-11-24T10:15:11.000021-05:00
<p>In the context of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xa322b58">Berlin SPARQL Benchmark</a>, I have repeatedly written about measurement procedures and steady state. The point is that the numbers at larger scales are unreliable due to cache behavior if one is not careful about measurement and does not have adequate warmup. Thus it came to pass that one cut of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x9524730">BSBM</a> paper had 3 seconds for <a href="http://dbpedia.org/resource/MySQL" id="link-id0x2ba8db0">MySQL</a> and 100 for <a href="http://virtuoso.openlinksw.com" id="link-id0xa9137d0">Virtuoso</a>, basically through ignoring cache effects.</p> <p>So we decided to do it ourselves.</p> <p>The score is (updated with revised <code>innodb_buffer_pool_size</code> setting, based on advice noted down below):</p> <table border="1" cellspacing="2" cellpadding="5"> <tr> <th>n-clients</th> <th>Virtuoso</th> <th>MySQL <br /> (with increased buffer pool size)</th> <th>MySQL <br /> (with default buffer poll size)</th> </tr> <tr align="right"> <td>1</td> <td> 41,161.33</td> <td> 27,023.11 </td> <td> 12,171.41</td> </tr> <tr align="right"> <td>4</td> <td> 127,918.30</td> <td> (pending) </td> <td> 37,566.82</td> </tr> <tr align="right"> <td>8</td> <td> 218,162.29 </td> <td> 105,524.23 </td> <td> 51,104.39 </td> </tr> <tr align="right"> <td>16</td> <td> 214,763.58 </td> <td> 98,852.42 </td> <td> 47,589.18 </td> </tr> </table> <p>The metric is the query mixes per hour from the BSBM test driver output. For the interested, the complete output is <a href="http://www.openlinksw.com/weblog/oerling/texts/bsbmres.txt" id="link-id1119f770">here</a>.</p> <p>The benchmark is pure <a href="http://dbpedia.org/resource/SQL" id="link-id0x2b61c88">SQL</a>, nothing to do with <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x17a6d408">SPARQL</a> or <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x9a0a968">RDF</a>.</p> <p>The hardware is 2 x Xeon 5345 (2 x quad core, 2.33 GHz), 16 G RAM. The OS is 64-bit Debian Linux.</p> <p>The benchmark was run at a scale of 200,000. Each run had 2000 warm-up query mixes and 500 measured query mixes, which gives steady state, eliminating any effects of OS disk cache and the like. Both databases were configured to use 8G for disk cache. The test effectively runs from memory. We ran an analyze table on each MySQL table but noticed that this had no effect. Virtuoso does the stats sampling on the go; possibly MySQL also since the explicit stats did not make any difference. The MySQL tables were served by the InnoDB engine. MySQL appears to cache results of queries in some cases. This was not apparent in the tests.</p> <p>The versions are 5.09 for Virtuoso and 5.1.29 for MySQL. You can download and examine --</p> <ul> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/virtuoso.ini" id="link-id14fe17f0">Virtuoso configuration file</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/my.cnf" id="link-id116fe490">MySQL configuration file</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/create_tables_and_rdf_view.sql" id="link-id14ce9268">Table definitions & RDF views</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/mysqlinx.sql" id="link-id1535e298">Indexes on MySQL tables</a> </li> </ul> <p> <strike>MySQL ought to do better. We suspect that here, just as in the TPC-D experiment we made way back, the query plans are not quite right. Also we rarely saw over 300% CPU utilization for MySQL. It is possible there is a config parameter that affects this. The public is invited to tell us about such.</strike> </p> <p> <b>Update:</b> </p> <p>Andreas Schultz of the BSBM team advised us to increase the <code>innodb_buffer_pool_size</code> setting in the MySQL config. We did and it produced some improvement. Indeed, this is more like it, as we now see CPU utilization around 700% instead of the 300% in the previously published run, which rendered it suspect. Also, our experiments with TPC-D led us to expect better. We ran these things a few times so as to have warm cache.</p> <p>On the first run, we noticed that the Innodb warm up time was somewhere well in excess of 2000 query mixes. Another time, we should make a graph of throughput as a function of time for both MySQL and Virtuoso. We recently made a greedy prefetch hack that should give us some mileage there. For the next BSBM, all we can advise is to run larger scale system for half an hour first and then measure and then measure again. If the second measurement is the same as the first then it is good.</p> <p>As always, since MySQL is not our specialty, we confidently invite the public to tell us how to make it run faster. So, unless something more turns up, our next trial is a revisit of <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x17a20498">TPC-H</a>.</p>
Virtuoso Vs. MySQL: Setting the Berlin Record Straight (update 2)
http://www.openlinksw.com/weblog/oerling/?date=2008-11-20#1484
2008-11-20T11:06:11Z
2008-11-24T10:15:05-05:00
<p>In the context of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xa5314d8">Berlin SPARQL Benchmark</a>, I have repeatedly written about measurement procedures and steady state. The point is that the numbers at larger scales are unreliable due to cache behavior if one is not careful about measurement and does not have adequate warmup. Thus it came to pass that one cut of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x18482c20">BSBM</a> paper had 3 seconds for <a href="http://dbpedia.org/resource/MySQL" id="link-id0xb8c54de8">MySQL</a> and 100 for <a href="http://virtuoso.openlinksw.com" id="link-id0x189b2210">Virtuoso</a>, basically through ignoring cache effects.</p> <p>So we decided to do it ourselves.</p> <p>The score is (updated with revised <code>innodb_buffer_pool_size</code> setting, based on advice noted down below):</p> <table border="1" cellspacing="2" cellpadding="5"> <tr> <th>n-clients</th> <th>Virtuoso</th> <th>MySQL <br /> (with increased buffer pool size)</th> <th>MySQL <br /> (with default buffer poll size)</th> </tr> <tr align="right"> <td>1</td> <td> 41,161.33</td> <td> 27,023.11 </td> <td> 12,171.41</td> </tr> <tr align="right"> <td>4</td> <td> 127,918.30</td> <td> (pending) </td> <td> 37,566.82</td> </tr> <tr align="right"> <td>8</td> <td> 218,162.29 </td> <td> 105,524.23 </td> <td> 51,104.39 </td> </tr> <tr align="right"> <td>16</td> <td> 214,763.58 </td> <td> 98,852.42 </td> <td> 47,589.18 </td> </tr> </table> <p>The metric is the query mixes per hour from the BSBM test driver output. For the interested, the complete output is <a href="http://www.openlinksw.com/weblog/oerling/texts/bsbmres.txt" id="link-id1119f770">here</a>.</p> <p>The benchmark is pure <a href="http://dbpedia.org/resource/SQL" id="link-id0x5257718">SQL</a>, nothing to do with <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xb8c463e0">SPARQL</a> or <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x16e68d50">RDF</a>.</p> <p>The hardware is 2 x Xeon 5345 (2 x quad core, 2.33 GHz), 16 G RAM. The OS is 64-bit Debian Linux.</p> <p>The benchmark was run at a scale of 200,000. Each run had 2000 warm-up query mixes and 500 measured query mixes, which gives steady state, eliminating any effects of OS disk cache and the like. Both databases were configured to use 8G for disk cache. The test effectively runs from memory. We ran an analyze table on each MySQL table but noticed that this had no effect. Virtuoso does the stats sampling on the go; possibly MySQL also since the explicit stats did not make any difference. The MySQL tables were served by the InnoDB engine. MySQL appears to cache results of queries in some cases. This was not apparent in the tests.</p> <p>The versions are 5.09 for Virtuoso and 5.1.29 for MySQL. You can download and examine --</p> <ul> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/virtuoso.ini" id="link-id14fe17f0">Virtuoso configuration file</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/my.cnf" id="link-id116fe490">MySQL configuration file</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/create_tables_and_rdf_view.sql" id="link-id14ce9268">Table definitions & RDF views</a> </li> <li> <a href="http://www.openlinksw.com/weblog/oerling/texts/mysqlinx.sql" id="link-id1535e298">Indexes on MySQL tables</a> </li> </ul> <p> <strike>MySQL ought to do better. We suspect that here, just as in the TPC-D experiment we made way back, the query plans are not quite right. Also we rarely saw over 300% CPU utilization for MySQL. It is possible there is a config parameter that affects this. The public is invited to tell us about such.</strike> </p> <p> <b>Update:</b> </p> <p>Andreas Schultz of the BSBM team advised us to increase the <code>innodb_buffer_pool_size</code> setting in the MySQL config. We did and it produced some improvement. Indeed, this is more like it, as we now see CPU utilization around 700% instead of the 300% in the previously published run, which rendered it suspect. Also, our experiments with TPC-D led us to expect better. We ran these things a few times so as to have warm cache.</p> <p>On the first run, we noticed that the Innodb warm up time was somewhere well in excess of 2000 query mixes. Another time, we should make a graph of throughput as a function of time for both MySQL and Virtuoso. We recently made a greedy prefetch hack that should give us some mileage there. For the next BSBM, all we can advise is to run larger scale system for half an hour first and then measure and then measure again. If the second measurement is the same as the first then it is good.</p> <p>As always, since MySQL is not our specialty, we confidently invite the public to tell us how to make it run faster. So, unless something more turns up, our next trial is a revisit of <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x122eaa00">TPC-H</a>.</p>
ISWC 2008: Some Questions
http://www.openlinksw.com/blog/vdb/blog/?date=2008-11-04#1481
2008-11-04T15:54:42Z
2008-11-04T14:37:01-05:00
<h2>Inference: Is it always forward chaining?</h2> <p>We got a number of questions about <a href="http://virtuoso.openlinksw.com" id="link-id0x13c64b60">Virtuoso</a>'s inference support. It seems that we are the odd one out, as we do not take it for granted that inference ought to consist of materializing entailment.</p> <p>Firstly, of course one can materialize all one wants with Virtuoso. The simplest way to do this is using SPARUL. With the recent transitivity extensions to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x14d17778">SPARQL</a>, it is also easy to materialize implications of transitivity with a single statement. Our point is that for trivial entailment such as subclass, sub-property, single transitive property, and <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x128e55d0">owl</a>:sameAs, we do not require materialization, as we can resolve these at run time also, with backward-chaining built into the engine.</p> <p>For more complex situations, one needs to materialize the entailment. At the present time, we know how to generalize our transitive feature to run arbitrary backward-chaining rules, including recursive ones. We could have a sort of Datalog backward-chaining embedded in our <a href="http://dbpedia.org/resource/SQL" id="link-id0x12614770">SQL</a>/SPARQL and could run this with good parallelism, as the transitive feature already works with clustering and partitioning without dying of message latency. Exactly when and how we do this will be seen. Even if users want entailment to be materialized, such a rule system could be used for producing the materialization at good speed.</p> <p>We had a word with <a href="http://web.comlab.ox.ac.uk/people/Ian.Horrocks/" id="link-id117c99d0">Ian Horrocks</a> on the question. He noted that it is often naive on behalf of the community to tend to equate description of semantics with description of algorithm. The <a href="http://dbpedia.org/resource/Data" id="link-id0x145b2980">data</a> need not always be blown up.</p> <p>The advantage of not always materializing is that the working set stays better. Once the working set is no longer in memory, response times jump disproportionately. Also, if the data changes or is retracted or is unreliable, one can end up doing a lot of extra work with materialization. Consider the effect of one malicious sameAs statement. This can lead to a lot of effects that are hard to retract. On the other hand, if running in memory with static data such as the LUBM benchmark, the queries run some 20% faster if entailment subclasses and sub-properties are materialized rather than done at run time.</p> <h2>Genetic Algorithms for SPARQL?</h2> <p>Our compliments for the wildest idea of the conference go to <a href="http://www.eyaloren.org/" id="link-id1a203af8">Eyal Oren</a>, <a href="http://www.few.vu.nl/~cgueret/" id="link-id16208758">Christophe Guéret</a>, and <a href="http://www.few.vu.nl/~schlobac/" id="link-id111923e0">Stefan Schlobach</a>, <i>et al</i>, for their <a href="http://www.informatik.uni-trier.de/~ley/db/conf/semweb/iswc2008.html#OrenGS08" id="link-id11793540">paper on using genetic algorithms for guessing how variables in a SPARQL query ought to be instantiated</a>. Prisoners of our "conventional wisdom" as we are, this might never have occurred to us.</p> <h2>Schema Last?</h2> <p>It is interesting to see how the industry comes to the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x12b57e90">semantic web</a> conferences talking about schema last while at the same time the traditional semantic web people stress enforcing schema constraints and making more predictably performing and database friendlier logics. So do the extremes converge.</p> <p>There is a point to schema last. <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x12a8ff48">RDF</a> is very good for getting a view of ad hoc or unknown data. One can just load and look at what there is. Also, additions of unforeseen optional properties or relations to the schema are easy and efficient. However, it seems that a really high traffic online application would always benefit from having some application specific data structures. Such could also save considerably in hardware.</p> <p>It is not a sharp divide between RDF and relational application oriented representation. We have the capabilities in our RDB to RDF mapping. We just need to show this and have SPARUL and data loading</p>
ISWC 2008: Some Questions
http://www.openlinksw.com/weblog/oerling/?date=2008-11-04#1479
2008-11-04T15:54:42Z
2008-11-04T14:36:50.000010-05:00
<h2>Inference: Is it always forward chaining?</h2> <p>We got a number of questions about <a href="http://virtuoso.openlinksw.com" id="link-id0x131604a8">Virtuoso</a>'s inference support. It seems that we are the odd one out, as we do not take it for granted that inference ought to consist of materializing entailment.</p> <p>Firstly, of course one can materialize all one wants with Virtuoso. The simplest way to do this is using SPARUL. With the recent transitivity extensions to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1422f910">SPARQL</a>, it is also easy to materialize implications of transitivity with a single statement. Our point is that for trivial entailment such as subclass, sub-property, single transitive property, and <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x145894a8">owl</a>:sameAs, we do not require materialization, as we can resolve these at run time also, with backward-chaining built into the engine.</p> <p>For more complex situations, one needs to materialize the entailment. At the present time, we know how to generalize our transitive feature to run arbitrary backward-chaining rules, including recursive ones. We could have a sort of Datalog backward-chaining embedded in our <a href="http://dbpedia.org/resource/SQL" id="link-id0x1458a288">SQL</a>/SPARQL and could run this with good parallelism, as the transitive feature already works with clustering and partitioning without dying of message latency. Exactly when and how we do this will be seen. Even if users want entailment to be materialized, such a rule system could be used for producing the materialization at good speed.</p> <p>We had a word with <a href="http://web.comlab.ox.ac.uk/people/Ian.Horrocks/" id="link-id117c99d0">Ian Horrocks</a> on the question. He noted that it is often naive on behalf of the community to tend to equate description of semantics with description of algorithm. The <a href="http://dbpedia.org/resource/Data" id="link-id0x14cf0b18">data</a> need not always be blown up.</p> <p>The advantage of not always materializing is that the working set stays better. Once the working set is no longer in memory, response times jump disproportionately. Also, if the data changes or is retracted or is unreliable, one can end up doing a lot of extra work with materialization. Consider the effect of one malicious sameAs statement. This can lead to a lot of effects that are hard to retract. On the other hand, if running in memory with static data such as the LUBM benchmark, the queries run some 20% faster if entailment subclasses and sub-properties are materialized rather than done at run time.</p> <h2>Genetic Algorithms for SPARQL?</h2> <p>Our compliments for the wildest idea of the conference go to <a href="http://www.eyaloren.org/" id="link-id1a203af8">Eyal Oren</a>, <a href="http://www.few.vu.nl/~cgueret/" id="link-id16208758">Christophe Guéret</a>, and <a href="http://www.few.vu.nl/~schlobac/" id="link-id111923e0">Stefan Schlobach</a>, <i>et al</i>, for their <a href="http://www.informatik.uni-trier.de/~ley/db/conf/semweb/iswc2008.html#OrenGS08" id="link-id11793540">paper on using genetic algorithms for guessing how variables in a SPARQL query ought to be instantiated</a>. Prisoners of our "conventional wisdom" as we are, this might never have occurred to us.</p> <h2>Schema Last?</h2> <p>It is interesting to see how the industry comes to the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x1154c1b0">semantic web</a> conferences talking about schema last while at the same time the traditional semantic web people stress enforcing schema constraints and making more predictably performing and database friendlier logics. So do the extremes converge.</p> <p>There is a point to schema last. <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x14c6a930">RDF</a> is very good for getting a view of ad hoc or unknown data. One can just load and look at what there is. Also, additions of unforeseen optional properties or relations to the schema are easy and efficient. However, it seems that a really high traffic online application would always benefit from having some application specific data structures. Such could also save considerably in hardware.</p> <p>It is not a sharp divide between RDF and relational application oriented representation. We have the capabilities in our RDB to RDF mapping. We just need to show this and have SPARUL and data loading</p>
ISWC 2008: RDB2RDF Face-to-Face
http://www.openlinksw.com/blog/vdb/blog/?date=2008-11-04#1477
2008-11-04T13:26:19Z
2008-11-04T17:20:35-05:00
<p>The W3C's RDB-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x153bdcf8">RDF</a> mapping incubator group (<a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0x13e3e6b8">RDB2RDF XG</a>) met in <a href="http://dbpedia.org/resource/Karlsruhe" id="link-id0x15236b08">Karlsruhe</a> after <a href="http://iswc2008.semanticweb.org/" id="link-id0x2450fba8">ISWC 2008</a>.</p> <p>The meeting was about writing a charter for a working group that would define a standard for mapping relational databases to RDF, either for purposes of import into RDF stores or of query mapping from <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x14c84338">SPARQL</a> to <a href="http://dbpedia.org/resource/SQL" id="link-id0x146db368">SQL</a>. There was a lot of agreement and the meeting even finished ahead of the allotted time.</p> <h2>Whose Identifiers?</h2> <p>There was discussion concerning using the <a href="http://dbpedia.org/resource/Entity" id="link-id0x12c15e58">Entity</a> Name Service from the Okkam project for assigning URIs to entities mapped from relational databases. This makes sense when talking about long-lived, legal entities, such as people or companies or geography. Of course, there are cases where this makes no sense; for example, a purchase order or maintenance call hardly needs an identifier registered with the ENS. The problem is, in practice, a CRM could mention customers that have an ENS registered ID (or even several such IDs) and others that have none. Of course, the CRM's reference cannot depend on any registration. Also, even when there is a stable <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x12b7b5c0">URI</a> for the entity, a CRM may need a key that specifies some administrative subdivision of the customer.</p> <p>Also we note that an on-demand RDB-to-RDF mapping may have some trouble dealing with "same as" assertions. If names that are anything other than string forms of the keys in the system must be returned, there will have to be a lookup added to the RDB. This is an administrative issue. Certainly going over the network to ask for names of items returned by queries has a prohibitive cost. It would be good for ad hoc integration to use shared URIs when possible. The trouble of adding and maintaining lookups for these, however, makes this more expensive than just mapping to RDF and using literals for joining between independently maintained systems.</p> <h2> <a href="http://dbpedia.org/resource/XML" id="link-id0x14bf7da0">XML</a> or RDF?</h2> <p>We talked about having a language for human consumption and another for discovery and machine processing of mappings. Would this latter be XML or RDF based? Describing every detail of syntax for a mapping as RDF is really tedious. Also such descriptions are very hard to query, just as <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x1493ffc0">OWL</a> ontologies are. One solution is to have opaque strings embedded into RDF, just like XSLT has <a href="http://dbpedia.org/resource/XPath" id="link-id0x1400fe98">XPath</a> in string form embedded into XML. Maybe it will end up in this way here also. Having a complete XML mapping of the parse tree for mappings, XQueryX-style, could be nice for automatic generation of mappings with XSLT from an XML view of the <a href="http://dbpedia.org/resource/Information" id="link-id0x14c846d8">information</a> schema. But then XSLT can also produce text, so an XML syntax that has every detail of a mapping language as distinct elements is not really necessary for this.</p> <p>Another matter is then describing the RDF generated by the mapping in terms of RDFS or OWL. This would be a by-product of declaring the mapping. Most often, I would presume the target ontology to be given, though, reducing the need for this feature. But if RDF mapping is used for discovery of <a href="http://dbpedia.org/resource/Data" id="link-id0x14f6f128">data</a>, such a description of the exposed data is essential.</p> <h2>Interoperability</h2> <p>We agreed with <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x1e776730">Sören Auer</a> that we could make <a href="http://virtuoso.openlinksw.com" id="link-id0x1477ad18">Virtuoso</a>'s mapping language compatible with <a href="http://triplify.org/" id="link-id0x15514388">Triplify</a>. Triplify is very simple, extraction only, no SPARQL, but does have the benefit of expressing everything in SQL. As it happens, I would be the last person to tell a web developer what language to program in. So if it is SQL, then let it stay SQL. Technically, a lot of the information the Virtuoso mapping expresses is contained in the Triplify SQL statements, but not all. Some extra declarations are needed still but can have reasonable defaults.</p> <p>There are two ways of stating a mapping. Virtuoso starts with the triple and says which tables and columns will produce the triple. Triplify starts with the SQL statement and says what triples it produces. These are fairly equivalent. For the web developer, the latter is likely more self-evident, while the former may be more compact and have less repetition.</p> <p>Virtuoso and Triplify alone would give us the two interoperable implementations required from a working group, supposing the language were annotations on top of SQL. This would be a guarantee of delivery, as we would be close enough to the result from the get go.</p> <h2>Related Web resources</h2> <ul> <li> <a href="http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSSQL2RDF" id="link-id14e27040">OpenLink Virtuoso: Open-Source Edition: Mapping SQL Data to RDF</a> </li> <li> <a href="http://virtuoso.openlinksw.com/Whitepapers/pdf/Virtuoso_SQL_to_RDF_Mapping.pdf" id="link-id1baad3a8">Virtuoso RDF Views â Getting Started Guide (PDF)</a> </li> </ul>
ISWC 2008: RDB2RDF Face-to-Face
http://www.openlinksw.com/weblog/oerling/?date=2008-11-04#1476
2008-11-04T13:26:19Z
2008-11-04T17:20:30.000002-05:00
<p>The W3C's RDB-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x141f0470">RDF</a> mapping incubator group (<a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0x13b8d018">RDB2RDF XG</a>) met in <a href="http://dbpedia.org/resource/Karlsruhe" id="link-id0x1e748060">Karlsruhe</a> after <a href="http://iswc2008.semanticweb.org/" id="link-id0x1eba8468">ISWC 2008</a>.</p> <p>The meeting was about writing a charter for a working group that would define a standard for mapping relational databases to RDF, either for purposes of import into RDF stores or of query mapping from <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1e5abe10">SPARQL</a> to <a href="http://dbpedia.org/resource/SQL" id="link-id0x13930368">SQL</a>. There was a lot of agreement and the meeting even finished ahead of the allotted time.</p> <h2>Whose Identifiers?</h2> <p>There was discussion concerning using the <a href="http://dbpedia.org/resource/Entity" id="link-id0x15587978">Entity</a> Name Service from the Okkam project for assigning URIs to entities mapped from relational databases. This makes sense when talking about long-lived, legal entities, such as people or companies or geography. Of course, there are cases where this makes no sense; for example, a purchase order or maintenance call hardly needs an identifier registered with the ENS. The problem is, in practice, a CRM could mention customers that have an ENS registered ID (or even several such IDs) and others that have none. Of course, the CRM's reference cannot depend on any registration. Also, even when there is a stable <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x144660f8">URI</a> for the entity, a CRM may need a key that specifies some administrative subdivision of the customer.</p> <p>Also we note that an on-demand RDB-to-RDF mapping may have some trouble dealing with "same as" assertions. If names that are anything other than string forms of the keys in the system must be returned, there will have to be a lookup added to the RDB. This is an administrative issue. Certainly going over the network to ask for names of items returned by queries has a prohibitive cost. It would be good for ad hoc integration to use shared URIs when possible. The trouble of adding and maintaining lookups for these, however, makes this more expensive than just mapping to RDF and using literals for joining between independently maintained systems.</p> <h2> <a href="http://dbpedia.org/resource/XML" id="link-id0x1edb8170">XML</a> or RDF?</h2> <p>We talked about having a language for human consumption and another for discovery and machine processing of mappings. Would this latter be XML or RDF based? Describing every detail of syntax for a mapping as RDF is really tedious. Also such descriptions are very hard to query, just as <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x2450fba8">OWL</a> ontologies are. One solution is to have opaque strings embedded into RDF, just like XSLT has <a href="http://dbpedia.org/resource/XPath" id="link-id0x234e5478">XPath</a> in string form embedded into XML. Maybe it will end up in this way here also. Having a complete XML mapping of the parse tree for mappings, XQueryX-style, could be nice for automatic generation of mappings with XSLT from an XML view of the <a href="http://dbpedia.org/resource/Information" id="link-id0x22e129f8">information</a> schema. But then XSLT can also produce text, so an XML syntax that has every detail of a mapping language as distinct elements is not really necessary for this.</p> <p>Another matter is then describing the RDF generated by the mapping in terms of RDFS or OWL. This would be a by-product of declaring the mapping. Most often, I would presume the target ontology to be given, though, reducing the need for this feature. But if RDF mapping is used for discovery of <a href="http://dbpedia.org/resource/Data" id="link-id0x155139c0">data</a>, such a description of the exposed data is essential.</p> <h2>Interoperability</h2> <p>We agreed with <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x132a64e0">Sören Auer</a> that we could make <a href="http://virtuoso.openlinksw.com" id="link-id0x1272c988">Virtuoso</a>'s mapping language compatible with <a href="http://triplify.org/" id="link-id0x12622738">Triplify</a>. Triplify is very simple, extraction only, no SPARQL, but does have the benefit of expressing everything in SQL. As it happens, I would be the last person to tell a web developer what language to program in. So if it is SQL, then let it stay SQL. Technically, a lot of the information the Virtuoso mapping expresses is contained in the Triplify SQL statements, but not all. Some extra declarations are needed still but can have reasonable defaults.</p> <p>There are two ways of stating a mapping. Virtuoso starts with the triple and says which tables and columns will produce the triple. Triplify starts with the SQL statement and says what triples it produces. These are fairly equivalent. For the web developer, the latter is likely more self-evident, while the former may be more compact and have less repetition.</p> <p>Virtuoso and Triplify alone would give us the two interoperable implementations required from a working group, supposing the language were annotations on top of SQL. This would be a guarantee of delivery, as we would be close enough to the result from the get go.</p> <h2>Related Web resources</h2> <ul> <li> <a href="http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSSQL2RDF" id="link-id14e27040">OpenLink Virtuoso: Open-Source Edition: Mapping SQL Data to RDF</a> </li> <li> <a href="http://virtuoso.openlinksw.com/Whitepapers/pdf/Virtuoso_SQL_to_RDF_Mapping.pdf" id="link-id1baad3a8">Virtuoso RDF Views â Getting Started Guide (PDF)</a> </li> </ul>
ISWC 2008: The Scalable Knowledge Systems Workshop
http://www.openlinksw.com/blog/vdb/blog/?date=2008-11-03#1473
2008-11-03T13:16:47Z
2008-11-03T12:33:54-05:00
<p>Mike Dean of <a href="http://dbpedia.org/resource/BBN_Technologies" id="link-id0x25699878">BBN Technologies</a> opened the Scalable <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1ed01750">Knowledge</a> Systems Workshop with an invited talk. He reminded us of the facts of nature as concern the cost of distributed computing and running out of space for the working set. Developers in the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x21fbb9a8">semantic web</a> field deplorably often ignore these facts, or alternatively recognize them and admit that they are unbeatable, that one just can't join across partitions.</p> <p>I gave a talk about the <a href="http://virtuoso.openlinksw.com" id="link-id0x20b6e020">Virtuoso</a> Cluster edition, wherein I repeated essentially the same ground facts as Mike and outlined how we (in spite of these) profit from distributed memory multiprocessing. To those not intimate with these questions, let me affirm that deriving benefit from threading in a symmetric multiprocessor box, let alone a cluster connected by a network, totally depends on having many relatively long running things going at a time and blocking as seldom as possible.</p> <p>Further, Mike Dean talked about <a href="http://www.asio.bbn.com/" id="link-id0x222252f0">ASIO</a>, the BBN suite of semantic web tools. His most challenging statement was about the storage engine, a network-database-inspired triple-store using memory-mapped files. </p> <p>Will the <a href="http://dbpedia.org/resource/CODASYL" id="link-id0x222d8730">CODASYL</a> days come back, and will the linked list on disk be the way to store triples/quads? I would say that this will have, especially with a memory-mapped file, probably a better best-case as a B-tree but that this also will be less predictable with fragmentation. With Virtuoso, using a B-tree index, we see about 20-30% of CPU time spent on index lookup when running LUBM queries. With a disk-based memory-mapped linked-list storage, we would see some improvements in this while getting hit probably worse than now in the case of fragmentation. Plus compaction on the fly would not be nearly as easy and surely far less local, if there were pointers between pages. So it is my intuition that trees are a safer bet with varying workloads while linked lists can be faster in a query-dominated in-memory situation.</p> <p>Chris Bizer presented the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x22e41c40">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1c909960">BSBM</a>), which has already been discussed here in some detail. He did acknowledge that the next round of the race must have a real steady-state rule. This just means that the benchmark must be run long enough for the system under test to reach a state where the cache is full and the performance remains indefinitely at the same level. Reaching steady state can take 20-30 minutes in some cases.</p> <p>Regardless of steady state, BSBM has two generally valid conclusions: </p> <ol> <li>mapping relational to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x21d01890">RDF</a>, where possible, is faster than triple storage; and </li> <li>the equivalent relational solution can be some 10x faster than the pure triples representation.</li> </ol> <p>Mike Dean asked whether BSBM was a case of a setup to have triple stores fail. Not necessarily, I would say; we should understand that one motivation of BSBM is testing mapping technologies. Therefore it must have a workload where mapping makes sense. Of course there are workloads where triples are unchallenged â take the <a href="http://challenge.semanticweb.org/" id="link-id0x1feb9250">Billion Triples Challenge</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x1fe12b60">data</a> set for one.</p> <p>Also, with BSBM, once should note that the query optimization time plays a fairly large role since most queries touch relatively little data. Also, even if the scale is large, the working set is not nearly the size of the database. This in fact penalizes mapping technologies against native <a href="http://dbpedia.org/resource/SQL" id="link-id0x1e275c88">SQL</a> since the difference there is compiling the query, especially since parameters are not used. So, Chris, since we both like to map, let's make a benchmark that shows mapping closer to native SQL.</p> <h2>Bridging the 10x Gap?</h2> <p>When we run Virtuoso relational against Virtuoso triple store with the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x22046d88">TPC-H</a> workload, we see that the relational case is significantly faster. These are long queries, thus query optimization time is negligible; we are here comparing memory-based access times. Why is this? The answer is that a single index lookup gives multiple column values with almost no penalty for the extra column. Also, since the number of total joins is lower, the overhead coming from moving from join to next join is likewise lower. This is just a meter of count of executed instructions.</p> <p>A column store joins in principle just as much as a triple store. However, since the BI workload often consists of scanning over large tables, the joins tend to be local, the needed lookup can often use the previous location as a starting point. A triple store can do the same if queries have high locality. We do this in some SQL situations and can try this with triples also. The RDF workload is typically more random in its access pattern, though. The other factor is the length of control path. A column store has a simpler control flow if it knows that the column will have exactly one value per row. With RDF, this is not a given. Also, the column store's row is identified by a single number and not a multipart key. These two factors give the column store running with a fixed schema some edge over the more generic RDF quad store.</p> <p>There was some discussion on how much closer a triple store could come to a relational one. Some gains are undoubtedly possible. We will see. For the ideal row store workload, the <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x22f837c0">RDBMS</a> will continue to have some edge. Large online systems typically have a large part of the workload that is simple and repetitive. There is nothing to prevent one having special indices for supporting such workload, even while retaining the possibility of arbitrary triples elsewhere. Some degree of application-specific data structure does make sense. We just need to show how this is done. In this way, we have a continuum and not an either/or choice of triples vs. tables.</p> <h2>Scale, Where Next?</h2> <p>Concerning the future direction of the workshop, there were a few directions suggested. One of the more interesting ones was Mike Dean's suggestion about dealing with a large volume of same-as assertions, specifically a volume where materializing all the entailed triples was no longer practical. Of course, there is the question of scale. This time, we were the only ones focusing on a parallel database with no restrictions on joining.</p>
ISWC 2008: The Scalable Knowledge Systems Workshop
http://www.openlinksw.com/weblog/oerling/?date=2008-11-03#1471
2008-11-03T13:16:47Z
2008-11-03T12:33:49-05:00
<p>Mike Dean of <a href="http://dbpedia.org/resource/BBN_Technologies" id="link-id0x21d04768">BBN Technologies</a> opened the Scalable <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x22348c58">Knowledge</a> Systems Workshop with an invited talk. He reminded us of the facts of nature as concern the cost of distributed computing and running out of space for the working set. Developers in the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x22570328">semantic web</a> field deplorably often ignore these facts, or alternatively recognize them and admit that they are unbeatable, that one just can't join across partitions.</p> <p>I gave a talk about the <a href="http://virtuoso.openlinksw.com" id="link-id0x23f313f0">Virtuoso</a> Cluster edition, wherein I repeated essentially the same ground facts as Mike and outlined how we (in spite of these) profit from distributed memory multiprocessing. To those not intimate with these questions, let me affirm that deriving benefit from threading in a symmetric multiprocessor box, let alone a cluster connected by a network, totally depends on having many relatively long running things going at a time and blocking as seldom as possible.</p> <p>Further, Mike Dean talked about <a href="http://www.asio.bbn.com/" id="link-id0x1d74c108">ASIO</a>, the BBN suite of semantic web tools. His most challenging statement was about the storage engine, a network-database-inspired triple-store using memory-mapped files. </p> <p>Will the <a href="http://dbpedia.org/resource/CODASYL" id="link-id0x1f8ee860">CODASYL</a> days come back, and will the linked list on disk be the way to store triples/quads? I would say that this will have, especially with a memory-mapped file, probably a better best-case as a B-tree but that this also will be less predictable with fragmentation. With Virtuoso, using a B-tree index, we see about 20-30% of CPU time spent on index lookup when running LUBM queries. With a disk-based memory-mapped linked-list storage, we would see some improvements in this while getting hit probably worse than now in the case of fragmentation. Plus compaction on the fly would not be nearly as easy and surely far less local, if there were pointers between pages. So it is my intuition that trees are a safer bet with varying workloads while linked lists can be faster in a query-dominated in-memory situation.</p> <p>Chris Bizer presented the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1d670da0">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x21928808">BSBM</a>), which has already been discussed here in some detail. He did acknowledge that the next round of the race must have a real steady-state rule. This just means that the benchmark must be run long enough for the system under test to reach a state where the cache is full and the performance remains indefinitely at the same level. Reaching steady state can take 20-30 minutes in some cases.</p> <p>Regardless of steady state, BSBM has two generally valid conclusions: </p> <ol> <li>mapping relational to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xab811020">RDF</a>, where possible, is faster than triple storage; and </li> <li>the equivalent relational solution can be some 10x faster than the pure triples representation.</li> </ol> <p>Mike Dean asked whether BSBM was a case of a setup to have triple stores fail. Not necessarily, I would say; we should understand that one motivation of BSBM is testing mapping technologies. Therefore it must have a workload where mapping makes sense. Of course there are workloads where triples are unchallenged â take the <a href="http://challenge.semanticweb.org/" id="link-id0x2538c3b8">Billion Triples Challenge</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x1d673760">data</a> set for one.</p> <p>Also, with BSBM, once should note that the query optimization time plays a fairly large role since most queries touch relatively little data. Also, even if the scale is large, the working set is not nearly the size of the database. This in fact penalizes mapping technologies against native <a href="http://dbpedia.org/resource/SQL" id="link-id0xac16cc10">SQL</a> since the difference there is compiling the query, especially since parameters are not used. So, Chris, since we both like to map, let's make a benchmark that shows mapping closer to native SQL.</p> <h2>Bridging the 10x Gap?</h2> <p>When we run Virtuoso relational against Virtuoso triple store with the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1d7dc518">TPC-H</a> workload, we see that the relational case is significantly faster. These are long queries, thus query optimization time is negligible; we are here comparing memory-based access times. Why is this? The answer is that a single index lookup gives multiple column values with almost no penalty for the extra column. Also, since the number of total joins is lower, the overhead coming from moving from join to next join is likewise lower. This is just a meter of count of executed instructions.</p> <p>A column store joins in principle just as much as a triple store. However, since the BI workload often consists of scanning over large tables, the joins tend to be local, the needed lookup can often use the previous location as a starting point. A triple store can do the same if queries have high locality. We do this in some SQL situations and can try this with triples also. The RDF workload is typically more random in its access pattern, though. The other factor is the length of control path. A column store has a simpler control flow if it knows that the column will have exactly one value per row. With RDF, this is not a given. Also, the column store's row is identified by a single number and not a multipart key. These two factors give the column store running with a fixed schema some edge over the more generic RDF quad store.</p> <p>There was some discussion on how much closer a triple store could come to a relational one. Some gains are undoubtedly possible. We will see. For the ideal row store workload, the <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x22e5b6f8">RDBMS</a> will continue to have some edge. Large online systems typically have a large part of the workload that is simple and repetitive. There is nothing to prevent one having special indices for supporting such workload, even while retaining the possibility of arbitrary triples elsewhere. Some degree of application-specific data structure does make sense. We just need to show how this is done. In this way, we have a continuum and not an either/or choice of triples vs. tables.</p> <h2>Scale, Where Next?</h2> <p>Concerning the future direction of the workshop, there were a few directions suggested. One of the more interesting ones was Mike Dean's suggestion about dealing with a large volume of same-as assertions, specifically a volume where materializing all the entailed triples was no longer practical. Of course, there is the question of scale. This time, we were the only ones focusing on a parallel database with no restrictions on joining.</p>
Virtuoso - Are We Too Clever for Our Own Good? (updated)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-10-26#1467
2008-10-26T12:15:35Z
2008-10-27T12:07:58-04:00
<p>"Physician, heal thyself," it is said. We profess to say what the messaging of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x1b4a25f0">semantic web</a> ought to be, but is our own perfect?</p> <p>I will here engage in some critical introspection as well as amplify on some answers given to <a href="http://virtuoso.openlinksw.com" id="link-id0x1e4f9928">Virtuoso</a>-related questions in recent times.</p> <p>I use some conversations from the <a href="http://dbpedia.org/resource/Vienna" id="link-id0x1e6c0ca8">Vienna</a> <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1e56df88">Linked Data</a> Practitioners meeting as a starting point. These views are mine and are limited to the Virtuoso server. These do not apply to the <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x1e680440">ODS</a> (<a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x1e140068">OpenLink Data Spaces</a>) applications line, <a href="http://oat.openlinksw.com/" id="link-id0x1f4ba630">OAT</a> (<a href="http://oat.openlinksw.com/" id="link-id0x1ba4bac8">OpenLink Ajax Toolkit</a>), or <a href="http://ode.openlinksw.com/" id="link-id0x1d4159b0">ODE</a> (<a href="http://ode.openlinksw.com/" id="link-id0x1e973c80">OpenLink Data Explorer</a>).</p> <h3>"It is not always clear what the main thrust is, we get the impression that you are spread too thin," said <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x1f8bafe0">Sören Auer</a>.</h3> <p>Well, personally, I am all for core competence. This is why I do not participate in all the online conversations and groups as much as I could, for example. Time and energy are critical resources and must be invested where they make a difference. In this case, the real core competence is running in the database race. This in itself, come to think of it, is a pretty broad concept.</p> <p>This is why we put a lot of emphasis on Linked Data and the <a href="http://dbpedia.org/resource/Data" id="link-id0x200bd1f0">Data</a> Web for now, as this is the emerging game. This is a deliberate choice, not an outside imperative or built-in limitation. More specifically, this means exposing any pre-existing relational data as linked data plus being the definitive <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1fb03528">RDF</a> store.</p> <p>We can do this because we own our database and <a href="http://dbpedia.org/resource/SQL" id="link-id0x1e7dcc70">SQL</a> and data access middleware and have a history of connecting to any <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x1e9baf18">RDBMS</a> out there.</p> <p>The principal message we have been hearing from the RDF field is the call for scale of triple storage. This is even louder than the call for relational mapping. We believe that in time mapping will exceed triple storage as such, once we get some real production strength mappings deployed, enough to outperform RDF warehousing.</p> <p>There are also RDF middleware things like RDF-ization and demand-driven web harvesting (i.e, the so-called Sponger). These are <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1f5f6b78">SPARQL</a> options, thus accessed via standard interfaces. We have little desire to create our own languages or APIs, or to tell people how to program. This is why we recently introduced <a href="http://sourceforge.net/projects/sesame/" id="link-id0x206818c8">Sesame</a>- and <a href="http://jena.sourceforge.net/" id="link-id0x202b3348">Jena</a>-compatible APIs to our RDF store. From what we hear, these work. On the other hand, we do not hesitate to move beyond the standards when there is obvious value or necessity. This is why we brought SPARQL up to and beyond SQL expressivity. It is not a case of E3 (Embrace, Extend, Extinguish).</p> <p>Now, this message could be better reflected in our material on the web. This <a href="http://dbpedia.org/resource/Blog" id="link-id0x1c82e508">blog</a> is a rather informal step in this direction; more is to come. For now we concentrate on delivering.</p> <p>The conventional communications wisdom is to split the message by target audience. For this, we should split the RDF, relational, and web services messages from each other. We believe that a challenger, like the semantic web technology stack, must have a compelling message to tell for it to be interesting. This is not a question of research prototypes. The new technology cannot lack something the installed technology takes for granted.</p> <p>This is why we do not tend to show things like how to insert and query a few triples: No business out there will insert and query triples for the sake of triples. There must be a more compelling story â for example, turning the whole world into a database. This is why our examples start with things like turning the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x20832510">TPC-H</a> database into RDF, queries and all. Anything less is not interesting. Why would an enterprise that has business intelligence and integration issues way more complex than the rather stereotypical TPC-H even look at a technology that pretends to be all for integration and all for expressivity of queries, yet cannot answer the first question of the entry exam?</p> <p>The world out there is complex. But maybe we ought to make some simple tutorials? So, as a call to the people out there, tell us what a good tutorial would be. The question is more about figuring out what is out there and adapting these and making a sort of compatibility list. Jena and Sesame stuff ought to run as is. We could offer a webinar to all the data web luminaries showing how to promote the data web message with Virtuoso. After all, why not show it on the best platform?</p> <h3>"You are arrogant. When I read your papers or documentation, the impression I get is that you say you are smart and the reader is stupid."</h3> <p>We should answer in multiple parts.</p> <p>For general collateral, like web sites and documentation:</p> <p>The web site gives a confused product image. For the Virtuoso product, we should divide at the top into</p> <ul> <li> Data web and RDF - Host linked data, expose relational assets as linked data;</li> <li> Relational Database - Full function, high performance, open source, Federated/Virtual Relational DBMS, expose heterogeneous RDB assets through one point of contact for integration;</li> <li> Web Services - access all the above over standard protocols, dynamic web pages, web hosting.</li> </ul> <p>For each point, one simple statement. We all know what the above things mean?</p> <p>Then we add a new point about scalability that impacts all the above, namely the Virtuoso version 6 Cluster, meaning that you can do all these things at 10 to 1000 times the scale. This means this much more data or in some cases this much more requests per second. This too is clear.</p> <p>Far as I am concerned, hosting Java or .<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x20283a88">NET</a> does not have to be on the front page. Also, we have no great interest in going against <a href="http://dbpedia.org/resource/Apache" id="link-id0x2024a068">Apache</a> when it comes to a web server only situation. The fact that we have a web listener is important for some things but our claim to fame does not rest on this.</p> <p>Then for documentation and training materials: The documentation should be better. Specifically it should have more of a how-to dimension since nobody reads the whole thing anyhow. About online tutorials, the order of presentation should be different. They do not really reflect what is important at the present moment either.</p> <p>Now for conference papers: Since taking the data web as a focus area, we have submitted some papers and had some rejected because these do not have enough references and do not explain what is obvious to ourselves.</p> <p>I think that the communications failure in this case is that we want to talk about end to end solutions and the reviewers expect research. For us, the solution is interesting and exists only if there is an adequate functionality mix for addressing a specific use case. This is why we do not make a paper about query cost model alone because the cost model, while indispensable, is a thing that is taken for granted where we come from. So we mention RDF adaptations to cost model, as these are important to the whole but do not find these to be the justification for a whole paper. If we made papers on this basis, we would have to make five times as many. Maybe we ought to.</p> <h3>"Virtuoso is very big and very difficult"</h3> <p>One thing that is not obvious from the Virtuoso packaging is that the minimum installation is an executable under 10MB and a config file. Two files.</p> <p>This gives you SQL and SPARQL out of the box. Adding <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x1ee61058">ODBC</a> and <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1b8c31c0">JDBC</a> clients is as simple as it gets. After this, there is basic database functionality. Tuning is a matter of a few parameters that are explained on this blog and elsewhere. Also, the full scale installation is available as an Amazon EC2 image, so no installation required.</p> <p>Now for the difficult side:</p> <p>Use SQL and SPARQL; use stored procedures whenever there is server side business logic. For some time critical web pages, use VSP. Do not use VSPX. Otherwise, use whatever you are used to â <a href="http://dbpedia.org/resource/PHP" id="link-id0x20a13c00">PHP</a> or Java or anything else. For web services, simple is best. Stick to basics. "The engineer is one who can invent a simple thing." Use SQL statements rather than admin UI.</p> <p>Know that you can start a server with no database file and you get an initial database with nothing extra. The demo database, the way it is produced by installers is cluttered.</p> <p>We should put this into a couple of use case oriented how-tos.</p> <p>Also, we should create a network of "friendly local virtuoso geeks" for providing basic training and services so we do not have to explain these things all the time. To all you data-web-ers out there â please sign up and we will provide instructions, etc. Contact Yrjänä Rankka (ghard[at-sign]openlinksw.com), or go through the mailing lists; do not contact me directly.</p> <h3>"OK, we understand that you may be good at the large end of the spectrum but how do you reconcile this with the lightweight or embedded end, like the semantic desktop?"</h3> <p>Now, what is good for one end is usually good for the other. Namely, a database, no matter the scale, needs to have space efficient storage, fast index lookup, and correct query plans. Then there are things that occur only at the high-end, like clustering, but these are separate things. For embedding, the initial memory footprint needs to be small. With Virtuoso, this is accomplished by leaving out some 200 built-in tables and 100,000 lines of SQL procedures that are normally in by default, supporting things such as DAV and diverse other protocols. After all, if SPARQL is all one wants these are not needed.</p> <p>If one really wants to do one's server logic (like web listener and thread dispatching) oneself, this is not impossible but requires some advice from us. On the other hand, if one wants to have logic for security close to the data, then using stored procedures is recommended; these execute right next to the data, and support inline SPARQL and SQL. Depending on the license status of the other code, some special licensing arrangements may apply.</p> <p>We are talking about such things with different parties at present.</p> <h3>"How webby are you? What is webby?"</h3> <p>"Webby means distributed, heterogeneous, open; not monolithic consolidation of everything."</p> <p>We are philosophically webby. We come from open standards; we are after all called OpenLink; our history consists of connecting things. We believe in choice â the user should be able to pick the best of breed for components and have them work together. We cannot and do not wish to force replacement of existing assets. Transforming data on the fly and connecting systems, leaving data where it originally resides, is the first preference. For the data web, the first preference is a federation of independent SPARQL end points. When there is harvesting, we prefer to do it on demand, as with our Sponger. With the immense amount of data out there we believe in finding what is relevant <i>when</i> it is relevant, preferably close at hand, leveraging things like social networks. With a data web, many things which are now siloized, such as marketplaces and social networks, will return to the open.</p> <p>Google-style crawling of everything becomes less practical if one needs to run complex <i>ad hoc</i> queries against the mass of data. For these types of scenarios, if one needs to warehouse, the data cloud will offer solutions where one pays for database on demand. While we believe in loosely coupled federation where possible, we have serious work on the scalability side for the data center and the compute-on-demand cloud.</p> <h3>"How does OpenLink see the next five years unfolding?"</h3> <p>Personally, I think we have the basics for the birth of a new inflection in the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1fb9ae58">knowledge</a> economy. The <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x1f07c648">URI</a> is the unit of exchange; its value and competitive edge lie in the data it links you with. A name without context is worth little, but as a name gets more use, more <a href="http://dbpedia.org/resource/Information" id="link-id0x1f007d60">information</a> can be found through that name. This is anything from financial statistics, to legal precedents, to news reporting or government data. Right now, if the SEC just added one line of markup to the XBRL template, this would instantaneously make all SEC-mandated reporting into linked data via GRDDL.</p> <p>The URI is a carrier of brand. An information brand gets traffic and references, and this can be monetized in diverse ways. The key word is <i>context</i>. Information overload is here to stay, and only better context offers the needed increase in productivity to stay ahead of the flood.</p> <p>Semantic technologies on the whole can help with this. Why these should be semantic web or data web technologies as opposed to just semantic is the linked data value proposition. Even smart islands are still islands. Agility, scale, and scope, depend on the possibility of combining things. Therefore common terminologies and dereferenceability and discoverability are important. Without these, we are at best dealing with closed systems even if they were smart. The expert systems of the 1980s are a case in point.</p> <p>Ever since the .com era, the <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id0x2048e670">URL</a> has been a brand. Now it becomes a URI. Thus, entirely hiding the URI from the user experience is not always desirable. The URI is a sort of handle on the provenance and where more can be found; besides, people are already used to these.</p> <p>With linked data, information value-add products become easy to build and deploy. They can be basically just canned SPARQL queries combining data in a useful and insightful manner. And where there is traffic there can be monetization, whether by advertizing, subscription, or other means. Such possibilities are a natural adjunct to the blogosphere. To publish analysis, one no longer needs to be a think tank or media company. We could call this scenario the birth of a meshup economy.</p> <p>For OpenLink itself, this is our roadmap. The immediate future is about getting our high end offerings like clustered RDF storage generally available, both on the cloud and for private data centers. Ourselves, we will offer the whole <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1c696170">Linked Open Data</a> cloud as a database. The single feature to come in version 2 of this is fully automatic partitioning and repartitioning for on-demand scale; now, you have to choose how many partitions you have.</p> <p>This makes some things possible that were hard thus far.</p> <p>On the mapping front, we go for real-scale data integration scenarios where we can show that SPARQL can unify terms and concepts across databases, yet bring no added cost for complex queries. Enterprises can use their existing warehouses and have an added level of abstraction, the possibility of cross systems interlinking, the advantages of using the same taxonomies and ontologies across systems, and so forth.</p> <p>Then there will be developments in the direction of smarter web harvesting on demand with the Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x206ab780">Sponger</a>, and federation of heterogeneous SPARQL end points. The federation is not so unlike clustering, except the time scales are 2 orders of magnitude longer. The work on SPARQL end point statistics and data set description and discovery is a good development in the community.</p> <p>Then there will be NLP integration, as exemplified by the Open Calais linked data wrapper and more.</p> <p>Can we pull this off or is this being spread too thin? We know from experience that all this can be accomplished. Scale is already here; we show it with the billion triples set. Mapping is here; we showed it last in the Berlin Benchmark. We will also show some TPC-H results after we get a little quiet after the ISWC event. Then there is ongoing maintenance but with this we have shown a steady turnaround and quick time to fix for pretty much anything.</p>
Virtuoso - Are We Too Clever for Our Own Good? (updated)
http://www.openlinksw.com/weblog/oerling/?date=2008-10-26#1465
2008-10-26T12:15:35Z
2008-10-27T12:07:52-04:00
<p>"Physician, heal thyself," it is said. We profess to say what the messaging of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x1fa3da18">semantic web</a> ought to be, but is our own perfect?</p> <p>I will here engage in some critical introspection as well as amplify on some answers given to <a href="http://virtuoso.openlinksw.com" id="link-id0x1e1eecf0">Virtuoso</a>-related questions in recent times.</p> <p>I use some conversations from the <a href="http://dbpedia.org/resource/Vienna" id="link-id0x1ec0b2e0">Vienna</a> <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x2045ac10">Linked Data</a> Practitioners meeting as a starting point. These views are mine and are limited to the Virtuoso server. These do not apply to the <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x2045ac38">ODS</a> (<a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x14f63c58">OpenLink Data Spaces</a>) applications line, <a href="http://oat.openlinksw.com/" id="link-id0x14f63c80">OAT</a> (<a href="http://oat.openlinksw.com/" id="link-id0x1e536928">OpenLink Ajax Toolkit</a>), or <a href="http://ode.openlinksw.com/" id="link-id0x1eaed7f8">ODE</a> (<a href="http://ode.openlinksw.com/" id="link-id0x1edfff88">OpenLink Data Explorer</a>).</p> <h3>"It is not always clear what the main thrust is, we get the impression that you are spread too thin," said <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x1b8a9580">Sören Auer</a>.</h3> <p>Well, personally, I am all for core competence. This is why I do not participate in all the online conversations and groups as much as I could, for example. Time and energy are critical resources and must be invested where they make a difference. In this case, the real core competence is running in the database race. This in itself, come to think of it, is a pretty broad concept.</p> <p>This is why we put a lot of emphasis on Linked Data and the <a href="http://dbpedia.org/resource/Data" id="link-id0x1b85fa38">Data</a> Web for now, as this is the emerging game. This is a deliberate choice, not an outside imperative or built-in limitation. More specifically, this means exposing any pre-existing relational data as linked data plus being the definitive <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1f5b4468">RDF</a> store.</p> <p>We can do this because we own our database and <a href="http://dbpedia.org/resource/SQL" id="link-id0x20076468">SQL</a> and data access middleware and have a history of connecting to any <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x1ffd6f98">RDBMS</a> out there.</p> <p>The principal message we have been hearing from the RDF field is the call for scale of triple storage. This is even louder than the call for relational mapping. We believe that in time mapping will exceed triple storage as such, once we get some real production strength mappings deployed, enough to outperform RDF warehousing.</p> <p>There are also RDF middleware things like RDF-ization and demand-driven web harvesting (i.e, the so-called Sponger). These are <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1316f720">SPARQL</a> options, thus accessed via standard interfaces. We have little desire to create our own languages or APIs, or to tell people how to program. This is why we recently introduced <a href="http://sourceforge.net/projects/sesame/" id="link-id0x20756a68">Sesame</a>- and <a href="http://jena.sourceforge.net/" id="link-id0x1ec01ac0">Jena</a>-compatible APIs to our RDF store. From what we hear, these work. On the other hand, we do not hesitate to move beyond the standards when there is obvious value or necessity. This is why we brought SPARQL up to and beyond SQL expressivity. It is not a case of E3 (Embrace, Extend, Extinguish).</p> <p>Now, this message could be better reflected in our material on the web. This <a href="http://dbpedia.org/resource/Blog" id="link-id0x2027b410">blog</a> is a rather informal step in this direction; more is to come. For now we concentrate on delivering.</p> <p>The conventional communications wisdom is to split the message by target audience. For this, we should split the RDF, relational, and web services messages from each other. We believe that a challenger, like the semantic web technology stack, must have a compelling message to tell for it to be interesting. This is not a question of research prototypes. The new technology cannot lack something the installed technology takes for granted.</p> <p>This is why we do not tend to show things like how to insert and query a few triples: No business out there will insert and query triples for the sake of triples. There must be a more compelling story â for example, turning the whole world into a database. This is why our examples start with things like turning the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x2051ff98">TPC-H</a> database into RDF, queries and all. Anything less is not interesting. Why would an enterprise that has business intelligence and integration issues way more complex than the rather stereotypical TPC-H even look at a technology that pretends to be all for integration and all for expressivity of queries, yet cannot answer the first question of the entry exam?</p> <p>The world out there is complex. But maybe we ought to make some simple tutorials? So, as a call to the people out there, tell us what a good tutorial would be. The question is more about figuring out what is out there and adapting these and making a sort of compatibility list. Jena and Sesame stuff ought to run as is. We could offer a webinar to all the data web luminaries showing how to promote the data web message with Virtuoso. After all, why not show it on the best platform?</p> <h3>"You are arrogant. When I read your papers or documentation, the impression I get is that you say you are smart and the reader is stupid."</h3> <p>We should answer in multiple parts.</p> <p>For general collateral, like web sites and documentation:</p> <p>The web site gives a confused product image. For the Virtuoso product, we should divide at the top into</p> <ul> <li> Data web and RDF - Host linked data, expose relational assets as linked data;</li> <li> Relational Database - Full function, high performance, open source, Federated/Virtual Relational DBMS, expose heterogeneous RDB assets through one point of contact for integration;</li> <li> Web Services - access all the above over standard protocols, dynamic web pages, web hosting.</li> </ul> <p>For each point, one simple statement. We all know what the above things mean?</p> <p>Then we add a new point about scalability that impacts all the above, namely the Virtuoso version 6 Cluster, meaning that you can do all these things at 10 to 1000 times the scale. This means this much more data or in some cases this much more requests per second. This too is clear.</p> <p>Far as I am concerned, hosting Java or .<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x1f297540">NET</a> does not have to be on the front page. Also, we have no great interest in going against <a href="http://dbpedia.org/resource/Apache" id="link-id0x1ea29578">Apache</a> when it comes to a web server only situation. The fact that we have a web listener is important for some things but our claim to fame does not rest on this.</p> <p>Then for documentation and training materials: The documentation should be better. Specifically it should have more of a how-to dimension since nobody reads the whole thing anyhow. About online tutorials, the order of presentation should be different. They do not really reflect what is important at the present moment either.</p> <p>Now for conference papers: Since taking the data web as a focus area, we have submitted some papers and had some rejected because these do not have enough references and do not explain what is obvious to ourselves.</p> <p>I think that the communications failure in this case is that we want to talk about end to end solutions and the reviewers expect research. For us, the solution is interesting and exists only if there is an adequate functionality mix for addressing a specific use case. This is why we do not make a paper about query cost model alone because the cost model, while indispensable, is a thing that is taken for granted where we come from. So we mention RDF adaptations to cost model, as these are important to the whole but do not find these to be the justification for a whole paper. If we made papers on this basis, we would have to make five times as many. Maybe we ought to.</p> <h3>"Virtuoso is very big and very difficult"</h3> <p>One thing that is not obvious from the Virtuoso packaging is that the minimum installation is an executable under 10MB and a config file. Two files.</p> <p>This gives you SQL and SPARQL out of the box. Adding <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x20a2e7d0">ODBC</a> and <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1e4cceb8">JDBC</a> clients is as simple as it gets. After this, there is basic database functionality. Tuning is a matter of a few parameters that are explained on this blog and elsewhere. Also, the full scale installation is available as an Amazon EC2 image, so no installation required.</p> <p>Now for the difficult side:</p> <p>Use SQL and SPARQL; use stored procedures whenever there is server side business logic. For some time critical web pages, use VSP. Do not use VSPX. Otherwise, use whatever you are used to â <a href="http://dbpedia.org/resource/PHP" id="link-id0x20b03f08">PHP</a> or Java or anything else. For web services, simple is best. Stick to basics. "The engineer is one who can invent a simple thing." Use SQL statements rather than admin UI.</p> <p>Know that you can start a server with no database file and you get an initial database with nothing extra. The demo database, the way it is produced by installers is cluttered.</p> <p>We should put this into a couple of use case oriented how-tos.</p> <p>Also, we should create a network of "friendly local virtuoso geeks" for providing basic training and services so we do not have to explain these things all the time. To all you data-web-ers out there â please sign up and we will provide instructions, etc. Contact Yrjänä Rankka (ghard[at-sign]openlinksw.com), or go through the mailing lists; do not contact me directly.</p> <h3>"OK, we understand that you may be good at the large end of the spectrum but how do you reconcile this with the lightweight or embedded end, like the semantic desktop?"</h3> <p>Now, what is good for one end is usually good for the other. Namely, a database, no matter the scale, needs to have space efficient storage, fast index lookup, and correct query plans. Then there are things that occur only at the high-end, like clustering, but these are separate things. For embedding, the initial memory footprint needs to be small. With Virtuoso, this is accomplished by leaving out some 200 built-in tables and 100,000 lines of SQL procedures that are normally in by default, supporting things such as DAV and diverse other protocols. After all, if SPARQL is all one wants these are not needed.</p> <p>If one really wants to do one's server logic (like web listener and thread dispatching) oneself, this is not impossible but requires some advice from us. On the other hand, if one wants to have logic for security close to the data, then using stored procedures is recommended; these execute right next to the data, and support inline SPARQL and SQL. Depending on the license status of the other code, some special licensing arrangements may apply.</p> <p>We are talking about such things with different parties at present.</p> <h3>"How webby are you? What is webby?"</h3> <p>"Webby means distributed, heterogeneous, open; not monolithic consolidation of everything."</p> <p>We are philosophically webby. We come from open standards; we are after all called OpenLink; our history consists of connecting things. We believe in choice â the user should be able to pick the best of breed for components and have them work together. We cannot and do not wish to force replacement of existing assets. Transforming data on the fly and connecting systems, leaving data where it originally resides, is the first preference. For the data web, the first preference is a federation of independent SPARQL end points. When there is harvesting, we prefer to do it on demand, as with our Sponger. With the immense amount of data out there we believe in finding what is relevant <i>when</i> it is relevant, preferably close at hand, leveraging things like social networks. With a data web, many things which are now siloized, such as marketplaces and social networks, will return to the open.</p> <p>Google-style crawling of everything becomes less practical if one needs to run complex <i>ad hoc</i> queries against the mass of data. For these types of scenarios, if one needs to warehouse, the data cloud will offer solutions where one pays for database on demand. While we believe in loosely coupled federation where possible, we have serious work on the scalability side for the data center and the compute-on-demand cloud.</p> <h3>"How does OpenLink see the next five years unfolding?"</h3> <p>Personally, I think we have the basics for the birth of a new inflection in the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x2018bd98">knowledge</a> economy. The <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x1ec110d8">URI</a> is the unit of exchange; its value and competitive edge lie in the data it links you with. A name without context is worth little, but as a name gets more use, more <a href="http://dbpedia.org/resource/Information" id="link-id0x1ecfba08">information</a> can be found through that name. This is anything from financial statistics, to legal precedents, to news reporting or government data. Right now, if the SEC just added one line of markup to the XBRL template, this would instantaneously make all SEC-mandated reporting into linked data via GRDDL.</p> <p>The URI is a carrier of brand. An information brand gets traffic and references, and this can be monetized in diverse ways. The key word is <i>context</i>. Information overload is here to stay, and only better context offers the needed increase in productivity to stay ahead of the flood.</p> <p>Semantic technologies on the whole can help with this. Why these should be semantic web or data web technologies as opposed to just semantic is the linked data value proposition. Even smart islands are still islands. Agility, scale, and scope, depend on the possibility of combining things. Therefore common terminologies and dereferenceability and discoverability are important. Without these, we are at best dealing with closed systems even if they were smart. The expert systems of the 1980s are a case in point.</p> <p>Ever since the .com era, the <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id0x1c4c9248">URL</a> has been a brand. Now it becomes a URI. Thus, entirely hiding the URI from the user experience is not always desirable. The URI is a sort of handle on the provenance and where more can be found; besides, people are already used to these.</p> <p>With linked data, information value-add products become easy to build and deploy. They can be basically just canned SPARQL queries combining data in a useful and insightful manner. And where there is traffic there can be monetization, whether by advertizing, subscription, or other means. Such possibilities are a natural adjunct to the blogosphere. To publish analysis, one no longer needs to be a think tank or media company. We could call this scenario the birth of a meshup economy.</p> <p>For OpenLink itself, this is our roadmap. The immediate future is about getting our high end offerings like clustered RDF storage generally available, both on the cloud and for private data centers. Ourselves, we will offer the whole <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x20791bf0">Linked Open Data</a> cloud as a database. The single feature to come in version 2 of this is fully automatic partitioning and repartitioning for on-demand scale; now, you have to choose how many partitions you have.</p> <p>This makes some things possible that were hard thus far.</p> <p>On the mapping front, we go for real-scale data integration scenarios where we can show that SPARQL can unify terms and concepts across databases, yet bring no added cost for complex queries. Enterprises can use their existing warehouses and have an added level of abstraction, the possibility of cross systems interlinking, the advantages of using the same taxonomies and ontologies across systems, and so forth.</p> <p>Then there will be developments in the direction of smarter web harvesting on demand with the Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x1f27e6d8">Sponger</a>, and federation of heterogeneous SPARQL end points. The federation is not so unlike clustering, except the time scales are 2 orders of magnitude longer. The work on SPARQL end point statistics and data set description and discovery is a good development in the community.</p> <p>Then there will be NLP integration, as exemplified by the Open Calais linked data wrapper and more.</p> <p>Can we pull this off or is this being spread too thin? We know from experience that all this can be accomplished. Scale is already here; we show it with the billion triples set. Mapping is here; we showed it last in the Berlin Benchmark. We will also show some TPC-H results after we get a little quiet after the ISWC event. Then there is ongoing maintenance but with this we have shown a steady turnaround and quick time to fix for pretty much anything.</p>
State of the Semantic Web, Part 2 - The Technical Questions (updated)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-10-26#1466
2008-10-26T12:02:43Z
2008-10-27T11:28:14-04:00
<p>Here I will talk about some more technical questions that came up. This is mostly general; <a href="http://virtuoso.openlinksw.com" id="link-id0x205901a0">Virtuoso</a> specific questions and answers are separate. </p> <h3>"How to Bootstrap? Where will the triples come from?"</h3> <p>There are already wrappers producing <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x13519ac8">RDF</a> from many applications. Since any structured or semi-structured <a href="http://dbpedia.org/resource/Data" id="link-id0x1c93b418">data</a> can be converted to RDF and often there is even a pre-existing terminology for the application domain, the availability of the data <i>per se</i> is not the concern.</p> <p>The triples may come from any application or database, but they will not come from the end user directly. There was a good talk about photograph annotation in <a href="http://dbpedia.org/resource/Vienna" id="link-id0x1ea9d150">Vienna</a>, describing many ways of deriving metadata for photos. The essential wisdom is annotating on the spot and wherever possible doing so automatically. The consumer is very unlikely to go annotate photos after the fact. Further, one can infer that photos made with the same camera around the same time are from the same location. There are other such heuristics. In this use case, the end user does not need to see triples. There is some benefit though in using commonly used geographical terminology for linking to other data sources.</p> <h3>"How will one develop applications?"</h3> <p>I'd say one will develop them much the same way as thus far. In <a href="http://dbpedia.org/resource/PHP" id="link-id0x207fca00">PHP</a>, for example. Whether one's query language is <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x20a5fde0">SPARQL</a> or <a href="http://dbpedia.org/resource/SQL" id="link-id0x1a0bb5e0">SQL</a> does not make a large difference in how basic web UI is made.</p> <p>A SPARQL end-point is no more an end-user item than a SQL command-line is.</p> <p>A common mistake among techies is that they think the data structure and user experience can or ought to be of the same structure. The UI dialogs do not, for example, have to have a 1:1 correspondence with SQL tables.</p> <p>The idea of generating UI from data, whether relational or data-web, is so seductive that generation upon generation of developers fall for it, repeatedly. Even I, at OpenLink, after supposedly having been around the block a couple of times made some experiments around the topic. What does make sense is putting a thin wrapper or HTML around the application, using XSLT and such for formatting. Since the model does allow for unforeseen properties of data, one can build a viewer for these alongside the regular forms. For this, Ajax technologies like <a href="http://oat.openlinksw.com/" id="link-id0x1e91d118">OAT</a> (the <a href="http://oat.openlinksw.com/" id="link-id0x174b7950">OpenLink AJAX Toolkit</a>) will be good.</p> <p>The UI ought not to completely hide the URIs of the data from the user. It should offer a drill down to faceted views of the triples for example. Remember when Xerox talked about graphical user interfaces in 1980? "Don't mode me in" was the slogan, as I recall.</p> <p>Since then, we have vacillated between modal and non-modal interaction models. Repetitive workflows like order entry go best modally and are anyway being replaced by web services. Also workflows that are very infrequent benefit from modality; take personal network setup wizards, for example. But enabling the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1ea14610">knowledge</a> worker is a domain that by its nature must retain some respect for human intelligence and not kill this by denying access to the underlying data, including provenance and URIs. Face it: the world is not getting simpler. It is increasingly data dependent and when this is so, having semantics and flexibility of access for the data is important.</p> <p>For a real-time task-oriented user interface like a fighter plane cockpit, one will not show URIs unless specifically requested. For planning fighter sorties though, there is some potential benefit in having all data such as friendly and hostile assets, geography, organizational structure, etc., as <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x207bcd20">linked data</a>. It makes for more flexible querying. Linked data does not <i>per se</i> mean open, so one can be joinable with open data through using the same identifiers even while maintaining arbitrary levels of security and compartmentalization.</p> <p>For automating tasks that every time involve the same data and queries, RDF has no intrinsic superiority. Thus the user interfaces in places where RDF will have real edge must be more capable of <i>ad hoc</i> viewing and navigation than regular real-time or line of business user interfaces.</p> <p>The <a href="http://ode.openlinksw.com/" id="link-id0x2083a6f0">OpenLink Data Explorer</a> idea of a "data behind the web page" view goes in this direction. Read the web as before, then hit a switch to go to the data view. There are and will be separate clarifications and demos about this.</p> <h3>"What of the proliferation of standards? Does this not look too tangled, no clear identity? How would one know where to begin?"</h3> <p>When <a href="http://www.w3.org/2001/sw/sweo/" id="link-id0x1e8eac68">SWEO</a> was beginning, there was an endlessly protracted discussion of the so-called layer cake. This acronym jungle is not good messaging. Just say linked, flexibly repurpose-able data, and rich vocabularies and structure. Just the right amount of structure for the application, less rigid and easier to change than relational.</p> <p>Do not even mention the different serialization formats. Just say that it fits on top of the accepted web infrastructure â <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1e3806b8">HTTP</a>, URIs, and <a href="http://dbpedia.org/resource/XML" id="link-id0x1f547288">XML</a> where desired.</p> <p>It is misleading to say inference is a box at some specific place in the diagram. Inference of different types may or may not take place at diverse points, whether presentation or storage, on demand or as a preprocessing step. Since there is structure and semantics, inference is possible if desired.</p> <h3>"Can I make a social network application in RDF only, with no <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x20553ee0">RDBMS</a>?"</h3> <p>Yes, in principle, but what do you have in mind? The answer is very context dependent. The person posing the question had an E-learning system in mind, with things such as course catalogues, course material, etc. In such a case, RDF is a great match, especially since the user count will not be in the millions. No university has that many students and anyway they do not hang online browsing the course catalogue.</p> <p>On the other hand, if I think of making a social network site with RDF as the exclusive data model, I see things that would be very inefficient. For example, keeping a count of logins or the last time of login would be by default several times less efficient than with a RDBMS.</p> <p>If some application is really large scale and has a knowable workload profile, like any social network does, then some task-specific data structure is simply economical. This does not mean that the application language cannot be SPARQL but this means that the storage format must be tuned to favor some operations over others, relational style. This is a matter of cost more than of feasibility. Ten servers cost less than a hundred and have failures ten times less frequently.</p> <p>In the near term we will see the birth of an application paradigm for the data web. The data will be open, exposed, first-class citizen; yet the user experience will not have to be in a 1:1 image of the data.</p>
State of the Semantic Web, Part 2 - The Technical Questions (updated)
http://www.openlinksw.com/weblog/oerling/?date=2008-10-26#1464
2008-10-26T12:02:43Z
2008-10-27T11:28:01-04:00
<p>Here I will talk about some more technical questions that came up. This is mostly general; <a href="http://virtuoso.openlinksw.com" id="link-id0x1f53d1a0">Virtuoso</a> specific questions and answers are separate. </p> <h3>"How to Bootstrap? Where will the triples come from?"</h3> <p>There are already wrappers producing <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1beda278">RDF</a> from many applications. Since any structured or semi-structured <a href="http://dbpedia.org/resource/Data" id="link-id0x1e57c648">data</a> can be converted to RDF and often there is even a pre-existing terminology for the application domain, the availability of the data <i>per se</i> is not the concern.</p> <p>The triples may come from any application or database, but they will not come from the end user directly. There was a good talk about photograph annotation in <a href="http://dbpedia.org/resource/Vienna" id="link-id0x2028b7e8">Vienna</a>, describing many ways of deriving metadata for photos. The essential wisdom is annotating on the spot and wherever possible doing so automatically. The consumer is very unlikely to go annotate photos after the fact. Further, one can infer that photos made with the same camera around the same time are from the same location. There are other such heuristics. In this use case, the end user does not need to see triples. There is some benefit though in using commonly used geographical terminology for linking to other data sources.</p> <h3>"How will one develop applications?"</h3> <p>I'd say one will develop them much the same way as thus far. In <a href="http://dbpedia.org/resource/PHP" id="link-id0x1eff1748">PHP</a>, for example. Whether one's query language is <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1d83dff8">SPARQL</a> or <a href="http://dbpedia.org/resource/SQL" id="link-id0x1e9f4e88">SQL</a> does not make a large difference in how basic web UI is made.</p> <p>A SPARQL end-point is no more an end-user item than a SQL command-line is.</p> <p>A common mistake among techies is that they think the data structure and user experience can or ought to be of the same structure. The UI dialogs do not, for example, have to have a 1:1 correspondence with SQL tables.</p> <p>The idea of generating UI from data, whether relational or data-web, is so seductive that generation upon generation of developers fall for it, repeatedly. Even I, at OpenLink, after supposedly having been around the block a couple of times made some experiments around the topic. What does make sense is putting a thin wrapper or HTML around the application, using XSLT and such for formatting. Since the model does allow for unforeseen properties of data, one can build a viewer for these alongside the regular forms. For this, Ajax technologies like <a href="http://oat.openlinksw.com/" id="link-id0x1d780520">OAT</a> (the <a href="http://oat.openlinksw.com/" id="link-id0x20943788">OpenLink AJAX Toolkit</a>) will be good.</p> <p>The UI ought not to completely hide the URIs of the data from the user. It should offer a drill down to faceted views of the triples for example. Remember when Xerox talked about graphical user interfaces in 1980? "Don't mode me in" was the slogan, as I recall.</p> <p>Since then, we have vacillated between modal and non-modal interaction models. Repetitive workflows like order entry go best modally and are anyway being replaced by web services. Also workflows that are very infrequent benefit from modality; take personal network setup wizards, for example. But enabling the <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1e14eb88">knowledge</a> worker is a domain that by its nature must retain some respect for human intelligence and not kill this by denying access to the underlying data, including provenance and URIs. Face it: the world is not getting simpler. It is increasingly data dependent and when this is so, having semantics and flexibility of access for the data is important.</p> <p>For a real-time task-oriented user interface like a fighter plane cockpit, one will not show URIs unless specifically requested. For planning fighter sorties though, there is some potential benefit in having all data such as friendly and hostile assets, geography, organizational structure, etc., as <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1e91d118">linked data</a>. It makes for more flexible querying. Linked data does not <i>per se</i> mean open, so one can be joinable with open data through using the same identifiers even while maintaining arbitrary levels of security and compartmentalization.</p> <p>For automating tasks that every time involve the same data and queries, RDF has no intrinsic superiority. Thus the user interfaces in places where RDF will have real edge must be more capable of <i>ad hoc</i> viewing and navigation than regular real-time or line of business user interfaces.</p> <p>The <a href="http://ode.openlinksw.com/" id="link-id0x1c7f8ee0">OpenLink Data Explorer</a> idea of a "data behind the web page" view goes in this direction. Read the web as before, then hit a switch to go to the data view. There are and will be separate clarifications and demos about this.</p> <h3>"What of the proliferation of standards? Does this not look too tangled, no clear identity? How would one know where to begin?"</h3> <p>When <a href="http://www.w3.org/2001/sw/sweo/" id="link-id0x1d73c268">SWEO</a> was beginning, there was an endlessly protracted discussion of the so-called layer cake. This acronym jungle is not good messaging. Just say linked, flexibly repurpose-able data, and rich vocabularies and structure. Just the right amount of structure for the application, less rigid and easier to change than relational.</p> <p>Do not even mention the different serialization formats. Just say that it fits on top of the accepted web infrastructure â <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1efefed0">HTTP</a>, URIs, and <a href="http://dbpedia.org/resource/XML" id="link-id0x1af89b18">XML</a> where desired.</p> <p>It is misleading to say inference is a box at some specific place in the diagram. Inference of different types may or may not take place at diverse points, whether presentation or storage, on demand or as a preprocessing step. Since there is structure and semantics, inference is possible if desired.</p> <h3>"Can I make a social network application in RDF only, with no <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x1cb62cd8">RDBMS</a>?"</h3> <p>Yes, in principle, but what do you have in mind? The answer is very context dependent. The person posing the question had an E-learning system in mind, with things such as course catalogues, course material, etc. In such a case, RDF is a great match, especially since the user count will not be in the millions. No university has that many students and anyway they do not hang online browsing the course catalogue.</p> <p>On the other hand, if I think of making a social network site with RDF as the exclusive data model, I see things that would be very inefficient. For example, keeping a count of logins or the last time of login would be by default several times less efficient than with a RDBMS.</p> <p>If some application is really large scale and has a knowable workload profile, like any social network does, then some task-specific data structure is simply economical. This does not mean that the application language cannot be SPARQL but this means that the storage format must be tuned to favor some operations over others, relational style. This is a matter of cost more than of feasibility. Ten servers cost less than a hundred and have failures ten times less frequently.</p> <p>In the near term we will see the birth of an application paradigm for the data web. The data will be open, exposed, first-class citizen; yet the user experience will not have to be in a 1:1 image of the data.</p>
State of the Semantic Web, Part 1 - Sociology, Business, and Messaging (update 2)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-10-24#1460
2008-10-24T10:19:03Z
2008-10-27T11:28:07-04:00
<p>I was in <a href="http://dbpedia.org/resource/Vienna" id="link-id0x1f18a540">Vienna</a> for the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1ec788a0">Linked Data</a> Practitioners gathering this week. Danny Ayers asked me if I would <a href="http://dbpedia.org/resource/Blog" id="link-id0x20838238">blog</a> about the State of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x20694ed8">Semantic Web</a> or write the <i>This Week's Semantic Web</i> column. I don't have the time to cover all that may have happened during the past week but I will editorialize about the questions that again were raised in Vienna. How these things relate to <a href="http://virtuoso.openlinksw.com" id="link-id0x20b1cd38">Virtuoso</a> will be covered separately. This is about the overarching questions of the times, not the finer points of geek craft.</p> <p> <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x1ff31b30">Sören Auer</a> asked me to say a few things about relational to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1f8118e0">RDF</a> mapping. I will cite some highlights from this, as they pertain to the general scene. There was an "open hacking" session Wednesday night featuring lightning talks. I will use some of these too as a starting point.</p> <h3>The messaging?</h3> <p>The <a href="http://www.w3.org/2001/sw/sweo/" id="link-id0x1dc39210">SWEO</a> (Semantic Web Education and Outreach) interest group of the W3C spent some time looking for an elevator pitch for the Semantic Web. It became "<a href="http://dbpedia.org/resource/Data" id="link-id0x1f24dd98">Data</a> Unleashed." Why not? Let's give this some context.</p> <p>So, if we are holding a <i>Semantic Web 101</i> session, where should we begin? I hazard to guess that we should not begin by writing a FOAF file in Turtle by hand, as this is one thing that is not likely to happen in the real world.</p> <p>Of course, the social aspect of the Data Web is the most immediately engaging, so a demo might be to go make an account with <a href="http://myopenlink.net/" id="link-id0x1f5e0198">myopenlink</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x1ec49a00">net</a> and see that after one has entered the data one normally enters for any social network, one has become a Data Web citizen. This means that one can be found, just like this, with a query against the set of data spaces hosted on the system. Then we just need a few pages that repurpose this data and relate it to other data. We show some samples of queries like this in our <a href="http://challenge.semanticweb.org/" id="link-id0x1ee35f70">Billion Triples Challenge</a> demo. We will make a webcast about this to make it all clearer.</p> <p>Behold: The Data Web is about the world becoming a database; writing <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x20644808">SPARQL</a> queries or triples is incidental. You will write FOAF files by hand just as little as you now write <a href="http://dbpedia.org/resource/SQL" id="link-id0x1fd9fbc0">SQL</a> insert statements for filling in your account <a href="http://dbpedia.org/resource/Information" id="link-id0x1dfd3540">information</a> on Myspace.</p> <p>Every time there is a major shift in technology, this shift needs to be motivated by addressing a new class of problem. This means doing something that could not be done before. The last time this happened was when the relational database became the dominant IT technology. At that time, the questions involved putting the enterprise in the database and building a cluster of Line Of Business (LOB) applications around the database. The argument for the <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x1e920868">RDBMS</a> was that you did not have to constrain the set of queries that might later be made, when designing the database. In other words, it was making things more <i>ad hoc</i>. This was opposed then on grounds of being less efficient than the hierarchical and network databases which the relational eventually replaced.</p> <p>Today, the point of the Data Web is that you do not have to constrain what your data can join or integrate with, when you design your database. The counter-argument is that this is slow and geeky and not scalable. See the similarity?</p> <p>A difference is that we are not specifically aiming at replacing the RDBMS. In fact, if you know exactly what you will query and have a well defined workload, a relational representation optimized for the workload will give you about 10x the performance of the equivalent RDF warehouse. OLTP remains a relational-only domain.</p> <p>However, when we are talking about doing queries and analytics against the Web, or even against more than a handful of relational systems, the things which make RDBMS good become problematic.</p> <h3>What is the business value of this?</h3> <p>The most reliable of human drives is the drive to make oneself known. This drives all, from any social scene to business communications to politics. Today, when you want to proclaim you exist, you do so first on the Web. The Web did not become the prevalent media because business loved it for its own sake, it became prevalent because business could not afford not to assert their presence there. If anything, the Web eroded the communications dominance of a lot of players, which was not welcome but still had to be dealt with, by embracing the Web.</p> <p>Today, in a world driven by data, the Data Web will be catalyzed by similar factors: If your data is not there, you will not figure in query results. Search engines will play some role there but also many social applications will have reports that are driven by published data. Also consider any e-commerce, any marketplace, and so forth. The Data Portability movement is a case in point: Users want to own their own content; silo operators want to capitalize on holding it. Right now, we see these things in silos; the Data Web will create bridges between these, and what is now in silo data centers will be increasingly available on an ad hoc basis with Open Data.</p> <p>Again, we see a movement from the specialized to the generic: What LinkedIn does in its data center can be done with ad hoc queries with <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1e715138">linked open data</a>. Of course, LinkedIn does these things somewhat more efficiently because their system is built just for this task, but the linked data approach has the built-in readiness to join with everything else at almost no cost, without making a new data warehouse for each new business question.</p> <p>We could call this the sociological aspect of the thing. Getting to more concrete business, we see an economy that, we could say, without being alarmists, is confronted with some issues. Well, generally when times are bad, this results in consolidation of property and power. Businesses fail and get split up and sold off in pieces, government adds controls and regulations and so forth. This means ad hoc data integration, as control without data is just pretense. If times are lean, this also means that there is little readiness to do wholesale replacement of systems, which will take years before producing anything. So we must play with what there is and make it deliver, in ways and conditions that were not necessarily anticipated. The agility of the Data Web, if correctly understood, can be of great benefit there, especially on the reporting and business intelligence side. Specifically mapping line-of-business systems into RDF on the fly will help with integration, making the specialized warehouse the slower and more expensive alternative. But this too is needed at times.</p> <p>But for the RDF community to be taken seriously there, the messaging must be geared in this direction. Writing FOAF files by hand is not where you begin the pitch. Well, what is more natural then having a global, queriable information space, when you have a global information driven economy?</p> <p>The Data Web is about making this happen. First with doing this in published generally available data; next with the enterprises having their private data for their own use but still linking toward the outside, even though private data stays private: You can still use standard terms and taxonomies, where they apply, when talking of proprietary information.</p> <h3>But let's get back to more specific issues</h3> <p>At the lightning talks in Vienna, one participant said, "Man's enemy is not the lion that eats men, it's his own brother. Semantic Web's enemy is the <a href="http://dbpedia.org/resource/XML" id="link-id0x1aeb61b8">XML</a> Web services stack that ate its lunch." There is some truth to the first part. The second part deserves some comment. The Web services stack is about transactions. When you have a fixed, often repeating task, it is a natural thing to make this a Web service. Even though SOA is not really prevalent in enterprise IT, it has value in things like managing supply-chain logistics with partners, etc. Lots of standard messages with unambiguous meaning. To make a parallel with the database world: first there was OLTP; then there was business intelligence. Of course, you must first have the transactions, to have something to analyze.</p> <p>SOA is for the transactions; the Data Web is for integration, analysis, and discovery. It is the <i>ad hoc</i> component of the real time enterprise, if you will. It is not a competitor against a transaction oriented SOA. In fact, RDF has no special genius for transactions. Another mistake that often gets made is stretching things beyond their natural niche. Doing transactions in RDF is this sort of over-stretching without real benefit.</p> <p>"I made an ontology and it really did solve a problem. How do I convince the enterprise people, the MBA who says it's too complex, the developer who says it is not what he's used to, and so on?"</p> <p>This is an education question. One of the findings of SWEO's enterprise survey was that there was awareness that difficult problems existed. There were and are corporate ontologies and taxonomies, diversely implemented. Some of these needs are recognized. RDF based technologies offer to make these more open standards based. open standards have proven economical in the past. What we also hear is that major enterprises do not even know what their information and human resources assets are: Experts can't be found even when they are in the next department, or reports and analysis gets buried in wikis, spreadsheets, and emails.</p> <p>Just as when SQL took off, we need vendors to do workshops on getting started with a technology. The affair in Vienna was a step in this direction. Another type of event specially focusing on vertical problems and their Data Web solutions is a next step. For example, one could do a workshop on integrating supply chain information with Data Web technologies. Or one on making enterprise <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1fbd3398">knowledge</a> bases from HR, CRM, office automation, wikis, etc. The good thing is that all these things are additions to, not replacements of, the existing mission-critical infrastructure. And better use of what you already have ought to be the theme of the day.</p>
State of the Semantic Web, Part 1 - Sociology, Business, and Messaging (update 2)
http://www.openlinksw.com/weblog/oerling/?date=2008-10-24#1459
2008-10-24T10:19:03Z
2008-10-27T11:27:54-04:00
<p>I was in <a href="http://dbpedia.org/resource/Vienna" id="link-id0x28471870">Vienna</a> for the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x26f0ec28">Linked Data</a> Practitioners gathering this week. Danny Ayers asked me if I would <a href="http://dbpedia.org/resource/Blog" id="link-id0x26cf7678">blog</a> about the State of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x273087e0">Semantic Web</a> or write the <i>This Week's Semantic Web</i> column. I don't have the time to cover all that may have happened during the past week but I will editorialize about the questions that again were raised in Vienna. How these things relate to <a href="http://virtuoso.openlinksw.com" id="link-id0x264e11b8">Virtuoso</a> will be covered separately. This is about the overarching questions of the times, not the finer points of geek craft.</p> <p> <a href="http://www.informatik.uni-leipzig.de/~auer/foaf.rdf#me" id="link-id0x2787de70">Sören Auer</a> asked me to say a few things about relational to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x280b12f8">RDF</a> mapping. I will cite some highlights from this, as they pertain to the general scene. There was an "open hacking" session Wednesday night featuring lightning talks. I will use some of these too as a starting point.</p> <h3>The messaging?</h3> <p>The <a href="http://www.w3.org/2001/sw/sweo/" id="link-id0x28078030">SWEO</a> (Semantic Web Education and Outreach) interest group of the W3C spent some time looking for an elevator pitch for the Semantic Web. It became "<a href="http://dbpedia.org/resource/Data" id="link-id0x290a48c0">Data</a> Unleashed." Why not? Let's give this some context.</p> <p>So, if we are holding a <i>Semantic Web 101</i> session, where should we begin? I hazard to guess that we should not begin by writing a FOAF file in Turtle by hand, as this is one thing that is not likely to happen in the real world.</p> <p>Of course, the social aspect of the Data Web is the most immediately engaging, so a demo might be to go make an account with <a href="http://myopenlink.net/" id="link-id0x272ed6d0">myopenlink</a>.<a href="http://dbpedia.org/resource/.NET_Framework" id="link-id0x277dbbd0">net</a> and see that after one has entered the data one normally enters for any social network, one has become a Data Web citizen. This means that one can be found, just like this, with a query against the set of data spaces hosted on the system. Then we just need a few pages that repurpose this data and relate it to other data. We show some samples of queries like this in our <a href="http://challenge.semanticweb.org/" id="link-id0x25fda5c8">Billion Triples Challenge</a> demo. We will make a webcast about this to make it all clearer.</p> <p>Behold: The Data Web is about the world becoming a database; writing <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x278c3878">SPARQL</a> queries or triples is incidental. You will write FOAF files by hand just as little as you now write <a href="http://dbpedia.org/resource/SQL" id="link-id0x27e6be18">SQL</a> insert statements for filling in your account <a href="http://dbpedia.org/resource/Information" id="link-id0x2727a278">information</a> on Myspace.</p> <p>Every time there is a major shift in technology, this shift needs to be motivated by addressing a new class of problem. This means doing something that could not be done before. The last time this happened was when the relational database became the dominant IT technology. At that time, the questions involved putting the enterprise in the database and building a cluster of Line Of Business (LOB) applications around the database. The argument for the <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x26020128">RDBMS</a> was that you did not have to constrain the set of queries that might later be made, when designing the database. In other words, it was making things more <i>ad hoc</i>. This was opposed then on grounds of being less efficient than the hierarchical and network databases which the relational eventually replaced.</p> <p>Today, the point of the Data Web is that you do not have to constrain what your data can join or integrate with, when you design your database. The counter-argument is that this is slow and geeky and not scalable. See the similarity?</p> <p>A difference is that we are not specifically aiming at replacing the RDBMS. In fact, if you know exactly what you will query and have a well defined workload, a relational representation optimized for the workload will give you about 10x the performance of the equivalent RDF warehouse. OLTP remains a relational-only domain.</p> <p>However, when we are talking about doing queries and analytics against the Web, or even against more than a handful of relational systems, the things which make RDBMS good become problematic.</p> <h3>What is the business value of this?</h3> <p>The most reliable of human drives is the drive to make oneself known. This drives all, from any social scene to business communications to politics. Today, when you want to proclaim you exist, you do so first on the Web. The Web did not become the prevalent media because business loved it for its own sake, it became prevalent because business could not afford not to assert their presence there. If anything, the Web eroded the communications dominance of a lot of players, which was not welcome but still had to be dealt with, by embracing the Web.</p> <p>Today, in a world driven by data, the Data Web will be catalyzed by similar factors: If your data is not there, you will not figure in query results. Search engines will play some role there but also many social applications will have reports that are driven by published data. Also consider any e-commerce, any marketplace, and so forth. The Data Portability movement is a case in point: Users want to own their own content; silo operators want to capitalize on holding it. Right now, we see these things in silos; the Data Web will create bridges between these, and what is now in silo data centers will be increasingly available on an ad hoc basis with Open Data.</p> <p>Again, we see a movement from the specialized to the generic: What LinkedIn does in its data center can be done with ad hoc queries with <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x261c7bc8">linked open data</a>. Of course, LinkedIn does these things somewhat more efficiently because their system is built just for this task, but the linked data approach has the built-in readiness to join with everything else at almost no cost, without making a new data warehouse for each new business question.</p> <p>We could call this the sociological aspect of the thing. Getting to more concrete business, we see an economy that, we could say, without being alarmists, is confronted with some issues. Well, generally when times are bad, this results in consolidation of property and power. Businesses fail and get split up and sold off in pieces, government adds controls and regulations and so forth. This means ad hoc data integration, as control without data is just pretense. If times are lean, this also means that there is little readiness to do wholesale replacement of systems, which will take years before producing anything. So we must play with what there is and make it deliver, in ways and conditions that were not necessarily anticipated. The agility of the Data Web, if correctly understood, can be of great benefit there, especially on the reporting and business intelligence side. Specifically mapping line-of-business systems into RDF on the fly will help with integration, making the specialized warehouse the slower and more expensive alternative. But this too is needed at times.</p> <p>But for the RDF community to be taken seriously there, the messaging must be geared in this direction. Writing FOAF files by hand is not where you begin the pitch. Well, what is more natural then having a global, queriable information space, when you have a global information driven economy?</p> <p>The Data Web is about making this happen. First with doing this in published generally available data; next with the enterprises having their private data for their own use but still linking toward the outside, even though private data stays private: You can still use standard terms and taxonomies, where they apply, when talking of proprietary information.</p> <h3>But let's get back to more specific issues</h3> <p>At the lightning talks in Vienna, one participant said, "Man's enemy is not the lion that eats men, it's his own brother. Semantic Web's enemy is the <a href="http://dbpedia.org/resource/XML" id="link-id0x26273118">XML</a> Web services stack that ate its lunch." There is some truth to the first part. The second part deserves some comment. The Web services stack is about transactions. When you have a fixed, often repeating task, it is a natural thing to make this a Web service. Even though SOA is not really prevalent in enterprise IT, it has value in things like managing supply-chain logistics with partners, etc. Lots of standard messages with unambiguous meaning. To make a parallel with the database world: first there was OLTP; then there was business intelligence. Of course, you must first have the transactions, to have something to analyze.</p> <p>SOA is for the transactions; the Data Web is for integration, analysis, and discovery. It is the <i>ad hoc</i> component of the real time enterprise, if you will. It is not a competitor against a transaction oriented SOA. In fact, RDF has no special genius for transactions. Another mistake that often gets made is stretching things beyond their natural niche. Doing transactions in RDF is this sort of over-stretching without real benefit.</p> <p>"I made an ontology and it really did solve a problem. How do I convince the enterprise people, the MBA who says it's too complex, the developer who says it is not what he's used to, and so on?"</p> <p>This is an education question. One of the findings of SWEO's enterprise survey was that there was awareness that difficult problems existed. There were and are corporate ontologies and taxonomies, diversely implemented. Some of these needs are recognized. RDF based technologies offer to make these more open standards based. open standards have proven economical in the past. What we also hear is that major enterprises do not even know what their information and human resources assets are: Experts can't be found even when they are in the next department, or reports and analysis gets buried in wikis, spreadsheets, and emails.</p> <p>Just as when SQL took off, we need vendors to do workshops on getting started with a technology. The affair in Vienna was a step in this direction. Another type of event specially focusing on vertical problems and their Data Web solutions is a next step. For example, one could do a workshop on integrating supply chain information with Data Web technologies. Or one on making enterprise <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x260172a8">knowledge</a> bases from HR, CRM, office automation, wikis, etc. The good thing is that all these things are additions to, not replacements of, the existing mission-critical infrastructure. And better use of what you already have ought to be the theme of the day.</p>
Virtuoso Cluster Paper Update
http://www.openlinksw.com/blog/vdb/blog/?date=2008-10-02#1451
2008-10-02T10:02:33Z
2008-10-03T04:38:06-04:00
<p>An updated version of the paper about <a href="http://virtuoso.openlinksw.com" id="link-id0xc0abc50">Virtuoso</a> Cluster is available at <a href="http://www.openlinksw.com/weblog/oerling/2008webscale_rdf.pdf" id="link-id16459248">2008webscale_rdf.pdf</a> </p>
Virtuoso Update, Billion Triples and Outlook
http://www.openlinksw.com/blog/vdb/blog/?date=2008-10-02#1450
2008-10-02T10:02:32Z
2008-10-02T12:47:07.000004-04:00
<div> <div style="display:none;">Virtuoso Update, Billion Triples and Outlook</div> <p>I will say a few things about what we have been doing and where we can go.</p> <p>Firstly, we have a fairly scalable platform with <a href="http://virtuoso.openlinksw.com" id="link-id0x1aa82dc0">Virtuoso</a> 6 Cluster. It was most recently tested with the workload discussed in the previous <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1445" id="link-id1638a5b8">Billion Triples post</a>.</p> <p>There is an updated version of <a href="http://www.openlinksw.com/weblog/oerling/2008webscale_rdf.pdf" id="link-id16280a68">the paper about this</a>. This will be presented at the web scale workshop of ISWC 2008 in Karlsruhe.</p> <p>Right now, we are polishing some things in Virtuoso 6 -- some optimizations for smarter balancing of interconnect traffic over multiple network interfaces, and some more <a href="http://dbpedia.org/resource/SQL" id="link-id0x1abd3f38">SQL</a> optimizations specific to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1adbe410">RDF</a>. The must-have basics, like parallel running of sub-queries and aggregates, and all-around unrolling of loops of every kind into large partitioned batches, is all there and proven to work.</p> <p>We spent a lot of time around the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1aaa0e78">Berlin SPARQL Benchmark</a> story, so we got to the more advanced stuff like the <a href="http://challenge.semanticweb.org/" id="link-id0x1a860a50">Billion Triples Challenge</a> rather late. We did along the way also run <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1a27f2a8">BSBM</a> with an <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1ad5c918">Oracle</a> back-end, with Virtuoso mapping <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1cf0e4a0">SPARQL</a> to SQL. This merits its own analysis in the near future. This will be the basic how-to of mapping OLTP systems to RDF. Depending on the case, one can use this for lookups in real-time or ETL.</p> <p>RDF will deliver value in complex situations. An example of a complex relational mapping use case came from Ordnance Survey, presented at the <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0x1ab96bb0">RDB2RDF XG</a>. Examples of complex warehouses include the <a href="http://neurocommons.org/page/Main_Page" id="link-id0x1adb2db0">Neurocommons</a> database, the Billion Triples Challenge, and the <a href="http://www.garlik.com/" id="link-id0x1925c7b0">Garlik DataPatrol</a>.</p> <p>In comparison, the Berlin workload is really simple and one where RDF is not at its best, as amply discussed on the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1c6d1480">Linked Data</a> forum. BSBM's primary value is as a demonstrator for the basic mapping tasks that will be repeated over and over for pretty much any online system when presence on the <a href="http://dbpedia.org/resource/Data" id="link-id0x1a937400">data</a> web becomes as indispensable as presence on the HTML web.</p> <p>I will now talk about the complex warehouse/web-harvesting side. I will come to the mapping in another post.</p> <p>Now, all the things shown in the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1445" id="link-id14de1d18">Billion Triples post</a> can be done with a relational system specially built for each purpose. Since we are a general purpose <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x1a457c70">RDBMS</a>, we use this capability where it makes sense. For example, storing statistics about which tags or interests occur with which other tags or interests as RDF blank nodes makes no sense. We do not even make the experiment; we know ahead of time that the result is at least an order of magnitude in favor of the relational row-oriented solution in both space and time.</p> <p>Whenever there is a data structure specially made for answering one specific question, like joint occurrence of tags, RDB and mapping is the way to go. With Virtuoso, this can fully-well coexist with physical triples, and can still be accessed in SPARQL and mixed with triples. This is territory that we have not extensively covered yet, but we will be giving some examples about this later.</p> <p>The real value of RDF is in agility. When there is no time to design and load a new warehouse for every new question, RDF is unparalleled. Also SPARQL, once it has the necessary extensions of aggregating and sub-queries, is nicer than SQL, especially when we have sub-classes and sub-properties, transitivity, and "same as" enabled. These things have some run time cost and if there is a report one is hitting absolutely all the time, then chances are that resolving terms and identity at load-time and using materialized views in SQL is the reasonable thing. If one is inventing a new report every time, then RDF has a lot more convenience and flexibility.</p> <p>We are just beginning to explore what we can do with data sets such as the online conversation space, linked data, and the open ontologies of <a href="http://umbel.org/about/" id="link-id0x1aa5ea18">UMBEL</a> and <a href="http://dbpedia.org/resource/Cyc" id="link-id0x1a631a20">OpenCyc</a>. It is safe to say that we can run with real world scale without loss of query expressivity. There is an incremental cost for performance but this is not prohibitive. Serving the whole billion triples set from memory would cost about $32K in hardware. $8K will do if one can wait for disk part of the time. One can use these numbers as a basis for costing larger systems. For online search applications, one will note that running the indexes pretty much from memory is necessary for flat response time. For back office analytics this is not necessarily as critical. It all depends on the use case.</p> <p>We expect to be able to combine geography, social proximity, subject matter, and <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0x1aebdcc8">named entities</a>, with hierarchical taxonomies and traditional full text, and to present this through a simple user interface.</p> <p>We expect to do this with online response times if we have a limited set of starting points and do not navigate more than 2 or 3 steps from each starting point. An example would be to have a full text pattern and news group, and get the cloud of interests from the authors of matching posts. Another would be to make a faceted view of the properties of the 1000 people most closely connected to one person.</p> <p>Queries like finding the fastest online responders to questions about romance across the global board-scape, or finding the person who initiates the most long running conversations about crime, take a bit longer but are entirely possible.</p> <p>The genius of RDF is to be able to do these things within a general purpose database, ad hoc, in a single query language, mostly without materializing intermediate results. Any of these things could be done with arbitrary efficiency in a custom built system. But what is special now is that the cost of access to this type of <a href="http://dbpedia.org/resource/Information" id="link-id0x1ab88490">information</a> and far beyond drops dramatically as we can do these things in a far less labor intensive way, with a general purpose system, with no redesigning and reloading of warehouses at every turn. The query becomes a commodity.</p> <p>Still, one must know what to ask. In this respect, the self-describing nature of RDF is unmatched. A query like <i>list the top 10 attributes with the most distinct values for all persons</i> cannot be done in SQL. SQL simply does not allow the columns to be variable.</p> <p>Further, we can accept queries as text, the way people are used to supplying them, and use structure for drill-down or result-relevance, and also recognize named entities and subject matter concepts in query text. Very simple NLP will go a long way towards keeping SPARQL out of the user experience.</p> <p>The other way of keeping query complexity hidden is to publish hand-written SPARQL as parameter-fed canned reports.</p> <p>Between now and ISWC 2008, the last week of October, we will put out demos showing some of these things. Stay tuned.</p> </div>
Virtuoso Cluster Paper Update
http://www.openlinksw.com/weblog/oerling/?date=2008-10-02#1449
2008-10-02T09:38:14Z
2008-10-03T04:38:01-04:00
<p>An updated version of the paper about <a href="http://virtuoso.openlinksw.com" id="link-id0x17b3d2c8">Virtuoso</a> Cluster is available at <a href="http://www.openlinksw.com/weblog/oerling/2008webscale_rdf.pdf" id="link-id16459248">2008webscale_rdf.pdf</a> </p>
Virtuoso Update, Billion Triples and Outlook
http://www.openlinksw.com/weblog/oerling/?date=2008-10-02#1448
2008-10-02T09:31:17Z
2008-10-02T12:47:02.000002-04:00
<p>I will say a few things about what we have been doing and where we can go.</p> <p>Firstly, we have a fairly scalable platform with <a href="http://virtuoso.openlinksw.com" id="link-id0xa412e450">Virtuoso</a> 6 Cluster. It was most recently tested with the workload discussed in the previous <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1445" id="link-id1638a5b8">Billion Triples post</a>.</p> <p>There is an updated version of <a href="http://www.openlinksw.com/weblog/oerling/2008webscale_rdf.pdf" id="link-id16280a68">the paper about this</a>. This will be presented at the web scale workshop of ISWC 2008 in Karlsruhe.</p> <p>Right now, we are polishing some things in Virtuoso 6 -- some optimizations for smarter balancing of interconnect traffic over multiple network interfaces, and some more <a href="http://dbpedia.org/resource/SQL" id="link-id0x1c1c5f48">SQL</a> optimizations specific to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1bcb6108">RDF</a>. The must-have basics, like parallel running of sub-queries and aggregates, and all-around unrolling of loops of every kind into large partitioned batches, is all there and proven to work.</p> <p>We spent a lot of time around the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x3a4e17c8">Berlin SPARQL Benchmark</a> story, so we got to the more advanced stuff like the <a href="http://challenge.semanticweb.org/" id="link-id0x1a66c568">Billion Triples Challenge</a> rather late. We did along the way also run <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x188c2608">BSBM</a> with an <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1aa97f98">Oracle</a> back-end, with Virtuoso mapping <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1abd87a0">SPARQL</a> to SQL. This merits its own analysis in the near future. This will be the basic how-to of mapping OLTP systems to RDF. Depending on the case, one can use this for lookups in real-time or ETL.</p> <p>RDF will deliver value in complex situations. An example of a complex relational mapping use case came from Ordnance Survey, presented at the <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0x1a941678">RDB2RDF XG</a>. Examples of complex warehouses include the <a href="http://neurocommons.org/page/Main_Page" id="link-id0x1aa5a9f8">Neurocommons</a> database, the Billion Triples Challenge, and the <a href="http://www.garlik.com/" id="link-id0x372df7b0">Garlik DataPatrol</a>.</p> <p>In comparison, the Berlin workload is really simple and one where RDF is not at its best, as amply discussed on the <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1a671cf0">Linked Data</a> forum. BSBM's primary value is as a demonstrator for the basic mapping tasks that will be repeated over and over for pretty much any online system when presence on the <a href="http://dbpedia.org/resource/Data" id="link-id0x1ab83dd0">data</a> web becomes as indispensable as presence on the HTML web.</p> <p>I will now talk about the complex warehouse/web-harvesting side. I will come to the mapping in another post.</p> <p>Now, all the things shown in the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1445" id="link-id14de1d18">Billion Triples post</a> can be done with a relational system specially built for each purpose. Since we are a general purpose <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x340d3470">RDBMS</a>, we use this capability where it makes sense. For example, storing statistics about which tags or interests occur with which other tags or interests as RDF blank nodes makes no sense. We do not even make the experiment; we know ahead of time that the result is at least an order of magnitude in favor of the relational row-oriented solution in both space and time.</p> <p>Whenever there is a data structure specially made for answering one specific question, like joint occurrence of tags, RDB and mapping is the way to go. With Virtuoso, this can fully-well coexist with physical triples, and can still be accessed in SPARQL and mixed with triples. This is territory that we have not extensively covered yet, but we will be giving some examples about this later.</p> <p>The real value of RDF is in agility. When there is no time to design and load a new warehouse for every new question, RDF is unparalleled. Also SPARQL, once it has the necessary extensions of aggregating and sub-queries, is nicer than SQL, especially when we have sub-classes and sub-properties, transitivity, and "same as" enabled. These things have some run time cost and if there is a report one is hitting absolutely all the time, then chances are that resolving terms and identity at load-time and using materialized views in SQL is the reasonable thing. If one is inventing a new report every time, then RDF has a lot more convenience and flexibility.</p> <p>We are just beginning to explore what we can do with data sets such as the online conversation space, linked data, and the open ontologies of <a href="http://umbel.org/about/" id="link-id0x19cabf38">UMBEL</a> and <a href="http://dbpedia.org/resource/Cyc" id="link-id0x19cecd10">OpenCyc</a>. It is safe to say that we can run with real world scale without loss of query expressivity. There is an incremental cost for performance but this is not prohibitive. Serving the whole billion triples set from memory would cost about $32K in hardware. $8K will do if one can wait for disk part of the time. One can use these numbers as a basis for costing larger systems. For online search applications, one will note that running the indexes pretty much from memory is necessary for flat response time. For back office analytics this is not necessarily as critical. It all depends on the use case.</p> <p>We expect to be able to combine geography, social proximity, subject matter, and <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0x1a8202e8">named entities</a>, with hierarchical taxonomies and traditional full text, and to present this through a simple user interface.</p> <p>We expect to do this with online response times if we have a limited set of starting points and do not navigate more than 2 or 3 steps from each starting point. An example would be to have a full text pattern and news group, and get the cloud of interests from the authors of matching posts. Another would be to make a faceted view of the properties of the 1000 people most closely connected to one person.</p> <p>Queries like finding the fastest online responders to questions about romance across the global board-scape, or finding the person who initiates the most long running conversations about crime, take a bit longer but are entirely possible.</p> <p>The genius of RDF is to be able to do these things within a general purpose database, ad hoc, in a single query language, mostly without materializing intermediate results. Any of these things could be done with arbitrary efficiency in a custom built system. But what is special now is that the cost of access to this type of <a href="http://dbpedia.org/resource/Information" id="link-id0x1ab0a918">information</a> and far beyond drops dramatically as we can do these things in a far less labor intensive way, with a general purpose system, with no redesigning and reloading of warehouses at every turn. The query becomes a commodity.</p> <p>Still, one must know what to ask. In this respect, the self-describing nature of RDF is unmatched. A query like <i>list the top 10 attributes with the most distinct values for all persons</i> cannot be done in SQL. SQL simply does not allow the columns to be variable.</p> <p>Further, we can accept queries as text, the way people are used to supplying them, and use structure for drill-down or result-relevance, and also recognize named entities and subject matter concepts in query text. Very simple NLP will go a long way towards keeping SPARQL out of the user experience.</p> <p>The other way of keeping query complexity hidden is to publish hand-written SPARQL as parameter-fed canned reports.</p> <p>Between now and ISWC 2008, the last week of October, we will put out demos showing some of these things. Stay tuned.</p>
OpenLink Software's Virtuoso Submission to the Billion Triples Challenge
http://www.openlinksw.com/blog/vdb/blog/?date=2008-09-30#1446
2008-09-30T16:24:34Z
2008-10-03T06:20:48.000094-04:00
<div> <h2>Introduction</h2> <p>We use <a href="http://virtuoso.openlinksw.com" id="link-id0xb03e418">Virtuoso</a> 6 Cluster Edition to demonstrate the following:</p> <ul> <li>Text and structured <a href="http://dbpedia.org/resource/Information" id="link-id0xbd9dae8">information</a> based lookups</li> <li>Analytics queries</li> <li>Analysis of co-occurrence of features like interests and tags.</li> <li>Dealing with identity of multiple IRI's (<a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0xb383dd8">owl</a>:sameAs)</li> </ul> <p>The demo is based on a set of canned <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xbda6298">SPARQL</a> queries that can be invoked using the <a href="http://ode.openlinksw.com/" id="link-id0xbb292f0">OpenLink Data Explorer</a> (<a href="http://ode.openlinksw.com/" id="link-id0xc263528">ODE</a>) Firefox extension.</p> <p>The demo queries can also be run directly against the SPARQL end point.</p> <p>The demo is being worked on at the time of submission and may be shown online by appointment.</p> <p>Automatic annotation of the <a href="http://dbpedia.org/resource/Data" id="link-id0xa173378">data</a> based on <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0xbdda558">named entity extraction</a> is being worked on at the time of this submission. By the time of ISWC 2008 the set of sample queries will be enhanced with queries based on extracted <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0xa66fbe0">named entities</a> and their relationships in the <a href="http://umbel.org/about/" id="link-id0xa06e2c8">UMBEL</a> and Open CYC ontologies. </p> <p>Also examples involving owl:sameAs are being added, likewise with similarity metrics and search hit scores.</p> <h2>The Data</h2> <p>The database consists of the billion triples data sets and some additions like Umbel. Also the Freebase extract is newer than the challenge original.</p> <p>The triple count is 1115 million.</p> <p>In the case of web harvested resources, the data is loaded in one graph per resource.</p> <p>In the case of larger data sets like <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xc2bf770">Dbpedia</a> or the US census, all triples of the provenance share a data set specific graph.</p> <p>All string literals are additionally indexed in a full text index. No stop words are used.</p> <p>Most queries do not specify a graph. Thus they are evaluated against the union of all the graphs in the database. The indexing scheme is SPOG, GPOS, POGS, OPGS. All indices ending in S are bitmap indices. </p> <h2>The Queries </h2> <p>The demo uses Virtuoso SPARQL extensions in most queries. These extensions consist on one hand of well known <a href="http://dbpedia.org/resource/SQL" id="link-id0xaf8cb40">SQL</a> features like aggregation with grouping and existence and value subqueries and on the other of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xafdceb8">RDF</a> specific features. The latter include run time RDFS and OWL inferencing support and backward chaining subclasses and transitivity. </p> <h3>Simple Lookups</h3> <pre>sparql select ?s ?p (bif:search_excerpt (bif:vector ('<a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0xbb64dd0">semantic', 'web</a>'), ?o)) where { ?s ?p ?o . filter (bif:contains (?o, "'semantic web'")) } limit 10 ; </pre> <p>This looks up triples with semantic web in the object and makes a search hit summary of the literal, highlighting the search terms. </p> <pre>sparql select ?tp count(*) where { ?s ?p2 ?o2 . ?o2 a ?tp . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?tp order by desc 2 limit 40 ; </pre> <p>This looks at what sorts of things are referenced by the properties of the foaf handle plaid_skirt.</p> <p>What are these things called?</p> <pre>sparql select ?lbl count(*) where { ?s ?p2 ?o2 . ?o2 rdfs:label ?lbl . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?lbl order by desc 2 ; </pre> <p>Many of these things do not have a rdfs:label. Let us use a more general concept of lable which groups dc:title, foaf:name and other name-like properties together. The subproperties are resolved at run time, there is no materialization. </p> <pre>sparql define input:inference 'b3s' select ?lbl count(*) where { ?s ?p2 ?o2 . ?o2 b3s:label ?lbl . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?lbl order by desc 2 ; </pre> <p>We can list sources by the topics they contain. Below we look for graphs that mention terrorist bombing. </p> <pre>sparql select ?g count(*) where { graph ?g { ?s ?p ?o . filter (bif:contains (?o, "'terrorist bombing'")) } } group by ?g order by desc 2 ; </pre> <p>Now some web 2.0 tagging of search results. The <a href="http://dbpedia.org/resource/Tag" id="link-id0xa8b89f8">tag</a> cloud of "computer"</p> <pre>sparql select ?lbl count (*) where { ?s ?p ?o . ?o bif:contains "computer" . ?s sioc:topic ?tg . optional { ?tg rdfs:label ?lbl } } group by ?lbl order by desc 2 limit 40 ; </pre> <p>This query will find the posters who talk the most about sex.</p> <pre>sparql select ?auth count (*) where { ?d dc:creator ?auth . ?d ?p ?o filter (bif:contains (?o, "sex")) } group by ?auth order by desc 2 ; </pre> <h3>Analytics </h3> <p>We look for people who are joined by having relatively uncommon interests but do not know each other.</p> <pre>sparql select ?i ?cnt ?n1 ?n2 ?p1 ?p2 where { { select ?i count (*) as ?cnt where { ?p foaf:interest ?i } group by ?i } filter ( ?cnt > 1 && ?cnt < 10) . ?p1 foaf:interest ?i . ?p2 foaf:interest ?i . filter (?p1 != ?p2 && !bif:exists ((select (1) where {?p1 foaf:knows ?p2 })) && !bif:exists ((select (1) where {?p2 foaf:knows ?p1 }))) . ?p1 foaf:nick ?n1 . ?p2 foaf:nick ?n2 . } order by ?cnt limit 50 ; </pre> <p>The query takes a fairly long time, mostly spent counting the interested in 25M interest triples. It then takes people that share the interest and checks that neither claims to know the other. It then sorts the results rarest interest first. The query can be written more efficently but is here just to show that database-wide scans of the population are possible ad hoc. </p> <p>Now we go to SQL to make a tag co-occurrence matrix. This can be used for showing a Technorati-style related tags line at the bottom of a search result page. This showcases the use of SQL together with SPARQL. The half-matrix of tags t1, t2 with the co-occurrence count at the intersection is much more efficiently done in SQL, specially since it gets updated as the data changes. This is an example of materialized intermediate results based on warehoused RDF. </p> <pre>create table tag_count (tcn_tag iri_id_8, tcn_count int, primary key (tcn_tag)); alter index tag_count on tag_count partition (tcn_tag int (0hexffff00)); create table tag_coincidence (tc_t1 iri_id_8, tc_t2 iri_id_8, tc_count int, tc_t1_count int, tc_t2_count int, primary key (tc_t1, tc_t2)) alter index tag_coincidence on tag_coincidence partition (tc_t1 int (0hexffff00)); create index tc2 on tag_coincidence (tc_t2, tc_t1) partition (tc_t2 int (0hexffff00)); </pre> <p>How many times each topic is mentioned?</p> <pre> insert into tag_count select * from (sparql define output:valmode "LONG" select ?t count (*) as ?cnt where { ?s sioc:topic ?t } group by ?t) xx option (quietcast); </pre> <p>Take all t1, t2 where t1 and t2 are tags of the same subject, store only the permutation where the internal id of t1 < that of t2.</p> <pre>insert into tag_coincidence (tc_t1, tc_t2, tc_count) select "t1", "t2", cnt from (select "t1", "t2", count (*) as cnt from (sparql define output:valmode "LONG" select ?t1 ?t2 where { ?s sioc:topic ?t1 . ?s sioc:topic ?t2 }) tags where "t1" < "t2" group by "t1", "t2") xx where isiri_id ("t1") and isiri_id ("t2") option (quietcast); </pre> <p>Now put the individual occurrence counts into the same table with the co-occurrence. This denormalization makes the related tags lookup faster. </p> <pre>update tag_coincidence set tc_t1_count = (select tcn_count from tag_count where tcn_tag = tc_t1), tc_t2_count = (select tcn_count from tag_count where tcn_tag = tc_t2); </pre> <p>Now each tag_coincidence row has the joint occurrence count and individual occurrence counts. A single select will return a Technorati-style related tags listing. </p> <p>To show the <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x9d4bc60">URI</a>'s of the tags: </p> <pre>select top 10 id_to_iri (tc_T1), id_to_iri (tc_t2), tc_count from tag_coincidence order by tc_count desc; </pre> <h3>Social Networks </h3> <p>We look at what interests people have </p> <pre>sparql select ?o ?cnt where { { select ?o count (*) as ?cnt where { ?s foaf:interest ?o } group by ?o } filter (?cnt > 100) } order by desc 2 limit 100 ; </pre> <p>Now the same for the Harry Potter fans </p> <pre>sparql select ?i2 count (*) where { ?p foaf:interest <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0xba0b390">http</a>://www.livejournal.com/interests.bml?int=harry+potter> . ?p foaf:interest ?i2 } group by ?i2 order by desc 2 limit 20 ; </pre> <p>We see whether knows relations are symmmetrical. We return the top n people that others claim to know without being reciprocally known.</p> <pre>sparql select ?celeb, count (*) where { ?claimant foaf:knows ?celeb . filter (!bif:exists ((select (1) where { ?celeb foaf:knows ?claimant }))) } group by ?celeb order by desc 2 limit 10 ; </pre> <p>We look for a well connected person to start from.</p> <pre>sparql select ?p count (*) where { ?p foaf:knows ?k } group by ?p order by desc 2 limit 50 ; </pre> <p>We look for the most connected of the many online identities of Stefan Decker.</p> <pre>sparql select ?sd count (distinct ?xx) where { ?sd a foaf:Person . ?sd ?name ?ns . filter (bif:contains (?ns, "'Stefan Decker'")) . ?sd foaf:knows ?xx } group by ?sd order by desc 2 ; </pre> <p>We count the transitive closure of Stefan Decker's connections </p> <pre>sparql select count (*) where { { select * where { ?s foaf:knows ?o } } option (transitive, t_distinct, t_in(?s), t_out(?o)) . filter (?s = <mailto:stefan.decker@deri.org>) } ; </pre> <p>Now we do the same while following owl:sameAs links.</p> <pre>sparql define input:same-as "yes" select count (*) where { { select * where { ?s foaf:knows ?o } } option (transitive, t_distinct, t_in(?s), t_out(?o)) . filter (?s = <mailto:stefan.decker@deri.org>) } ; </pre> <h2>Demo System</h2> <p>The system runs on Virtuoso 6 Cluster Edition. The database is partitioned into 12 partitions, each served by a distinct server process. The system demonstrated hosts these 12 servers on 2 machines, each with 2 xXeon 5345 and 16GB memory and 4 SATA disks. For scaling, the processes and corresponding partitions can be spread over a larger number of machines. If each ran on its own server with 16GB RAM, the whole data set could be served from memory. This is desirable for search engine or fast analytics applications. Most of the demonstrated queries run in memory on second invocation. The timing difference between first and second run is easily an order of magnitude. </p> </div>
OpenLink Software's Virtuoso Submission to the Billion Triples Challenge
http://www.openlinksw.com/weblog/oerling/?date=2008-09-30#1445
2008-09-30T15:39:26Z
2008-10-03T06:20:43.000002-04:00
<div> <h2>Introduction</h2> <p>We use <a href="http://virtuoso.openlinksw.com" id="link-id0xa278560">Virtuoso</a> 6 Cluster Edition to demonstrate the following:</p> <ul> <li>Text and structured <a href="http://dbpedia.org/resource/Information" id="link-id0xb3a4490">information</a> based lookups</li> <li>Analytics queries</li> <li>Analysis of co-occurrence of features like interests and tags.</li> <li>Dealing with identity of multiple IRI's (<a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0xa904bd8">owl</a>:sameAs)</li> </ul> <p>The demo is based on a set of canned <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xac185d0">SPARQL</a> queries that can be invoked using the <a href="http://ode.openlinksw.com/" id="link-id0xb8efe28">OpenLink Data Explorer</a> (<a href="http://ode.openlinksw.com/" id="link-id0xb341808">ODE</a>) Firefox extension.</p> <p>The demo queries can also be run directly against the SPARQL end point.</p> <p>The demo is being worked on at the time of submission and may be shown online by appointment.</p> <p>Automatic annotation of the <a href="http://dbpedia.org/resource/Data" id="link-id0xa2fcc88">data</a> based on <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0xc085440">named entity extraction</a> is being worked on at the time of this submission. By the time of ISWC 2008 the set of sample queries will be enhanced with queries based on extracted <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0xa92b3e0">named entities</a> and their relationships in the <a href="http://umbel.org/about/" id="link-id0xa1c7c38">UMBEL</a> and Open CYC ontologies. </p> <p>Also examples involving owl:sameAs are being added, likewise with similarity metrics and search hit scores.</p> <h2>The Data</h2> <p>The database consists of the billion triples data sets and some additions like Umbel. Also the Freebase extract is newer than the challenge original.</p> <p>The triple count is 1115 million.</p> <p>In the case of web harvested resources, the data is loaded in one graph per resource.</p> <p>In the case of larger data sets like <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xa949850">Dbpedia</a> or the US census, all triples of the provenance share a data set specific graph.</p> <p>All string literals are additionally indexed in a full text index. No stop words are used.</p> <p>Most queries do not specify a graph. Thus they are evaluated against the union of all the graphs in the database. The indexing scheme is SPOG, GPOS, POGS, OPGS. All indices ending in S are bitmap indices. </p> <h2>The Queries </h2> <p>The demo uses Virtuoso SPARQL extensions in most queries. These extensions consist on one hand of well known <a href="http://dbpedia.org/resource/SQL" id="link-id0xc116190">SQL</a> features like aggregation with grouping and existence and value subqueries and on the other of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xa9047f0">RDF</a> specific features. The latter include run time RDFS and OWL inferencing support and backward chaining subclasses and transitivity. </p> <h3>Simple Lookups</h3> <pre>sparql select ?s ?p (bif:search_excerpt (bif:vector ('<a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0xbe38070">semantic', 'web</a>'), ?o)) where { ?s ?p ?o . filter (bif:contains (?o, "'semantic web'")) } limit 10 ; </pre> <p>This looks up triples with semantic web in the object and makes a search hit summary of the literal, highlighting the search terms. </p> <pre>sparql select ?tp count(*) where { ?s ?p2 ?o2 . ?o2 a ?tp . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?tp order by desc 2 limit 40 ; </pre> <p>This looks at what sorts of things are referenced by the properties of the foaf handle plaid_skirt.</p> <p>What are these things called?</p> <pre>sparql select ?lbl count(*) where { ?s ?p2 ?o2 . ?o2 rdfs:label ?lbl . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?lbl order by desc 2 ; </pre> <p>Many of these things do not have a rdfs:label. Let us use a more general concept of lable which groups dc:title, foaf:name and other name-like properties together. The subproperties are resolved at run time, there is no materialization. </p> <pre>sparql define input:inference 'b3s' select ?lbl count(*) where { ?s ?p2 ?o2 . ?o2 b3s:label ?lbl . ?s foaf:nick ?o . filter (bif:contains (?o, "plaid_skirt")) } group by ?lbl order by desc 2 ; </pre> <p>We can list sources by the topics they contain. Below we look for graphs that mention terrorist bombing. </p> <pre>sparql select ?g count(*) where { graph ?g { ?s ?p ?o . filter (bif:contains (?o, "'terrorist bombing'")) } } group by ?g order by desc 2 ; </pre> <p>Now some web 2.0 tagging of search results. The <a href="http://dbpedia.org/resource/Tag" id="link-id0xa366510">tag</a> cloud of "computer"</p> <pre>sparql select ?lbl count (*) where { ?s ?p ?o . ?o bif:contains "computer" . ?s sioc:topic ?tg . optional { ?tg rdfs:label ?lbl } } group by ?lbl order by desc 2 limit 40 ; </pre> <p>This query will find the posters who talk the most about sex.</p> <pre>sparql select ?auth count (*) where { ?d dc:creator ?auth . ?d ?p ?o filter (bif:contains (?o, "sex")) } group by ?auth order by desc 2 ; </pre> <h3>Analytics </h3> <p>We look for people who are joined by having relatively uncommon interests but do not know each other.</p> <pre>sparql select ?i ?cnt ?n1 ?n2 ?p1 ?p2 where { { select ?i count (*) as ?cnt where { ?p foaf:interest ?i } group by ?i } filter ( ?cnt > 1 && ?cnt < 10) . ?p1 foaf:interest ?i . ?p2 foaf:interest ?i . filter (?p1 != ?p2 && !bif:exists ((select (1) where {?p1 foaf:knows ?p2 })) && !bif:exists ((select (1) where {?p2 foaf:knows ?p1 }))) . ?p1 foaf:nick ?n1 . ?p2 foaf:nick ?n2 . } order by ?cnt limit 50 ; </pre> <p>The query takes a fairly long time, mostly spent counting the interested in 25M interest triples. It then takes people that share the interest and checks that neither claims to know the other. It then sorts the results rarest interest first. The query can be written more efficently but is here just to show that database-wide scans of the population are possible ad hoc. </p> <p>Now we go to SQL to make a tag co-occurrence matrix. This can be used for showing a Technorati-style related tags line at the bottom of a search result page. This showcases the use of SQL together with SPARQL. The half-matrix of tags t1, t2 with the co-occurrence count at the intersection is much more efficiently done in SQL, specially since it gets updated as the data changes. This is an example of materialized intermediate results based on warehoused RDF. </p> <pre>create table tag_count (tcn_tag iri_id_8, tcn_count int, primary key (tcn_tag)); alter index tag_count on tag_count partition (tcn_tag int (0hexffff00)); create table tag_coincidence (tc_t1 iri_id_8, tc_t2 iri_id_8, tc_count int, tc_t1_count int, tc_t2_count int, primary key (tc_t1, tc_t2)) alter index tag_coincidence on tag_coincidence partition (tc_t1 int (0hexffff00)); create index tc2 on tag_coincidence (tc_t2, tc_t1) partition (tc_t2 int (0hexffff00)); </pre> <p>How many times each topic is mentioned?</p> <pre> insert into tag_count select * from (sparql define output:valmode "LONG" select ?t count (*) as ?cnt where { ?s sioc:topic ?t } group by ?t) xx option (quietcast); </pre> <p>Take all t1, t2 where t1 and t2 are tags of the same subject, store only the permutation where the internal id of t1 < that of t2.</p> <pre>insert into tag_coincidence (tc_t1, tc_t2, tc_count) select "t1", "t2", cnt from (select "t1", "t2", count (*) as cnt from (sparql define output:valmode "LONG" select ?t1 ?t2 where { ?s sioc:topic ?t1 . ?s sioc:topic ?t2 }) tags where "t1" < "t2" group by "t1", "t2") xx where isiri_id ("t1") and isiri_id ("t2") option (quietcast); </pre> <p>Now put the individual occurrence counts into the same table with the co-occurrence. This denormalization makes the related tags lookup faster. </p> <pre>update tag_coincidence set tc_t1_count = (select tcn_count from tag_count where tcn_tag = tc_t1), tc_t2_count = (select tcn_count from tag_count where tcn_tag = tc_t2); </pre> <p>Now each tag_coincidence row has the joint occurrence count and individual occurrence counts. A single select will return a Technorati-style related tags listing. </p> <p>To show the <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0xaf355c8">URI</a>'s of the tags: </p> <pre>select top 10 id_to_iri (tc_T1), id_to_iri (tc_t2), tc_count from tag_coincidence order by tc_count desc; </pre> <h3>Social Networks </h3> <p>We look at what interests people have </p> <pre>sparql select ?o ?cnt where { { select ?o count (*) as ?cnt where { ?s foaf:interest ?o } group by ?o } filter (?cnt > 100) } order by desc 2 limit 100 ; </pre> <p>Now the same for the Harry Potter fans </p> <pre>sparql select ?i2 count (*) where { ?p foaf:interest <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0xa274410">http</a>://www.livejournal.com/interests.bml?int=harry+potter> . ?p foaf:interest ?i2 } group by ?i2 order by desc 2 limit 20 ; </pre> <p>We see whether knows relations are symmmetrical. We return the top n people that others claim to know without being reciprocally known.</p> <pre>sparql select ?celeb, count (*) where { ?claimant foaf:knows ?celeb . filter (!bif:exists ((select (1) where { ?celeb foaf:knows ?claimant }))) } group by ?celeb order by desc 2 limit 10 ; </pre> <p>We look for a well connected person to start from.</p> <pre>sparql select ?p count (*) where { ?p foaf:knows ?k } group by ?p order by desc 2 limit 50 ; </pre> <p>We look for the most connected of the many online identities of Stefan Decker.</p> <pre>sparql select ?sd count (distinct ?xx) where { ?sd a foaf:Person . ?sd ?name ?ns . filter (bif:contains (?ns, "'Stefan Decker'")) . ?sd foaf:knows ?xx } group by ?sd order by desc 2 ; </pre> <p>We count the transitive closure of Stefan Decker's connections </p> <pre>sparql select count (*) where { { select * where { ?s foaf:knows ?o } } option (transitive, t_distinct, t_in(?s), t_out(?o)) . filter (?s = <mailto:stefan.decker@deri.org>) } ; </pre> <p>Now we do the same while following owl:sameAs links.</p> <pre>sparql define input:same-as "yes" select count (*) where { { select * where { ?s foaf:knows ?o } } option (transitive, t_distinct, t_in(?s), t_out(?o)) . filter (?s = <mailto:stefan.decker@deri.org>) } ; </pre> <h2>Demo System</h2> <p>The system runs on Virtuoso 6 Cluster Edition. The database is partitioned into 12 partitions, each served by a distinct server process. The system demonstrated hosts these 12 servers on 2 machines, each with 2 xXeon 5345 and 16GB memory and 4 SATA disks. For scaling, the processes and corresponding partitions can be spread over a larger number of machines. If each ran on its own server with 16GB RAM, the whole data set could be served from memory. This is desirable for search engine or fast analytics applications. Most of the demonstrated queries run in memory on second invocation. The timing difference between first and second run is easily an order of magnitude. </p> </div>
Requirements for Relational-to-RDF Mapping
http://www.openlinksw.com/blog/vdb/blog/?date=2008-09-08#1436
2008-09-08T09:41:25Z
2008-09-08T15:03:09-04:00
<div> <div style="display:none;">Requirements for Relational-to-RDF Mapping</div> <p>Many of you will know about the W3C relational-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1e1be0a8">RDF</a> mapping incubator activity. The group is planning to suggest forming a working group for drawing up a specification for relational-to-RDF mapping.</p> <p>To this effect, I recently summarized the group discussions and some of our own experiences around the topic at <<a href="http://esw.w3.org/topic/Rdb2RdfXG/ReqForMappingByOErling" id="link-id146030e8">http://esw.w3.org/topic/Rdb2RdfXG/ReqForMappingByOErling</a>>.</p> <p>I will here discuss this less formally and more in the light of our own experience. A working group goal statement must be neutral vis à vis the following points, even if any working group will unavoidably encounter these issues on the way. A <a href="http://dbpedia.org/resource/Blog" id="link-id0x1e6b3950">blog</a> post on the other hand can be more specific.</p> <p>I gave a talk to the <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0xa0932c68">RDB2RDF XG</a> this spring, with these <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/Relational2RDF.ppt" id="link-id14572540">slides</a>.</p> <p>The main point is that people would really like to map on-the-fly, if they only could. Making an RDF warehouse is not of value in itself, but it is true that in some cases this cannot be avoided.</p> <p>At first sight, one would think that a mapping specification could be neutral as regards whether one stores the mapped triples as triples or makes them on demand. There is almost no comparison between the complexity of doing non-trivial mappings on-the-fly versus mapping as ETL. Some of this complexity spills over into the requirements for a mapping language. </p> <h2>Eliminating JOINs</h2> <p>We expect to have a situation where one virtual triple can have many possible sources. The mapping is a union of mapped databases. Any integration scenario will have this feature. In such a situation, if we are <code>JOIN</code>ing using such triples, we end up with <code>UNION</code>s of all databases that could produce the triples in question. This is generally not desired. Therefore, in the on-demand mapping case, there must be a lot of type inference logic that is not relevant in the ETL scenario.</p> <p>To make the point clearer, suppose a query like "list the organizations whose representatives have published about <i>xx</i>." Suppose that there are three databases mapped, all of which have a table of organizations, a table of persons with affiliation to organizations, a table of publications by these persons, and finally a table of tags for the publications. Now, we want the laboratories that have published with articles with <a href="http://dbpedia.org/resource/Tag" id="link-id0xa0977bf0">tag</a> <i>XX</i>. It is a matter of common sense in this scenario that a publication will have the author and the author's affiliation in the same database. However, the RDB-to-RDF mapping does not necessarily know this, if all that it is told is that a table makes IRIs of publications by applying a certain pattern to the primary key of the publications table. To infer what needs to be inferred, the system must realize that IRIs from one mapping are disjoint from IRIs from another: A paper in database <i>X</i> will usually not have an author in database <i>Y</i>. The IDs in database <i>Y</i>, even if perchance equal to the IDs in <i>X</i>, do not mean the same thing, and there is no point joining across databases by them.</p> <p>This entire question is a non-issue in the ETL scenario, but is absolutely vital in the real-time mapping. This is also something that must be stated, at least implicitly, in any mapping. If a mapping translates keys of one place to IRIs with one pattern, and keys from another using another pattern, it must be inferable from the patterns whether the sets of IRIs will be disjoint.</p> <p>This is critical. Otherwise we will be joining everything to everything else, and there will be orders of magnitude of penalty compared to hand-crafted <a href="http://dbpedia.org/resource/SQL" id="link-id0xa09490f8">SQL</a> over the same <a href="http://dbpedia.org/resource/Data" id="link-id0xa095efd0">data</a> sources.</p> <h2>Expectations and Limitations on Queries</h2> <p> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1e360230">SPARQL</a> queries translate quite well to SQL when there is only one table that can produce a triple with a subject of a given class, when there are few columns that can map to a given predicate, and when classes and predicates are literals in the query.</p> <p> <a href="http://virtuoso.openlinksw.com" id="link-id0x1f5edb30">Virtuoso</a> has some SQL extensions for dealing with breaking a wide table into a row per column. This facilitates dealing with predicates that are not known at query compile time. If the table in question is not managed by Virtuoso, Virtuoso's SQL virtualization/federation takes care of the matter. If a mapping system goes directly to third-party SQL, no such tricks can be used.</p> <p>The above example suggests that for supporting on-the-fly mapping without relying on owning the SQL underneath, some subsets of SPARQL may have to be defined. For example, one will probably have to require that all predicates be literals. The alternative is prohibitive run-time cost and complexity.</p> <p>But we must not lose the baby with the bath-water. Aside from offering global identifiers, RDF's attractions include subclasses and sub-predicates. In relational terms, these translate to <code>UNION</code>s and do involve some added cost. A mapping system just has to have means of dealing with this cost, and of recognizing cases where this cost is prohibitive. Some further work is likely to be required for defining well-behaved subsets of SPARQL and mappings.</p> <h2>ETL Ou Ne Pas ETL?</h2> <p>Whether to warehouse or not? If one has hundreds of sources, of which some are not even relational, some ETL would seem necessary. Kashiup Vipul gave a position paper at last year's RDB-to-RDF mapping workshop in Cambridge, Massachusetts, about a system of relational mapping and on-demand RDF-izers of diverse semi-structured biomedical data, e.g., spreadsheets. The issue certainly exists, and any mapping work will likely encounter integration scenarios where one part is fairly neatly mapped from relational stores, and another part comes from a less structured repository of ETLed physical triples.</p> <p>Our take is that if something is a large or very large relational store, then map; else, ETL. With Virtuoso, we can mix mapped and local triples, but this is not a generally available feature of triple stores and standardization will likely have to wait until there are more implementations.</p> <h2>Conclusions</h2> <ul> <li>If you map on demand, watch out for an explosion of <code>UNION</code>s when integrating sources that talk of similar things.</li> <li>If you integrate lots of sources, some ETL is likely unavoidable. Look for ways of dealing with part ETL, part mapping. ETLing everything is not always best or even possible.</li> <li>If you map a single fairly-clean RDB to RDF, mapping will work well, potentially much faster than triple storage. Higher storage density and more data per index lookup on the relational side.</li> <li>If you map on demand, some restrictions to SPARQL may be practically necessary. These have to do with variables in predicate position, variables in class position, etc. Individual implementations may support these, but standardization will likely have to put limits on them.</li> </ul> <p>This was a quick summary, by no means comprehensive, on what an eventual RDB2RDF working group would come across. This is a sort of addendum to the requirements I outlined on the ESW wiki.</p> </div>
Transitivity and Graphs for SQL
http://www.openlinksw.com/blog/vdb/blog/?date=2008-09-08#1435
2008-09-08T09:41:24Z
2008-09-08T15:43:07-04:00
<div> <div style="display:none;">Transitivity and Graphs for SQL</div> <h2>Background</h2> <p>I have mentioned on a couple of prior occasions that basic graph operations ought to be integrated into the <a href="http://dbpedia.org/resource/SQL" id="link-id0xa1a18c58">SQL</a> query language.</p> <p>The history of databases is by and large about moving from specialized applications toward a generic platform. The introduction of the DBMS itself is the archetypal example. It is all about extracting the common features of applications and making these the features of a platform instead.</p> <p>It is now time to apply this principle to graph traversal.</p> <p>The rationale is that graph operations are somewhat tedious to write in a parallelize-able, latency-tolerant manner. Writing them as one would for memory-based <a href="http://dbpedia.org/resource/Data" id="link-id0xaf8c730">data</a> structures is easier but totally unscalable as soon as there is any latency involved, i.e., disk reads or messages between cluster peers.</p> <p>The ad-hoc nature and very large volume of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xae41ef0">RDF</a> data makes this a timely question. Up until now, the answer to this question has been to materialize any implied facts in RDF stores. If <i>a</i> was part of <i>b</i>, and <i>b</i> part of <i><a href="http://dbpedia.org/resource/C_(programming_language)" id="link-id0xac9d8790">c</a></i>, the implied fact that <i>a</i> is part of <i>c</i> would be inserted explicitly into the database as a pre-query step.</p> <p>This is simple and often efficient, but tends to have the downside that one makes a specialized warehouse for each new type of query. The activity becomes less ad-hoc.</p> <p>Also, this becomes next to impossible when the scale approaches web scale, or if some of the data is liable to be on-and-off included-into or excluded-from the set being analyzed. This is why with <a href="http://virtuoso.openlinksw.com" id="link-id0xb68f9d0">Virtuoso</a> we have tended to favor inference on demand ("backward chaining") and mapping of relational data into RDF without copying.</p> <p>The SQL world has taken steps towards dealing with recursion with the <code>WITH - UNION</code> construct which allows definition of recursive views. The idea there is to define, for example, a tree walk as a <code>UNION</code> of the data of the starting node plus the recursive walk of the starting node's immediate children.</p> <p>The main problem with this is that I do not very well see how a SQL optimizer could effectively rearrange queries involving <code>JOIN</code>s between such recursive views. This model of recursion seems to lose SQL's non-procedural nature. One can no longer easily rearrange <code>JOIN</code>s based on what data is given and what is to be retrieved. If the recursion is written from root to leaf, it is not obvious how to do this from leaf to root. At any rate, queries written in this way are so complex to write, let alone optimize, that I decided to take another approach.</p> <p>Take a question like "list the parts of products of category <i>C</i> which have materials that are classified as toxic." Suppose that the product categories are a tree, the product parts are a tree, and the materials classification is a tree taxonomy where "toxic" has a multilevel substructure.</p> <p>Depending on the count of products and materials, the query can be evaluated as either going from products to parts to materials and then climbing up the materials tree to see if the material is toxic. Or one could do it in reverse, starting with the different toxic materials, looking up the parts containing these, going to the part tree to the product, and up the product hierarchy to see if the product is in the right category. One should be able to evaluate the identical query either way depending on what indices exist, what the cardinalities of the relations are, and so forth â regular cost based optimization.</p> <p>Especially with RDF, there are many problems of this type. In regular SQL, it is a long-standing cultural practice to flatten hierarchies, but this is not the case with RDF.</p> <p>In Virtuoso, we see <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xb3bdcc0">SPARQL</a> as reducing to SQL. Any RDF-oriented database-engine or query-optimization feature is accessed via SQL. Thus, if we address run-time-recursion in the Virtuoso query engine, this becomes, <i>ipso facto</i>, an SQL feature. Besides, we remember that SQL is a much more mature and expressive language than the current SPARQL recommendation.</p> <h2> SQL and Transitivity </h2> <p>We will here look at some simple social network queries. A later article will show how to do more general graph operations. We extend the SQL derived table construct, i.e., <code>SELECT</code> in another <code>SELECT</code>'s <code>FROM</code> clause, with a <code>TRANSITIVE</code> clause.</p> <p>Consider the data:</p> <blockquote> <pre><code>CREATE TABLE "knows" ("p1" INT, "p2" INT, PRIMARY KEY ("p1", "p2") ); ALTER INDEX "knows" ON "knows" PARTITION ("p1" INT); CREATE INDEX "knows2" ON "knows" ("p2", "p1") PARTITION ("p2" INT); </code> </pre></blockquote> <p>We represent a social network with the many-to-many relation "knows". The persons are identified by integers.</p> <blockquote> <pre><code>INSERT INTO "knows" VALUES (1, 2); INSERT INTO "knows" VALUES (1, 3); INSERT INTO "knows" VALUES (2, 4);</code> </pre> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM "knows" ) "k" WHERE "k"."p1" = 1;</code></pre></blockquote> <p>We obtain the result:</p> <blockquote> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">1</td> <td align="center">3</td> </tr> <tr> <td align="center">1</td> <td align="center">2</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> </table> </blockquote> <p>The operation is reversible:</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM "knows" ) "k" WHERE "k"."p2" = 4; </code> </pre> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">2</td> <td align="center">4</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> </table> </blockquote> <p>Since now we give <i>p2</i>, we traverse from <i>p2</i> towards <i>p1</i>. The result set states that 4 is known by 2 and 2 is known by 1.</p> <p>To see what would happen if <i>x</i> knowing <i>y</i> also meant <i>y</i> knowing <i>x</i>, one could write:</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM (SELECT "p1", "p2" FROM "knows" UNION ALL SELECT "p2", "p1" FROM "knows" ) "k2" ) "k" WHERE "k"."p2" = 4;</code> </pre> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">2</td> <td align="center">4</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> <tr> <td align="center">3</td> <td align="center">4</td> </tr> </table> </blockquote> <p>Now, since we know that 1 and 4 are related, we can ask how they are related.</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2", T_STEP (1) AS "via", T_STEP ('step_no') AS "step", T_STEP ('path_id') AS "path" FROM "knows" ) "k" WHERE "p1" = 1 AND "p2" = 4;</code> </pre> <table width="250"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> <th align="center" width="50">via</th> <th align="center" width="50">step</th> <th align="center" width="50">path</th> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">1</td> <td align="center">0</td> <td align="center">0</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">2</td> <td align="center">1</td> <td align="center">0</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">4</td> <td align="center">2</td> <td align="center">0</td> </tr> </table> </blockquote> <p>The two first columns are the ends of the path. The next column is the person that is a step on the path. The next one is the number of the step, counting from 0, so that the end of the path that corresponds to the end condition on the column designated as input, i.e., <i>p1</i>, has number 0. Since there can be multiple solutions, the last column is a sequence number allowing distinguishing multiple alternative paths from each other.</p> <p>For LinkedIn users, the friends ordered by distance and descending friend count query, which is at the basis of most LinkedIn search result views can be written as: </p> <blockquote> <pre><code>SELECT p2, dist, (SELECT COUNT (*) FROM "knows" "c" WHERE "c"."p1" = "k"."p2" ) FROM (SELECT TRANSITIVE t_in (1) t_out (2) t_distinct "p1", "p2", t_step ('step_no') AS "dist" FROM "knows" ) "k" WHERE "p1" = 1 ORDER BY "dist", 3 DESC;</code> </pre> <table width="150"> <tr> <th align="center" width="50">p2</th> <th align="center" width="50">dist</th> <th align="center" width="50">aggregate</th> </tr> <tr> <td align="center">2</td> <td align="center">1</td> <td align="center">1</td> </tr> <tr> <td align="center">3</td> <td align="center">1</td> <td align="center">0</td> </tr> <tr> <td align="center">4</td> <td align="center">2</td> <td align="center">0</td> </tr> </table> </blockquote> <h2>How?</h2> <p>The queries shown above work on Virtuoso v6. When running in cluster mode, several thousand graph traversal steps may be proceeding at the same time, meaning that all database access is parallelized and that the algorithm is internally latency-tolerant. By default, all results are produced in a deterministic order, permitting predictable slicing of result sets.</p> <p>Furthermore, for queries where both ends of a path are given, the optimizer may decide to attack the path from both ends simultaneously. So, supposing that every member of a social network has an average of 30 contacts, and we need to find a path between two users that are no more than 6 steps apart, we begin at both ends, expanding each up to 3 levels, and we stop when we find the first intersection. Thus, we reach 2 * 30^3 = 54,000 nodes, and not 30^6 = 729,000,000 nodes.</p> <p>Writing a generic database driven graph traversal framework on the application side, say in Java over <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xa8a9ef8">JDBC</a>, would easily be over a thousand lines. This is much more work than can be justified just for a one-off, ad-hoc query. Besides, the traversal order in such a case could not be optimized by the DBMS.</p> <h2>Next</h2> <p>In a future <a href="http://dbpedia.org/resource/Blog" id="link-id0xb526a40">blog</a> post I will show how this feature can be used for common graph tasks like critical path, itinerary planning, traveling salesman, the 8 queens chess problem, etc. There are lots of switches for controlling different parameters of the traversal. This is just the beginning. I will also give examples of the use of this in SPARQL.</p> </div>
Requirements for Relational-to-RDF Mapping
http://www.openlinksw.com/weblog/oerling/?date=2008-09-08#1434
2008-09-08T09:40:06Z
2008-09-08T15:03:06.000009-04:00
<p>Many of you will know about the W3C relational-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1d61a948">RDF</a> mapping incubator activity. The group is planning to suggest forming a working group for drawing up a specification for relational-to-RDF mapping.</p> <p>To this effect, I recently summarized the group discussions and some of our own experiences around the topic at <<a href="http://esw.w3.org/topic/Rdb2RdfXG/ReqForMappingByOErling" id="link-id146030e8">http://esw.w3.org/topic/Rdb2RdfXG/ReqForMappingByOErling</a>>.</p> <p>I will here discuss this less formally and more in the light of our own experience. A working group goal statement must be neutral vis à vis the following points, even if any working group will unavoidably encounter these issues on the way. A <a href="http://dbpedia.org/resource/Blog" id="link-id0x21334340">blog</a> post on the other hand can be more specific.</p> <p>I gave a talk to the <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id0x1ef85f58">RDB2RDF XG</a> this spring, with these <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/Relational2RDF.ppt" id="link-id14572540">slides</a>.</p> <p>The main point is that people would really like to map on-the-fly, if they only could. Making an RDF warehouse is not of value in itself, but it is true that in some cases this cannot be avoided.</p> <p>At first sight, one would think that a mapping specification could be neutral as regards whether one stores the mapped triples as triples or makes them on demand. There is almost no comparison between the complexity of doing non-trivial mappings on-the-fly versus mapping as ETL. Some of this complexity spills over into the requirements for a mapping language. </p> <h2>Eliminating JOINs</h2> <p>We expect to have a situation where one virtual triple can have many possible sources. The mapping is a union of mapped databases. Any integration scenario will have this feature. In such a situation, if we are <code>JOIN</code>ing using such triples, we end up with <code>UNION</code>s of all databases that could produce the triples in question. This is generally not desired. Therefore, in the on-demand mapping case, there must be a lot of type inference logic that is not relevant in the ETL scenario.</p> <p>To make the point clearer, suppose a query like "list the organizations whose representatives have published about <i>xx</i>." Suppose that there are three databases mapped, all of which have a table of organizations, a table of persons with affiliation to organizations, a table of publications by these persons, and finally a table of tags for the publications. Now, we want the laboratories that have published with articles with <a href="http://dbpedia.org/resource/Tag" id="link-id0x1d8270d0">tag</a> <i>XX</i>. It is a matter of common sense in this scenario that a publication will have the author and the author's affiliation in the same database. However, the RDB-to-RDF mapping does not necessarily know this, if all that it is told is that a table makes IRIs of publications by applying a certain pattern to the primary key of the publications table. To infer what needs to be inferred, the system must realize that IRIs from one mapping are disjoint from IRIs from another: A paper in database <i>X</i> will usually not have an author in database <i>Y</i>. The IDs in database <i>Y</i>, even if perchance equal to the IDs in <i>X</i>, do not mean the same thing, and there is no point joining across databases by them.</p> <p>This entire question is a non-issue in the ETL scenario, but is absolutely vital in the real-time mapping. This is also something that must be stated, at least implicitly, in any mapping. If a mapping translates keys of one place to IRIs with one pattern, and keys from another using another pattern, it must be inferable from the patterns whether the sets of IRIs will be disjoint.</p> <p>This is critical. Otherwise we will be joining everything to everything else, and there will be orders of magnitude of penalty compared to hand-crafted <a href="http://dbpedia.org/resource/SQL" id="link-id0x1d8b0b40">SQL</a> over the same <a href="http://dbpedia.org/resource/Data" id="link-id0x1ee63530">data</a> sources.</p> <h2>Expectations and Limitations on Queries</h2> <p> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1dcfd7b0">SPARQL</a> queries translate quite well to SQL when there is only one table that can produce a triple with a subject of a given class, when there are few columns that can map to a given predicate, and when classes and predicates are literals in the query.</p> <p> <a href="http://virtuoso.openlinksw.com" id="link-id0x2006b798">Virtuoso</a> has some SQL extensions for dealing with breaking a wide table into a row per column. This facilitates dealing with predicates that are not known at query compile time. If the table in question is not managed by Virtuoso, Virtuoso's SQL virtualization/federation takes care of the matter. If a mapping system goes directly to third-party SQL, no such tricks can be used.</p> <p>The above example suggests that for supporting on-the-fly mapping without relying on owning the SQL underneath, some subsets of SPARQL may have to be defined. For example, one will probably have to require that all predicates be literals. The alternative is prohibitive run-time cost and complexity.</p> <p>But we must not lose the baby with the bath-water. Aside from offering global identifiers, RDF's attractions include subclasses and sub-predicates. In relational terms, these translate to <code>UNION</code>s and do involve some added cost. A mapping system just has to have means of dealing with this cost, and of recognizing cases where this cost is prohibitive. Some further work is likely to be required for defining well-behaved subsets of SPARQL and mappings.</p> <h2>ETL Ou Ne Pas ETL?</h2> <p>Whether to warehouse or not? If one has hundreds of sources, of which some are not even relational, some ETL would seem necessary. Kashiup Vipul gave a position paper at last year's RDB-to-RDF mapping workshop in Cambridge, Massachusetts, about a system of relational mapping and on-demand RDF-izers of diverse semi-structured biomedical data, e.g., spreadsheets. The issue certainly exists, and any mapping work will likely encounter integration scenarios where one part is fairly neatly mapped from relational stores, and another part comes from a less structured repository of ETLed physical triples.</p> <p>Our take is that if something is a large or very large relational store, then map; else, ETL. With Virtuoso, we can mix mapped and local triples, but this is not a generally available feature of triple stores and standardization will likely have to wait until there are more implementations.</p> <h2>Conclusions</h2> <ul> <li>If you map on demand, watch out for an explosion of <code>UNION</code>s when integrating sources that talk of similar things.</li> <li>If you integrate lots of sources, some ETL is likely unavoidable. Look for ways of dealing with part ETL, part mapping. ETLing everything is not always best or even possible.</li> <li>If you map a single fairly-clean RDB to RDF, mapping will work well, potentially much faster than triple storage. Higher storage density and more data per index lookup on the relational side.</li> <li>If you map on demand, some restrictions to SPARQL may be practically necessary. These have to do with variables in predicate position, variables in class position, etc. Individual implementations may support these, but standardization will likely have to put limits on them.</li> </ul> <p>This was a quick summary, by no means comprehensive, on what an eventual RDB2RDF working group would come across. This is a sort of addendum to the requirements I outlined on the ESW wiki.</p>
Transitivity and Graphs for SQL
http://www.openlinksw.com/weblog/oerling/?date=2008-09-08#1433
2008-09-08T09:20:11Z
2008-09-08T15:43:04.000006-04:00
<h2>Background</h2> <p>I have mentioned on a couple of prior occasions that basic graph operations ought to be integrated into the <a href="http://dbpedia.org/resource/SQL" id="link-id0xb1fe830">SQL</a> query language.</p> <p>The history of databases is by and large about moving from specialized applications toward a generic platform. The introduction of the DBMS itself is the archetypal example. It is all about extracting the common features of applications and making these the features of a platform instead.</p> <p>It is now time to apply this principle to graph traversal.</p> <p>The rationale is that graph operations are somewhat tedious to write in a parallelize-able, latency-tolerant manner. Writing them as one would for memory-based <a href="http://dbpedia.org/resource/Data" id="link-id0x1cb37218">data</a> structures is easier but totally unscalable as soon as there is any latency involved, i.e., disk reads or messages between cluster peers.</p> <p>The ad-hoc nature and very large volume of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1e1850a0">RDF</a> data makes this a timely question. Up until now, the answer to this question has been to materialize any implied facts in RDF stores. If <i>a</i> was part of <i>b</i>, and <i>b</i> part of <i><a href="http://dbpedia.org/resource/C_(programming_language)" id="link-id0xa1a08d38">c</a></i>, the implied fact that <i>a</i> is part of <i>c</i> would be inserted explicitly into the database as a pre-query step.</p> <p>This is simple and often efficient, but tends to have the downside that one makes a specialized warehouse for each new type of query. The activity becomes less ad-hoc.</p> <p>Also, this becomes next to impossible when the scale approaches web scale, or if some of the data is liable to be on-and-off included-into or excluded-from the set being analyzed. This is why with <a href="http://virtuoso.openlinksw.com" id="link-id0xa51bd10">Virtuoso</a> we have tended to favor inference on demand ("backward chaining") and mapping of relational data into RDF without copying.</p> <p>The SQL world has taken steps towards dealing with recursion with the <code>WITH - UNION</code> construct which allows definition of recursive views. The idea there is to define, for example, a tree walk as a <code>UNION</code> of the data of the starting node plus the recursive walk of the starting node's immediate children.</p> <p>The main problem with this is that I do not very well see how a SQL optimizer could effectively rearrange queries involving <code>JOIN</code>s between such recursive views. This model of recursion seems to lose SQL's non-procedural nature. One can no longer easily rearrange <code>JOIN</code>s based on what data is given and what is to be retrieved. If the recursion is written from root to leaf, it is not obvious how to do this from leaf to root. At any rate, queries written in this way are so complex to write, let alone optimize, that I decided to take another approach.</p> <p>Take a question like "list the parts of products of category <i>C</i> which have materials that are classified as toxic." Suppose that the product categories are a tree, the product parts are a tree, and the materials classification is a tree taxonomy where "toxic" has a multilevel substructure.</p> <p>Depending on the count of products and materials, the query can be evaluated as either going from products to parts to materials and then climbing up the materials tree to see if the material is toxic. Or one could do it in reverse, starting with the different toxic materials, looking up the parts containing these, going to the part tree to the product, and up the product hierarchy to see if the product is in the right category. One should be able to evaluate the identical query either way depending on what indices exist, what the cardinalities of the relations are, and so forth â regular cost based optimization.</p> <p>Especially with RDF, there are many problems of this type. In regular SQL, it is a long-standing cultural practice to flatten hierarchies, but this is not the case with RDF.</p> <p>In Virtuoso, we see <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xb4b3ce8">SPARQL</a> as reducing to SQL. Any RDF-oriented database-engine or query-optimization feature is accessed via SQL. Thus, if we address run-time-recursion in the Virtuoso query engine, this becomes, <i>ipso facto</i>, an SQL feature. Besides, we remember that SQL is a much more mature and expressive language than the current SPARQL recommendation.</p> <h2> SQL and Transitivity </h2> <p>We will here look at some simple social network queries. A later article will show how to do more general graph operations. We extend the SQL derived table construct, i.e., <code>SELECT</code> in another <code>SELECT</code>'s <code>FROM</code> clause, with a <code>TRANSITIVE</code> clause.</p> <p>Consider the data:</p> <blockquote> <pre><code>CREATE TABLE "knows" ("p1" INT, "p2" INT, PRIMARY KEY ("p1", "p2") ); ALTER INDEX "knows" ON "knows" PARTITION ("p1" INT); CREATE INDEX "knows2" ON "knows" ("p2", "p1") PARTITION ("p2" INT); </code> </pre></blockquote> <p>We represent a social network with the many-to-many relation "knows". The persons are identified by integers.</p> <blockquote> <pre><code>INSERT INTO "knows" VALUES (1, 2); INSERT INTO "knows" VALUES (1, 3); INSERT INTO "knows" VALUES (2, 4);</code> </pre> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM "knows" ) "k" WHERE "k"."p1" = 1;</code></pre></blockquote> <p>We obtain the result:</p> <blockquote> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">1</td> <td align="center">3</td> </tr> <tr> <td align="center">1</td> <td align="center">2</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> </table> </blockquote> <p>The operation is reversible:</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM "knows" ) "k" WHERE "k"."p2" = 4; </code> </pre> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">2</td> <td align="center">4</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> </table> </blockquote> <p>Since now we give <i>p2</i>, we traverse from <i>p2</i> towards <i>p1</i>. The result set states that 4 is known by 2 and 2 is known by 1.</p> <p>To see what would happen if <i>x</i> knowing <i>y</i> also meant <i>y</i> knowing <i>x</i>, one could write:</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2" FROM (SELECT "p1", "p2" FROM "knows" UNION ALL SELECT "p2", "p1" FROM "knows" ) "k2" ) "k" WHERE "k"."p2" = 4;</code> </pre> <table width="100"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> </tr> <tr> <td align="center">2</td> <td align="center">4</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> </tr> <tr> <td align="center">3</td> <td align="center">4</td> </tr> </table> </blockquote> <p>Now, since we know that 1 and 4 are related, we can ask how they are related.</p> <blockquote> <pre><code>SELECT * FROM (SELECT TRANSITIVE T_IN (1) T_OUT (2) T_DISTINCT "p1", "p2", T_STEP (1) AS "via", T_STEP ('step_no') AS "step", T_STEP ('path_id') AS "path" FROM "knows" ) "k" WHERE "p1" = 1 AND "p2" = 4;</code> </pre> <table width="250"> <tr> <th align="center" width="50">p1</th> <th align="center" width="50">p2</th> <th align="center" width="50">via</th> <th align="center" width="50">step</th> <th align="center" width="50">path</th> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">1</td> <td align="center">0</td> <td align="center">0</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">2</td> <td align="center">1</td> <td align="center">0</td> </tr> <tr> <td align="center">1</td> <td align="center">4</td> <td align="center">4</td> <td align="center">2</td> <td align="center">0</td> </tr> </table> </blockquote> <p>The two first columns are the ends of the path. The next column is the person that is a step on the path. The next one is the number of the step, counting from 0, so that the end of the path that corresponds to the end condition on the column designated as input, i.e., <i>p1</i>, has number 0. Since there can be multiple solutions, the last column is a sequence number allowing distinguishing multiple alternative paths from each other.</p> <p>For LinkedIn users, the friends ordered by distance and descending friend count query, which is at the basis of most LinkedIn search result views can be written as: </p> <blockquote> <pre><code>SELECT p2, dist, (SELECT COUNT (*) FROM "knows" "c" WHERE "c"."p1" = "k"."p2" ) FROM (SELECT TRANSITIVE t_in (1) t_out (2) t_distinct "p1", "p2", t_step ('step_no') AS "dist" FROM "knows" ) "k" WHERE "p1" = 1 ORDER BY "dist", 3 DESC;</code> </pre> <table width="150"> <tr> <th align="center" width="50">p2</th> <th align="center" width="50">dist</th> <th align="center" width="50">aggregate</th> </tr> <tr> <td align="center">2</td> <td align="center">1</td> <td align="center">1</td> </tr> <tr> <td align="center">3</td> <td align="center">1</td> <td align="center">0</td> </tr> <tr> <td align="center">4</td> <td align="center">2</td> <td align="center">0</td> </tr> </table> </blockquote> <h2>How?</h2> <p>The queries shown above work on Virtuoso v6. When running in cluster mode, several thousand graph traversal steps may be proceeding at the same time, meaning that all database access is parallelized and that the algorithm is internally latency-tolerant. By default, all results are produced in a deterministic order, permitting predictable slicing of result sets.</p> <p>Furthermore, for queries where both ends of a path are given, the optimizer may decide to attack the path from both ends simultaneously. So, supposing that every member of a social network has an average of 30 contacts, and we need to find a path between two users that are no more than 6 steps apart, we begin at both ends, expanding each up to 3 levels, and we stop when we find the first intersection. Thus, we reach 2 * 30^3 = 54,000 nodes, and not 30^6 = 729,000,000 nodes.</p> <p>Writing a generic database driven graph traversal framework on the application side, say in Java over <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xb595050">JDBC</a>, would easily be over a thousand lines. This is much more work than can be justified just for a one-off, ad-hoc query. Besides, the traversal order in such a case could not be optimized by the DBMS.</p> <h2>Next</h2> <p>In a future <a href="http://dbpedia.org/resource/Blog" id="link-id0x1e4d4f18">blog</a> post I will show how this feature can be used for common graph tasks like critical path, itinerary planning, traveling salesman, the 8 queens chess problem, etc. There are lots of switches for controlling different parameters of the traversal. This is just the beginning. I will also give examples of the use of this in SPARQL.</p>
Epistemology of the Sponger, or How Virtuoso Drives a Web Query
http://www.openlinksw.com/blog/vdb/blog/?date=2008-09-05#1432
2008-09-05T09:20:56Z
2008-09-05T16:04:28-04:00
<div> <div style="display:none;">Epistemology of the Sponger, or How Virtuoso Drives a Web Query</div> <p> <a href="http://virtuoso.openlinksw.com" id="link-id0x1ed6cf28">Virtuoso</a> has an extensive collection of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1f8d1f78">RDF</a>-izers called Sponger Cartridges. These take a web resource in one of 30+ formats (so far) and extract RDF from it. The Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x1edc90e8">Sponger</a> is a device which evaluates a query and along the way, finds dereferenceable links, dereferences them, and iteratively re-evaluates the query, until either nothing new is found or some limit is reached.</p> <p>We could call this <i>query-driven crawling</i>. The idea is intuitive â what one looks for, determines what one finds.</p> <p>This does however raise certain questions pertaining to the nature and ultimate possibility of <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1f836b68">knowledge</a>, i.e., epistemology.</p> <p>The process of querying could be said to go from the few to the many, just like the process of harvesting <a href="http://dbpedia.org/resource/Data" id="link-id0x1edb1648">data</a> from the web, the way any search engine does. One follows links or makes joins and thereby increases one's reach.</p> <p>The difference is that a query has no <i>a priori</i> direction. If I ask for the phone numbers of my friends and there are no phone numbers in the database, then it is valid to give an empty result without looking at my friends at all. <a href="http://dbpedia.org/resource/Closed_world_assumption" id="link-id0x1edf1f30">Closed world</a>, as it is said. Never mind that the friends would have had a "see also" link to a retrievable document that did have a phone number.</p> <p>The problem is that a query execution plan determines what possible dereferenceable material the query will encounter during its execution. What is worse, a query plan tends toward the minimal, i.e., toward minimizing the chances of encountering something dereferenceable along the way. Where query and crawl appeared to have a similarity, in fact they have two opposite goals.</p> <p>The user generally has no idea of the execution plan. In the general case, the user <i>cannot</i> have an idea of this plan. There are valid, over 40 year old reasons for leaving the query planning to the database. In exceptional situations the user can read or direct these, but this is really quite tedious and requires understanding that is basically never present.</p> <p>So, given a query, how do we find data that will match it, short of having a pre-loaded database of absolutely everything? This is certainly a desirable goal, and all in the <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id0x1eb46548">open world</a>, distributed spirit of the web.</p> <p>Let us limit ourselves to queries that have some literals in the object or subject positions. A <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1ed293f8">SPARQL</a> query is basically a graph. Its vertices are variables and literals, and its edges are triple patterns. An edge is labeled by a predicate. For now, we will consider the predicate to always be a literal. From each literal, we can draw a tree, following each edge starting at this literal and descending until we find another literal. Each tree is not always a spanning tree of the graph, but all the trees collectively span the graph.</p> <p>Consider the query </p> <blockquote> <code>{ <john> knows ?x . <mary> knows ?x . ?x label ?l }.</code> </blockquote> The starting points are the literals <code>john</code> and <code>mary</code>. The <code>john</code> tree has one child, <code>?x</code>, which has the children <code>mary</code> and <code>?l</code>. One could notate it as <blockquote> <code>{ <john> knows ?x . {{ <mary> knows ?x} UNION {?x label ?l}}}</code> </blockquote> That is, the head first, and if it has more than one child, a union listing them, recursively. <p>If one composed such queries for each literal in the original pattern and evaluated each as a breadth first walk of the tree, no query optimization tricks, and for each binding of each variable, recorded whether there was something to dereference, one would in a finite time have reached all the directly reachable data. Then one could evaluate the original query, using whatever plan was preferred.</p> <p>The check for dereferenceable data applied to each IRI-valued binding formed in the above evaluation, would consist of looking for "see also", "same as", and other such properties of the IRI. It could also consult text based search engines. Since the evaluation is breadth first, it generates a large number of parallel tasks and is fairly latency tolerant, i.e., it will not die if it must retrieve a few pages from remote sources. We will leave the exact rewrite rules for unions, optionals, aggregates, subqueries, and so on, as an exercise; the general idea should be clear enough.</p> <p>We have here shown a way of transforming SPARQL queries in such a way as to guarantee dereferencing of findable links, without requiring the end user to either explicitly specify or understand query plans.</p> <p>The present Sponger does not work exactly in this manner but it will be developed in this direction. Fortunately, the algorithms outlined above are nothing complicated.</p> </div>
Epistemology of the Sponger, or How Virtuoso Drives a Web Query
http://www.openlinksw.com/weblog/oerling/?date=2008-09-05#1431
2008-09-05T09:16:20Z
2008-09-05T16:04:26-04:00
<p> <a href="http://virtuoso.openlinksw.com" id="link-id0x1fabb368">Virtuoso</a> has an extensive collection of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1f8625b8">RDF</a>-izers called Sponger Cartridges. These take a web resource in one of 30+ formats (so far) and extract RDF from it. The Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x1f841060">Sponger</a> is a device which evaluates a query and along the way, finds dereferenceable links, dereferences them, and iteratively re-evaluates the query, until either nothing new is found or some limit is reached.</p> <p>We could call this <i>query-driven crawling</i>. The idea is intuitive â what one looks for, determines what one finds.</p> <p>This does however raise certain questions pertaining to the nature and ultimate possibility of <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1f895b08">knowledge</a>, i.e., epistemology.</p> <p>The process of querying could be said to go from the few to the many, just like the process of harvesting <a href="http://dbpedia.org/resource/Data" id="link-id0x1f879410">data</a> from the web, the way any search engine does. One follows links or makes joins and thereby increases one's reach.</p> <p>The difference is that a query has no <i>a priori</i> direction. If I ask for the phone numbers of my friends and there are no phone numbers in the database, then it is valid to give an empty result without looking at my friends at all. <a href="http://dbpedia.org/resource/Closed_world_assumption" id="link-id0x1f8b0658">Closed world</a>, as it is said. Never mind that the friends would have had a "see also" link to a retrievable document that did have a phone number.</p> <p>The problem is that a query execution plan determines what possible dereferenceable material the query will encounter during its execution. What is worse, a query plan tends toward the minimal, i.e., toward minimizing the chances of encountering something dereferenceable along the way. Where query and crawl appeared to have a similarity, in fact they have two opposite goals.</p> <p>The user generally has no idea of the execution plan. In the general case, the user <i>cannot</i> have an idea of this plan. There are valid, over 40 year old reasons for leaving the query planning to the database. In exceptional situations the user can read or direct these, but this is really quite tedious and requires understanding that is basically never present.</p> <p>So, given a query, how do we find data that will match it, short of having a pre-loaded database of absolutely everything? This is certainly a desirable goal, and all in the <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id0x1f845188">open world</a>, distributed spirit of the web.</p> <p>Let us limit ourselves to queries that have some literals in the object or subject positions. A <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1f84f348">SPARQL</a> query is basically a graph. Its vertices are variables and literals, and its edges are triple patterns. An edge is labeled by a predicate. For now, we will consider the predicate to always be a literal. From each literal, we can draw a tree, following each edge starting at this literal and descending until we find another literal. Each tree is not always a spanning tree of the graph, but all the trees collectively span the graph.</p> <p>Consider the query </p> <blockquote> <code>{ <john> knows ?x . <mary> knows ?x . ?x label ?l }.</code> </blockquote> The starting points are the literals <code>john</code> and <code>mary</code>. The <code>john</code> tree has one child, <code>?x</code>, which has the children <code>mary</code> and <code>?l</code>. One could notate it as <blockquote> <code>{ <john> knows ?x . {{ <mary> knows ?x} UNION {?x label ?l}}}</code> </blockquote> That is, the head first, and if it has more than one child, a union listing them, recursively. <p>If one composed such queries for each literal in the original pattern and evaluated each as a breadth first walk of the tree, no query optimization tricks, and for each binding of each variable, recorded whether there was something to dereference, one would in a finite time have reached all the directly reachable data. Then one could evaluate the original query, using whatever plan was preferred.</p> <p>The check for dereferenceable data applied to each IRI-valued binding formed in the above evaluation, would consist of looking for "see also", "same as", and other such properties of the IRI. It could also consult text based search engines. Since the evaluation is breadth first, it generates a large number of parallel tasks and is fairly latency tolerant, i.e., it will not die if it must retrieve a few pages from remote sources. We will leave the exact rewrite rules for unions, optionals, aggregates, subqueries, and so on, as an exercise; the general idea should be clear enough.</p> <p>We have here shown a way of transforming SPARQL queries in such a way as to guarantee dereferencing of findable links, without requiring the end user to either explicitly specify or understand query plans.</p> <p>The present Sponger does not work exactly in this manner but it will be developed in this direction. Fortunately, the algorithms outlined above are nothing complicated.</p>
A quick look at SP2B, the SPARQL Performance Benchmark
http://www.openlinksw.com/blog/vdb/blog/?date=2008-08-27#1423
2008-08-27T16:03:40Z
2008-09-02T09:49:57-04:00
<div> <div style="display:none;">A quick look at SP2B, the SPARQL Performance Benchmark</div> <p>I finally got around to running the <a href="http://dbis.informatik.uni-freiburg.de/index.php?project=SP2B" id="link-id17bac628">SP<sup>2</sup>B SPARQL Performance Benchmark</a> on the current <a href="http://virtuoso.openlinksw.com" id="link-id0x1dcaaa48">Virtuoso</a> Open Source Edition, v5.0.8.</p> <p>I ran it with the 5M triples scale, which is the highest scale for which the authors give numbers.</p> <p>I got a run time of 25 minutes for the 12 queries, giving an arithmetic mean of the query time of 125 seconds. This is better than the 800 or so seconds that the authors had measured. Also, Q6 of the set had failed for the authors, but we have since fixed this; the fix is in the v5.0.8 cut.</p> <p>I also tried it with a scale of 25M, but this became I/O bound and took a bit longer. I will try this with v6 and v7 cluster later, which are vastly better at anything I/O bound.</p> <p>The machine was a 2GHz Xeon with 8G RAM. The query text was the one from the authors, with an explicit <code>FROM</code> clause added; the client was the command line Interactive <a href="http://dbpedia.org/resource/SQL" id="link-id0x1be2c808">SQL</a> (iSQL).</p> <p>If one does the test with the default index layout without specifying a graph, things will not work very well. Also, returning the million-row results of these queries over the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x1d7ac018">SPARQL protocol</a> is not practical.</p> <p>I will say something more about SP<sup>2</sup>B when I get to have a closer look.</p> </div>
A quick look at SP2B, the SPARQL Performance Benchmark
http://www.openlinksw.com/weblog/oerling/?date=2008-08-27#1422
2008-08-27T16:00:07Z
2008-09-02T09:49:55-04:00
<p>I finally got around to running the <a href="http://dbis.informatik.uni-freiburg.de/index.php?project=SP2B" id="link-id17bac628">SP<sup>2</sup>B SPARQL Performance Benchmark</a> on the current <a href="http://virtuoso.openlinksw.com" id="link-id0x1d2a6838">Virtuoso</a> Open Source Edition, v5.0.8.</p> <p>I ran it with the 5M triples scale, which is the highest scale for which the authors give numbers.</p> <p>I got a run time of 25 minutes for the 12 queries, giving an arithmetic mean of the query time of 125 seconds. This is better than the 800 or so seconds that the authors had measured. Also, Q6 of the set had failed for the authors, but we have since fixed this; the fix is in the v5.0.8 cut.</p> <p>I also tried it with a scale of 25M, but this became I/O bound and took a bit longer. I will try this with v6 and v7 cluster later, which are vastly better at anything I/O bound.</p> <p>The machine was a 2GHz Xeon with 8G RAM. The query text was the one from the authors, with an explicit <code>FROM</code> clause added; the client was the command line Interactive <a href="http://dbpedia.org/resource/SQL" id="link-id0x19e74ce0">SQL</a> (iSQL).</p> <p>If one does the test with the default index layout without specifying a graph, things will not work very well. Also, returning the million-row results of these queries over the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x1c4231a0">SPARQL protocol</a> is not practical.</p> <p>I will say something more about SP<sup>2</sup>B when I get to have a closer look.</p>
Configuring Virtuoso for Benchmarking
http://www.openlinksw.com/blog/vdb/blog/?date=2008-08-25#1419
2008-08-25T14:06:11Z
2008-08-25T15:29:06.000036-04:00
<div> <div style="display:none;">Configuring Virtuoso for Benchmarking</div> <p>I will here summarize what should be known about running benchmarks with <a href="http://virtuoso.openlinksw.com" id="link-id0xc152cf0">Virtuoso</a>.</p> <h2>Physical Memory</h2> <p>For 8G RAM, in the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> NumberOfBuffers = 550000 </code> </blockquote> <p>For 16G RAM, double thisâ</p> <blockquote> <code> [Parameters]<br /> ...<br /> NumberOfBuffers = 1100000 </code> </blockquote> <h2>Transaction Isolation</h2> <p>For most cases, certainly all <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xb7ba270">RDF</a> cases, <i>Read Committed</i> should be the default transaction isolation. In the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> DefaultIsolation = 2 </code> </blockquote> <h2>Multiuser Workload</h2> <p>If <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x1a40f308">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1e003cf8">JDBC</a>, or similarly connected client applications are used, there must be more <code>ServerThreads</code> available than there will be client connections. In the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> ServerThreads = 100 </code> </blockquote> <p>With web clients (unlike ODBC, JDBC, or similar clients), it may be justified to have fewer <code>ServerThreads</code> than there are concurrent clients. The <code>MaxKeepAlives</code> should be the maximum number of expected web clients. This can be more than the <code>ServerThreads</code> count. In the <code>[HTTPServer]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [HTTPServer]<br /> ...<br /> ServerThreads = 100 <br /> MaxKeepAlives = 1000 <br /> KeepAliveTimeout = 10 </code> </blockquote> <p> <i><b>Note</b> â The <code>[HTTPServer] ServerThreads</code> are taken from the total pool made available by the <code>[Parameters] ServerThreads</code>. Thus, the <code>[Parameters] ServerThreads</code> should always be at least as large as (and is best set greater than) the <code>[HTTPServer] ServerThreads</code>, and if using the closed-source Commercial Version, should not exceed the licensed thread count.</i> </p> <h2>Disk Use</h2> <p>The basic rule is to use one stripe (file) per distinct physical device (not per file system), using no RAID. For example, one might stripe a database over 6 files (6 physical disks), with an initial size of 60000 pages (the files will grow as needed). </p> <p>For the above described example, in the <code>[Database]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Database]<br /> ...<br /> Striping = 1<br /> MaxCheckpointRemap = 2000000 </code> </blockquote> <p>â and in the <code>[Striping]</code> stanza, on one line per <code>SegmentName</code>, set â</p> <blockquote> <code> [Striping]<br /> ...<br /> Segment1 = 60000 , /virtdev/db/virt-seg1.db = q1 , /data1/db/virt-seg1-str2.db = q2 , /data2/db/virt-seg1-str3.db = q3 , /data3/db/virt-seg1-str4.db = q4 , /data4/db/virt-seg1-str5.db = q5 , /data5/db/virt-seg1-str6.db = q6</code> </blockquote> <p>As can be seen here, each file gets a background IO thread (the <code>= q<i>xxx</i></code> clause). It should be noted that all files on the same physical device should have the same <code>q<i>xxx</i></code> value. This is not directly relevant to the benchmarking scenario above, because we have only one file per device, and thus only one file per IO queue.</p> <h2> <a href="http://dbpedia.org/resource/SQL" id="link-id0xc8b97c0">SQL</a> Optimization</h2> <p>If queries have lots of joins but access little <a href="http://dbpedia.org/resource/Data" id="link-id0x193b2fa8">data</a>, as with the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1b283ca0">Berlin SPARQL Benchmark</a>, the SQL compiler must be told not to look for better plans if the best plan so far is quicker than the compilation time expended so far. Thus, in the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> StopCompilerWhenXOverRunTime = 1 </code> </blockquote> </div>
Configuring Virtuoso for Benchmarking
http://www.openlinksw.com/weblog/oerling/?date=2008-08-25#1418
2008-08-25T14:05:46Z
2008-08-25T15:29:04-04:00
<p>I will here summarize what should be known about running benchmarks with <a href="http://virtuoso.openlinksw.com" id="link-id0xc53af18">Virtuoso</a>.</p> <h2>Physical Memory</h2> <p>For 8G RAM, in the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> NumberOfBuffers = 550000 </code> </blockquote> <p>For 16G RAM, double thisâ</p> <blockquote> <code> [Parameters]<br /> ...<br /> NumberOfBuffers = 1100000 </code> </blockquote> <h2>Transaction Isolation</h2> <p>For most cases, certainly all <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xc2f07a0">RDF</a> cases, <i>Read Committed</i> should be the default transaction isolation. In the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> DefaultIsolation = 2 </code> </blockquote> <h2>Multiuser Workload</h2> <p>If <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0xc1c7178">ODBC</a>, <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xd16fb40">JDBC</a>, or similarly connected client applications are used, there must be more <code>ServerThreads</code> available than there will be client connections. In the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> ServerThreads = 100 </code> </blockquote> <p>With web clients (unlike ODBC, JDBC, or similar clients), it may be justified to have fewer <code>ServerThreads</code> than there are concurrent clients. The <code>MaxKeepAlives</code> should be the maximum number of expected web clients. This can be more than the <code>ServerThreads</code> count. In the <code>[HTTPServer]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [HTTPServer]<br /> ...<br /> ServerThreads = 100 <br /> MaxKeepAlives = 1000 <br /> KeepAliveTimeout = 10 </code> </blockquote> <p> <i><b>Note</b> â The <code>[HTTPServer] ServerThreads</code> are taken from the total pool made available by the <code>[Parameters] ServerThreads</code>. Thus, the <code>[Parameters] ServerThreads</code> should always be at least as large as (and is best set greater than) the <code>[HTTPServer] ServerThreads</code>, and if using the closed-source Commercial Version, should not exceed the licensed thread count.</i> </p> <h2>Disk Use</h2> <p>The basic rule is to use one stripe (file) per distinct physical device (not per file system), using no RAID. For example, one might stripe a database over 6 files (6 physical disks), with an initial size of 60000 pages (the files will grow as needed). </p> <p>For the above described example, in the <code>[Database]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Database]<br /> ...<br /> Striping = 1<br /> MaxCheckpointRemap = 2000000 </code> </blockquote> <p>â and in the <code>[Striping]</code> stanza, on one line per <code>SegmentName</code>, set â</p> <blockquote> <code> [Striping]<br /> ...<br /> Segment1 = 60000 , /virtdev/db/virt-seg1.db = q1 , /data1/db/virt-seg1-str2.db = q2 , /data2/db/virt-seg1-str3.db = q3 , /data3/db/virt-seg1-str4.db = q4 , /data4/db/virt-seg1-str5.db = q5 , /data5/db/virt-seg1-str6.db = q6</code> </blockquote> <p>As can be seen here, each file gets a background IO thread (the <code>= q<i>xxx</i></code> clause). It should be noted that all files on the same physical device should have the same <code>q<i>xxx</i></code> value. This is not directly relevant to the benchmarking scenario above, because we have only one file per device, and thus only one file per IO queue.</p> <h2> <a href="http://dbpedia.org/resource/SQL" id="link-id0xc9fa298">SQL</a> Optimization</h2> <p>If queries have lots of joins but access little <a href="http://dbpedia.org/resource/Data" id="link-id0xb4e0aa0">data</a>, as with the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xb2de990">Berlin SPARQL Benchmark</a>, the SQL compiler must be told not to look for better plans if the best plan so far is quicker than the compilation time expended so far. Thus, in the <code>[Parameters]</code> stanza of <code>virtuoso.ini</code>, set â</p> <blockquote> <code> [Parameters]<br /> ...<br /> StopCompilerWhenXOverRunTime = 1 </code> </blockquote>
BSBM With Triples and Mapped Relational Data
http://www.openlinksw.com/blog/vdb/blog/?date=2008-08-06#1410
2008-08-06T19:41:50Z
2008-08-06T16:29:44.000003-04:00
<div> <div style="display:none;">BSBM With Triples and Mapped Relational Data</div> <p>The special contribution of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id10039db0">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id106b2538">BSBM</a>) to the <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id101a75f8">RDF</a> world is to raise the question of doing OLTP with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xae54170">RDF</a>.</p> <p>Of course, here we immediately hit the question of comparisons with relational databases. To this effect, <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0x1e847b08">BSBM</a> also specifies a relational schema and can generate the <a href="http://dbpedia.org/resource/Data" id="link-id1206c378">data</a> as either triples or <a href="http://dbpedia.org/resource/SQL" id="link-id1667f040">SQL</a> inserts.</p> <p>The benchmark effectively simulates the case of exposing an existing <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id10a93518">RDBMS</a> as RDF. <a href="http://www.openlinksw.com/dataspace/organization/openlink#this" id="link-id13e46d80">OpenLink Software</a> calls this <i>RDF Views</i>. <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id12027578">Oracle</a> is beginning to call this <i>semantic covers</i>. The <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id161dc678">RDB2RDF XG</a>, a W3C incubator group, has been active in this area since Spring, 2008.</p> <h3>But why an OLTP workload with RDF to begin with?</h3> <p>We believe this is relevant because RDF promises to be the interoperability factor between potentially all of traditional IS. If <a href="http://dbpedia.org/resource/Data" id="link-id0x1e7119d8">data</a> is online for human consumption, it may be online via a <a href="http://dbpedia.org/resource/SPARQL" id="link-id106a8908">SPARQL</a> end-point as well. The economic justification will come from discoverability and from applications integrating multi-source structured data. Online shopping is a fine use case.</p> <p>Warehousing all the world's publishable data as RDF is not our first preference, nor would it be the publisher's. Considerations of duplicate infrastructure and maintenance are reason enough. Consequently, we need to show that mapping can outperform an RDF warehouse, which is what we'll do here.</p> <h3>What We Got </h3> <p>First, we found that <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1400" id="link-id150ea748">making the query plan took much too long</a> in proportion to the run time. With BSBM this is an issue because the queries have lots of joins but access relatively little data. So we made a faster compiler and along the way retouched the cost model a bit.</p> <p>But the really interesting part with BSBM is mapping relational data to RDF. For us, BSBM is a great way of showing that mapping can outperform even the best triple store. A relational row store is as good as unbeatable with the query mix. And when there is a clear mapping, there is no reason the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xae5aff0">SPARQL</a> could not be directly translated.</p> <p>If Chris Bizer et al launched the mapping ship, we will be the ones to pilot it to harbor!</p> <p>We filled two <a href="http://virtuoso.openlinksw.com" id="link-id12dbdc70">Virtuoso</a> instances with a BSBM200000 data set, for 100M triples. One was filled with physical triples; the other was filled with the equivalent relational data plus mapping to triples. Performance figures are given in "query mixes per hour". (An update or follow-on to this post will provide elapsed times for each test run.)</p> <p>With the unmodified benchmark we got:</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td>1297 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td><b>3144 qmph</b> </td> </tr> </table> </blockquote> <p>In both cases, most of the time was spent on Q6, which looks for products with one of three words in the label. We altered Q6 to use text index for the mapping, and altered the databases accordingly. (There is no such thing as an e-commerce site without a text index, so we are amply justified in making this change.)</p> <p>The following were measured on the second run of a 100 query mix series, single test driver, warm cache.</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td> 5746 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td> <b>7525 qmph</b> </td> </tr> </table> </blockquote> <p>We then ran the same with 4 concurrent instances of the test driver. The qmph here is 400 / the longest run time.</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td> 19459 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td> <b>24531 qmph</b> </td> </tr> </table> </blockquote> <p>The system used was 64-bit Linux, 2GHz dual-Xeon 5130 (8 cores) with 8G RAM. The concurrent throughputs are a little under 4 times the single thread throughput, which is normal for SMP due to memory contention. The numbers do not evidence significant overhead from thread synchronization.</p> <p>The query compilation represents about 1/3 of total server side CPU. In an actual online application of this type, queries would be parameterized, so the throughputs would be accordingly higher. We used the <code>StopCompilerWhenXOverRunTime = 1</code> option here to cut needless compiler overhead, the queries being straightforward enough.</p> <p>We also see that the advantage of mapping can be further increased by more compiler optimizations, so we expect in the end mapping will lead RDF warehousing by a factor of 4 or so.</p> <h3>Suggestions for BSBM</h3> <ul> <li> <p> <b>Reporting Rules.</b> The benchmark spec should specify a form for disclosure of test run data, TPC style. This includes things like configuration parameters and exact text of queries. There should be accepted variants of query text, as with the TPC.</p> </li> <li> <p> <b>Multiuser operation.</b> The test driver should get a stream number as parameter, so that each client makes a different query sequence. Also, disk performance in this type of benchmark can only be reasonably assessed with a naturally parallel multiuser workload.</p> </li> <li> <p> <b>Add business intelligence.</b> SPARQL has aggregates now, at least with <a href="http://jena.sourceforge.net/" id="link-id11a25ac0">Jena</a> and <a href="http://virtuoso.openlinksw.com" id="link-id0xb003180">Virtuoso</a>, so let's use these. The BSBM business intelligence metric should be a separate metric off the same data. Adding synthetic sales figures would make more interesting queries possible. For example, producing recommendations like "customers who bought this also bought xxx."</p> </li> <li> <p> <b>For the SPARQL community</b>, BSBM sends the message that one ought to support parameterized queries and stored procedures. This would be a <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id109e2448">SPARQL protocol</a> extension; the SPARUL syntax should also have a way of calling a procedure. Something like <code>select proc (??, ??)</code> would be enough, where <code>??</code> is a parameter marker, like <code>?</code> in <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id13febf48">ODBC</a>/<a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id120416a8">JDBC</a>.</p> </li> <li> <p> <b>Add transactions.</b>Especially if we are contrasting mapping vs. storing triples, having an update flow is relevant. In practice, this could be done by having the test driver send web service requests for order entry and the SUT could implement these as updates to the triples or a mapped relational store. This could use stored procedures or logic in an app server.</p> </li> </ul> <h3>Comments on Query Mix</h3> <p>The time of most queries is less than linear to the scale factor. Q6 is an exception if it is not implemented using a text index. Without the text index, Q6 will inevitably come to dominate query time as the scale is increased, and thus will make the benchmark less relevant at larger scales.</p> <h2>Next</h2> <p>We include the sources of our RDF view definitions and other material for running BSBM with our forthcoming Virtuoso Open Source 5.0.8 release. This also includes all the query optimization work done for BSBM. This will be available in the coming days.</p> </div>
BSBM With Triples and Mapped Relational Data
http://www.openlinksw.com/weblog/oerling/?date=2008-08-06#1409
2008-08-06T19:35:27Z
2008-08-06T16:29:40-04:00
<p>The special contribution of the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id10039db0">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id106b2538">BSBM</a>) to the <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id101a75f8">RDF</a> world is to raise the question of doing OLTP with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xb230eb0">RDF</a>.</p> <p>Of course, here we immediately hit the question of comparisons with relational databases. To this effect, <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xa832da8">BSBM</a> also specifies a relational schema and can generate the <a href="http://dbpedia.org/resource/Data" id="link-id1206c378">data</a> as either triples or <a href="http://dbpedia.org/resource/SQL" id="link-id1667f040">SQL</a> inserts.</p> <p>The benchmark effectively simulates the case of exposing an existing <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id10a93518">RDBMS</a> as RDF. <a href="http://www.openlinksw.com/dataspace/organization/openlink#this" id="link-id13e46d80">OpenLink Software</a> calls this <i>RDF Views</i>. <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id12027578">Oracle</a> is beginning to call this <i>semantic covers</i>. The <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id161dc678">RDB2RDF XG</a>, a W3C incubator group, has been active in this area since Spring, 2008.</p> <h3>But why an OLTP workload with RDF to begin with?</h3> <p>We believe this is relevant because RDF promises to be the interoperability factor between potentially all of traditional IS. If <a href="http://dbpedia.org/resource/Data" id="link-id0xabe48a0">data</a> is online for human consumption, it may be online via a <a href="http://dbpedia.org/resource/SPARQL" id="link-id106a8908">SPARQL</a> end-point as well. The economic justification will come from discoverability and from applications integrating multi-source structured data. Online shopping is a fine use case.</p> <p>Warehousing all the world's publishable data as RDF is not our first preference, nor would it be the publisher's. Considerations of duplicate infrastructure and maintenance are reason enough. Consequently, we need to show that mapping can outperform an RDF warehouse, which is what we'll do here.</p> <h3>What We Got </h3> <p>First, we found that <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1400" id="link-id150ea748">making the query plan took much too long</a> in proportion to the run time. With BSBM this is an issue because the queries have lots of joins but access relatively little data. So we made a faster compiler and along the way retouched the cost model a bit.</p> <p>But the really interesting part with BSBM is mapping relational data to RDF. For us, BSBM is a great way of showing that mapping can outperform even the best triple store. A relational row store is as good as unbeatable with the query mix. And when there is a clear mapping, there is no reason the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x96bb5e0">SPARQL</a> could not be directly translated.</p> <p>If Chris Bizer et al launched the mapping ship, we will be the ones to pilot it to harbor!</p> <p>We filled two <a href="http://virtuoso.openlinksw.com" id="link-id12dbdc70">Virtuoso</a> instances with a BSBM200000 data set, for 100M triples. One was filled with physical triples; the other was filled with the equivalent relational data plus mapping to triples. Performance figures are given in "query mixes per hour". (An update or follow-on to this post will provide elapsed times for each test run.)</p> <p>With the unmodified benchmark we got:</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td>1297 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td><b>3144 qmph</b> </td> </tr> </table> </blockquote> <p>In both cases, most of the time was spent on Q6, which looks for products with one of three words in the label. We altered Q6 to use text index for the mapping, and altered the databases accordingly. (There is no such thing as an e-commerce site without a text index, so we are amply justified in making this change.)</p> <p>The following were measured on the second run of a 100 query mix series, single test driver, warm cache.</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td> 5746 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td> <b>7525 qmph</b> </td> </tr> </table> </blockquote> <p>We then ran the same with 4 concurrent instances of the test driver. The qmph here is 400 / the longest run time.</p> <blockquote> <table> <tr> <td><i>Physical Triples:</i> </td> <td>Â Â </td> <td> 19459 qmph</td> </tr> <tr> <td><i>Mapped Triples:</i> </td> <td>Â Â </td> <td> <b>24531 qmph</b> </td> </tr> </table> </blockquote> <p>The system used was 64-bit Linux, 2GHz dual-Xeon 5130 (8 cores) with 8G RAM. The concurrent throughputs are a little under 4 times the single thread throughput, which is normal for SMP due to memory contention. The numbers do not evidence significant overhead from thread synchronization.</p> <p>The query compilation represents about 1/3 of total server side CPU. In an actual online application of this type, queries would be parameterized, so the throughputs would be accordingly higher. We used the <code>StopCompilerWhenXOverRunTime = 1</code> option here to cut needless compiler overhead, the queries being straightforward enough.</p> <p>We also see that the advantage of mapping can be further increased by more compiler optimizations, so we expect in the end mapping will lead RDF warehousing by a factor of 4 or so.</p> <h3>Suggestions for BSBM</h3> <ul> <li> <p> <b>Reporting Rules.</b> The benchmark spec should specify a form for disclosure of test run data, TPC style. This includes things like configuration parameters and exact text of queries. There should be accepted variants of query text, as with the TPC.</p> </li> <li> <p> <b>Multiuser operation.</b> The test driver should get a stream number as parameter, so that each client makes a different query sequence. Also, disk performance in this type of benchmark can only be reasonably assessed with a naturally parallel multiuser workload.</p> </li> <li> <p> <b>Add business intelligence.</b> SPARQL has aggregates now, at least with <a href="http://jena.sourceforge.net/" id="link-id11a25ac0">Jena</a> and <a href="http://virtuoso.openlinksw.com" id="link-id0xa83f490">Virtuoso</a>, so let's use these. The BSBM business intelligence metric should be a separate metric off the same data. Adding synthetic sales figures would make more interesting queries possible. For example, producing recommendations like "customers who bought this also bought xxx."</p> </li> <li> <p> <b>For the SPARQL community</b>, BSBM sends the message that one ought to support parameterized queries and stored procedures. This would be a <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id109e2448">SPARQL protocol</a> extension; the SPARUL syntax should also have a way of calling a procedure. Something like <code>select proc (??, ??)</code> would be enough, where <code>??</code> is a parameter marker, like <code>?</code> in <a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id13febf48">ODBC</a>/<a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id120416a8">JDBC</a>.</p> </li> <li> <p> <b>Add transactions.</b>Especially if we are contrasting mapping vs. storing triples, having an update flow is relevant. In practice, this could be done by having the test driver send web service requests for order entry and the SUT could implement these as updates to the triples or a mapped relational store. This could use stored procedures or logic in an app server.</p> </li> </ul> <h3>Comments on Query Mix</h3> <p>The time of most queries is less than linear to the scale factor. Q6 is an exception if it is not implemented using a text index. Without the text index, Q6 will inevitably come to dominate query time as the scale is increased, and thus will make the benchmark less relevant at larger scales.</p> <h2>Next</h2> <p>We include the sources of our RDF view definitions and other material for running BSBM with our forthcoming Virtuoso Open Source 5.0.8 release. This also includes all the query optimization work done for BSBM. This will be available in the coming days.</p>
Virtuoso Optimizations for the Berlin SPARQL Benchmark
http://www.openlinksw.com/blog/vdb/blog/?date=2008-07-30#1401
2008-07-30T18:52:11Z
2008-08-06T16:29:42-04:00
<div> <div style="display:none;">Virtuoso Optimizations for the Berlin SPARQL Benchmark </div> <p>We had a look at Chris Bizer's initial results with the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id105c9f78">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id102d62b0">BSBM</a>) on <a href="http://virtuoso.openlinksw.com" id="link-id13eb9780">Virtuoso</a>. The first results were rather bad, as nearly all of the run time was spent optimizing the <a href="http://dbpedia.org/resource/SPARQL" id="link-id14a51258">SPARQL</a> statements and under 10% actually running them.</p> <p>So I spent a couple of days on the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xaad28d0">SPARQL</a>/<a href="http://dbpedia.org/resource/SQL" id="link-id108745b0">SQL</a> compiler, to the effect of making it do a better guess of initial execution plan and streamlining some operations. In fact, many of the queries in <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xaa230b8">BSBM</a> are not particularly sensitive to execution plan, as they access a very small portion of the database. So to close the matter, I put in a flag that makes the <a href="http://dbpedia.org/resource/SQL" id="link-id0x1e9e8e28">SQL</a> compiler give up on devising new plans if the time of the best plan so far is less than the time spent compiling so far.</p> <p>With these changes, available now as a diff on top of 5.0.7, we run quite well, several times better than initially. With the compiler time cut-off in place (ini parameter <code>StopCompilerWhenXOverRunTime = 1</code>), we get the following times, output from the BSBM test driver:</p> <blockquote> <pre> Starting test... 0: 1031.22 ms, total: 1151 ms 1: 982.89 ms, total: 1040 ms 2: 923.27 ms, total: 968 ms 3: 898.37 ms, total: 932 ms 4: 855.70 ms, total: 865 ms Scale factor: 10000 Number of query mix runs: 5 times min/max Query mix runtime: 0.8557 s / 1.0312 s Total runtime: 4.691 seconds QMpH: 3836.77 query mixes per hour CQET: 0.93829 seconds average runtime of query mix CQET (geom.): 0.93625 seconds geometric mean runtime of query mix Metrics for Query 1: Count: 5 times executed in whole run AQET: 0.012212 seconds (arithmetic mean) AQET(geom.): 0.009934 seconds (geometric mean) QPS: 81.89 Queries per second minQET/maxQET: 0.00684000s / 0.03115700s Average result count: 7.0 min/max result count: 3 / 10 Metrics for Query 2: Count: 35 times executed in whole run AQET: 0.030490 seconds (arithmetic mean) AQET(geom.): 0.029776 seconds (geometric mean) QPS: 32.80 Queries per second minQET/maxQET: 0.02467300s / 0.06753000s Average result count: 22.5 min/max result count: 15 / 30 Metrics for Query 3: Count: 5 times executed in whole run AQET: 0.006947 seconds (arithmetic mean) AQET(geom.): 0.006905 seconds (geometric mean) QPS: 143.95 Queries per second minQET/maxQET: 0.00580000s / 0.00795100s Average result count: 4.0 min/max result count: 0 / 10 Metrics for Query 4: Count: 5 times executed in whole run AQET: 0.008858 seconds (arithmetic mean) AQET(geom.): 0.008829 seconds (geometric mean) QPS: 112.89 Queries per second minQET/maxQET: 0.00804400s / 0.01019500s Average result count: 3.4 min/max result count: 0 / 10 Metrics for Query 5: Count: 5 times executed in whole run AQET: 0.087542 seconds (arithmetic mean) AQET(geom.): 0.087327 seconds (geometric mean) QPS: 11.42 Queries per second minQET/maxQET: 0.08165600s / 0.09889200s Average result count: 5.0 min/max result count: 5 / 5 Metrics for Query 6: Count: 5 times executed in whole run AQET: 0.131222 seconds (arithmetic mean) AQET(geom.): 0.131216 seconds (geometric mean) QPS: 7.62 Queries per second minQET/maxQET: 0.12924200s / 0.13298200s Average result count: 3.6 min/max result count: 3 / 5 Metrics for Query 7: Count: 20 times executed in whole run AQET: 0.043601 seconds (arithmetic mean) AQET(geom.): 0.040890 seconds (geometric mean) QPS: 22.94 Queries per second minQET/maxQET: 0.01984400s / 0.06012600s Average result count: 26.4 min/max result count: 5 / 96 Metrics for Query 8: Count: 10 times executed in whole run AQET: 0.018168 seconds (arithmetic mean) AQET(geom.): 0.016205 seconds (geometric mean) QPS: 55.04 Queries per second minQET/maxQET: 0.01097600s / 0.05066900s Average result count: 12.8 min/max result count: 6 / 20 Metrics for Query 9: Count: 20 times executed in whole run AQET: 0.043813 seconds (arithmetic mean) AQET(geom.): 0.043807 seconds (geometric mean) QPS: 22.82 Queries per second minQET/maxQET: 0.04274900s / 0.04504100s Average result count: 0.0 min/max result count: 0 / 0 Metrics for Query 10: Count: 15 times executed in whole run AQET: 0.030697 seconds (arithmetic mean) AQET(geom.): 0.029651 seconds (geometric mean) QPS: 32.58 Queries per second minQET/maxQET: 0.02072000s / 0.03975700s Average result count: 1.1 min/max result count: 0 / 4 real 0 m 5.485 s user 0 m 2.233 s sys 0 m 0.170 s </pre></blockquote> <p>Of the approximately 5.5 seconds of running five query mixes, the test driver spends 2.2 s. The server side processing time is 3.1 s, of which SQL compilation is 1.35 s. The rest is miscellaneous system time. The measurement is on 64-bit Linux, 2GHz dual-Xeon 5130 (8 cores) with 8G RAM. </p> <p>We note that this type of workload would be done with stored procedures or prepared, parameterized queries in the SQL world.</p> <p>There will be some further tuning still but this addresses the bulk of the matter. There will be a separate message about the patch containing these improvements.</p> </div>
Virtuoso Optimizations for the Berlin SPARQL Benchmark
http://www.openlinksw.com/weblog/oerling/?date=2008-07-30#1400
2008-07-30T18:17:54Z
2008-08-06T16:29:37.000003-04:00
<p>We had a look at Chris Bizer's initial results with the <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id105c9f78">Berlin SPARQL Benchmark</a> (<a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id102d62b0">BSBM</a>) on <a href="http://virtuoso.openlinksw.com" id="link-id13eb9780">Virtuoso</a>. The first results were rather bad, as nearly all of the run time was spent optimizing the <a href="http://dbpedia.org/resource/SPARQL" id="link-id14a51258">SPARQL</a> statements and under 10% actually running them.</p> <p>So I spent a couple of days on the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xa5a8d0e8">SPARQL</a>/<a href="http://dbpedia.org/resource/SQL" id="link-id108745b0">SQL</a> compiler, to the effect of making it do a better guess of initial execution plan and streamlining some operations. In fact, many of the queries in <a href="http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/spec/index.html" id="link-id0xaf04af8">BSBM</a> are not particularly sensitive to execution plan, as they access a very small portion of the database. So to close the matter, I put in a flag that makes the <a href="http://dbpedia.org/resource/SQL" id="link-id0x1e8d2360">SQL</a> compiler give up on devising new plans if the time of the best plan so far is less than the time spent compiling so far.</p> <p>With these changes, available now as a diff on top of 5.0.7, we run quite well, several times better than initially. With the compiler time cut-off in place (ini parameter <code>StopCompilerWhenXOverRunTime = 1</code>), we get the following times, output from the BSBM test driver:</p> <blockquote> <pre> Starting test... 0: 1031.22 ms, total: 1151 ms 1: 982.89 ms, total: 1040 ms 2: 923.27 ms, total: 968 ms 3: 898.37 ms, total: 932 ms 4: 855.70 ms, total: 865 ms Scale factor: 10000 Number of query mix runs: 5 times min/max Query mix runtime: 0.8557 s / 1.0312 s Total runtime: 4.691 seconds QMpH: 3836.77 query mixes per hour CQET: 0.93829 seconds average runtime of query mix CQET (geom.): 0.93625 seconds geometric mean runtime of query mix Metrics for Query 1: Count: 5 times executed in whole run AQET: 0.012212 seconds (arithmetic mean) AQET(geom.): 0.009934 seconds (geometric mean) QPS: 81.89 Queries per second minQET/maxQET: 0.00684000s / 0.03115700s Average result count: 7.0 min/max result count: 3 / 10 Metrics for Query 2: Count: 35 times executed in whole run AQET: 0.030490 seconds (arithmetic mean) AQET(geom.): 0.029776 seconds (geometric mean) QPS: 32.80 Queries per second minQET/maxQET: 0.02467300s / 0.06753000s Average result count: 22.5 min/max result count: 15 / 30 Metrics for Query 3: Count: 5 times executed in whole run AQET: 0.006947 seconds (arithmetic mean) AQET(geom.): 0.006905 seconds (geometric mean) QPS: 143.95 Queries per second minQET/maxQET: 0.00580000s / 0.00795100s Average result count: 4.0 min/max result count: 0 / 10 Metrics for Query 4: Count: 5 times executed in whole run AQET: 0.008858 seconds (arithmetic mean) AQET(geom.): 0.008829 seconds (geometric mean) QPS: 112.89 Queries per second minQET/maxQET: 0.00804400s / 0.01019500s Average result count: 3.4 min/max result count: 0 / 10 Metrics for Query 5: Count: 5 times executed in whole run AQET: 0.087542 seconds (arithmetic mean) AQET(geom.): 0.087327 seconds (geometric mean) QPS: 11.42 Queries per second minQET/maxQET: 0.08165600s / 0.09889200s Average result count: 5.0 min/max result count: 5 / 5 Metrics for Query 6: Count: 5 times executed in whole run AQET: 0.131222 seconds (arithmetic mean) AQET(geom.): 0.131216 seconds (geometric mean) QPS: 7.62 Queries per second minQET/maxQET: 0.12924200s / 0.13298200s Average result count: 3.6 min/max result count: 3 / 5 Metrics for Query 7: Count: 20 times executed in whole run AQET: 0.043601 seconds (arithmetic mean) AQET(geom.): 0.040890 seconds (geometric mean) QPS: 22.94 Queries per second minQET/maxQET: 0.01984400s / 0.06012600s Average result count: 26.4 min/max result count: 5 / 96 Metrics for Query 8: Count: 10 times executed in whole run AQET: 0.018168 seconds (arithmetic mean) AQET(geom.): 0.016205 seconds (geometric mean) QPS: 55.04 Queries per second minQET/maxQET: 0.01097600s / 0.05066900s Average result count: 12.8 min/max result count: 6 / 20 Metrics for Query 9: Count: 20 times executed in whole run AQET: 0.043813 seconds (arithmetic mean) AQET(geom.): 0.043807 seconds (geometric mean) QPS: 22.82 Queries per second minQET/maxQET: 0.04274900s / 0.04504100s Average result count: 0.0 min/max result count: 0 / 0 Metrics for Query 10: Count: 15 times executed in whole run AQET: 0.030697 seconds (arithmetic mean) AQET(geom.): 0.029651 seconds (geometric mean) QPS: 32.58 Queries per second minQET/maxQET: 0.02072000s / 0.03975700s Average result count: 1.1 min/max result count: 0 / 4 real 0 m 5.485 s user 0 m 2.233 s sys 0 m 0.170 s </pre></blockquote> <p>Of the approximately 5.5 seconds of running five query mixes, the test driver spends 2.2 s. The server side processing time is 3.1 s, of which SQL compilation is 1.35 s. The rest is miscellaneous system time. The measurement is on 64-bit Linux, 2GHz dual-Xeon 5130 (8 cores) with 8G RAM. </p> <p>We note that this type of workload would be done with stored procedures or prepared, parameterized queries in the SQL world.</p> <p>There will be some further tuning still but this addresses the bulk of the matter. There will be a separate message about the patch containing these improvements.</p>
Virtuoso 5.0.7 Release, Now With Jena and Sesame APIs
http://www.openlinksw.com/blog/vdb/blog/?date=2008-07-17#1393
2008-07-17T17:18:09Z
2008-07-17T15:28:22.000002-04:00
<div> <div style="display:none;">Virtuoso 5.0.7 Release, Now With Jena and Sesame APIs</div> <h2>Improvements</h2> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfnativestorageproviders.html" id="link-id13e54d98">Full operation</a> with <a href="http://jena.sourceforge.net/" id="link-id0x11a3d360">Jena</a> and <a href="http://sourceforge.net/projects/sesame/" id="link-id0x1108d428">Sesame</a> <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1288aa00">RDF</a> Frameworks. This fully replaces any previous attempts at interop, and introduces samples and test suites.</li> <li>Better support for alternate RDF indexing schemes</li> <li>Parallel operation of the RDF Sponger, importing multiple sources concurrently.</li> <li>New <a href="http://dbpedia.org/resource/Data" id="link-id0x128a9810">data</a> formats supported for on-demand RDF-ization in the Sponger</li> <li>More efficient support for inference of subclass and sub-property; now capable of efficiently handling taxonomies of tens of thousands of classes</li> <li> <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x6af0678">OWL</a> <a href="http://docs.openlinksw.com:80/virtuoso/rdfsparqlrule.html#rdfsparqlruleintro" id="link-id104d58d8">equivalentClass and equivalentProperty</a> support.</li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfdatarepresentation.html#rdfdynamiclocal" id="link-id109606a8">Dynamic IRI host part</a> support for mapped data and for metadata of local resources. Renaming the host or using multiple virtual hosts will accept URIs with the right host part and refer to the same thing, no duplicate storage required.</li> <li> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x12e0cc38">SPARQL</a> optimizations for <code>LIMIT</code> and <code>OFFSET</code> </li> </ul> <h2>Documentation</h2> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/perfdiag.html#perfdiagqueryplans" id="link-id10a56dd0">How to read query plans and how to use the key performance meters</a> </li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfperformancetuning.html#rdfperfcost" id="link-id106cb5c0">How to diagnose SPARQL queries and how to decide what indexing scheme is right for each RDF use case</a> </li> <li>How to debug RDF views</li> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/sparqldebug.html" id="link-id133b4420">Better documentation of SPARQL extensions and options</a> </li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfviews.html#rdfviewnorthwindexample1" id="link-id1060fdd8">A sample of correct RDF view usage with the Northwind demo data</a> </li> </ul> </ul> <h2>Bug Fixes</h2> <ul> <li>Generally improved safety of built-in functions, better argument checking.</li> <li>Verified UTF8 international character support in all RDF use cases, <a href="http://dbpedia.org/resource/SQL" id="link-id0x12839fd0">SQL</a> client/<a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x1288f350">SPARQL protocol</a>/all data formats.</li> </ul> </div>
Virtuoso 5.0.7 Release, Now With Jena and Sesame APIs
http://www.openlinksw.com/weblog/oerling/?date=2008-07-17#1392
2008-07-17T17:16:19Z
2008-07-17T15:28:20-04:00
<h2>Improvements</h2> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfnativestorageproviders.html" id="link-id13e54d98">Full operation</a> with <a href="http://jena.sourceforge.net/" id="link-id0x11839970">Jena</a> and <a href="http://sourceforge.net/projects/sesame/" id="link-id0x118521a0">Sesame</a> <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x11e14758">RDF</a> Frameworks. This fully replaces any previous attempts at interop, and introduces samples and test suites.</li> <li>Better support for alternate RDF indexing schemes</li> <li>Parallel operation of the RDF Sponger, importing multiple sources concurrently.</li> <li>New <a href="http://dbpedia.org/resource/Data" id="link-id0x13661868">data</a> formats supported for on-demand RDF-ization in the Sponger</li> <li>More efficient support for inference of subclass and sub-property; now capable of efficiently handling taxonomies of tens of thousands of classes</li> <li> <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x9df079b8">OWL</a> <a href="http://docs.openlinksw.com:80/virtuoso/rdfsparqlrule.html#rdfsparqlruleintro" id="link-id104d58d8">equivalentClass and equivalentProperty</a> support.</li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfdatarepresentation.html#rdfdynamiclocal" id="link-id109606a8">Dynamic IRI host part</a> support for mapped data and for metadata of local resources. Renaming the host or using multiple virtual hosts will accept URIs with the right host part and refer to the same thing, no duplicate storage required.</li> <li> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x110e7688">SPARQL</a> optimizations for <code>LIMIT</code> and <code>OFFSET</code> </li> </ul> <h2>Documentation</h2> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/perfdiag.html#perfdiagqueryplans" id="link-id10a56dd0">How to read query plans and how to use the key performance meters</a> </li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfperformancetuning.html#rdfperfcost" id="link-id106cb5c0">How to diagnose SPARQL queries and how to decide what indexing scheme is right for each RDF use case</a> </li> <li>How to debug RDF views</li> <ul> <li> <a href="http://docs.openlinksw.com:80/virtuoso/sparqldebug.html" id="link-id133b4420">Better documentation of SPARQL extensions and options</a> </li> <li> <a href="http://docs.openlinksw.com:80/virtuoso/rdfviews.html#rdfviewnorthwindexample1" id="link-id1060fdd8">A sample of correct RDF view usage with the Northwind demo data</a> </li> </ul> </ul> <h2>Bug Fixes</h2> <ul> <li>Generally improved safety of built-in functions, better argument checking.</li> <li>Verified UTF8 international character support in all RDF use cases, <a href="http://dbpedia.org/resource/SQL" id="link-id0x11140c28">SQL</a> client/<a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x110947e8">SPARQL protocol</a>/all data formats.</li> </ul>
The DARQ Matter of Federation
http://www.openlinksw.com/blog/vdb/blog/?date=2008-06-09#1381
2008-06-09T14:02:19Z
2008-06-11T15:15:14-04:00
<div> <div style="display:none;">The DARQ Matter of Federation</div> <p>Astronomers propose that the universe is held together, so to speak, by the gravity of invisible "dark matter" spread in interstellar and intergalactic space.</p> <p>For the <a href="http://dbpedia.org/resource/Data" id="link-id0x19dbf410">data</a> web, it will be held together by federation, also an invisible factor. As in Minkowski space, so in <a href="http://dbpedia.org/resource/Cyberspace" id="link-id0x9fc13ff8">cyberspace</a>.</p> <p>To take the astronomical analogy further, putting too much visible stuff in one place makes a black hole, whose chief properties are that it is very heavy, can only get heavier and that nothing comes out.</p> <p> <a href="http://darq.sourceforge.net/" id="link-id0x1d06bd88">DARQ</a> is Bastian Quilitz's federated extension of the <a href="http://jena.sourceforge.net/" id="link-id0x1cf28f70">Jena</a> <a href="http://jena.sourceforge.net/ARQ/" id="link-id0x1cba22c8">ARQ</a> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x171c7dc8">SPARQL</a> processor. It has existed for a while and was also presented at <a href="http://www.eswc2008.org/" id="link-id0x1ed53cd0">ESWC2008</a>. There is also SPARQL FED from Andy Seaborne, an explicit means of specifying which end point will process which fragment of a distributed SPARQL query. Still, for federation to deliver in an open, decentralized world, it must be transparent. For a specific application, with a predictable workload, it is of course OK to partition queries explicitly.</p> <p>Bastian had split <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1ce846c0">DBpedia</a> among five <a href="http://virtuoso.openlinksw.com" id="link-id0x1cad0640">Virtuoso</a> servers and was querying this set with DARQ. The end result was that there was a rather frightful cost of federation as opposed to all the data residing in a single Virtuoso. The other result was that if selectivity of predicates was not correctly guessed by the federation engine, the proposition was a non-starter. With correct join order it worked, though.</p> <p>Yet, we really want federation. Looking further down the road, we simply must make federation work. This is just as necessary as running on a server cluster for mid-size workloads.</p> <p>Since we are convinced of the cause, let's talk about the means.</p> <p>For DARQ as it now stands, there's probably an order of magnitude or even more to gain from a couple of simple tricks. If going to a SPARQL end point that is not the outermost in the loop join sequence, batch the requests together in one <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x19a48280">HTTP</a>/1.1 message. So, if the query is "get me my friends living in cities of over a million people," there will be the fragment "get city where x lives" and later "ask if population of x greater than 1000000". If I have 100 friends, I send the 100 requests in a batch to each eligible server.</p> <p>Further, if running against a server of known brand, use a client-server connection and prepared statements with array parameters. This can well improve the processing speed at the remote end point by another order of magnitude. This gain may however not be as great as the latency savings from message batching. We will provide a sample of how to do this with Virtuoso over <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x1cf18278">JDBC</a> so Bastian can try this if interested.</p> <p>These simple things will give a lot of mileage and may even decide whether federation is an option in specific applications. For the open web however, these measures will not yet win the day.</p> <p>When federating <a href="http://dbpedia.org/resource/SQL" id="link-id0x1cf7d0e8">SQL</a>, colocation of data is sort of explicit. If two tables are joined and they are in the same source, then the join can go to the source. For SPARQL this is also so but with a twist:</p> <p>If a foaf:Person is found on a given server, this does not mean that the Person's geek code or email hash will be on the same server. Thus <code>{?p name "Johnny" . ?p geekCode ?g . ?p emailHash ?h }</code> does not necessarily denote a colocated join if many servers serve items of the vocabulary.</p> <p>However, in most practical cases, for obtaining a rapid answer, treating this as a colocated fragment will be appropriate. Thus, it may be necessary to be able to declare that geek codes will be assumed colocated with names. This will save a lot of message passing and offer decent, if not theoretically total recall. For search style applications, starting with such assumptions will make sense. If nothing is found, then we can partition each join step separately for the unlikely case that there were a server that gave geek codes but not names.</p> <p>For Virtuoso, we find that a federated query's asynchronous, parallel evaluation model is not so different from that on a local cluster. So the cluster version could have the option of federated query. The difference is that a cluster is local and tightly coupled and predictably partitioned but a federated setting is none of these.</p> <p>For description, we would take DARQ's description model and maybe extend it a little where needed. Also we would enhance the protocol to allow just asking for the query cost estimate given a query with literals specified. We will do this eventually.</p> <p>We would like to talk to Bastian about large improvements to DARQ, specially when working with Virtuoso. We'll see.</p> <p>Of course, one mode of federating is the crawl-as-you-go approach of the Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x1e163140">Sponger</a>. This will bring in fragments following seeAlso or sameAs declarations or other references. This will however not have the recall of a warehouse or federation over well described SPARQL end-points. But up to a certain volume it has the speed of local storage.</p> <p>The emergence of voiD (Vocabulary of Interlinked Data) is a step in the direction of making federation a reality. There is <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-id1109a4c8">a separate post</a> about this.</p> </div>
Aspects of RDF to RDF Mapping
http://www.openlinksw.com/blog/vdb/blog/?date=2008-06-09#1380
2008-06-09T14:02:18Z
2008-06-11T13:15:39-04:00
<div> <div style="display:none;">Aspects of RDF to RDF Mapping</div> <p>The W3C has recently launched an <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-idd763f48">incubator group about mapping relational data to RDF</a>.</p> <p>From participating in the group for the few initial sessions, I get the following impressions.</p> <p>There is a segment of users, for example from the biomedical community, who do heavy duty <a href="http://dbpedia.org/resource/Data" id="link-id0x17f9e6f8">data</a> integration and look to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x17eabf48">RDF</a> for managing complexity. Unifying heterogeneous data under OWL ontologies, reasoning, and data integrity, are points of interest.</p> <p>There is another segment that is concerned with semantifying the document web, which topic includes initiatives such as <a href="http://triplify.org/" id="link-id0x1a25cd28">Triplify</a> and <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x182c41e8">semantic web</a> search such as <a href="http://sindice.org/" id="link-id0x1a29c5e8">Sindice</a>. The emphasis there is on minimizing entry cost and creating critical mass. The next one to come will clean up the semantics, if these need be cleaned up at all.</p> <p>(Some cleanup is taking place with <a href="http://www.mpi-inf.mpg.de/~suchanek/downloads/yago/" id="link-id0x17fd2b70">Yago</a> and <a href="http://zitgist.com/about/" id="link-id0x17e6ab88">Zitgist</a>, but this is a matter for a different post.)</p> <p>Thus, technically speaking, the mapping landscape is diverse, but ETL (extract-transform-load) seems to predominate. The biomedical people make data warehouses for answering specific questions. The web people are interested in putting data out in the expectation that the next player will warehouse it and allow running complex meshups against the whole of the RDF-ized web.</p> <p>As one would expect, these groups see different issues and needs. Roughly speaking, one is about quality and structure and the other is about volume.</p> <p>Where do we stand?</p> <p>We are with the research data warehousers in saying that the mapping question is very complex and that it would indeed be nice to bypass ETL and go to the source <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x182acd68">RDBMS</a>(s) on demand. Projects in this direction are ongoing.</p> <p>We are with the web people in building large RDF stores with scalable query answering for arbitrary RDF, for example, hosting a lot of the Linking Open Data sets, and working with Zitgist.</p> <p>These things are somewhat different.</p> <p>At present, both the research warehousers and the web scalers predominantly go for ETL.</p> <p>This is fine by us as we definitely are in the large RDF store race.</p> <p>Still, mapping has its point. A relational store will perform quite a bit faster than a quad store if it has the right covering indices or application-specific compressed columnar layout. Thus, there is nothing to block us from querying analytics in <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x16c91438">SPARQL</a>, once the obviously necessary extensions of sub-query, expressions and aggregation are in place.</p> <p>To cite an example, the Ordnance Survey of the UK has a GIS system running on <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x17ee37c8">Oracle</a> with an entry pretty much for each mailbox, lamp post, and hedgerow in the country. According to Ordnance Survey, this would be 1 petatriple, 1e15 triples. "Such a big server farm that we'd have to put it on our map," as Jenny Harding put it at <a href="http://www.eswc2008.org/" id="link-id0x1cab6330">ESWC2008</a>. I'd add that an even bigger map entry would be the power plant needed to run the 100,000 or so PCs this would take. This is counting 10 gigatriples per PC, which would not even give very good working sets.</p> <p>So, on-the-fly RDBMS-to-RDF mapping in some cases is simply necessary. Still, the benefits of RDF for integration can be preserved if the translation middleware is smart enough. Specifically, this entails knowing what tables can be joined with what other tables and pushing maximum processing to the RDBMS(s) involved in the query.</p> <p>You can download the slide set I used for the <a href="http://virtuoso.openlinksw.com" id="link-id0xa1fb7e8">Virtuoso</a> presentation for the RDB to RDF mapping incubator group (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/Relational2RDF.ppt" id="link-id106f9e88">PPT</a>; <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id10a8dc90">other formats</a> coming soon). The main point is that real integration is hard and needs smart query splitting and optimization, as well as real understanding of the databases and subject matter from the <a href="http://dbpedia.org/resource/Information" id="link-id0x17ee38a0">information</a> architect. Sometimes in the web space it can suffice to put data out there with trivial RDF translation and hope that a search engine or such will figure out how to join this with something else. For the enterprise, things are not so. Benefits are clear if one can navigate between disjoint silos but making this accurate enough for deriving business conclusions, as well as efficient enough for production, is a soluble and non-trivial question.</p> <p>We will show the basics of this with the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1844d718">TPC-H</a> mapping, and by joining this with physical triples. We will also make a set of TPC-H format table sets, make mappings between keys in one to keys in the other, and show joins between the two. The SPARQL querying of one such data store is a done deal, including the SPARQL extensions for this. There is even a demo paper, Business Intelligence Extensions for SPARQL (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/RDFAndMapped_BI.pdf" id="link-id12ea4b18">PDF</a>; <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id106e1810">other formats</a> coming soon), by us on the subject in the ESWC 2008 proceedings. If there is an issue left, it is just the technicality of always producing <a href="http://dbpedia.org/resource/SQL" id="link-id0x17fc8d60">SQL</a> that looks hand-crafted and hence is better understood by the target RDBMS(s). For example, Oracle works better if one uses an <code>IN</code> sub-query instead of the equivalent existence test.</p> <p>Follow this <a href="http://dbpedia.org/resource/Blog" id="link-id0xa9bcef8">blog</a> for more on the topic; published papers are always a limited view on the matter.</p> </div>
ESWC 2008
http://www.openlinksw.com/blog/vdb/blog/?date=2008-06-09#1379
2008-06-09T14:02:16Z
2008-06-11T13:15:33-04:00
<div> <div style="display:none;">ESWC 2008</div> <p>Yrjänä Rankka and I attended <a href="http://www.eswc2008.org/" id="link-id10b7a038">ESWC2008</a> on behalf of OpenLink.</p> <p>We were invited at the last minute to give a <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id105df758">Linked Open Data</a> talk at Paolo Bouquet's Identity and Reference workshop. We also had a demo of <a href="http://dbpedia.org/resource/SPARQL" id="link-id12eacca0">SPARQL</a> BI (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/ESWC2008%20SPARQL%20BI%20OpenLink.ppt" id="link-id10b43e58">PPT</a>); <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id1116d8f0">other formats coming soon</a>), our business intelligence extensions to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x16c9bfc8">SPARQL</a> as well as joining between relational <a href="http://dbpedia.org/resource/Data" id="link-id10badc40">data</a> mapped to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id108edaf8">RDF</a> and native <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x181a5ed8">RDF</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x17e69910">data</a>. i was also speaking at the social networks panel chaired by Harry Halpin.</p> <p>I have gathered a few impressions that I will share in the next few posts (<a href="http://www.openlinksw.com/weblog/oerling/?id=1375" id="link-id107298e0">1 - RDF Mapping</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1376" id="link-id10b3a530">2 - DARQ</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-id107290e0">3 - voiD</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1378" id="link-id1071a950">4 - Paradigmata</a>). <i>Caveat: This is not meant to be complete or impartial press coverage of the event but rather some quick comments on issues of personal/OpenLink interest. The fact that I do not mention something does not mean that it is unimportant.</i> </p> <h2>The voiD Graph</h2> <p> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1a87f110">Linked Open Data</a> was well represented, with Chris Bizer, Tom Heath, ourselves and many others. The great advance for <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id108f3c48">LOD</a> this time around is <a href="http://community.linkeddata.org/MediaWiki/index.php?MetaLOD#Kick-off_meeting_at_ESWC08" id="link-id10df9830">voiD, the Vocabulary of Interlinked Datasets</a>, a means to describe what in fact is inside the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1a089980">LOD</a> cloud, how to join it with what and so forth. Big time important if there is to be a <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-iddf74578">web of federatable data sources</a>, feeding directly into what we have been saying for a while about SPARQL end-point self-description and discovery. There is reasonable hope of having something by the date of <a href="http://www.linkeddataplanet.com/" id="link-id10dd0848">Linked Data Planet</a> in a couple of weeks.</p> <h2>Federating</h2> <p>Bastian Quilitz gave a talk about his <a href="http://darq.sourceforge.net/" id="link-id108746e8">DARQ</a>, a federated version of Jena's ARQ.</p> <p>Something like <a href="http://darq.sourceforge.net/" id="link-id0x1a2d9860">DARQ</a>'s optimization statistics should make their way into the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id10992348">SPARQL protocol</a> as well as the voiD data set description.</p> <p>We really need federation but more on this in <a href="http://www.openlinksw.com/weblog/oerling/?id=1376" id="link-id1059d688">a separate post</a>.</p> <h2> <a href="http://xsparql.deri.ie/" id="link-id10314308">XSPARQL</a> </h2> <p>Axel Polleres et al had a paper about <a href="http://xsparql.deri.ie/" id="link-id0x1ad77490">XSPARQL</a>, a merge of <a href="http://dbpedia.org/resource/XQuery" id="link-id10b98e90">XQuery</a> and SPARQL. While visiting DERI a couple of weeks back and again at the conference, we talked about OpenLink implementing the spec. It is evident that the engines must be in the same process and not communicate via the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x17e75190">SPARQL protocol</a> for this to be practical. We could do this. We'll have to see when.</p> <p>Politically, using <a href="http://dbpedia.org/resource/XQuery" id="link-id0x18a9bf10">XQuery</a> to give expressions and XML synthesis to SPARQL would be fitting. These things are needed anyhow, as surely as aggregation and sub-queries but the latter would not so readily come from XQuery. Some rapprochement between RDF and XML folks is desirable anyhow.</p> <h2>Panel: Will the Sem Web Rise to the Challenge of the Social Web?</h2> <p>The social web panel presented the question of whether the sem web was ready for prime time with data portability.</p> <p>The main thrust was expressed in Harry Halpin's rousing closing words: "Men will fight in a battle and lose a battle for a cause they believe in. Even if the battle is lost, the cause may come back and prevail, this time changed and under a different name. Thus, there may well come to be something like our <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id122f4da0">semantic web</a>, but it may not be the one we have worked all these years to build if we do not rise to the occasion before us right now."</p> <p>So, how to do this? Dan Brickley asked the audience how many supported, or were aware of, the latest Web 2.0 things, such as <a href="http://dbpedia.org/page/OAuth" id="link-idf300bc0">OAuth</a> and <a href="http://dbpedia.org/page/OpenID" id="link-id10ce7a40">OpenID</a>. A few were. The general idea was that research (after all, this was a research event) should be more integrated and open to the world at large, not living at the "outdated pace" of a 3 year funding cycle. Stefan Decker of DERI acquiesced in principle. Of course there is impedance mismatch between specialization and interfacing with everything.</p> <p>I said that triples and vocabularies existed, that OpenLink had <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id1210dbf8">ODS</a> (<a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id11076be8">OpenLink Data Spaces</a>, <a href="http://community.linkeddata.org/" id="link-id10d46710">Community LinkedData</a>) for managing one's data-web presence, but that scale would be the next thing. Rather large scale even, with 100 gigatriples (Gtriples) reached before one even noticed. It takes a lot of PCs to host this, maybe $400K worth at today's prices, without replication. Count 16G ram and a few cores per Gtriple so that one is not waiting for disk all the time.</p> <p>The tricks that Web 2.0 silos do with app-specific data structures and app-specific partitioning do not really work for RDF without compromising the whole point of smooth schema evolution and tolerance of ragged data.</p> <p>So, simple vocabularies, minimal inference, minimal blank nodes. Besides, note that the inference will have to be done at run time, not forward-chained at load time, if only because users will not agree on what sameAs and other declarations they want for their queries. Not to mention spam or malicious sameAs declarations!</p> <p>As always, there was the question of business models for the open data web and for semantic technologies in general. As we see it, <a href="http://dbpedia.org/resource/Information" id="link-id108b7688">information</a> overload is the factor driving the demand. Better contextuality will justify semantic technologies. Due to the large volumes and complex processing, a data-as-service model will arise. The data may be open, but its query infrastructure, cleaning, and keeping up-to-date, can be monetized as services.</p> <h2>Identity and Reference</h2> <p>For the identity and reference workshop, the ultimate question is metaphysical and has no single universal answer, even though people, ever since the dawn of time and earlier, have occupied themselves with the issue. Consequently, I started with the Genesis quote where Adam called things by <i>nominibus suis</i>, off-hand implying that things would have some intrinsic ontologically-due names. This would be among the older references to the question, at least in widely known sources.</p> <p>For present purposes, the consensus seemed to be that what would be considered the same as something else depended entirely on the application. What was similar enough to warrant a sameAs for cooking purposes might not warrant a sameAs for chemistry. In fact, complete and exact sameness for URIs would be very rare. So, instead of making generic weak similarity assertions like similarTo or seeAlso, one would choose a set of strong sameAs assertions and have these in effect for query answering if they were appropriate to the granularity demanded by the application.</p> <p>Therefore sameAs is our permanent companion, and there will in time be malicious and spam sameAs. So, nothing much should be materialized on the basis of sameAs assertions in an <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id10c4dfd0">open world</a>. For an app-specific warehouse, sameAs can be resolved at load time.</p> <p>There was naturally some apparent tension between the Occam camp of <a href="http://dbpedia.org/resource/Entity" id="link-id105fd240">entity</a> name services and the LOD camp. I would say that the issue is more a perceived polarity than a real one. People will, inevitably, continue giving things names regardless of any centralized authority. Just look at natural language. But having a dictionary that is commonly accepted for established domains of discourse is immensely helpful.</p> <h2>CYC and NLP</h2> <p>The semantic search workshop was interesting, especially CYC's presentation. CYC is, as it were, the grand old man of <a href="http://dbpedia.org/resource/Knowledge" id="link-id10568158">knowledge</a> representation. Over the long term, I would have support of the CYC inference language inside a database query processor. This would mostly be for repurposing the huge <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1acff9d0">knowledge</a> base for helping in search type queries. If it is for transactions or financial reporting, then queries will be <a href="http://dbpedia.org/resource/SQL" id="link-id130a0a80">SQL</a> and make little or no use of any sort of inference. If it is for summarization or finding things, the opposite holds. For scaling, the issue is just making correct cardinality guesses for query planning, which is harder when inference is involved. We'll see.</p> <p>I will also have a closer look at natural language one of these days, quite inevitably, since <a href="http://zitgist.com/about/" id="link-id10795828">Zitgist</a> (for example) is into <a href="http://dbpedia.org/resource/Entity" id="link-id0x18a12918">entity</a> disambiguation.</p> <h2>Scale</h2> <p>Garlic gave a talk about their Data Patrol and QDOS. We agree that storing the data for these as triples instead of 1000 or so constantly changing relational tables could well make the difference between next-to-unmanageable and efficiently adaptive.</p> <p>Garlic probably has the largest triple collection in constant online use to date. We will soon join them with our hosting of the whole LOD cloud and <a href="http://sindice.org/" id="link-id0x17f18a38">Sindice</a>/<a href="http://zitgist.com/about/" id="link-id0x184e9e90">Zitgist</a> as triples.</p> <h2>Conclusions</h2> <p>There is a mood to deliver applications. Consequently, scale remains a central, even the principal topic. So for now we make bigger centrally-managed databases. At the next turn around the corner we will have to turn to federation. The point here is that a planetary-scale, centrally-managed, online system can be made when the workload is uniform and anticipatable, but if it is free-form queries and complex analysis, we have a problem. So we move in the direction of federating and charging based on usage whenever the workload is more complex than making simple lookups now and then.</p> <p>For the <a href="http://virtuoso.openlinksw.com" id="link-id1026ac28">Virtuoso</a> roadmap, this changes little. Next we make data sets available on Amazon EC2, as widely promised at ESWC. With big scale also comes rescaling and repartitioning, so this gets additional weight, as does further parallelizing of single user workloads. As it happens, the same medicine helps for both. At <a href="http://www.linkeddataplanet.com/" id="link-id0x17ff5c20">Linked Data Planet</a>, we will make more announcements.</p> </div>
The DARQ Matter of Federation
http://www.openlinksw.com/weblog/oerling/?date=2008-06-09#1376
2008-06-09T13:57:30Z
2008-06-11T15:15:00-04:00
<p>Astronomers propose that the universe is held together, so to speak, by the gravity of invisible "dark matter" spread in interstellar and intergalactic space.</p> <p>For the <a href="http://dbpedia.org/resource/Data" id="link-id0x19bbd830">data</a> web, it will be held together by federation, also an invisible factor. As in Minkowski space, so in <a href="http://dbpedia.org/resource/Cyberspace" id="link-id0x19af2488">cyberspace</a>.</p> <p>To take the astronomical analogy further, putting too much visible stuff in one place makes a black hole, whose chief properties are that it is very heavy, can only get heavier and that nothing comes out.</p> <p> <a href="http://darq.sourceforge.net/" id="link-id0x19b7a9c8">DARQ</a> is Bastian Quilitz's federated extension of the <a href="http://jena.sourceforge.net/" id="link-id0x19ce3da0">Jena</a> <a href="http://jena.sourceforge.net/ARQ/" id="link-id0xa569a258">ARQ</a> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1a8d2270">SPARQL</a> processor. It has existed for a while and was also presented at <a href="http://www.eswc2008.org/" id="link-id0x1aad1d00">ESWC2008</a>. There is also SPARQL FED from Andy Seaborne, an explicit means of specifying which end point will process which fragment of a distributed SPARQL query. Still, for federation to deliver in an open, decentralized world, it must be transparent. For a specific application, with a predictable workload, it is of course OK to partition queries explicitly.</p> <p>Bastian had split <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1a8ac770">DBpedia</a> among five <a href="http://virtuoso.openlinksw.com" id="link-id0x19601d30">Virtuoso</a> servers and was querying this set with DARQ. The end result was that there was a rather frightful cost of federation as opposed to all the data residing in a single Virtuoso. The other result was that if selectivity of predicates was not correctly guessed by the federation engine, the proposition was a non-starter. With correct join order it worked, though.</p> <p>Yet, we really want federation. Looking further down the road, we simply must make federation work. This is just as necessary as running on a server cluster for mid-size workloads.</p> <p>Since we are convinced of the cause, let's talk about the means.</p> <p>For DARQ as it now stands, there's probably an order of magnitude or even more to gain from a couple of simple tricks. If going to a SPARQL end point that is not the outermost in the loop join sequence, batch the requests together in one <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x19b94818">HTTP</a>/1.1 message. So, if the query is "get me my friends living in cities of over a million people," there will be the fragment "get city where x lives" and later "ask if population of x greater than 1000000". If I have 100 friends, I send the 100 requests in a batch to each eligible server.</p> <p>Further, if running against a server of known brand, use a client-server connection and prepared statements with array parameters. This can well improve the processing speed at the remote end point by another order of magnitude. This gain may however not be as great as the latency savings from message batching. We will provide a sample of how to do this with Virtuoso over <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0x17822258">JDBC</a> so Bastian can try this if interested.</p> <p>These simple things will give a lot of mileage and may even decide whether federation is an option in specific applications. For the open web however, these measures will not yet win the day.</p> <p>When federating <a href="http://dbpedia.org/resource/SQL" id="link-id0x1a651628">SQL</a>, colocation of data is sort of explicit. If two tables are joined and they are in the same source, then the join can go to the source. For SPARQL this is also so but with a twist:</p> <p>If a foaf:Person is found on a given server, this does not mean that the Person's geek code or email hash will be on the same server. Thus <code>{?p name "Johnny" . ?p geekCode ?g . ?p emailHash ?h }</code> does not necessarily denote a colocated join if many servers serve items of the vocabulary.</p> <p>However, in most practical cases, for obtaining a rapid answer, treating this as a colocated fragment will be appropriate. Thus, it may be necessary to be able to declare that geek codes will be assumed colocated with names. This will save a lot of message passing and offer decent, if not theoretically total recall. For search style applications, starting with such assumptions will make sense. If nothing is found, then we can partition each join step separately for the unlikely case that there were a server that gave geek codes but not names.</p> <p>For Virtuoso, we find that a federated query's asynchronous, parallel evaluation model is not so different from that on a local cluster. So the cluster version could have the option of federated query. The difference is that a cluster is local and tightly coupled and predictably partitioned but a federated setting is none of these.</p> <p>For description, we would take DARQ's description model and maybe extend it a little where needed. Also we would enhance the protocol to allow just asking for the query cost estimate given a query with literals specified. We will do this eventually.</p> <p>We would like to talk to Bastian about large improvements to DARQ, specially when working with Virtuoso. We'll see.</p> <p>Of course, one mode of federating is the crawl-as-you-go approach of the Virtuoso <a href="http://virtuoso.openlinksw.com/Whitepapers/html/VirtSpongerWhitePaper.html" id="link-id0x1dddce48">Sponger</a>. This will bring in fragments following seeAlso or sameAs declarations or other references. This will however not have the recall of a warehouse or federation over well described SPARQL end-points. But up to a certain volume it has the speed of local storage.</p> <p>The emergence of voiD (Vocabulary of Interlinked Data) is a step in the direction of making federation a reality. There is <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-id1109a4c8">a separate post</a> about this.</p>
Aspects of RDF to RDF Mapping
http://www.openlinksw.com/weblog/oerling/?date=2008-06-09#1375
2008-06-09T13:52:20Z
2008-06-11T13:15:19.000010-04:00
<p>The W3C has recently launched an <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-idd763f48">incubator group about mapping relational data to RDF</a>.</p> <p>From participating in the group for the few initial sessions, I get the following impressions.</p> <p>There is a segment of users, for example from the biomedical community, who do heavy duty <a href="http://dbpedia.org/resource/Data" id="link-id0x1b388bf0">data</a> integration and look to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1a24b198">RDF</a> for managing complexity. Unifying heterogeneous data under OWL ontologies, reasoning, and data integrity, are points of interest.</p> <p>There is another segment that is concerned with semantifying the document web, which topic includes initiatives such as <a href="http://triplify.org/" id="link-id0x16cb5c48">Triplify</a> and <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x1adcd2b8">semantic web</a> search such as <a href="http://sindice.org/" id="link-id0x1a462ee0">Sindice</a>. The emphasis there is on minimizing entry cost and creating critical mass. The next one to come will clean up the semantics, if these need be cleaned up at all.</p> <p>(Some cleanup is taking place with <a href="http://www.mpi-inf.mpg.de/~suchanek/downloads/yago/" id="link-id0x17faa940">Yago</a> and <a href="http://zitgist.com/about/" id="link-id0x1acd23f0">Zitgist</a>, but this is a matter for a different post.)</p> <p>Thus, technically speaking, the mapping landscape is diverse, but ETL (extract-transform-load) seems to predominate. The biomedical people make data warehouses for answering specific questions. The web people are interested in putting data out in the expectation that the next player will warehouse it and allow running complex meshups against the whole of the RDF-ized web.</p> <p>As one would expect, these groups see different issues and needs. Roughly speaking, one is about quality and structure and the other is about volume.</p> <p>Where do we stand?</p> <p>We are with the research data warehousers in saying that the mapping question is very complex and that it would indeed be nice to bypass ETL and go to the source <a href="http://dbpedia.org/resource/Relational_database_management_system" id="link-id0x17f28d60">RDBMS</a>(s) on demand. Projects in this direction are ongoing.</p> <p>We are with the web people in building large RDF stores with scalable query answering for arbitrary RDF, for example, hosting a lot of the Linking Open Data sets, and working with Zitgist.</p> <p>These things are somewhat different.</p> <p>At present, both the research warehousers and the web scalers predominantly go for ETL.</p> <p>This is fine by us as we definitely are in the large RDF store race.</p> <p>Still, mapping has its point. A relational store will perform quite a bit faster than a quad store if it has the right covering indices or application-specific compressed columnar layout. Thus, there is nothing to block us from querying analytics in <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1a2c81c8">SPARQL</a>, once the obviously necessary extensions of sub-query, expressions and aggregation are in place.</p> <p>To cite an example, the Ordnance Survey of the UK has a GIS system running on <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x18a82010">Oracle</a> with an entry pretty much for each mailbox, lamp post, and hedgerow in the country. According to Ordnance Survey, this would be 1 petatriple, 1e15 triples. "Such a big server farm that we'd have to put it on our map," as Jenny Harding put it at <a href="http://www.eswc2008.org/" id="link-id0x16533418">ESWC2008</a>. I'd add that an even bigger map entry would be the power plant needed to run the 100,000 or so PCs this would take. This is counting 10 gigatriples per PC, which would not even give very good working sets.</p> <p>So, on-the-fly RDBMS-to-RDF mapping in some cases is simply necessary. Still, the benefits of RDF for integration can be preserved if the translation middleware is smart enough. Specifically, this entails knowing what tables can be joined with what other tables and pushing maximum processing to the RDBMS(s) involved in the query.</p> <p>You can download the slide set I used for the <a href="http://virtuoso.openlinksw.com" id="link-id0x16c57ed0">Virtuoso</a> presentation for the RDB to RDF mapping incubator group (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/Relational2RDF.ppt" id="link-id106f9e88">PPT</a>; <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id10a8dc90">other formats</a> coming soon). The main point is that real integration is hard and needs smart query splitting and optimization, as well as real understanding of the databases and subject matter from the <a href="http://dbpedia.org/resource/Information" id="link-id0x1b132910">information</a> architect. Sometimes in the web space it can suffice to put data out there with trivial RDF translation and hope that a search engine or such will figure out how to join this with something else. For the enterprise, things are not so. Benefits are clear if one can navigate between disjoint silos but making this accurate enough for deriving business conclusions, as well as efficient enough for production, is a soluble and non-trivial question.</p> <p>We will show the basics of this with the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x17fc7b58">TPC-H</a> mapping, and by joining this with physical triples. We will also make a set of TPC-H format table sets, make mappings between keys in one to keys in the other, and show joins between the two. The SPARQL querying of one such data store is a done deal, including the SPARQL extensions for this. There is even a demo paper, Business Intelligence Extensions for SPARQL (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/RDFAndMapped_BI.pdf" id="link-id12ea4b18">PDF</a>; <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id106e1810">other formats</a> coming soon), by us on the subject in the ESWC 2008 proceedings. If there is an issue left, it is just the technicality of always producing <a href="http://dbpedia.org/resource/SQL" id="link-id0x18439b70">SQL</a> that looks hand-crafted and hence is better understood by the target RDBMS(s). For example, Oracle works better if one uses an <code>IN</code> sub-query instead of the equivalent existence test.</p> <p>Follow this <a href="http://dbpedia.org/resource/Blog" id="link-id0x16c29ea0">blog</a> for more on the topic; published papers are always a limited view on the matter.</p>
ESWC 2008
http://www.openlinksw.com/weblog/oerling/?date=2008-06-09#1374
2008-06-09T13:49:15Z
2008-06-11T13:15:11.000008-04:00
<p>Yrjänä Rankka and I attended <a href="http://www.eswc2008.org/" id="link-id10b7a038">ESWC2008</a> on behalf of OpenLink.</p> <p>We were invited at the last minute to give a <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id105df758">Linked Open Data</a> talk at Paolo Bouquet's Identity and Reference workshop. We also had a demo of <a href="http://dbpedia.org/resource/SPARQL" id="link-id12eacca0">SPARQL</a> BI (<a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations/ESWC2008%20SPARQL%20BI%20OpenLink.ppt" id="link-id10b43e58">PPT</a>); <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VirtPresentations" id="link-id1116d8f0">other formats coming soon</a>), our business intelligence extensions to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1843a368">SPARQL</a> as well as joining between relational <a href="http://dbpedia.org/resource/Data" id="link-id10badc40">data</a> mapped to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id108edaf8">RDF</a> and native <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1843a3b0">RDF</a> <a href="http://dbpedia.org/resource/Data" id="link-id0x1843a3c8">data</a>. i was also speaking at the social networks panel chaired by Harry Halpin.</p> <p>I have gathered a few impressions that I will share in the next few posts (<a href="http://www.openlinksw.com/weblog/oerling/?id=1375" id="link-id107298e0">1 - RDF Mapping</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1376" id="link-id10b3a530">2 - DARQ</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-id107290e0">3 - voiD</a>, <a href="http://www.openlinksw.com/weblog/oerling/?id=1378" id="link-id1071a950">4 - Paradigmata</a>). <i>Caveat: This is not meant to be complete or impartial press coverage of the event but rather some quick comments on issues of personal/OpenLink interest. The fact that I do not mention something does not mean that it is unimportant.</i> </p> <h2>The voiD Graph</h2> <p> <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x16c781e0">Linked Open Data</a> was well represented, with Chris Bizer, Tom Heath, ourselves and many others. The great advance for <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id108f3c48">LOD</a> this time around is <a href="http://community.linkeddata.org/MediaWiki/index.php?MetaLOD#Kick-off_meeting_at_ESWC08" id="link-id10df9830">voiD, the Vocabulary of Interlinked Datasets</a>, a means to describe what in fact is inside the <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x16c78228">LOD</a> cloud, how to join it with what and so forth. Big time important if there is to be a <a href="http://www.openlinksw.com/weblog/oerling/?id=1377" id="link-iddf74578">web of federatable data sources</a>, feeding directly into what we have been saying for a while about SPARQL end-point self-description and discovery. There is reasonable hope of having something by the date of <a href="http://www.linkeddataplanet.com/" id="link-id10dd0848">Linked Data Planet</a> in a couple of weeks.</p> <h2>Federating</h2> <p>Bastian Quilitz gave a talk about his <a href="http://darq.sourceforge.net/" id="link-id108746e8">DARQ</a>, a federated version of Jena's ARQ.</p> <p>Something like <a href="http://darq.sourceforge.net/" id="link-id0x16c782e8">DARQ</a>'s optimization statistics should make their way into the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id10992348">SPARQL protocol</a> as well as the voiD data set description.</p> <p>We really need federation but more on this in <a href="http://www.openlinksw.com/weblog/oerling/?id=1376" id="link-id1059d688">a separate post</a>.</p> <h2> <a href="http://xsparql.deri.ie/" id="link-id10314308">XSPARQL</a> </h2> <p>Axel Polleres et al had a paper about <a href="http://xsparql.deri.ie/" id="link-id0x1a2d8458">XSPARQL</a>, a merge of <a href="http://dbpedia.org/resource/XQuery" id="link-id10b98e90">XQuery</a> and SPARQL. While visiting DERI a couple of weeks back and again at the conference, we talked about OpenLink implementing the spec. It is evident that the engines must be in the same process and not communicate via the <a href="http://www.w3.org/TR/rdf-sparql-protocol/" id="link-id0x1d99c1d0">SPARQL protocol</a> for this to be practical. We could do this. We'll have to see when.</p> <p>Politically, using <a href="http://dbpedia.org/resource/XQuery" id="link-id0x1acae1f0">XQuery</a> to give expressions and XML synthesis to SPARQL would be fitting. These things are needed anyhow, as surely as aggregation and sub-queries but the latter would not so readily come from XQuery. Some rapprochement between RDF and XML folks is desirable anyhow.</p> <h2>Panel: Will the Sem Web Rise to the Challenge of the Social Web?</h2> <p>The social web panel presented the question of whether the sem web was ready for prime time with data portability.</p> <p>The main thrust was expressed in Harry Halpin's rousing closing words: "Men will fight in a battle and lose a battle for a cause they believe in. Even if the battle is lost, the cause may come back and prevail, this time changed and under a different name. Thus, there may well come to be something like our <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id122f4da0">semantic web</a>, but it may not be the one we have worked all these years to build if we do not rise to the occasion before us right now."</p> <p>So, how to do this? Dan Brickley asked the audience how many supported, or were aware of, the latest Web 2.0 things, such as <a href="http://dbpedia.org/page/OAuth" id="link-idf300bc0">OAuth</a> and <a href="http://dbpedia.org/page/OpenID" id="link-id10ce7a40">OpenID</a>. A few were. The general idea was that research (after all, this was a research event) should be more integrated and open to the world at large, not living at the "outdated pace" of a 3 year funding cycle. Stefan Decker of DERI acquiesced in principle. Of course there is impedance mismatch between specialization and interfacing with everything.</p> <p>I said that triples and vocabularies existed, that OpenLink had <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id1210dbf8">ODS</a> (<a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id11076be8">OpenLink Data Spaces</a>, <a href="http://community.linkeddata.org/" id="link-id10d46710">Community LinkedData</a>) for managing one's data-web presence, but that scale would be the next thing. Rather large scale even, with 100 gigatriples (Gtriples) reached before one even noticed. It takes a lot of PCs to host this, maybe $400K worth at today's prices, without replication. Count 16G ram and a few cores per Gtriple so that one is not waiting for disk all the time.</p> <p>The tricks that Web 2.0 silos do with app-specific data structures and app-specific partitioning do not really work for RDF without compromising the whole point of smooth schema evolution and tolerance of ragged data.</p> <p>So, simple vocabularies, minimal inference, minimal blank nodes. Besides, note that the inference will have to be done at run time, not forward-chained at load time, if only because users will not agree on what sameAs and other declarations they want for their queries. Not to mention spam or malicious sameAs declarations!</p> <p>As always, there was the question of business models for the open data web and for semantic technologies in general. As we see it, <a href="http://dbpedia.org/resource/Information" id="link-id108b7688">information</a> overload is the factor driving the demand. Better contextuality will justify semantic technologies. Due to the large volumes and complex processing, a data-as-service model will arise. The data may be open, but its query infrastructure, cleaning, and keeping up-to-date, can be monetized as services.</p> <h2>Identity and Reference</h2> <p>For the identity and reference workshop, the ultimate question is metaphysical and has no single universal answer, even though people, ever since the dawn of time and earlier, have occupied themselves with the issue. Consequently, I started with the Genesis quote where Adam called things by <i>nominibus suis</i>, off-hand implying that things would have some intrinsic ontologically-due names. This would be among the older references to the question, at least in widely known sources.</p> <p>For present purposes, the consensus seemed to be that what would be considered the same as something else depended entirely on the application. What was similar enough to warrant a sameAs for cooking purposes might not warrant a sameAs for chemistry. In fact, complete and exact sameness for URIs would be very rare. So, instead of making generic weak similarity assertions like similarTo or seeAlso, one would choose a set of strong sameAs assertions and have these in effect for query answering if they were appropriate to the granularity demanded by the application.</p> <p>Therefore sameAs is our permanent companion, and there will in time be malicious and spam sameAs. So, nothing much should be materialized on the basis of sameAs assertions in an <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id10c4dfd0">open world</a>. For an app-specific warehouse, sameAs can be resolved at load time.</p> <p>There was naturally some apparent tension between the Occam camp of <a href="http://dbpedia.org/resource/Entity" id="link-id105fd240">entity</a> name services and the LOD camp. I would say that the issue is more a perceived polarity than a real one. People will, inevitably, continue giving things names regardless of any centralized authority. Just look at natural language. But having a dictionary that is commonly accepted for established domains of discourse is immensely helpful.</p> <h2>CYC and NLP</h2> <p>The semantic search workshop was interesting, especially CYC's presentation. CYC is, as it were, the grand old man of <a href="http://dbpedia.org/resource/Knowledge" id="link-id10568158">knowledge</a> representation. Over the long term, I would have support of the CYC inference language inside a database query processor. This would mostly be for repurposing the huge <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x17f7dd40">knowledge</a> base for helping in search type queries. If it is for transactions or financial reporting, then queries will be <a href="http://dbpedia.org/resource/SQL" id="link-id130a0a80">SQL</a> and make little or no use of any sort of inference. If it is for summarization or finding things, the opposite holds. For scaling, the issue is just making correct cardinality guesses for query planning, which is harder when inference is involved. We'll see.</p> <p>I will also have a closer look at natural language one of these days, quite inevitably, since <a href="http://zitgist.com/about/" id="link-id10795828">Zitgist</a> (for example) is into <a href="http://dbpedia.org/resource/Entity" id="link-id0x1a2c8bd0">entity</a> disambiguation.</p> <h2>Scale</h2> <p>Garlic gave a talk about their Data Patrol and QDOS. We agree that storing the data for these as triples instead of 1000 or so constantly changing relational tables could well make the difference between next-to-unmanageable and efficiently adaptive.</p> <p>Garlic probably has the largest triple collection in constant online use to date. We will soon join them with our hosting of the whole LOD cloud and <a href="http://sindice.org/" id="link-id0x1b383720">Sindice</a>/<a href="http://zitgist.com/about/" id="link-id0x1b383738">Zitgist</a> as triples.</p> <h2>Conclusions</h2> <p>There is a mood to deliver applications. Consequently, scale remains a central, even the principal topic. So for now we make bigger centrally-managed databases. At the next turn around the corner we will have to turn to federation. The point here is that a planetary-scale, centrally-managed, online system can be made when the workload is uniform and anticipatable, but if it is free-form queries and complex analysis, we have a problem. So we move in the direction of federating and charging based on usage whenever the workload is more complex than making simple lookups now and then.</p> <p>For the <a href="http://virtuoso.openlinksw.com" id="link-id1026ac28">Virtuoso</a> roadmap, this changes little. Next we make data sets available on Amazon EC2, as widely promised at ESWC. With big scale also comes rescaling and repartitioning, so this gets additional weight, as does further parallelizing of single user workloads. As it happens, the same medicine helps for both. At <a href="http://www.linkeddataplanet.com/" id="link-id0x1a2c7eb0">Linked Data Planet</a>, we will make more announcements.</p>
Virtuoso Cluster Paper
http://www.openlinksw.com/blog/vdb/blog/?date=2008-05-30#1369
2008-05-30T10:02:04Z
2008-05-30T06:02:05-04:00
<div> <div style="display:none;">Virtuoso Cluster Paper</div> <div> <div>We have a new article on <a href="http://virtuoso.openlinksw.com" id="link-id10424890">Virtuoso</a> cluster, submitted to ISWC 2008.</div> <div> Right now we are working on hosting the billion triples challenge <a href="http://dbpedia.org/resource/Data" id="link-id1077f800">data</a> set at Amazon EC2 using <a href="http://virtuoso.openlinksw.com" id="link-id102117f0">Virtuoso</a> Cluster. This will be the first publicly available instance of <a href="http://virtuoso.openlinksw.com" id="link-id0x20387e80">Virtuoso</a> Cluster and all interested may then instantiate their own copy on the EC2 infrastructure. </div> <br /> <div> <a href="http://www.openlinksw.com/weblog/oerling/2008iswc_webscale_rdf.pdf" id="link-id10af2f30">Towards Web Scale RDF</a> <br /> <a href="http://www.openlinksw.com/weblog/oerling/RDFAndMapped_BI.pdf" id="link-idfedf9f0">Integrating Open Sources and Relational Data with SPARQL</a> <br /> <a href="http://www.openlinksw.com/weblog/oerling/bisparql2.pdf" id="link-id106e5418">Business Intelligence Extensions for SPARQL</a> <br /> </div> <br /> <div> Look for a separate announcement in the near future. </div> </div> </div>
Virtuoso Cluster Paper
http://www.openlinksw.com/weblog/oerling/?date=2008-05-30#1368
2008-05-30T09:57:47Z
2008-05-30T10:36:31-04:00
<p>We have a new article on <a href="http://virtuoso.openlinksw.com">Virtuoso</a> Cluster, submitted to ISWC 2008.</p> <p>Right now we are working on hosting the billion triples challenge <a href="http://dbpedia.org/resource/Data">data</a> set on Amazon EC2 using Virtuoso Cluster. This will be the first publicly available instance of Virtuoso Cluster and all interested may then instantiate their own copy on the EC2 infrastructure.</p> <ul> <li> <p> <a href="http://www.openlinksw.com/weblog/oerling/2008iswc_webscale_rdf.pdf" id="link-id1062bc98">Towards Web Scale RDF</a> (PDF)</p> </li> <li> <p> <a href="http://www.openlinksw.com/weblog/oerling/RDFAndMapped_BI.pdf" id="link-id1063efa8">Integrating Open Sources and Relational Data with SPARQL</a> (PDF)</p> </li> <li> <p> <a href="http://www.openlinksw.com/weblog/oerling/bisparql2.pdf" id="link-id10c37708">Business Intelligence Extensions for SPARQL</a> (PDF)</p> </li> </ul> <p>Look for a separate announcement in the near future.</p><a href="index.vspx?tag=database" rel="tag" style="display:none;">database</a><a href="index.vspx?tag=databases" rel="tag" style="display:none;">databases</a><a href="index.vspx?tag=rdf" rel="tag" style="display:none;">rdf</a><a href="index.vspx?tag=semanticweb" rel="tag" style="display:none;">semanticweb</a><a href="index.vspx?tag=sparql" rel="tag" style="display:none;">sparql</a><a href="index.vspx?tag=virtuoso" rel="tag" style="display:none;">virtuoso</a>
DBpedia Benchmark Revisited
http://www.openlinksw.com/blog/vdb/blog/?date=2008-05-09#1359
2008-05-09T19:33:42Z
2008-05-12T11:24:43-04:00
<div> <div style="display:none;">DBpedia Benchmark Revisited</div> <p>We ran the <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1cd6d0c8">DBpedia</a> benchmark queries again with different configurations of <a href="http://virtuoso.openlinksw.com" id="link-id0x1bf01048">Virtuoso</a>. I had not studied the details of the matter previously but now did have a closer look at the queries.</p> <p>Comparing numbers given by different parties is a constant problem. In the case reported here, we loaded the full DBpedia 3, all languages, with about 198M triples, onto Virtuoso v5 and Virtuoso Cluster v6, all on the same 4 core 2GHz Xeon with 8G RAM. All databases were striped on 6 disks. The Cluster configuration was with 4 processes in the same box.</p> <p>We ran the queries in two variants:</p> <ul> <li>With graph specified in the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1b9d3ca0">SPARQL</a> <code>FROM</code> clause, using the default indices.</li> <li>With no graph specified anywhere, using an alternate indexing scheme.</li> </ul> <p>The times below are for the sequence of 5 queries; individual query times are not reported. I did not do a line-by-line review of the execution plans since they seem to run well enough. We could get some extra mileage from cost model tweaks, especially for the numeric range conditions, but we will do this when somebody comes up with better times.</p> <p>First, about Virtuoso v5: Because there is a query in the set that specifies no condition on S or O and only P, this simply cannot be done with the default indices. With Virtuoso Cluster v6 it sort-of can, because v6 is more space efficient.</p> <p>So we added the index:</p> <blockquote> <code> create bitmap index <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1c364a58">rdf</a>_quad_pogs on rdf_quad (p, o, g, s); </code> </blockquote> <table> <tr> <td>Â </td> <td align="center"><b>Virtuoso v5 with<br /> gspo, ogps, pogs</b> </td> <td align="center"><b>Virtuoso Cluster v6 with <br />gspo, ogps</b> </td> <td align="center"><b>Virtuoso Cluster v6 with <br />gspo, ogps, pogs</b> </td> </tr> <tr> <td><b>cold</b> </td> <td align="center">210 s</td> <td align="center">136 s</td> <td align="center">33.4 s</td> </tr> <tr> <td><b>warm</b> </td> <td align="center">0.600 s</td> <td align="center">4.01 s</td> <td align="center">0.628 s</td> </tr> </table> <p>OK, so now let us do it without a graph being specified. For all platforms, we drop any existing indices, and --</p> <blockquote> <code> create table r2 (g iri_id_8, s, iri_id_8, p iri_id_8, o any, primary key (s, p, o, g)) <br /> alter index R2 on R2 partition (s int (0hexffff00)); <br /> <br /> log_enable (2); <br /> insert into r2 (g, s, p, o) select g, s, p, o from rdf_quad; <br /> <br /> drop table rdf_quad; <br /> alter table r2 rename RDF_QUAD; <br /> create bitmap index rdf_quad_opgs on rdf_quad (o, p, g, s) partition (o varchar (-1, 0hexffff)); <br /> create bitmap index rdf_quad_pogs on rdf_quad (p, o, g, s) partition (o varchar (-1, 0hexffff)); <br /> create bitmap index rdf_quad_gpos on rdf_quad (g, p, o, s) partition (o varchar (-1, 0hexffff)); </code> </blockquote> <p>The code is identical for v5 and v6, except that with v5 we use <code>iri_id (32 bit)</code> for the type, not <code>iri_id_8 (64 bit)</code>. We note that we run out of IDs with v5 around a few billion triples, so with v6 we have double the ID length and still manage to be vastly more space efficient.</p> <p>With the above 4 indices, we can query the <a href="http://dbpedia.org/resource/Data" id="link-id0x1bae4cd8">data</a> pretty much in any combination without hitting a full scan of any index. We note that all indices that do not begin with s end with s as a bitmap. This takes about 60% of the space of a non-bitmap index for data such as DBpedia.</p> <p>If you intend to do completely arbitrary RDF queries in Virtuoso, then chances are you are best off with the above index scheme.</p> <table> <tr> <td>Â </td> <td align="center"><b> Virtuoso v5 with<br /> gspo, ogps, pogs</b> </td> <td align="center"><b> Virtuoso Cluster v6 with <br /> spog, pogs, opgs, gpos </b> </td> </tr> <tr> <td><b>warm</b> </td> <td align="center">0.595 s</td> <td align="center">0.617 s</td> </tr> </table> <p>The cold times were about the same as above, so not reproduced.</p> <h3>Graph or No Graph?</h3> <p>It is in the SPARQL spirit to specify a graph and for pretty much any application, there are entirely sensible ways of keeping the data in graphs and specifying which ones are concerned by queries. This is why Virtuoso is set up for this by default.</p> <p>On the other hand, for the open web scenario, dealing with an unknown large number of graphs, enumerating graphs is not possible and questions like which graph of which source asserts x become relevant. We have two distinct use cases which warrant different setups of the database, simple as that.</p> <p>The latter use case is not really within the SPARQL spec, so implementations may or may not support this. For example <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1cd2db78">Oracle</a> or Vertica would not do this well since they partition data according to graph or predicate, respectively. On the other hand, stores that work with one quad table, which is most of the ones out there, should do it maybe with some configuring, as shown above.</p> <p>Frameworks like Jena are not to my <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1b300390">knowledge</a> geared towards having a wildcard for graph, although I would suppose this can be arranged by adding some "super-graph" object, a graph of all graphs. I don't think this is directly supported and besides most apps would not need it.</p> <p>Once the indices are right, there is no difference between specifying a graph and not specifying a graph with the queries considered. With more complex queries, specifying a graph or set of graphs does allow some optimizations that cannot be done with no graph specified. For example, bitmap intersections are possible only when all leading key parts are given.</p> <h3>Conclusions</h3> <p>The best warm cache time is with v5; the five queries run under 600 ms after the first go. This is noted to show that all-in-memory with a single thread of execution is hard to beat.</p> <p>Cluster v6 performs the same queries in 623 ms. What is gained in parallelism is lost in latency if all operations complete in microseconds. On the other hand, Cluster v6 leaves v5 in the dust in any situation that has less than 100% hit rate. This is due to actual benefit from parallelism if operations take longer than a few microseconds, such as in the case of disk reads. Cluster v6 has substantially better data layout on disk, as well as fewer pages to load for the same content.</p> <p>This makes it possible to run the queries without the pogs index on Cluster v6 even when v5 takes prohibitively long.</p> <p>The morale of the story is to have a lot of RAM and space-efficient data representation.</p> <p>The DBpedia benchmark does not specify any random access pattern that would give a measure of sustained throughput under load, so we are left with the extremes of cold and warm cache of which neither is quite realistic.</p> <p>Chris Bizer and I have talked on and off about benchmarks and I have made suggestions that we will see incorporated into the Berlin SPARQL benchmark, which will, I believe, be much more informative.</p> <h3>Appendix: Query Text</h3> <p>For reference, the query texts specifying the graph are below. To run without specifying the graph, just drop the <code>FROM <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1c371db0">http</a>://dbpedia.org></code> from each query. The returned row counts are indicated below each query's text.</p> <blockquote> <code><pre> sparql SELECT ?p ?o FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/Metropolitan_Museum_of_Art> ?p ?o }; -- 1337 rows sparql PREFIX p: <http://dbpedia.org/property/> SELECT ?film1 ?actor1 ?film2 ?actor2 FROM <http://dbpedia.org> WHERE { ?film1 p:starring <http://dbpedia.org/resource/Kevin_Bacon> . ?film1 p:starring ?actor1 . ?film2 p:starring ?actor1 . ?film2 p:starring ?actor2 . }; -- 23910 rows sparql PREFIX p: <http://dbpedia.org/property/> SELECT ?artist ?artwork ?museum ?director FROM <http://dbpedia.org> WHERE { ?artwork p:artist ?artist . ?artwork p:museum ?museum . ?museum p:director ?director }; -- 303 rows sparql PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> SELECT ?s ?homepage FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/Berlin> geo:lat ?berlinLat . <http://dbpedia.org/resource/Berlin> geo:long ?berlinLong . ?s geo:lat ?lat . ?s geo:long ?long . ?s foaf:homepage ?homepage . FILTER ( ?lat <= ?berlinLat + 0.03190235436 && ?long >= ?berlinLong - 0.08679199218 && ?lat >= ?berlinLat - 0.03190235436 && ?long <= ?berlinLong + 0.08679199218) }; -- 56 rows sparql PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> PREFIX p: <http://dbpedia.org/property/> SELECT ?s ?a ?homepage FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/New_York_City> geo:lat ?nyLat . <http://dbpedia.org/resource/New_York_City> geo:long ?nyLong . ?s geo:lat ?lat . ?s geo:long ?long . ?s p:architect ?a . ?a foaf:homepage ?homepage . FILTER ( ?lat <= ?nyLat + 0.3190235436 && ?long >= ?nyLong - 0.8679199218 && ?lat >= ?nyLat - 0.3190235436 && ?long <= ?nyLong + 0.8679199218) }; -- 13 rows </pre> </code> </blockquote> </div>
DBpedia Benchmark Revisited
http://www.openlinksw.com/weblog/oerling/?date=2008-05-09#1358
2008-05-09T19:27:00Z
2008-05-12T11:24:36-04:00
<p>We ran the <a href="http://dbpedia.org/resource/DBpedia" id="link-id0x1b7f9688">DBpedia</a> benchmark queries again with different configurations of <a href="http://virtuoso.openlinksw.com" id="link-id0x1cca2e00">Virtuoso</a>. I had not studied the details of the matter previously but now did have a closer look at the queries.</p> <p>Comparing numbers given by different parties is a constant problem. In the case reported here, we loaded the full DBpedia 3, all languages, with about 198M triples, onto Virtuoso v5 and Virtuoso Cluster v6, all on the same 4 core 2GHz Xeon with 8G RAM. All databases were striped on 6 disks. The Cluster configuration was with 4 processes in the same box.</p> <p>We ran the queries in two variants:</p> <ul> <li>With graph specified in the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1b77f758">SPARQL</a> <code>FROM</code> clause, using the default indices.</li> <li>With no graph specified anywhere, using an alternate indexing scheme.</li> </ul> <p>The times below are for the sequence of 5 queries; individual query times are not reported. I did not do a line-by-line review of the execution plans since they seem to run well enough. We could get some extra mileage from cost model tweaks, especially for the numeric range conditions, but we will do this when somebody comes up with better times.</p> <p>First, about Virtuoso v5: Because there is a query in the set that specifies no condition on S or O and only P, this simply cannot be done with the default indices. With Virtuoso Cluster v6 it sort-of can, because v6 is more space efficient.</p> <p>So we added the index:</p> <blockquote> <code> create bitmap index <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1cb0b180">rdf</a>_quad_pogs on rdf_quad (p, o, g, s); </code> </blockquote> <table> <tr> <td>Â </td> <td align="center"><b>Virtuoso v5 with<br /> gspo, ogps, pogs</b> </td> <td align="center"><b>Virtuoso Cluster v6 with <br />gspo, ogps</b> </td> <td align="center"><b>Virtuoso Cluster v6 with <br />gspo, ogps, pogs</b> </td> </tr> <tr> <td><b>cold</b> </td> <td align="center">210 s</td> <td align="center">136 s</td> <td align="center">33.4 s</td> </tr> <tr> <td><b>warm</b> </td> <td align="center">0.600 s</td> <td align="center">4.01 s</td> <td align="center">0.628 s</td> </tr> </table> <p>OK, so now let us do it without a graph being specified. For all platforms, we drop any existing indices, and --</p> <blockquote> <code> create table r2 (g iri_id_8, s, iri_id_8, p iri_id_8, o any, primary key (s, p, o, g)) <br /> alter index R2 on R2 partition (s int (0hexffff00)); <br /> <br /> log_enable (2); <br /> insert into r2 (g, s, p, o) select g, s, p, o from rdf_quad; <br /> <br /> drop table rdf_quad; <br /> alter table r2 rename RDF_QUAD; <br /> create bitmap index rdf_quad_opgs on rdf_quad (o, p, g, s) partition (o varchar (-1, 0hexffff)); <br /> create bitmap index rdf_quad_pogs on rdf_quad (p, o, g, s) partition (o varchar (-1, 0hexffff)); <br /> create bitmap index rdf_quad_gpos on rdf_quad (g, p, o, s) partition (o varchar (-1, 0hexffff)); </code> </blockquote> <p>The code is identical for v5 and v6, except that with v5 we use <code>iri_id (32 bit)</code> for the type, not <code>iri_id_8 (64 bit)</code>. We note that we run out of IDs with v5 around a few billion triples, so with v6 we have double the ID length and still manage to be vastly more space efficient.</p> <p>With the above 4 indices, we can query the <a href="http://dbpedia.org/resource/Data" id="link-id0x6339b80">data</a> pretty much in any combination without hitting a full scan of any index. We note that all indices that do not begin with s end with s as a bitmap. This takes about 60% of the space of a non-bitmap index for data such as DBpedia.</p> <p>If you intend to do completely arbitrary RDF queries in Virtuoso, then chances are you are best off with the above index scheme.</p> <table> <tr> <td>Â </td> <td align="center"><b> Virtuoso v5 with<br /> gspo, ogps, pogs</b> </td> <td align="center"><b> Virtuoso Cluster v6 with <br /> spog, pogs, opgs, gpos </b> </td> </tr> <tr> <td><b>warm</b> </td> <td align="center">0.595 s</td> <td align="center">0.617 s</td> </tr> </table> <p>The cold times were about the same as above, so not reproduced.</p> <h3>Graph or No Graph?</h3> <p>It is in the SPARQL spirit to specify a graph and for pretty much any application, there are entirely sensible ways of keeping the data in graphs and specifying which ones are concerned by queries. This is why Virtuoso is set up for this by default.</p> <p>On the other hand, for the open web scenario, dealing with an unknown large number of graphs, enumerating graphs is not possible and questions like which graph of which source asserts x become relevant. We have two distinct use cases which warrant different setups of the database, simple as that.</p> <p>The latter use case is not really within the SPARQL spec, so implementations may or may not support this. For example <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x11ed7028">Oracle</a> or Vertica would not do this well since they partition data according to graph or predicate, respectively. On the other hand, stores that work with one quad table, which is most of the ones out there, should do it maybe with some configuring, as shown above.</p> <p>Frameworks like Jena are not to my <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x1a49ded0">knowledge</a> geared towards having a wildcard for graph, although I would suppose this can be arranged by adding some "super-graph" object, a graph of all graphs. I don't think this is directly supported and besides most apps would not need it.</p> <p>Once the indices are right, there is no difference between specifying a graph and not specifying a graph with the queries considered. With more complex queries, specifying a graph or set of graphs does allow some optimizations that cannot be done with no graph specified. For example, bitmap intersections are possible only when all leading key parts are given.</p> <h3>Conclusions</h3> <p>The best warm cache time is with v5; the five queries run under 600 ms after the first go. This is noted to show that all-in-memory with a single thread of execution is hard to beat.</p> <p>Cluster v6 performs the same queries in 623 ms. What is gained in parallelism is lost in latency if all operations complete in microseconds. On the other hand, Cluster v6 leaves v5 in the dust in any situation that has less than 100% hit rate. This is due to actual benefit from parallelism if operations take longer than a few microseconds, such as in the case of disk reads. Cluster v6 has substantially better data layout on disk, as well as fewer pages to load for the same content.</p> <p>This makes it possible to run the queries without the pogs index on Cluster v6 even when v5 takes prohibitively long.</p> <p>The morale of the story is to have a lot of RAM and space-efficient data representation.</p> <p>The DBpedia benchmark does not specify any random access pattern that would give a measure of sustained throughput under load, so we are left with the extremes of cold and warm cache of which neither is quite realistic.</p> <p>Chris Bizer and I have talked on and off about benchmarks and I have made suggestions that we will see incorporated into the Berlin SPARQL benchmark, which will, I believe, be much more informative.</p> <h3>Appendix: Query Text</h3> <p>For reference, the query texts specifying the graph are below. To run without specifying the graph, just drop the <code>FROM <<a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x1905bfd0">http</a>://dbpedia.org></code> from each query. The returned row counts are indicated below each query's text.</p> <blockquote> <code><pre> sparql SELECT ?p ?o FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/Metropolitan_Museum_of_Art> ?p ?o }; -- 1337 rows sparql PREFIX p: <http://dbpedia.org/property/> SELECT ?film1 ?actor1 ?film2 ?actor2 FROM <http://dbpedia.org> WHERE { ?film1 p:starring <http://dbpedia.org/resource/Kevin_Bacon> . ?film1 p:starring ?actor1 . ?film2 p:starring ?actor1 . ?film2 p:starring ?actor2 . }; -- 23910 rows sparql PREFIX p: <http://dbpedia.org/property/> SELECT ?artist ?artwork ?museum ?director FROM <http://dbpedia.org> WHERE { ?artwork p:artist ?artist . ?artwork p:museum ?museum . ?museum p:director ?director }; -- 303 rows sparql PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> SELECT ?s ?homepage FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/Berlin> geo:lat ?berlinLat . <http://dbpedia.org/resource/Berlin> geo:long ?berlinLong . ?s geo:lat ?lat . ?s geo:long ?long . ?s foaf:homepage ?homepage . FILTER ( ?lat <= ?berlinLat + 0.03190235436 && ?long >= ?berlinLong - 0.08679199218 && ?lat >= ?berlinLat - 0.03190235436 && ?long <= ?berlinLong + 0.08679199218) }; -- 56 rows sparql PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> PREFIX p: <http://dbpedia.org/property/> SELECT ?s ?a ?homepage FROM <http://dbpedia.org> WHERE { <http://dbpedia.org/resource/New_York_City> geo:lat ?nyLat . <http://dbpedia.org/resource/New_York_City> geo:long ?nyLong . ?s geo:lat ?lat . ?s geo:long ?long . ?s p:architect ?a . ?a foaf:homepage ?homepage . FILTER ( ?lat <= ?nyLat + 0.3190235436 && ?long >= ?nyLong - 0.8679199218 && ?lat >= ?nyLat - 0.3190235436 && ?long <= ?nyLong + 0.8679199218) }; -- 13 rows </pre> </code> </blockquote>
SPARQL at WWW 2008
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-30#1354
2008-04-30T16:28:10Z
2008-08-28T11:26:06.000004-04:00
<div> <div style="display:none;">SPARQL at WWW 2008</div> <p>Andy Seaborne and Eric Prud'hommeaux, editors of the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x183b13a8">SPARQL</a> recommendation, convened a SPARQL birds of a feather session at <a href="http://www2008.org/" id="link-id0xd31c2d0">WWW 2008</a>. The administrative outcome was that implementors could now experiment with extensions, hopefully keeping each other current about their efforts and that towards the end of 2008, a new W3C working group might begin formalizing the experiences into a new SPARQL spec.</p> <p>The session drew a good crowd, including many users and developers. The wishes were largely as expected, with a few new ones added. Many of the wishes already had diverse implementations, however most often without interop. I will below give some comments on the main issues discussed.</p> </div> <li> <p> <b>SPARQL Update</b> - This is likely the most universally agreed upon extension. Implementations exist, largely along the lines of Andy Seaborne's SPARUL spec, which is also likely material for a W3C member submission. The issue is without much controversy; transactions fall outside the scope, which is reasonable enough. With triple stores, we can define things as combinations of inserts and deletes, and isolation we just leave aside. If anything, operating on a transactional platform such as <a href="http://virtuoso.openlinksw.com" id="link-id0x1d442cd8">Virtuoso</a>, one wishes to disable transactions for any operations such as bulk loads and long-running inserts and deletes. Transactionality has pretty much no overhead for a few hundred rows, but for a few hundred million rows the cost of locking and rollback is prohibitive. With Virtuoso, we have a row auto-commit mode which we recommend for use with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xec62c58">RDF</a>: It commits by itself now and then, optionally keeping a roll forward log, and is transactional enough not to leave half triples around, i.e., inserted in one index but not another.</p> <p>As far as we are concerned, updating physical triples along the SPARUL lines is pretty much a done deal.</p> <p>The matter of updating relational <a href="http://dbpedia.org/resource/Data" id="link-id0x19f995c8">data</a> mapped to RDF is a whole other kettle of fish. On this, I should say that RDF has no special virtues for expressing transactions but rather has a special genius for integration. Updating is best left to web service interfaces that use <a href="http://dbpedia.org/resource/SQL" id="link-id0x125b2b98">SQL</a> on the inside. Anyway, updating union views, which most mappings will be, is complicated. Besides, for transactions, one usually knows exactly what one wishes to update.</p> </li> <li> <p> <b>Full Text</b> - Many people expressed a desire for full text access. Here we run into a deplorable confusion with regexps. The closest SPARQL has to full text in its native form is regexps, but these are not really mappable to full text except in rare special cases and I would despair of explaining to an end user what exactly these cases are. So, in principle, some regexps are equivalent to full text but in practice I find it much preferable to keep these entirely separate.</p> <p>It was noted that what the users want is a text box for search words. This is a front end to the CONTAINS predicate of most SQL implementations. Ours is MS SQL Server compatible and has a SPARQL version called <code>bif:contains</code>. One must still declare which triples one wants indexed for full text, though. This admin overhead seems inevitable, as text indexing is a large overhead and not needed by all applications.</p> <p>Also, text hits are not boolean; usually they come with a hit score. Thus, a SPARQL extension for this could look like </p> <blockquote> <code>select * where { ?thing has_description ?d . ?d ftcontains "gizmo" ftand "widget" score ?score . }</code> </blockquote> <p>This would return all the subjects, descriptions, and scores, from subjects with a has_description property containing widget and gizmo. Extending the basic pattern is better than having the match in a filter, since the match binds a variable.</p> <p>The <a href="http://dbpedia.org/resource/XQuery" id="link-id0xfec6788">XQuery</a>/<a href="http://dbpedia.org/resource/XPath" id="link-id0x1a789e38">XPath</a> groups have recently come up with a full-text spec, so I used their style of syntax above. We already have a full-text extension, as do some others. but for standardization, it is probably most appropriate to take the XQuery work as a basis. The XQuery full-text spec is quite complex, but I would expect most uses to get by with a small subset, and the structure seems better thought out, at first glance, than the more ad-hoc implementations in diverse SQLs.</p> <p>Again, declaring any text index to support the search, as well as its timeliness or transactionality, are best left to implementations.</p> </li> <li> <p> <b>Federation</b> - This is a tricky matter. <a href="http://jena.sourceforge.net/ARQ/" id="link-id0xba487f0">ARQ</a> has a SPARQL extension for sending a nested set of triple patterns to a specific end-point. The <a href="http://darq.sourceforge.net/" id="link-id0xf8a5ab0">DARQ</a> project has something more, including a selectivity model for SPARQL.</p> <p>With federated SQL, life is simpler since after the views are expanded, we have a query where each table is at a known server and has more or less known statistics. Generally, execution plans where as much work as possible is pushed to the remote servers are preferred, and modeling the latencies is not overly hard. With SPARQL, each triple pattern could in principle come from any of the federated servers. Associating a specific end-point to a fragment of the query just passes the problem to the user. It is my guess that this is the best we can do without getting very elaborate, and possibly buggy, end-point content descriptions for routing federated queries.</p> <p>Having said this, there remains the problem of join order. I suggested that we enhance the protocol by allowing asking an end-point for the query cost for a given SPARQL query. Since they all must have a cost model for optimization, this should not be an impossible request. A time cost and estimated cardinality would be enough. Making statistics available <i>Ã la</i> DARQ was also discussed. Being able to declare cardinalities expected of a remote end-point is probably necessary anyway, since not all will implement the cost model interface. For standardization, agreeing on what is a proper description of content and cardinality and how fine grained this must be will be so difficult that I would not wait for it. A cost model interface would nicely hide this within the end-point itself.</p> <p>With Virtuoso, we do not have a federated SPARQL scheme but we could have the ARQ-like service construct. We'd use our own cost model with explicit declarations of cardinalities of the remote data for guessing a join order. Still, this is a bit of work. We'll see.</p> <p>For practicality, the service construct coupled with join order hints is the best short term bet. Making this pretty enough for standardization is not self-evident, as it requires end-point description and/or cost model hooks for things to stay declarative.</p> </li> <li> <p> <b>End-point description</b> - This question has been around for a while; I have <a href="http://www.openlinksw.com/weblog/oerling/?id=1085" id="link-id101d3440">blogged about it earlier</a>, but we are not really at a point where there would be even rough consensus about an end-point ontology. We should probably do something on our own to demonstrate some application of this, as we host lots of <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x162d0de8">linked open data</a> sets.</p> </li> <li> <p> <b>SQL equivalence</b> - There were many requests for aggregation, some for subqueries and nesting, expressions in select, negation, existence and so on. I would call these all SQL equivalence. One use case was taking all the teams in the database and for all with over 5 members, add the big_team class and a property for member count.</p> <p>With Virtuoso, we could write this as -- </p> <blockquote> <code>construct { ?team a big_team . ?team member_count ?ct } from ... where {?team a team . { select ?team2 count (*) as ?ct where { ?m member_of ?team2 } . filter (?team = ?team2 and ? ct > 5) }}</code> </blockquote> <p>We have pretty much all the SQL equivalence features, as we have been working for some time at translating the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x11c870c8">TPC-H</a> workload into SPARQL.</p> <p>The usefulness of these things is uncontested but standardization could be hard as there are subtle questions about variable scope and the like.</p> </li> <li> <p> <b>Inference</b> - The SPARQL spec does not deal with transitivity or such matters because it is assumed that these are handled by an underlying inference layer. This is however most often not so. There was interest in more fine grained control of inference, for example declaring that just one property in a query would be transitive or that subclasses should be taken into account in only one triple pattern. As far as I am concerned, this is very reasonable, and we even offer extensions for this sort of thing in Virtuoso's SPARQL. This however only makes sense if the inference is done at query time and pattern by pattern. For instance, if forward chaining is used, this no longer makes sense. Specifying that some forward chaining ought to be done at query time is impractical, as the operation can be very large and time consuming and it is the DBA's task to determine what should be stored and for how long, how changes should be propagated, and so on. All these are application dependent and standardizing will be difficult.</p> <p>Support for RDF features like lists and bags would all fall into the functions an underlying inference layer should perform. These things are of special interest when querying <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0x156f3830">OWL</a> models, for example.</p> </li> <li> <p> <b>Path expressions</b> - Path expressions were requested by a few people. We have implemented some, as in </p> <blockquote> <code>?product+?has_supplier+>s_name = "Gizmos, Inc.".</code> </blockquote> This means that one supplier of product has name "Gizmo, Inc.". This is a nice shorthand but we run into problems if we start supporting repetitive steps, optional steps, and the like.</li> <p>In conclusion, update, full text, and basic counting and grouping would seem straightforward at this point. Nesting queries, value subqueries, views, and the like should not be too hard if an agreement is reached on scope rules. Inference and federation will probably need more experimentation but a lot can be had already with very simple fine grained control of backward chaining, if such applies, or with explicit end-point references and explicit join order. These are practical but not pretty enough for committee consensus, would be my guess. Anyway, it will be a few months before anything formal will happen.</p>
SPARQL at WWW 2008
http://www.openlinksw.com/weblog/oerling/?date=2008-04-30#1353
2008-04-30T15:59:15Z
2008-08-28T11:26:00-04:00
<p>Andy Seaborne and Eric Prud'hommeaux, editors of the <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xddcd418">SPARQL</a> recommendation, convened a SPARQL birds of a feather session at <a href="http://www2008.org/" id="link-id0xf6c0510">WWW 2008</a>. The administrative outcome was that implementors could now experiment with extensions, hopefully keeping each other current about their efforts and that towards the end of 2008, a new W3C working group might begin formalizing the experiences into a new SPARQL spec.</p> <p>The session drew a good crowd, including many users and developers. The wishes were largely as expected, with a few new ones added. Many of the wishes already had diverse implementations, however most often without interop. I will below give some comments on the main issues discussed.</p> <li> <p> <b>SPARQL Update</b> - This is likely the most universally agreed upon extension. Implementations exist, largely along the lines of Andy Seaborne's SPARUL spec, which is also likely material for a W3C member submission. The issue is without much controversy; transactions fall outside the scope, which is reasonable enough. With triple stores, we can define things as combinations of inserts and deletes, and isolation we just leave aside. If anything, operating on a transactional platform such as <a href="http://virtuoso.openlinksw.com" id="link-id0xf9e8d18">Virtuoso</a>, one wishes to disable transactions for any operations such as bulk loads and long-running inserts and deletes. Transactionality has pretty much no overhead for a few hundred rows, but for a few hundred million rows the cost of locking and rollback is prohibitive. With Virtuoso, we have a row auto-commit mode which we recommend for use with <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xe098028">RDF</a>: It commits by itself now and then, optionally keeping a roll forward log, and is transactional enough not to leave half triples around, i.e., inserted in one index but not another.</p> <p>As far as we are concerned, updating physical triples along the SPARUL lines is pretty much a done deal.</p> <p>The matter of updating relational <a href="http://dbpedia.org/resource/Data" id="link-id0xf55ec50">data</a> mapped to RDF is a whole other kettle of fish. On this, I should say that RDF has no special virtues for expressing transactions but rather has a special genius for integration. Updating is best left to web service interfaces that use <a href="http://dbpedia.org/resource/SQL" id="link-id0xff00f38">SQL</a> on the inside. Anyway, updating union views, which most mappings will be, is complicated. Besides, for transactions, one usually knows exactly what one wishes to update.</p> </li> <li> <p> <b>Full Text</b> - Many people expressed a desire for full text access. Here we run into a deplorable confusion with regexps. The closest SPARQL has to full text in its native form is regexps, but these are not really mappable to full text except in rare special cases and I would despair of explaining to an end user what exactly these cases are. So, in principle, some regexps are equivalent to full text but in practice I find it much preferable to keep these entirely separate.</p> <p>It was noted that what the users want is a text box for search words. This is a front end to the CONTAINS predicate of most SQL implementations. Ours is MS SQL Server compatible and has a SPARQL version called <code>bif:contains</code>. One must still declare which triples one wants indexed for full text, though. This admin overhead seems inevitable, as text indexing is a large overhead and not needed by all applications.</p> <p>Also, text hits are not boolean; usually they come with a hit score. Thus, a SPARQL extension for this could look like </p> <blockquote> <code>select * where { ?thing has_description ?d . ?d ftcontains "gizmo" ftand "widget" score ?score . }</code> </blockquote> <p>This would return all the subjects, descriptions, and scores, from subjects with a has_description property containing widget and gizmo. Extending the basic pattern is better than having the match in a filter, since the match binds a variable.</p> <p>The <a href="http://dbpedia.org/resource/XQuery" id="link-id0xee42f50">XQuery</a>/<a href="http://dbpedia.org/resource/XPath" id="link-id0xd169630">XPath</a> groups have recently come up with a full-text spec, so I used their style of syntax above. We already have a full-text extension, as do some others. but for standardization, it is probably most appropriate to take the XQuery work as a basis. The XQuery full-text spec is quite complex, but I would expect most uses to get by with a small subset, and the structure seems better thought out, at first glance, than the more ad-hoc implementations in diverse SQLs.</p> <p>Again, declaring any text index to support the search, as well as its timeliness or transactionality, are best left to implementations.</p> </li> <li> <p> <b>Federation</b> - This is a tricky matter. <a href="http://jena.sourceforge.net/ARQ/" id="link-id0xcbe1e18">ARQ</a> has a SPARQL extension for sending a nested set of triple patterns to a specific end-point. The <a href="http://darq.sourceforge.net/" id="link-id0xe7c0088">DARQ</a> project has something more, including a selectivity model for SPARQL.</p> <p>With federated SQL, life is simpler since after the views are expanded, we have a query where each table is at a known server and has more or less known statistics. Generally, execution plans where as much work as possible is pushed to the remote servers are preferred, and modeling the latencies is not overly hard. With SPARQL, each triple pattern could in principle come from any of the federated servers. Associating a specific end-point to a fragment of the query just passes the problem to the user. It is my guess that this is the best we can do without getting very elaborate, and possibly buggy, end-point content descriptions for routing federated queries.</p> <p>Having said this, there remains the problem of join order. I suggested that we enhance the protocol by allowing asking an end-point for the query cost for a given SPARQL query. Since they all must have a cost model for optimization, this should not be an impossible request. A time cost and estimated cardinality would be enough. Making statistics available <i>Ã la</i> DARQ was also discussed. Being able to declare cardinalities expected of a remote end-point is probably necessary anyway, since not all will implement the cost model interface. For standardization, agreeing on what is a proper description of content and cardinality and how fine grained this must be will be so difficult that I would not wait for it. A cost model interface would nicely hide this within the end-point itself.</p> <p>With Virtuoso, we do not have a federated SPARQL scheme but we could have the ARQ-like service construct. We'd use our own cost model with explicit declarations of cardinalities of the remote data for guessing a join order. Still, this is a bit of work. We'll see.</p> <p>For practicality, the service construct coupled with join order hints is the best short term bet. Making this pretty enough for standardization is not self-evident, as it requires end-point description and/or cost model hooks for things to stay declarative.</p> </li> <li> <p> <b>End-point description</b> - This question has been around for a while; I have <a href="http://www.openlinksw.com/weblog/oerling/?id=1085" id="link-id101d3440">blogged about it earlier</a>, but we are not really at a point where there would be even rough consensus about an end-point ontology. We should probably do something on our own to demonstrate some application of this, as we host lots of <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0xfaec780">linked open data</a> sets.</p> </li> <li> <p> <b>SQL equivalence</b> - There were many requests for aggregation, some for subqueries and nesting, expressions in select, negation, existence and so on. I would call these all SQL equivalence. One use case was taking all the teams in the database and for all with over 5 members, add the big_team class and a property for member count.</p> <p>With Virtuoso, we could write this as -- </p> <blockquote> <code>construct { ?team a big_team . ?team member_count ?ct } from ... where {?team a team . { select ?team2 count (*) as ?ct where { ?m member_of ?team2 } . filter (?team = ?team2 and ? ct > 5) }}</code> </blockquote> <p>We have pretty much all the SQL equivalence features, as we have been working for some time at translating the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xe2392e8">TPC-H</a> workload into SPARQL.</p> <p>The usefulness of these things is uncontested but standardization could be hard as there are subtle questions about variable scope and the like.</p> </li> <li> <p> <b>Inference</b> - The SPARQL spec does not deal with transitivity or such matters because it is assumed that these are handled by an underlying inference layer. This is however most often not so. There was interest in more fine grained control of inference, for example declaring that just one property in a query would be transitive or that subclasses should be taken into account in only one triple pattern. As far as I am concerned, this is very reasonable, and we even offer extensions for this sort of thing in Virtuoso's SPARQL. This however only makes sense if the inference is done at query time and pattern by pattern. For instance, if forward chaining is used, this no longer makes sense. Specifying that some forward chaining ought to be done at query time is impractical, as the operation can be very large and time consuming and it is the DBA's task to determine what should be stored and for how long, how changes should be propagated, and so on. All these are application dependent and standardizing will be difficult.</p> <p>Support for RDF features like lists and bags would all fall into the functions an underlying inference layer should perform. These things are of special interest when querying <a href="http://dbpedia.org/resource/Web_Ontology_Language" id="link-id0xfd87f78">OWL</a> models, for example.</p> </li> <li> <p> <b>Path expressions</b> - Path expressions were requested by a few people. We have implemented some, as in </p> <blockquote> <code>?product+?has_supplier+>s_name = "Gizmos, Inc.".</code> </blockquote> This means that one supplier of product has name "Gizmo, Inc.". This is a nice shorthand but we run into problems if we start supporting repetitive steps, optional steps, and the like.</li> <p>In conclusion, update, full text, and basic counting and grouping would seem straightforward at this point. Nesting queries, value subqueries, views, and the like should not be too hard if an agreement is reached on scope rules. Inference and federation will probably need more experimentation but a lot can be had already with very simple fine grained control of backward chaining, if such applies, or with explicit end-point references and explicit join order. These are practical but not pretty enough for committee consensus, would be my guess. Anyway, it will be a few months before anything formal will happen.</p>
Linked Data and Information Architecture
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-29#1350
2008-04-29T14:37:22Z
2008-04-29T17:18:21.000048-04:00
<div> <div style="display:none;">Linked Data and Information Architecture</div> <p>We had a workshop on <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1437ac70">Linked Open Data</a> (<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x1315f788">LOD</a>) last week in <a href="http://www2008.org/" id="link-id0x13737468">Beijing</a>. You can see the papers in <a href="http://events.linkeddata.org/ldow2008/#program" id="link-id10651ab8">the program</a>. The event was a success with plenty of good talks and animated conversation. I will not go into every paper here but will comment a little on the conversation and draw some technology requirements going forward.</p> <p>Tim Berners-Lee showed a read-write version of <a href="http://dig.csail.mit.edu/2005/ajar/release/tabulator/0.8/tab.html" id="link-id0x15633520">Tabulator</a>. This raises the question of updating on the <a href="http://dbpedia.org/resource/Data" id="link-id0x1350a178">Data</a> Web. The consensus was that one could assert what one wanted in one's own space but that others' spaces would be read-only. What spaces one considered relevant would be the user's or developer's business, as in the document web.</p> <p>It seems to me that a significant use case of LOD is an open-web situation where the user picks a broad read-only "data wallpaper" or backdrop of assertions, and then uses this combined with a much smaller, local, writable data set. This is certainly the case when editing data for publishing, as in Tim's demo. This will also be the case when developing mesh-ups combining multiple distinct data sets bound together by sets of SameAs assertions, for example. Questions like, "What is the minimum subset of n data sets needed for deriving the result?" will be common. This will also be the case in applications using proprietary data combined with open data.</p> <p>This means that databases will have to deal with queries that specify large lists of included graphs, all graphs in the store or all graphs with an exclusion list. All this is quite possible but again should be considered when architecting systems for an open <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0xa27bae8">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0x155c3f18">web</a>.</p> <p>"There is data but what can we really do with it? How far can we trust it, and what can we confidently decide based on it?"</p> <p>As an answer to this question, <a href="http://zitgist.com/about/" id="link-id0xd447580">Zitgist</a> has compiled the <a href="http://umbel.org/about/" id="link-id0x14735008">UMBEL</a> taxonomy using <a href="http://dbpedia.org/resource/SKOS" id="link-id0x15ab1c48">SKOS</a>. This draws on Wikipedia, Open CYC, Wordnet, and <a href="http://www.mpi-inf.mpg.de/~suchanek/downloads/yago/" id="link-id0x15d5aa88">YAGO</a>, hence the acronym WOWY. UMBEL is both a taxononmy and a set of instance data, containing a large set of <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0x9fe45d98">named entities</a>, including persons, organizations, geopolitical entities, and so forth. By extracting references to this set of named entities from documents and correlating this to the taxonomy, one gets a good idea of what a document (or part thereof) is about.</p> <p>Kingsley presented this in the Zitgist demo. This is our answer to the criticism about <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xa1920800">DBpedia</a> having errors in classification. DBpedia, as a bootstrap stage, is about giving names to all things. Subsequent efforts like UMBEL are about refining the relationships.</p> <p>"Should there be a global <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x12cd5290">URI</a> dictionary?"</p> <p>There was a talk by Paolo Bouquet about <a href="http://dbpedia.org/resource/Entity" id="link-id0x12d03400">Entity</a> Name System, a a sort of data DNS, with the purpose of associating some description and rough classification to URIs. This would allow discovering URIs for reuse. I'd say that this is good if it can cut down on the SameAs proliferation and if this can be widely distributed and replicated for resilience, <i>Ã la</i> DNS. On the other hand, it was pointed out that this was not quite in the LOD spirit, where parties would mint their own dereferenceable URIs, in their own domains. We'll see.</p> <p>"What to do when identity expires?"</p> <p>Giovanni of Sindice said that a document should be removed from search if it was no longer available. Kingsley pointed out that resilience of reference requires some way to recover data. The data web cannot be less resilient than the document web, and there is a point to having access to history. He recommended hooking up with the <a href="http://dbpedia.org/resource/Internet" id="link-id0x143e4130">Internet</a> Archive, since they make long term persistence their business. In this way, if an application depends on data, and the URIs on which it depends are no longer dereferenceable or or provide content from a new owner of the domain, those who need the old version can still get it and host it themselves.</p> <p>It is increasingly clear that OWL SameAs is both the blessing and bane of linked data. We can easily have tens of URIs for the same thing, especially with people. Still, these should be considered the same.</p> <p>Returning every synonym in a query answer hardly makes sense but accepting them as input seems almost necessary. This is what we do with <a href="http://virtuoso.openlinksw.com" id="link-id0x15a2a930">Virtuoso</a>'s SameAs support. Even so, this can easily double query times even when there are no synonyms.</p> <p>Be that as it may, SameAs is here to stay; just consider the mapping of DBpedia to Geonames, for example.</p> <p>Also, making aberrant SameAs statements can completely poison a data set and lead to absurd query results. Hence choosing which SameAs assertions from which source will be considered seems necessary. In an open web scenario, this leads inevitably to multi-graph queries that can be complex to write with regular <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x12bb8ce8">SPARQL</a>. By extension, it seems that a good query would also include the graphs actually used for deriving each result row. This is of course possible but has some implications on how databases should be organized.</p> <p>Yves Raymond gave a talk about deriving identity between Musicbrainz and Jamendo. I see the issue as a core question of linked data in general. The algorithm Yves presented started with attribute value similarities and then followed related entities. Artists would be the same if they had similar names and similar names of albums with similar song titles, for example. We can find the same basic question in any analysis, for example, looking at how news reporting differs between media, supposing there is adequate entity extraction.</p> <p>There is basic graph diffing in <a href="http://data.semanticweb.org/conference/iswc-aswc/2007/tracks/research/papers/533/html" id="link-id0x153c1fa8">RDFSync</a>, for example. But here we are expanding the context significantly. We will traverse references to some depth, allow similarity matches, SameAs, and so forth. Having presumed identity of two URIs, we can then look at the difference in their environment to produce a human readable summary. This could then be evaluated for purposes of analysis or of combining content.</p> <p>At first sight, these algorithms seem well parallelizable, as long as all threads have access to all data. For scaling, this means a probably message-bound distributed algorithm. This is something to look into for the next stage of linked data.</p> <p>Some inference is needed, but if everybody has their own choice of data sets to query, then everybody would also have their own entailed triples. This will make for an explosion of entailed graphs if forward chaining is used. Forward chaining is very nice because it keeps queries simple and easy to optimize. With Virtuoso, we still favor backward chaining since we expect a great diversity of graph combinations and near infinite volume in the open web scenario. With private repositories of slowly changing data put together for a special application, the situation is different.</p> <p>In conclusion, we have a real LOD movement with actual momentum and a good idea of what to do next. The next step is promoting this to the broader community, starting with <a href="http://www.linkeddataplanet.com/" id="link-id0x155d1d00">Linked Data Planet</a> in New York in June.</p> </div>
On Sem Web Search
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-29#1349
2008-04-29T14:37:21Z
2008-10-02T11:37:12.000008-04:00
<div> <div style="display:none;">On Sem Web Search</div> <p> <i>"I give the search keywords and you give me a <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1a603f18">SPARQL</a> end-point and a query that will get the <a href="http://dbpedia.org/resource/Data" id="link-id0x1bda5c00">data</a>."</i> </p> <p>Thus did one SPARQL user describe the task of a semantic/data web search engine.</p> <p>In <a href="http://www.openlinksw.com/weblog/oerling/?id=1336" id="link-idff98750">a previous post</a>, I suggested that if the data web were the size of the document web, we'd be looking at two orders of magnitude more search complexity. It just might be so.</p> <p>In the conversation, I pointed out that a search engine might have a copy of everything and even a capability to do SPARQL and full text on it all, yet still the users would be better off doing the queries against the SPARQL end-points of the data publishers. It is a bit like the fact that not all web browsing runs off Google's cache. With the data web, the point is even more pronounced, as serving a hit from Google's cache is a small operation but a complex query might be a very large one.</p> <p>Yet, the data web is about ad-hoc joining between data sets of different origins. Thus a search engine of the data web ought to be capable of joining also, even if large queries ought to be run against individual publishers' end-points or the user's own data warehouse.</p> <p>For ranking, the general consensus was that no single hit-ranking would be good for the data web. Thus word frequency-based hit-scores are OK for text hits but more is not obvious. I would think that some link analysis could apply but this will take some more experimentation.</p> <p>For search summaries, if we have splitting of data sets into small fragments <i>Ã la</i> <a href="http://sindice.com/" id="link-id0x1d2b7288">Sindice</a>, search summaries are pretty much the same as with just text search. If we store triples, then we can give text style summaries of text hits in literals and Fresnel lens views of the structured data around the literal. For showing a page of hits, the lenses must abbreviate heavily but this is still feasible. The engine would know about the most common ontologies and summarize instance data accordingly.</p> <p>Chris Bizer pointed out that trust and provenance are critical, especially if an answer is arrived at by joining multiple data sets. The trust of the conclusion is no greater than that of the weakest participating document. Different users will have different trusted sources.</p> <p>A mature data web search engine would combine a provenance/trust specification, a search condition consisting of SPARQL or full text or both, and a specification for hit rank. Again, most searches would use defaults, but these three components should in principle be orthogonally specifiable.</p> <p>Many places may host the same data set either for download or SPARQL access. The <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x1b2317d0">URI</a> of the data set is not its <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id0x1c55dd68">URL</a>. Different places may further host multiple data sets on one end-point. Thus the search engine ought to return all end-points where the set is to be found. The end-points themselves ought to be able to say what data sets they contain, under what graph IRIs. Since there is no consensus about end-point self description, this too would be left to the search engine. In practice, this could be accomplished by extending Sindice's semantic site map specification. A possible query would be to find an end-point containing a set of named data sets. If none were found, the search engine itself could run a query joining all the sets since it at least would hold them all.</p> <p>Since many places will host sets like Wordnet or Uniprot, indexing these once for each copy hardly makes sense. Thus a site should identify its data by the data set's URI and not the copy's URL.</p> <p>It came up in the discussion that search engines should share a ping format so that a single message format would be enough to notify any engine about data being updated. This is already partly the case with Sindice and <a href="http://www.pingthesemanticweb.com/" id="link-id0xa405ebd0">PTSW</a> (<a href="http://www.pingthesemanticweb.com/" id="link-id0x1c051a00">PingTheSemanticWeb</a>) sharing a ping format. </p> <p>Further, since it is no trouble to publish a copy of the 45G Uniprot file but a fair amount of work to index it, search engines should be smart about processing requests to index things, since these can amount to a denial of service attack. </p> <p>Probably very large data sets should be indexed only in the form supplied by their publisher, and others hosting copies would just state that they hold a copy. If the claim to the copy proved false, users could complain and the search engine administrator would remove the listing. It seems that some manual curating cannot be avoided here. </p> <h2>On Data Web Search Business Model</h2> <p>It seems there can be an overlap between the data web search and the data web hosting businesses. For example, Talis rents space for hosting <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1a60c7e0">RDF</a> data with SPARQL access. A search engine should offer basic indexing of everything for free, but could charge either data publishers or end users for running SPARQL queries across data sets. These do not have the nicely anticipatable and fairly uniform resource consumption of text lookups. In this manner, a search provider could cost-justify the capacity for allowing arbitrary queries. </p> <p>The value of the data web consists of unexpected joining. Such joining takes place most efficiently if the sources are at least in some proximity, for example in the same data center. Thus the search provider could monetize functioning as the database provider for mesh-ups. In the document web, publishing pages is very simple and there is no great benefit from co-locating search and pages, rather the opposite. For the data web, the hosting with SPARQL and all is more complex and resembles providing search. Thus providing search can combine with providing SPARQL hosting, once we accept in principle that search should have arbitrary inter-document joining, even if it is at an extra premium.</p> <p>The present search business model is advertising. If the data web is to be accessed by automated agents such as mesh-up code, display of ads is not self-evident. This is quite separate from the fact that semantics can lead to better ad targeting.</p> <p>One model would be to do text lookups for free from a regular web page but show ads, just a la Google search ads. Using the service via web services for text or SPARQL would have a cost paid by the searching or publishing party and would not be financed by advertising.</p> <p>In the case of data used in value-add data products (mesh-ups) that have financial value to their users, the original publisher of the data could even be paid for keeping the data up-to-date. This would hold for any time-sensitive feeds like news or financial feeds. Thus the hosting/search provider would be a broker of data-use fees and the data producer would be in the position of an AdSense inventory owner, i.e., a web site which shows AdSense ads. Organizing this under a hub providing back-office functions similar to an ad network could make sense even if the actual processing were divided among many sites.</p> <p>Kingsley has repeatedly formulated the core value proposition of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x3728a2f8">semantic web</a> in terms of dealing with <a href="http://dbpedia.org/resource/Information" id="link-id0x1bbcbeb8">information</a> overload: There is the real-time enterprise and the real-time individual and both are beasts of perception. Their image is won and lost in the <a href="http://dbpedia.org/resource/Internet" id="link-id0x1843b020">Internet</a> online conversation space. We know that allegations, even if later proven false, will stick if left unchallenged. The function of semantics on the web is to allow one to track and manage where one stands. In fact, Garlik has made a business of just this, but now from a privacy and security angle. The <a href="http://www.garlik.com/" id="link-id0x1aa76ab0">Garlik DataPatrol</a> harvests data from diverse sources and allows assessing vulnerability to identity theft, for example.</p> <p>If one is in the business of collating all the structured data in the world, as a data web search engine is, then providing custom alerts for both security or public image management is quite natural. This can be a very valuable service if it works well.</p> <p>At OpenLink, we will now experiment with the Sindice/<a href="http://zitgist.com/about/" id="link-id0x18800228">Zitgist</a>/PingTheSemanticWeb content. This is a regular part of the productization of <a href="http://virtuoso.openlinksw.com" id="link-id0x1adf39c8">Virtuoso</a>'s cluster edition. We expect to release some results in the next 4 weeks.</p> </div>
WWW 2008
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-29#1348
2008-04-29T14:37:20Z
2008-04-29T13:35:23-04:00
<div> <div style="display:none;">WWW 2008</div> <p>Following my return from WWW 2008 in <a href="http://www2008.org/" id="link-id0x9ff7d5d0">Beijing</a>, I will write a series of <a href="http://dbpedia.org/resource/Blog" id="link-id0x9e4a7650">blog</a> posts discussing diverse topics that were brought up in presentations and conversations during the week.</p> <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x9e7ae398">Linked data</a> was our main interest in the conference and there was a one day workshop on this, unfortunately overlapping with a day of W3C Advisory Committee meetings. Hence Tim Berners-Lee, one of the chairs of the workshop, could not attend for most of the day. Still, he was present to say that "<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0xa287d38">Linked open data</a> is the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x15372940">semantic web</a> and the web done as it ought to be done." <p>For my part, I will draw some architecture conclusions from the different talks and extrapolate about the requirements on database platforms for linked data.</p> <p>Chris Bizer predicted that 2008 would be the year of <a href="http://dbpedia.org/resource/Data" id="link-id0xa1454c58">data</a> web search, if 2007 was the year of <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xa0f73c50">SPARQL</a>. This may be the case, as linked data is now pretty much a reality and the questions of discovery become prevalent. There was a birds-of-a-feather session on this and I will make some comments on what we intend to explore in bridging between the text index based semantic web search engines and SPARQL.</p> <p>Andy Seaborne convened a birds-of-a-feather session on the future of SPARQL. Many of the already anticipated and implemented requirements were confirmed and a few were introduced. A separate blog post will discuss these further.</p> <p>From the various discussions held throughout the conference, we conclude that plug-and-play operation with the major semantic web frameworks of Jena, Sesame, and Redland, is our major immediate-term deliverable. Our efforts in this direction thus far are insufficient and we will next have these done with the right supervision and proper interop testing. The issues are fortunately simple but doing things totally right require some small server side support and some <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xa5d4d5b8">JDBC</a>/<a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x9dc28d10">ODBC</a> tweaks, so to the interested, we advise to wait for an update to be published on this blog.</p> <p>I further had a conversation with Andy Seaborne about using Jena reasoning capabilities with <a href="http://virtuoso.openlinksw.com" id="link-id0xa2754050">Virtuoso</a> and generally the issues of "impedance mismatch" between reasoning and typical database workloads. More on this later. </p> </div>
Linked Data and Information Architecture
http://www.openlinksw.com/weblog/oerling/?date=2008-04-29#1347
2008-04-29T12:08:24Z
2008-04-29T17:18:15.000009-04:00
<p>We had a workshop on <a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0x9e7e5098">Linked Open Data</a> (<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0xb9e86c8">LOD</a>) last week in <a href="http://www2008.org/" id="link-id0x131a72a0">Beijing</a>. You can see the papers in <a href="http://events.linkeddata.org/ldow2008/#program" id="link-id10651ab8">the program</a>. The event was a success with plenty of good talks and animated conversation. I will not go into every paper here but will comment a little on the conversation and draw some technology requirements going forward.</p> <p>Tim Berners-Lee showed a read-write version of <a href="http://dig.csail.mit.edu/2005/ajar/release/tabulator/0.8/tab.html" id="link-id0x9da015a0">Tabulator</a>. This raises the question of updating on the <a href="http://dbpedia.org/resource/Data" id="link-id0x14813578">Data</a> Web. The consensus was that one could assert what one wanted in one's own space but that others' spaces would be read-only. What spaces one considered relevant would be the user's or developer's business, as in the document web.</p> <p>It seems to me that a significant use case of LOD is an open-web situation where the user picks a broad read-only "data wallpaper" or backdrop of assertions, and then uses this combined with a much smaller, local, writable data set. This is certainly the case when editing data for publishing, as in Tim's demo. This will also be the case when developing mesh-ups combining multiple distinct data sets bound together by sets of SameAs assertions, for example. Questions like, "What is the minimum subset of n data sets needed for deriving the result?" will be common. This will also be the case in applications using proprietary data combined with open data.</p> <p>This means that databases will have to deal with queries that specify large lists of included graphs, all graphs in the store or all graphs with an exclusion list. All this is quite possible but again should be considered when architecting systems for an open <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x9f1a1d8">linked data</a> <a href="http://dbpedia.org/resource/Giant_Global_Graph" id="link-id0xa5a3e1b0">web</a>.</p> <p>"There is data but what can we really do with it? How far can we trust it, and what can we confidently decide based on it?"</p> <p>As an answer to this question, <a href="http://zitgist.com/about/" id="link-id0xad44dc0">Zitgist</a> has compiled the <a href="http://umbel.org/about/" id="link-id0x9ebde358">UMBEL</a> taxonomy using <a href="http://dbpedia.org/resource/SKOS" id="link-id0xa04a85c0">SKOS</a>. This draws on Wikipedia, Open CYC, Wordnet, and <a href="http://www.mpi-inf.mpg.de/~suchanek/downloads/yago/" id="link-id0x9fdd9018">YAGO</a>, hence the acronym WOWY. UMBEL is both a taxononmy and a set of instance data, containing a large set of <a href="http://dbpedia.org/resource/Named_entity_recognition" id="link-id0xa0b9cb70">named entities</a>, including persons, organizations, geopolitical entities, and so forth. By extracting references to this set of named entities from documents and correlating this to the taxonomy, one gets a good idea of what a document (or part thereof) is about.</p> <p>Kingsley presented this in the Zitgist demo. This is our answer to the criticism about <a href="http://dbpedia.org/resource/DBpedia" id="link-id0xdc5d940">DBpedia</a> having errors in classification. DBpedia, as a bootstrap stage, is about giving names to all things. Subsequent efforts like UMBEL are about refining the relationships.</p> <p>"Should there be a global <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0xa0aa2cc0">URI</a> dictionary?"</p> <p>There was a talk by Paolo Bouquet about <a href="http://dbpedia.org/resource/Entity" id="link-id0x9e6f4b28">Entity</a> Name System, a a sort of data DNS, with the purpose of associating some description and rough classification to URIs. This would allow discovering URIs for reuse. I'd say that this is good if it can cut down on the SameAs proliferation and if this can be widely distributed and replicated for resilience, <i>Ã la</i> DNS. On the other hand, it was pointed out that this was not quite in the LOD spirit, where parties would mint their own dereferenceable URIs, in their own domains. We'll see.</p> <p>"What to do when identity expires?"</p> <p>Giovanni of Sindice said that a document should be removed from search if it was no longer available. Kingsley pointed out that resilience of reference requires some way to recover data. The data web cannot be less resilient than the document web, and there is a point to having access to history. He recommended hooking up with the <a href="http://dbpedia.org/resource/Internet" id="link-id0xcc203f8">Internet</a> Archive, since they make long term persistence their business. In this way, if an application depends on data, and the URIs on which it depends are no longer dereferenceable or or provide content from a new owner of the domain, those who need the old version can still get it and host it themselves.</p> <p>It is increasingly clear that OWL SameAs is both the blessing and bane of linked data. We can easily have tens of URIs for the same thing, especially with people. Still, these should be considered the same.</p> <p>Returning every synonym in a query answer hardly makes sense but accepting them as input seems almost necessary. This is what we do with <a href="http://virtuoso.openlinksw.com" id="link-id0xa0d28c98">Virtuoso</a>'s SameAs support. Even so, this can easily double query times even when there are no synonyms.</p> <p>Be that as it may, SameAs is here to stay; just consider the mapping of DBpedia to Geonames, for example.</p> <p>Also, making aberrant SameAs statements can completely poison a data set and lead to absurd query results. Hence choosing which SameAs assertions from which source will be considered seems necessary. In an open web scenario, this leads inevitably to multi-graph queries that can be complex to write with regular <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xc89a768">SPARQL</a>. By extension, it seems that a good query would also include the graphs actually used for deriving each result row. This is of course possible but has some implications on how databases should be organized.</p> <p>Yves Raymond gave a talk about deriving identity between Musicbrainz and Jamendo. I see the issue as a core question of linked data in general. The algorithm Yves presented started with attribute value similarities and then followed related entities. Artists would be the same if they had similar names and similar names of albums with similar song titles, for example. We can find the same basic question in any analysis, for example, looking at how news reporting differs between media, supposing there is adequate entity extraction.</p> <p>There is basic graph diffing in <a href="http://data.semanticweb.org/conference/iswc-aswc/2007/tracks/research/papers/533/html" id="link-id0x9fe62620">RDFSync</a>, for example. But here we are expanding the context significantly. We will traverse references to some depth, allow similarity matches, SameAs, and so forth. Having presumed identity of two URIs, we can then look at the difference in their environment to produce a human readable summary. This could then be evaluated for purposes of analysis or of combining content.</p> <p>At first sight, these algorithms seem well parallelizable, as long as all threads have access to all data. For scaling, this means a probably message-bound distributed algorithm. This is something to look into for the next stage of linked data.</p> <p>Some inference is needed, but if everybody has their own choice of data sets to query, then everybody would also have their own entailed triples. This will make for an explosion of entailed graphs if forward chaining is used. Forward chaining is very nice because it keeps queries simple and easy to optimize. With Virtuoso, we still favor backward chaining since we expect a great diversity of graph combinations and near infinite volume in the open web scenario. With private repositories of slowly changing data put together for a special application, the situation is different.</p> <p>In conclusion, we have a real LOD movement with actual momentum and a good idea of what to do next. The next step is promoting this to the broader community, starting with <a href="http://www.linkeddataplanet.com/" id="link-id0xa16319a8">Linked Data Planet</a> in New York in June.</p>
On Sem Web Search
http://www.openlinksw.com/weblog/oerling/?date=2008-04-29#1346
2008-04-29T12:03:47Z
2008-10-02T11:37:07.000002-04:00
<p> <i>"I give the search keywords and you give me a <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1a474e80">SPARQL</a> end-point and a query that will get the <a href="http://dbpedia.org/resource/Data" id="link-id0x18e93100">data</a>."</i> </p> <p>Thus did one SPARQL user describe the task of a semantic/data web search engine.</p> <p>In <a href="http://www.openlinksw.com/weblog/oerling/?id=1336" id="link-idff98750">a previous post</a>, I suggested that if the data web were the size of the document web, we'd be looking at two orders of magnitude more search complexity. It just might be so.</p> <p>In the conversation, I pointed out that a search engine might have a copy of everything and even a capability to do SPARQL and full text on it all, yet still the users would be better off doing the queries against the SPARQL end-points of the data publishers. It is a bit like the fact that not all web browsing runs off Google's cache. With the data web, the point is even more pronounced, as serving a hit from Google's cache is a small operation but a complex query might be a very large one.</p> <p>Yet, the data web is about ad-hoc joining between data sets of different origins. Thus a search engine of the data web ought to be capable of joining also, even if large queries ought to be run against individual publishers' end-points or the user's own data warehouse.</p> <p>For ranking, the general consensus was that no single hit-ranking would be good for the data web. Thus word frequency-based hit-scores are OK for text hits but more is not obvious. I would think that some link analysis could apply but this will take some more experimentation.</p> <p>For search summaries, if we have splitting of data sets into small fragments <i>Ã la</i> <a href="http://sindice.com/" id="link-id0x19f64110">Sindice</a>, search summaries are pretty much the same as with just text search. If we store triples, then we can give text style summaries of text hits in literals and Fresnel lens views of the structured data around the literal. For showing a page of hits, the lenses must abbreviate heavily but this is still feasible. The engine would know about the most common ontologies and summarize instance data accordingly.</p> <p>Chris Bizer pointed out that trust and provenance are critical, especially if an answer is arrived at by joining multiple data sets. The trust of the conclusion is no greater than that of the weakest participating document. Different users will have different trusted sources.</p> <p>A mature data web search engine would combine a provenance/trust specification, a search condition consisting of SPARQL or full text or both, and a specification for hit rank. Again, most searches would use defaults, but these three components should in principle be orthogonally specifiable.</p> <p>Many places may host the same data set either for download or SPARQL access. The <a href="http://dbpedia.org/resource/Uniform_Resource_Identifier" id="link-id0x19f34660">URI</a> of the data set is not its <a href="http://dbpedia.org/resource/Uniform_Resource_Locator" id="link-id0x1afbb680">URL</a>. Different places may further host multiple data sets on one end-point. Thus the search engine ought to return all end-points where the set is to be found. The end-points themselves ought to be able to say what data sets they contain, under what graph IRIs. Since there is no consensus about end-point self description, this too would be left to the search engine. In practice, this could be accomplished by extending Sindice's semantic site map specification. A possible query would be to find an end-point containing a set of named data sets. If none were found, the search engine itself could run a query joining all the sets since it at least would hold them all.</p> <p>Since many places will host sets like Wordnet or Uniprot, indexing these once for each copy hardly makes sense. Thus a site should identify its data by the data set's URI and not the copy's URL.</p> <p>It came up in the discussion that search engines should share a ping format so that a single message format would be enough to notify any engine about data being updated. This is already partly the case with Sindice and <a href="http://www.pingthesemanticweb.com/" id="link-id0x17e36628">PTSW</a> (<a href="http://www.pingthesemanticweb.com/" id="link-id0x1a2a9060">PingTheSemanticWeb</a>) sharing a ping format. </p> <p>Further, since it is no trouble to publish a copy of the 45G Uniprot file but a fair amount of work to index it, search engines should be smart about processing requests to index things, since these can amount to a denial of service attack. </p> <p>Probably very large data sets should be indexed only in the form supplied by their publisher, and others hosting copies would just state that they hold a copy. If the claim to the copy proved false, users could complain and the search engine administrator would remove the listing. It seems that some manual curating cannot be avoided here. </p> <h2>On Data Web Search Business Model</h2> <p>It seems there can be an overlap between the data web search and the data web hosting businesses. For example, Talis rents space for hosting <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x192732f8">RDF</a> data with SPARQL access. A search engine should offer basic indexing of everything for free, but could charge either data publishers or end users for running SPARQL queries across data sets. These do not have the nicely anticipatable and fairly uniform resource consumption of text lookups. In this manner, a search provider could cost-justify the capacity for allowing arbitrary queries. </p> <p>The value of the data web consists of unexpected joining. Such joining takes place most efficiently if the sources are at least in some proximity, for example in the same data center. Thus the search provider could monetize functioning as the database provider for mesh-ups. In the document web, publishing pages is very simple and there is no great benefit from co-locating search and pages, rather the opposite. For the data web, the hosting with SPARQL and all is more complex and resembles providing search. Thus providing search can combine with providing SPARQL hosting, once we accept in principle that search should have arbitrary inter-document joining, even if it is at an extra premium.</p> <p>The present search business model is advertising. If the data web is to be accessed by automated agents such as mesh-up code, display of ads is not self-evident. This is quite separate from the fact that semantics can lead to better ad targeting.</p> <p>One model would be to do text lookups for free from a regular web page but show ads, just a la Google search ads. Using the service via web services for text or SPARQL would have a cost paid by the searching or publishing party and would not be financed by advertising.</p> <p>In the case of data used in value-add data products (mesh-ups) that have financial value to their users, the original publisher of the data could even be paid for keeping the data up-to-date. This would hold for any time-sensitive feeds like news or financial feeds. Thus the hosting/search provider would be a broker of data-use fees and the data producer would be in the position of an AdSense inventory owner, i.e., a web site which shows AdSense ads. Organizing this under a hub providing back-office functions similar to an ad network could make sense even if the actual processing were divided among many sites.</p> <p>Kingsley has repeatedly formulated the core value proposition of the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0x192611e0">semantic web</a> in terms of dealing with <a href="http://dbpedia.org/resource/Information" id="link-id0x1b0abcf0">information</a> overload: There is the real-time enterprise and the real-time individual and both are beasts of perception. Their image is won and lost in the <a href="http://dbpedia.org/resource/Internet" id="link-id0x1b2d4078">Internet</a> online conversation space. We know that allegations, even if later proven false, will stick if left unchallenged. The function of semantics on the web is to allow one to track and manage where one stands. In fact, Garlik has made a business of just this, but now from a privacy and security angle. The <a href="http://www.garlik.com/" id="link-id0x1a563b50">Garlik DataPatrol</a> harvests data from diverse sources and allows assessing vulnerability to identity theft, for example.</p> <p>If one is in the business of collating all the structured data in the world, as a data web search engine is, then providing custom alerts for both security or public image management is quite natural. This can be a very valuable service if it works well.</p> <p>At OpenLink, we will now experiment with the Sindice/<a href="http://zitgist.com/about/" id="link-id0x1bb17320">Zitgist</a>/PingTheSemanticWeb content. This is a regular part of the productization of <a href="http://virtuoso.openlinksw.com" id="link-id0x1b20cfc8">Virtuoso</a>'s cluster edition. We expect to release some results in the next 4 weeks.</p>
WWW 2008
http://www.openlinksw.com/weblog/oerling/?date=2008-04-29#1345
2008-04-29T11:59:16Z
2008-04-29T13:35:17-04:00
<p>Following my return from WWW 2008 in <a href="http://www2008.org/" id="link-id0xa1901330">Beijing</a>, I will write a series of <a href="http://dbpedia.org/resource/Blog" id="link-id0xd356660">blog</a> posts discussing diverse topics that were brought up in presentations and conversations during the week.</p> <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x1290cb78">Linked data</a> was our main interest in the conference and there was a one day workshop on this, unfortunately overlapping with a day of W3C Advisory Committee meetings. Hence Tim Berners-Lee, one of the chairs of the workshop, could not attend for most of the day. Still, he was present to say that "<a href="http://community.linkeddata.org/dataspace/organization/lod#this" id="link-id0xa2754050">Linked open data</a> is the <a href="http://dbpedia.org/resource/Semantic_Web" id="link-id0xa099dea8">semantic web</a> and the web done as it ought to be done." <p>For my part, I will draw some architecture conclusions from the different talks and extrapolate about the requirements on database platforms for linked data.</p> <p>Chris Bizer predicted that 2008 would be the year of <a href="http://dbpedia.org/resource/Data" id="link-id0x9e0f0a98">data</a> web search, if 2007 was the year of <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xd9e64a0">SPARQL</a>. This may be the case, as linked data is now pretty much a reality and the questions of discovery become prevalent. There was a birds-of-a-feather session on this and I will make some comments on what we intend to explore in bridging between the text index based semantic web search engines and SPARQL.</p> <p>Andy Seaborne convened a birds-of-a-feather session on the future of SPARQL. Many of the already anticipated and implemented requirements were confirmed and a few were introduced. A separate blog post will discuss these further.</p> <p>From the various discussions held throughout the conference, we conclude that plug-and-play operation with the major semantic web frameworks of Jena, Sesame, and Redland, is our major immediate-term deliverable. Our efforts in this direction thus far are insufficient and we will next have these done with the right supervision and proper interop testing. The issues are fortunately simple but doing things totally right require some small server side support and some <a href="http://dbpedia.org/resource/Java_Database_Connectivity" id="link-id0xacc8c7e8">JDBC</a>/<a href="http://dbpedia.org/resource/Open_Database_Connectivity" id="link-id0x9e48d258">ODBC</a> tweaks, so to the interested, we advise to wait for an update to be published on this blog.</p> <p>I further had a conversation with Andy Seaborne about using Jena reasoning capabilities with <a href="http://virtuoso.openlinksw.com" id="link-id0x134b8e98">Virtuoso</a> and generally the issues of "impedance mismatch" between reasoning and typical database workloads. More on this later. </p>
Partitioning and Cluster Config
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-14#1340
2008-04-14T09:41:43Z
2008-04-14T16:32:43-04:00
<div> <div style="display:none;">Partitioning and Cluster Config</div> <p>Now that it is time to nail down the final database format and configuration steps for <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x1c278550">Virtuoso</a> Cluster, we have to make a couple of small choices.</p> <h3>Design Objective</h3> <p>While fixed partitioning is good for testing, it is impractical even for measuring scalability, let alone deployment. So fixed partitioning is really a no-go. The best would be entirely dynamic partitioning where the data file split after reaching a certain size and where data files migrated between any number of heterogeneous boxes, so as to equalize pressure, like a liquid fills a container. It would have to be a sluggish cool liquid with a high surface tension, else we would get "Brownian motion" in trying to equalize the pressure too often. But a liquid still.</p> <p>Sure. Now go implement this with generic RDBMS ACID semantics and the works. Feasible. But while I am a fair-to-good geek, I don't have time for this right now.</p> <p>So something in between, then. The auto-migrating files are really a problem for keeping locks, keeping tractable roll forward, special cases in message routing etc. The papers by Google and Amazon allude to this and they stop far short of serializable transaction semantics.</p> <p>So what is the design target? In practice, running a few hundred database nodes with fault tolerance. The nodes must be allowed to be of different sizes since a one time replace of hundreds of PC's fits ill with the economy.</p> <p>Addition and removal of nodes must be without global downtime but putting some partitions in read only mode for restricted periods is OK. Assigning the percentages of the data mass to the nodes can be a DBA task with a utility making suggestions based on measured disk cache hit rates and the like. Partitions and the makeup of the cluster must be maintainable from a single place, no copying of config files or such, nobody can get this right and the screw ups from having cluster nodes disagree about some part of the partition map are so untractable you don't even want to go there.</p> <p>So, how is this done? Divide the space of partitioning hashes into say 1K slices. For each slice, give the node numbers that hold this slice of the partitioning space. If there are more nodes, just divide the space in more slices. When a node comes new to the cluster, it manages no slices. Slices from other nodes can be offloaded to the new one by copying their contents. The slice holders put the slice in read only mode and the new node just does an insert-select of the partitions it is meant to take over, no logs or locks needed and all comes in order so insert is 100% in processor cache. When the copy is complete, the slice comes back in read write mode in its new location and the old holders of the slices delete theirs in the background. This is not as quick as shifting database files around but takes far less programming.</p> <p>To remove a node, reassign its slices and have the assignees read the data, as above. When the copy is done, the node can go off line.</p> <p>A slight modification of this is for cases where a slice always has more than one holder. Now, if we allocate slices to nodes on the go, keeping would-be replicas on different machines but otherwise equalizing load, we run into a problem with redundancy when we perform an operation that has no specific recipient: Suppose we count a table's rows, we should send the count request once per every slice. Now, the slice is not the recipient of the request, the cluster node hosting the slice is. We should either qualify the request with what slices it pertains to, which means extra reading and filtering or have it so that no matter the choice of replicas for any operation, there is no overlap between their contents, yet the contents cover every slice. The only simple way to enforce the latter is to have cluster nodes pair-wise as each others' replicas. Nodes will be adding nodes in pairs or threes or whatever number of replicas there will be. Google's GFS, the file system redundancy layer under Bigtable or Dynamo do not have this problem since they do not deal with generic DBMS semantics. The downside is that if a pair has two different types of boxes, the sizing should go according to the smaller one. No big deal.</p> <p>If replicas are assigned box by box instead of slice by slice, life is also simpler in terms of roll forward and reconstituting lost nodes.</p> <p>The complete list of cluster nodes and their slice allocations are kept on a master node. Each node also knows which slices it holds. With the loss of the master the situation can be reconstructed from the others. For normal boot, the nodes get the cluster layout, slice allocation and some config parameters from the master, so that if network addresses change they do not have to be written to each node. These are remembered by each node though, for the event of master failure.</p> <p>When a Virtuoso 6 database is made, the system objects are partitioned from the get-go. On a single process this has no effect. But since all the data structures exist, the transition from a single process to cluster and back is smooth.</p> <p>At any time, a database, whether single or cluster, is a self-contained unit. One server process serves one database. It is one-to-many from database to server process. A server process will not mount multiple databases. This could be done but then this would be more changes than fit in the time available so this will not be supported. One can always have multiple server processes and attached tables for genuinely inter-database operations. This said, a single database holds arbitrarily many catalogs and schemas and application objects of all sorts.</p> <p>In terms of schedule, we do the single copy per partition right now. Duplicate and triplicate copies of partitions are needed as we do some of our web scale things in the pipeline. So a degree of this supported even now but without seamless recovery, so when a replica is offline, the remaining copy is read only. Making this read-write is a matter of a little programming. DDL operations will continue to require all nodes to be online.</p> <p>As of this writing, we are making the regular test suite run on a cluster with the above described partitioning, a single copy per partition. After this, the database layout will stay constant. The first deployments will go out without replicated partitions. Replicated partitions will follow shortly, together with some of the optimizations mentioned in previous posts.</p> </div>
Architectures for Infinity
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-14#1339
2008-04-14T09:41:42Z
2008-04-14T13:19:58.000010-04:00
<div> <div style="display:none;">Architectures for Infinity</div> <p>A while back, a friend suggested he and I go check out the <a href="http://www.kurzweilai.net/" id="link-idfed3e58">Singularity Summit</a>, a conference where they talk of strong AI. Well, I am not a singularist. But since singularists are so brazenly provocative, I looked a little to see if there is anything there, engineering-wise.</p> <p>So, for a change, we'll be visionary. Read on, I also mention <a href="http://dbpedia.org/resource/RDF" id="link-id109a58e8">RDF</a> at the end.</p> <p>I will not even begin with arguments about indefinitely continuing a trend. I will just say that nature has continuous and discontinuous features. Computing is at a qualitative bend and things are not as simple as they are blithely cracked up to be. Read HEC, the high end crusader; he has good commentary on architecture. Having absorbed and understood this, you can talk again about a billion-fold increase in computing price/performance.</p> <p>When I looked further, about uploading, i.e., the sci-fi process of scanning a brain and having it continue its function inside a computer simulation, I actually found some serious work. <a href="http://p9.hostingprod.com/@modha.org/" id="link-id10bf26d0">Dharmendra Modha</a> at IBM had made a rat-scale cortical simulator running on a 32K node IBM BlueGene, with a 9x slowdown from real time. This is real stuff and in no way meant to be associated with singularism by being mentioned in the same paragraph. Anyway, I was intrigued.</p> <p>So I asked myself if I could do better. I gave it a fair try, as fair as can be without an actual experiment. The end result is that latency rules. To have deterministic semantics, one must sync across tens of thousands of processors and one cannot entirely eliminate multi-hop messages on the interconnect fabric. If one optimistically precalculates and spreads optimistic results over the cluster, rolling them back will be expensive. Local optimistic computation may have little point since most data points will directly depend on non local data. One needs two sets of wiring, a 3D torus for predominantly close range bulk and a tree for sync, just like the BlueGene has. Making the same from newer hardware makes it more economical but there's another 8 or so orders of magnitude to go before energy efficiency parity with biology. Anyway, a many-times-over qualitative gap. Human scale in real time might be just there somewhere in reach with the stuff now in pre-release if there were no bounds on cost or power consumption. We'd be talking billions at least and even then it is iffy. But this is of little relevance as long as the rat scale is at the level of a systems test for the platform and not actually simulating a plausible organism in some sort of virtual environment. Anyway, of biology I cannot judge and for the computer science, Modha et al have it figured out about as well as can.</p> <p>Simulation workloads are not the same as database. Database is easier in a way since any global sync is very rare. 2PC seldom touches every partition and if it does, the time to update was anyway greater than the time to commit. Databases are generally multi-user, with a low level of sync between users and a lot of asynchrony. On the other hand, the general case of database does not have predictable cluster locality. Well, if one has an OLTP app with a controlled set of transactions and reports, then one can partition just for that and have almost 100% affinity between the host serving the connection and the data. With <a href="http://dbpedia.org/resource/RDF" id="link-id10c92378">RDF</a> for example, such is not generally possible or would require such acrobatics of data layout that a DBA would not even begin.</p> <p>So, for database, there is a pretty much even probability for any connection between the node running the query and any other. True, function shipping can make the messages large and fairly async and latency tolerant.</p> <p>On the simulation side, it would seem that the wiring can mimic locality of the simulated process. Messages to neighbors are more likely than messages to remote nodes. So a 3D torus works well there, complemented by a tree for control messages, like sending all nodes the count of messages to expect. Of course, the control wiring (reduce tree) must have far less latency than a long path through the torus wiring and steps with long message deliveries on the torus must be rare for this to bring any profit. Also, long distance messages can go through the tree wiring if the volume is not excessive, else the tree top gets congested.</p> <p>So, looking past the multicore/multithread and a single level switched interconnect, what would the architecture of the total knowledge machine be? For the neural simulation, the above-described is the best I can come up with and IBM already has come up with it anyway. But here I am more concerned with a database/symbol processing workload than about physical simulation. For the record, I'll say that I expect no strong AI to emerge from these pursuits but that they will still be useful, basically as a support for a linked data "planetary datasphere." Like Google, except it supports arbitrary queries joining arbitrary data at web scale. This would be a couple of orders of magnitude above the text index of same. Now, in practice, most queries will be rather trivial, so it is not that the 2 orders of magnitude are always realized. This would also involve a bit more updating than the equivalent text index since reports would now and then include private materialized inference results. As a general rule, backward chaining would be nicer, since this is read only but some private workspaces for distilling data cannot be avoided.</p> <p>So, given this general spec, where should architecture go? We can talk of processors, networks and software in turn.</p> <h3>Processors </h3> <p>For evaluating processors, the archetypal task is doing a single random lookup out of a big index. Whether it is a B tree or hash is not really essential since both will have to be built of fixed size pages and will have more than one level. Both need some serialization for checking if things are in memory and for pinning them in cache for the duration of processing. This is eternally true as long as updates are permitted, even if RAM were the bottom of the hierarchy. And if not, then there is the checking of whether a page is in memory and the logic for pinning it.</p> <p>This means critical sections on shared data structures with small memory writes inside. This is anathema, yet true. So, what the processor needs is shared memory for threads and if possible an instruction for entry into a read-write lock. If the lock is busy, there should be a settable interval before the thread is removed from the core. With a multithread core, this should be just like a memory cache miss. Only if this were really prolonged would there be an interrupt to run the OS scheduler to vacate the thread and load something else, and not even this if the number of executable threads were less or equal the number of threads on the cores. </p> <p>For thread sync structures, the transactional memory ideas on the SPARC ROC might be going in this direction. For on-core threading, I have not tried how well SPARC T2 does but with the right OS support, it might be in the ballpark. The X86_64 chips have nice thread speed but the OS, whether Solaris or Linux, is a disaster if a mutex is busy. Don't know why but so it is.</p> <p>Things like IBM Cell, with multiple integrated distributed memory processors on a chip might be workable if they had hardware for global critical sections and if sub-microsecond tasks could be profitably dispatched to the specialized cores (synergistic unit in Cell terminology). If an index lookup, about 2-4 microseconds of real time, could be profitably carried out on a synergistic core without sinking it all in sync delays on the main core, there might be some gain to this. Still, operations like cache lookups tend to be pointer chasing and latency of small memory reads and sometimes writes is a big factor. I have not measured Cell for this but it is not advertised for this sort of workload. It might do nicely for the neuron simulator but not for generic database, I would guess.</p> <h3>Networks and Data Center </h3> <p>The point at which distributed memory wins over shared determines the size of the single compute node. Problems of threads and critical sections fall off but are replaced by network and the troubles of scheduling and serializing and de-serializing messages. There are really huge shared memory systems (by Cray, for example) but even there, hitting the wrong address sends a high latency message over an internal switched fabric, a bit like a network page fault. Well, if one has millions of threads runnable for catching the slack of memory access over interconnect, this might not be so bad, but this is not really the architecture for a general purpose database. So, at some point, we have clearly demarcated distributed memory with affinity of data to processor, simple as that.</p> <p>For now, the high end clustered database benchmarks run off 1 Gbit ethernet fabrics with some 30 compute nodes on a single switch. This is for shared nothing systems. For shared disk, cache fusion systems like Oracle RAC, we have more heavy duty networking like Infiniband, as one would expect. I have discussed the merits of cache fusion vs shared nothing partitioning on in a previous post.</p> <p>As long as we are at a scale that fits on a single switch with even port to port latency, we are set. For an <a href="http://dbpedia.org/resource/RDF" id="link-id0x1c4a5de8">RDF</a> workload, throughput is not really the issue but latency can be. With today's technology, nodes with 4-8 cores and 16G RAM are practical and their number is most often not really large. Adding two orders of magnitude, we get more questions. Let's say that 2 billion triples fit with relative comfort but not without disk on a 16G RAM node. This would make 500 nodes for a trillion triples. </p> <p>This is an entirely relevant and reasonable scale, considering that loading all public biomedical sets plus a pharma company's in house data could approach this ballpark. Let alone anything on the scale of the social web, i.e., the online conversation space.</p> <p>So how to manage clusters like this? The current cluster gear is oriented toward switch trees and is fairly expensive, about $1000 per node. To make this easy, we would need a wiring free, modular architecture. Picture a shelf with SATA drive size bays, each would get a compute node of 8 cores and 16G plus interconnect. To simplify the network, the module would have a laser on each side, for a cube network topology, the enclosure could connect the edges with fiber optics for the 3D torus. Mass storage would be in the same form factor, as disks or flashes to be interspersed in the proper ratio with compute nodes. All would have the same communication chip, something like a Cray SeaStar with 6 or 7 external ports. A seventh port could be used for a reduce tree. The rack would provide cooling on the shelves by circulating a coolant fluid. Reconfiguring and scaling would be a matter of adding shelves to the cabinet and laying out Lego bricks, blue for compute and red for storage. The network could achieve a latency for an arbitrary point to point message of no more than 10-20 microseconds by the right mix of cube and tree. This in a box of thousands of nodes.</p> <p>This type of layout would accommodate web scale without needing rows and rows of rack cabinets.</p> <p>The external interfaces for web requests and replies are no big deal after this, since the intra-cluster traffic is orders of magnitude higher than the result set traffic. After all, the end user does not want dumps of the database but highly refined and ranked results.</p> <h3>Software </h3> <p>We have previously talked about dynamic partitions a la Google Bigtable or Amazon Dynamo. These techniques are entirely fine and will serve for the universal knowledge store. </p> <p>But what about query logic? OK, having a consistent map of partitions shared over tens of thousands of nodes is entirely possible. So is replication and logic for using a spatially closer partition when multiple copies are know to exist. These things take some programming but there is nothing really new about them. These are a direct straightforward extension of what we do for clustering right now. </p> <p>But look to windward. How to run complex queries and inference on the platform outlined above? There are some features of RDF querying like same-as that can be easily parallelized, backward-chaining style: Just proceed with the value at hand and initiate the lookup of synonyms and let them get processed when they become available. Same for subclass and sub-property. We already do this, but could do it with more parallelism.</p> <p>No matter what advances in architecture take place, I do not see a world where every user materializes the entailment of their own same-as-es and rules over a web scale data set. So, backward chaining approaches to inference must develop. Luckily, what most queries need is simple. A rule-oriented language, like Prolog without cut will parallelize well enough. Some degree of memoization may be appropriate for cutting down on re-proving the same thing over and over. Memoization over a cluster is a problem though, since this involves messages. I should say that one should not go looking for pre-proven things beyond the node at hand and that computation should not spread too quickly or too promiscuously so as not to make long message paths. We must remember that a totally even round trip time on a large cluster just will not happen.</p> <p>Query planning on any system critically depends on correct ballpark guesses on cardinality. If predicates are amplified with transitivity and same-as at run time, the cardinality guessing becomes harder. This can kill any plan no matter the hardware. Probably, an executing query must be annotated with the underlying cardinality assumptions. If these prove radically false, the execution may abort and a new plan be made to better match what was found out. This bears some looking into. </p> <p>There are some network algorithms like shortest path, traveling salesman and similar that probably deserve a special operator in the query language. These can benefit from parallelization and a sequential implementation running on a cluster with latency will be a disaster. Expressing the message flow in a rule language is not really simple and pretty much no programmer will either appreciate the necessity or go to the trouble. Therefore such things should likely be offered by the platform and made by the few people who understand such matters.</p> <p>For forward chaining, it seems that any results should generally go into their own graph so as not to pollute the base data. This graph, supposing it is small enough, can have different partitioning from the large base data set. If the data comes from far and wide but results are local, there is better usage of a RETE like algorithm for triggering inference when data comes in. RETE will parallelize well enough, also for clusters,results just have to be broadcast to the nodes that may have a use for them.</p> <p>The programming model will typically be using a set of local overlays on a large shared data set. Queries will most often not be against a single graph. Strict transactionality will be the exception rather than the rule. At the database node level, there must be real transactionality even if the whole system most often did not run with strict ACID semantics. This is due to ACID requirements of some internal ops, e.g., some bitmap index operations, log checkpoints, DDL changes, etc.</p> <p>For procedural tasks, map-reduce is OK. We have it even <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1270" id="link-id10b7ac00">in our SQL</a>. Map-reduce is not the basis for making a DBMS but it is a nice feature for some parts of query evaluation and application logic.</p> <p>We have not talked about linking the data itself, but there is a whole workshop on this next week in <a href="http://www2008.org/" id="link-id14a5aa00">Beijing</a>; I will write about it separately. Let this just serve to state that we are serious about the platform for this, present and future.</p> <h3>Conclusion </h3> <p>The web scale database, the Google with arbitrary joining and inference, is one generation away, talking of economical implementation. Today, a price tag of $100K will buy some 50-100 billion triples worth with reasonable query response. Unlimited budget will take this a bit further, like one order of magnitude. and then, returns might be decreasing.</p> <p>Of course, this is worth nothing if the software isn't there. <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-idfdcf058">Virtuoso</a> with its cluster edition will allow one to use unlimited RAM and processors. The frontier is now at getting just the right parallelism for each task, including inference ones.</p> </div>
Virtuoso Cluster Update
http://www.openlinksw.com/blog/vdb/blog/?date=2008-04-14#1338
2008-04-14T09:41:41Z
2008-04-14T12:42:22.000006-04:00
<div> <div style="display:none;">Virtuoso Cluster Update</div> <p>We now have <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id10beda60">Virtuoso</a> 6 running most of its test suite in single process and cluster modes. It is now time to finalize how it is to be configured and deployed. A bit more on this later.</p> <p>We would have been done in about half time if we had not also redone the database physical layout with key compression. Still, if we get 3x more data in the same memory, using 64 bit ids for everything, the effort is justified. For any size above 2 billion triples, this means 3x less cost.</p> <p>A good amount of the time and effort goes into everything except the core. Of course, we first do the optimizations we find appropriate and measure them. After all, the rest has no point if these do not run in the desired ballpark.</p> <p>For delivering something, the requirements are quite the opposite: For example, when when defining a unique index, what to do when the billionth key turns out not to be unique? And then what if one of the processes is killed during the operation? Does it all come out right also when played from the roll forward log? See what I mean? There is no end of such.</p> <p>So, well after we are done with the basic functionality, we have to deal with this sort of thing. Even if we limited ourselves to <a href="http://dbpedia.org/resource/RDF" id="link-id0xa1c2a768">RDF</a> workloads only in the first cut, we still would need to do this since maintaining this would simply not be possible without some generic DBMS functionality. So we get the full feature generic clustered RDBMS in the same cut, no splitting the deliverable.</p> <p>The basic cluster execution model is described <a href="http://docs.openlinksw.com/virtuoso/clusterprogrammingsqlexmod.html" id="link-id14a5cab0">here</a>.</p> <p>There are some further optimizations that we will do at and around the time of first public cut.</p> <p>These have to do mostly with execution scheduling. For example, a bitmap intersection join must be done differently from a single server when there is latency in getting the next chunk of bits. Value sub-queries, derived tables and existences must be started as batches, just like joined tables.</p> <p>Having too many threads on an index is no good. But having a large batch of random lookups to work with, even when each of them does not have its own thread, gives some possibilities for IO optimization. When one would block for disk, start the disk asynchronously, like with a read ahead and do the next index lookup from the batch. This is specially so in cluster situations where the index lookups naturally come in "pre-vectored" batches. You could say that the loop join is rolled out. This is done anyhow for message latency reasons.</p> <p>Do we optimize for the right stuff? Well, looking into the future, it does not look like regular RAM will be the bottom of the storage hierarchy, no matter how you look at it. With solid state disks, locality may not be so important but latency is there to stay. With everything now growing sideways, as in number of cores and core multithreading, we are just looking to deepening our already warm and intimate relationship with the Moira which cuts the thread, Atropos, Lady Latency. The attention of the best minds of the industry is devoted to thee. pouring forth of the effort of the best minds in the industry.</p> </div>
Partitioning and Cluster Config
http://www.openlinksw.com/weblog/oerling/?date=2008-04-14#1337
2008-04-14T09:37:29Z
2008-04-14T16:32:38-04:00
<p>Now that it is time to nail down the final database format and configuration steps for <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x16952828">Virtuoso</a> Cluster, we have to make a couple of small choices.</p> <h3>Design Objective</h3> <p>While fixed partitioning is good for testing, it is impractical even for measuring scalability, let alone deployment. So fixed partitioning is really a no-go. The best would be entirely dynamic partitioning where the data file split after reaching a certain size and where data files migrated between any number of heterogeneous boxes, so as to equalize pressure, like a liquid fills a container. It would have to be a sluggish cool liquid with a high surface tension, else we would get "Brownian motion" in trying to equalize the pressure too often. But a liquid still.</p> <p>Sure. Now go implement this with generic RDBMS ACID semantics and the works. Feasible. But while I am a fair-to-good geek, I don't have time for this right now.</p> <p>So something in between, then. The auto-migrating files are really a problem for keeping locks, keeping tractable roll forward, special cases in message routing etc. The papers by Google and Amazon allude to this and they stop far short of serializable transaction semantics.</p> <p>So what is the design target? In practice, running a few hundred database nodes with fault tolerance. The nodes must be allowed to be of different sizes since a one time replace of hundreds of PC's fits ill with the economy.</p> <p>Addition and removal of nodes must be without global downtime but putting some partitions in read only mode for restricted periods is OK. Assigning the percentages of the data mass to the nodes can be a DBA task with a utility making suggestions based on measured disk cache hit rates and the like. Partitions and the makeup of the cluster must be maintainable from a single place, no copying of config files or such, nobody can get this right and the screw ups from having cluster nodes disagree about some part of the partition map are so untractable you don't even want to go there.</p> <p>So, how is this done? Divide the space of partitioning hashes into say 1K slices. For each slice, give the node numbers that hold this slice of the partitioning space. If there are more nodes, just divide the space in more slices. When a node comes new to the cluster, it manages no slices. Slices from other nodes can be offloaded to the new one by copying their contents. The slice holders put the slice in read only mode and the new node just does an insert-select of the partitions it is meant to take over, no logs or locks needed and all comes in order so insert is 100% in processor cache. When the copy is complete, the slice comes back in read write mode in its new location and the old holders of the slices delete theirs in the background. This is not as quick as shifting database files around but takes far less programming.</p> <p>To remove a node, reassign its slices and have the assignees read the data, as above. When the copy is done, the node can go off line.</p> <p>A slight modification of this is for cases where a slice always has more than one holder. Now, if we allocate slices to nodes on the go, keeping would-be replicas on different machines but otherwise equalizing load, we run into a problem with redundancy when we perform an operation that has no specific recipient: Suppose we count a table's rows, we should send the count request once per every slice. Now, the slice is not the recipient of the request, the cluster node hosting the slice is. We should either qualify the request with what slices it pertains to, which means extra reading and filtering or have it so that no matter the choice of replicas for any operation, there is no overlap between their contents, yet the contents cover every slice. The only simple way to enforce the latter is to have cluster nodes pair-wise as each others' replicas. Nodes will be adding nodes in pairs or threes or whatever number of replicas there will be. Google's GFS, the file system redundancy layer under Bigtable or Dynamo do not have this problem since they do not deal with generic DBMS semantics. The downside is that if a pair has two different types of boxes, the sizing should go according to the smaller one. No big deal.</p> <p>If replicas are assigned box by box instead of slice by slice, life is also simpler in terms of roll forward and reconstituting lost nodes.</p> <p>The complete list of cluster nodes and their slice allocations are kept on a master node. Each node also knows which slices it holds. With the loss of the master the situation can be reconstructed from the others. For normal boot, the nodes get the cluster layout, slice allocation and some config parameters from the master, so that if network addresses change they do not have to be written to each node. These are remembered by each node though, for the event of master failure.</p> <p>When a Virtuoso 6 database is made, the system objects are partitioned from the get-go. On a single process this has no effect. But since all the data structures exist, the transition from a single process to cluster and back is smooth.</p> <p>At any time, a database, whether single or cluster, is a self-contained unit. One server process serves one database. It is one-to-many from database to server process. A server process will not mount multiple databases. This could be done but then this would be more changes than fit in the time available so this will not be supported. One can always have multiple server processes and attached tables for genuinely inter-database operations. This said, a single database holds arbitrarily many catalogs and schemas and application objects of all sorts.</p> <p>In terms of schedule, we do the single copy per partition right now. Duplicate and triplicate copies of partitions are needed as we do some of our web scale things in the pipeline. So a degree of this supported even now but without seamless recovery, so when a replica is offline, the remaining copy is read only. Making this read-write is a matter of a little programming. DDL operations will continue to require all nodes to be online.</p> <p>As of this writing, we are making the regular test suite run on a cluster with the above described partitioning, a single copy per partition. After this, the database layout will stay constant. The first deployments will go out without replicated partitions. Replicated partitions will follow shortly, together with some of the optimizations mentioned in previous posts.</p>
Architectures for Infinity
http://www.openlinksw.com/weblog/oerling/?date=2008-04-14#1336
2008-04-14T09:32:40Z
2008-04-14T13:19:56-04:00
<p>A while back, a friend suggested he and I go check out the <a href="http://www.kurzweilai.net/" id="link-idfed3e58">Singularity Summit</a>, a conference where they talk of strong AI. Well, I am not a singularist. But since singularists are so brazenly provocative, I looked a little to see if there is anything there, engineering-wise.</p> <p>So, for a change, we'll be visionary. Read on, I also mention <a href="http://dbpedia.org/resource/RDF" id="link-id109a58e8">RDF</a> at the end.</p> <p>I will not even begin with arguments about indefinitely continuing a trend. I will just say that nature has continuous and discontinuous features. Computing is at a qualitative bend and things are not as simple as they are blithely cracked up to be. Read HEC, the high end crusader; he has good commentary on architecture. Having absorbed and understood this, you can talk again about a billion-fold increase in computing price/performance.</p> <p>When I looked further, about uploading, i.e., the sci-fi process of scanning a brain and having it continue its function inside a computer simulation, I actually found some serious work. <a href="http://p9.hostingprod.com/@modha.org/" id="link-id10bf26d0">Dharmendra Modha</a> at IBM had made a rat-scale cortical simulator running on a 32K node IBM BlueGene, with a 9x slowdown from real time. This is real stuff and in no way meant to be associated with singularism by being mentioned in the same paragraph. Anyway, I was intrigued.</p> <p>So I asked myself if I could do better. I gave it a fair try, as fair as can be without an actual experiment. The end result is that latency rules. To have deterministic semantics, one must sync across tens of thousands of processors and one cannot entirely eliminate multi-hop messages on the interconnect fabric. If one optimistically precalculates and spreads optimistic results over the cluster, rolling them back will be expensive. Local optimistic computation may have little point since most data points will directly depend on non local data. One needs two sets of wiring, a 3D torus for predominantly close range bulk and a tree for sync, just like the BlueGene has. Making the same from newer hardware makes it more economical but there's another 8 or so orders of magnitude to go before energy efficiency parity with biology. Anyway, a many-times-over qualitative gap. Human scale in real time might be just there somewhere in reach with the stuff now in pre-release if there were no bounds on cost or power consumption. We'd be talking billions at least and even then it is iffy. But this is of little relevance as long as the rat scale is at the level of a systems test for the platform and not actually simulating a plausible organism in some sort of virtual environment. Anyway, of biology I cannot judge and for the computer science, Modha et al have it figured out about as well as can.</p> <p>Simulation workloads are not the same as database. Database is easier in a way since any global sync is very rare. 2PC seldom touches every partition and if it does, the time to update was anyway greater than the time to commit. Databases are generally multi-user, with a low level of sync between users and a lot of asynchrony. On the other hand, the general case of database does not have predictable cluster locality. Well, if one has an OLTP app with a controlled set of transactions and reports, then one can partition just for that and have almost 100% affinity between the host serving the connection and the data. With <a href="http://dbpedia.org/resource/RDF" id="link-id10c92378">RDF</a> for example, such is not generally possible or would require such acrobatics of data layout that a DBA would not even begin.</p> <p>So, for database, there is a pretty much even probability for any connection between the node running the query and any other. True, function shipping can make the messages large and fairly async and latency tolerant.</p> <p>On the simulation side, it would seem that the wiring can mimic locality of the simulated process. Messages to neighbors are more likely than messages to remote nodes. So a 3D torus works well there, complemented by a tree for control messages, like sending all nodes the count of messages to expect. Of course, the control wiring (reduce tree) must have far less latency than a long path through the torus wiring and steps with long message deliveries on the torus must be rare for this to bring any profit. Also, long distance messages can go through the tree wiring if the volume is not excessive, else the tree top gets congested.</p> <p>So, looking past the multicore/multithread and a single level switched interconnect, what would the architecture of the total knowledge machine be? For the neural simulation, the above-described is the best I can come up with and IBM already has come up with it anyway. But here I am more concerned with a database/symbol processing workload than about physical simulation. For the record, I'll say that I expect no strong AI to emerge from these pursuits but that they will still be useful, basically as a support for a linked data "planetary datasphere." Like Google, except it supports arbitrary queries joining arbitrary data at web scale. This would be a couple of orders of magnitude above the text index of same. Now, in practice, most queries will be rather trivial, so it is not that the 2 orders of magnitude are always realized. This would also involve a bit more updating than the equivalent text index since reports would now and then include private materialized inference results. As a general rule, backward chaining would be nicer, since this is read only but some private workspaces for distilling data cannot be avoided.</p> <p>So, given this general spec, where should architecture go? We can talk of processors, networks and software in turn.</p> <h3>Processors </h3> <p>For evaluating processors, the archetypal task is doing a single random lookup out of a big index. Whether it is a B tree or hash is not really essential since both will have to be built of fixed size pages and will have more than one level. Both need some serialization for checking if things are in memory and for pinning them in cache for the duration of processing. This is eternally true as long as updates are permitted, even if RAM were the bottom of the hierarchy. And if not, then there is the checking of whether a page is in memory and the logic for pinning it.</p> <p>This means critical sections on shared data structures with small memory writes inside. This is anathema, yet true. So, what the processor needs is shared memory for threads and if possible an instruction for entry into a read-write lock. If the lock is busy, there should be a settable interval before the thread is removed from the core. With a multithread core, this should be just like a memory cache miss. Only if this were really prolonged would there be an interrupt to run the OS scheduler to vacate the thread and load something else, and not even this if the number of executable threads were less or equal the number of threads on the cores. </p> <p>For thread sync structures, the transactional memory ideas on the SPARC ROC might be going in this direction. For on-core threading, I have not tried how well SPARC T2 does but with the right OS support, it might be in the ballpark. The X86_64 chips have nice thread speed but the OS, whether Solaris or Linux, is a disaster if a mutex is busy. Don't know why but so it is.</p> <p>Things like IBM Cell, with multiple integrated distributed memory processors on a chip might be workable if they had hardware for global critical sections and if sub-microsecond tasks could be profitably dispatched to the specialized cores (synergistic unit in Cell terminology). If an index lookup, about 2-4 microseconds of real time, could be profitably carried out on a synergistic core without sinking it all in sync delays on the main core, there might be some gain to this. Still, operations like cache lookups tend to be pointer chasing and latency of small memory reads and sometimes writes is a big factor. I have not measured Cell for this but it is not advertised for this sort of workload. It might do nicely for the neuron simulator but not for generic database, I would guess.</p> <h3>Networks and Data Center </h3> <p>The point at which distributed memory wins over shared determines the size of the single compute node. Problems of threads and critical sections fall off but are replaced by network and the troubles of scheduling and serializing and de-serializing messages. There are really huge shared memory systems (by Cray, for example) but even there, hitting the wrong address sends a high latency message over an internal switched fabric, a bit like a network page fault. Well, if one has millions of threads runnable for catching the slack of memory access over interconnect, this might not be so bad, but this is not really the architecture for a general purpose database. So, at some point, we have clearly demarcated distributed memory with affinity of data to processor, simple as that.</p> <p>For now, the high end clustered database benchmarks run off 1 Gbit ethernet fabrics with some 30 compute nodes on a single switch. This is for shared nothing systems. For shared disk, cache fusion systems like Oracle RAC, we have more heavy duty networking like Infiniband, as one would expect. I have discussed the merits of cache fusion vs shared nothing partitioning on in a previous post.</p> <p>As long as we are at a scale that fits on a single switch with even port to port latency, we are set. For an <a href="http://dbpedia.org/resource/RDF" id="link-id0x1830ce48">RDF</a> workload, throughput is not really the issue but latency can be. With today's technology, nodes with 4-8 cores and 16G RAM are practical and their number is most often not really large. Adding two orders of magnitude, we get more questions. Let's say that 2 billion triples fit with relative comfort but not without disk on a 16G RAM node. This would make 500 nodes for a trillion triples. </p> <p>This is an entirely relevant and reasonable scale, considering that loading all public biomedical sets plus a pharma company's in house data could approach this ballpark. Let alone anything on the scale of the social web, i.e., the online conversation space.</p> <p>So how to manage clusters like this? The current cluster gear is oriented toward switch trees and is fairly expensive, about $1000 per node. To make this easy, we would need a wiring free, modular architecture. Picture a shelf with SATA drive size bays, each would get a compute node of 8 cores and 16G plus interconnect. To simplify the network, the module would have a laser on each side, for a cube network topology, the enclosure could connect the edges with fiber optics for the 3D torus. Mass storage would be in the same form factor, as disks or flashes to be interspersed in the proper ratio with compute nodes. All would have the same communication chip, something like a Cray SeaStar with 6 or 7 external ports. A seventh port could be used for a reduce tree. The rack would provide cooling on the shelves by circulating a coolant fluid. Reconfiguring and scaling would be a matter of adding shelves to the cabinet and laying out Lego bricks, blue for compute and red for storage. The network could achieve a latency for an arbitrary point to point message of no more than 10-20 microseconds by the right mix of cube and tree. This in a box of thousands of nodes.</p> <p>This type of layout would accommodate web scale without needing rows and rows of rack cabinets.</p> <p>The external interfaces for web requests and replies are no big deal after this, since the intra-cluster traffic is orders of magnitude higher than the result set traffic. After all, the end user does not want dumps of the database but highly refined and ranked results.</p> <h3>Software </h3> <p>We have previously talked about dynamic partitions a la Google Bigtable or Amazon Dynamo. These techniques are entirely fine and will serve for the universal knowledge store. </p> <p>But what about query logic? OK, having a consistent map of partitions shared over tens of thousands of nodes is entirely possible. So is replication and logic for using a spatially closer partition when multiple copies are know to exist. These things take some programming but there is nothing really new about them. These are a direct straightforward extension of what we do for clustering right now. </p> <p>But look to windward. How to run complex queries and inference on the platform outlined above? There are some features of RDF querying like same-as that can be easily parallelized, backward-chaining style: Just proceed with the value at hand and initiate the lookup of synonyms and let them get processed when they become available. Same for subclass and sub-property. We already do this, but could do it with more parallelism.</p> <p>No matter what advances in architecture take place, I do not see a world where every user materializes the entailment of their own same-as-es and rules over a web scale data set. So, backward chaining approaches to inference must develop. Luckily, what most queries need is simple. A rule-oriented language, like Prolog without cut will parallelize well enough. Some degree of memoization may be appropriate for cutting down on re-proving the same thing over and over. Memoization over a cluster is a problem though, since this involves messages. I should say that one should not go looking for pre-proven things beyond the node at hand and that computation should not spread too quickly or too promiscuously so as not to make long message paths. We must remember that a totally even round trip time on a large cluster just will not happen.</p> <p>Query planning on any system critically depends on correct ballpark guesses on cardinality. If predicates are amplified with transitivity and same-as at run time, the cardinality guessing becomes harder. This can kill any plan no matter the hardware. Probably, an executing query must be annotated with the underlying cardinality assumptions. If these prove radically false, the execution may abort and a new plan be made to better match what was found out. This bears some looking into. </p> <p>There are some network algorithms like shortest path, traveling salesman and similar that probably deserve a special operator in the query language. These can benefit from parallelization and a sequential implementation running on a cluster with latency will be a disaster. Expressing the message flow in a rule language is not really simple and pretty much no programmer will either appreciate the necessity or go to the trouble. Therefore such things should likely be offered by the platform and made by the few people who understand such matters.</p> <p>For forward chaining, it seems that any results should generally go into their own graph so as not to pollute the base data. This graph, supposing it is small enough, can have different partitioning from the large base data set. If the data comes from far and wide but results are local, there is better usage of a RETE like algorithm for triggering inference when data comes in. RETE will parallelize well enough, also for clusters,results just have to be broadcast to the nodes that may have a use for them.</p> <p>The programming model will typically be using a set of local overlays on a large shared data set. Queries will most often not be against a single graph. Strict transactionality will be the exception rather than the rule. At the database node level, there must be real transactionality even if the whole system most often did not run with strict ACID semantics. This is due to ACID requirements of some internal ops, e.g., some bitmap index operations, log checkpoints, DDL changes, etc.</p> <p>For procedural tasks, map-reduce is OK. We have it even <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1270" id="link-id10b7ac00">in our SQL</a>. Map-reduce is not the basis for making a DBMS but it is a nice feature for some parts of query evaluation and application logic.</p> <p>We have not talked about linking the data itself, but there is a whole workshop on this next week in <a href="http://www2008.org/" id="link-id14a5aa00">Beijing</a>; I will write about it separately. Let this just serve to state that we are serious about the platform for this, present and future.</p> <h3>Conclusion </h3> <p>The web scale database, the Google with arbitrary joining and inference, is one generation away, talking of economical implementation. Today, a price tag of $100K will buy some 50-100 billion triples worth with reasonable query response. Unlimited budget will take this a bit further, like one order of magnitude. and then, returns might be decreasing.</p> <p>Of course, this is worth nothing if the software isn't there. <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-idfdcf058">Virtuoso</a> with its cluster edition will allow one to use unlimited RAM and processors. The frontier is now at getting just the right parallelism for each task, including inference ones.</p>
Virtuoso Cluster Update
http://www.openlinksw.com/weblog/oerling/?date=2008-04-14#1335
2008-04-14T09:26:00Z
2008-04-14T12:42:20.000001-04:00
<p>We now have <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id10beda60">Virtuoso</a> 6 running most of its test suite in single process and cluster modes. It is now time to finalize how it is to be configured and deployed. A bit more on this later.</p> <p>We would have been done in about half time if we had not also redone the database physical layout with key compression. Still, if we get 3x more data in the same memory, using 64 bit ids for everything, the effort is justified. For any size above 2 billion triples, this means 3x less cost.</p> <p>A good amount of the time and effort goes into everything except the core. Of course, we first do the optimizations we find appropriate and measure them. After all, the rest has no point if these do not run in the desired ballpark.</p> <p>For delivering something, the requirements are quite the opposite: For example, when when defining a unique index, what to do when the billionth key turns out not to be unique? And then what if one of the processes is killed during the operation? Does it all come out right also when played from the roll forward log? See what I mean? There is no end of such.</p> <p>So, well after we are done with the basic functionality, we have to deal with this sort of thing. Even if we limited ourselves to <a href="http://dbpedia.org/resource/RDF" id="link-id0x1b4d3580">RDF</a> workloads only in the first cut, we still would need to do this since maintaining this would simply not be possible without some generic DBMS functionality. So we get the full feature generic clustered RDBMS in the same cut, no splitting the deliverable.</p> <p>The basic cluster execution model is described <a href="http://docs.openlinksw.com/virtuoso/clusterprogrammingsqlexmod.html" id="link-id14a5cab0">here</a>.</p> <p>There are some further optimizations that we will do at and around the time of first public cut.</p> <p>These have to do mostly with execution scheduling. For example, a bitmap intersection join must be done differently from a single server when there is latency in getting the next chunk of bits. Value sub-queries, derived tables and existences must be started as batches, just like joined tables.</p> <p>Having too many threads on an index is no good. But having a large batch of random lookups to work with, even when each of them does not have its own thread, gives some possibilities for IO optimization. When one would block for disk, start the disk asynchronously, like with a read ahead and do the next index lookup from the batch. This is specially so in cluster situations where the index lookups naturally come in "pre-vectored" batches. You could say that the loop join is rolled out. This is done anyhow for message latency reasons.</p> <p>Do we optimize for the right stuff? Well, looking into the future, it does not look like regular RAM will be the bottom of the storage hierarchy, no matter how you look at it. With solid state disks, locality may not be so important but latency is there to stay. With everything now growing sideways, as in number of cores and core multithreading, we are just looking to deepening our already warm and intimate relationship with the Moira which cuts the thread, Atropos, Lady Latency. The attention of the best minds of the industry is devoted to thee. pouring forth of the effort of the best minds in the industry.</p>
Virtuoso 5.0.6 Updates
http://www.openlinksw.com/blog/vdb/blog/?date=2008-03-25#1327
2008-03-25T17:23:24Z
2008-04-14T14:02:55-04:00
<div> <div style="display:none;">Virtuoso 5.0.6 Updates</div> <p>I will here summarize the developments since the last <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x17765540">Virtuoso</a> 5 Open Source release.</p> <p>On the <a href="http://dbpedia.org/resource/RDF" id="link-id0x1d8eb970">RDF</a> side, the bitmap intersection join has been improved quite a bit so that it is now almost always more than 2x more efficient than the equivalent nested loop join.</p> <p>XML trees in the object position in RDF quads were in some cases incorrectly indexed, leading to failure to retrieve quads. This is fixed and should problems occur in existing databases, they can be corrected by simply dropping and re-creating an index.</p> <p>Also the cost model has been further tuned. We have run the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x17508ee8">TPC-H</a> queries with larger databases and have profiled it extensively. There are improvements to locking, especially for concurrency of transactions with large shared lock sets, as is the case in the TPC-H queries. The rules stipulate that these have to be run with repeatable read. There are also optimizations for decimal floating point.</p> <p>A sampling of TPC-H queries translated into <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x18365440">SPARQL</a> comes with the new demo database. These show a live sample of the TPC-H schema translated into linked data, complete with SPARQL translations of the original queries. Some work is still ongoing there but the relational to RDF mapping is mature enough for real business intelligence applications now.</p> <p>On the closed source side, we have some adjustments to the virtual database. When using Virtuoso as a front end to Oracle, using the TPC-H queries as a metric, the virtual database overhead is minimal. Previously, we had some overhead because some queries were rewritten in a way that Oracle would not optimize as well as the original TPC-H text. Specifically, turning an IN sub-query predicate into an equivalent EXISTS did not sit well with Oracle.</p> </div>
Virtuoso 5.0.6 Updates
http://www.openlinksw.com/weblog/oerling/?date=2008-03-25#1326
2008-03-25T16:59:08Z
2008-03-26T11:59:02-04:00
<p>I will here summarize the developments since the last <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id15843368">Virtuoso</a> 5 Open Source release.</p> <p>On the <a href="http://dbpedia.org/resource/RDF" id="link-id101cae58">RDF</a> side, the bitmap intersection join has been improved quite a bit so that it is now almost always more than 2x more efficient than the equivalent nested loop join.</p> <p>XML trees in the object position in <a href="http://dbpedia.org/resource/RDF" id="link-id1172c108">RDF</a> quads were in some cases incorrectly indexed, leading to failure to retrieve quads. This is fixed and should problems occur in existing databases, they can be corrected by simply dropping and re-creating an index.</p> <p>Also the cost model has been further tuned. We have run the <a href="http://dbpedia.org/resource/TPC-H" id="link-idd65a998">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id11a2bf48">H</a> queries with larger databases and have profiled it extensively. There are improvements to locking, especially for concurrency of transactions with large shared lock sets, as is the case in the <a href="http://dbpedia.org/resource/TPC-H" id="link-id12cfd690">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id15891ae0">H</a> queries. The rules stipulate that these have to be run with repeatable read. There are also optimizations for decimal floating point.</p> <p>A sampling of <a href="http://dbpedia.org/resource/TPC-H" id="link-id15b12eb0">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id1172c740">H</a> queries translated into <a href="http://dbpedia.org/resource/SPARQL" id="link-id15533aa8">SPARQL</a> comes with the new demo database. These show a live sample of the <a href="http://dbpedia.org/resource/TPC-H" id="link-id15b82cd8">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id15521d50">H</a> schema translated into linked data, complete with <a href="http://dbpedia.org/resource/SPARQL" id="link-id15ae14d8">SPARQL</a> translations of the original queries. Some work is still ongoing there but the relational to <a href="http://dbpedia.org/resource/RDF" id="link-id101b1240">RDF</a> mapping is mature enough for real business intelligence applications now.</p> <p>On the closed source side, we have some adjustments to the virtual database. When using <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id10888df0">Virtuoso</a> as a front end to Oracle, using the <a href="http://dbpedia.org/resource/TPC-H" id="link-id134a9378">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-id10366320">H</a> queries as a metric, the virtual database overhead is minimal. Previously, we had some overhead because some queries were rewritten in a way that Oracle would not optimize as well as the original <a href="http://dbpedia.org/resource/TPC-H" id="link-id15536320">TPC</a>-<a href="http://dbpedia.org/resource/TPC-H" id="link-idfd4e278">H</a> text. Specifically, turning an IN sub-query predicate into an equivalent EXISTS did not sit well with Oracle.</p>
TPC H as Linked Data (Updated 2)
http://www.openlinksw.com/blog/vdb/blog/?date=2008-03-06#1322
2008-03-06T16:34:12Z
2008-08-28T11:26:03.000004-04:00
<div> <div style="display:none;">TPC H as Linked Data (Updated 2)</div> <p>We have a new demo online at <a href="http://demo.openlinksw.com/tpc-h" id="link-id1829c9a0">http://demo.openlinksw.com/tpc-h</a>. This takes the industry standard <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xf0fff10">TPC-H</a> benchmark <a href="http://dbpedia.org/resource/Data" id="link-id0x1387cd90">data</a> and presents it as <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x152407c0">linked data</a> with a <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x11657940">SPARQL</a> end point and dereferenceable URIs. </p> <p>This is an example of using <a href="http://virtuoso.openlinksw.com" id="link-id0xe560628">Virtuoso</a>'s relational-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xa07e2588">RDF</a> mapping for publishing business data, for browsing using the linked data principles and opening it to analytics queries in SPARQL.</p> <p> As noted before, we have extended SPARQL with aggregation and nested queries, thus making it a viable <a href="http://dbpedia.org/resource/SQL" id="link-id0xa3666b00">SQL</a> substitute for decision support queries. </p> <p>The article at <a href="http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSTPCHLinkedData" id="link-id10799d10">http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSTPCHLinkedData</a> gives details and the source code for the implementation.</p> <p> We are still working on some aspects of the more complex TPC-H queries, thus the demo is not complete with all the 22 queries. This is however enough to see a representative sample of how analytics queries work with SPARQL and Virtuoso's SQL-to-RDF mapping. The demo will be part of the next Virtuoso Open Source download, probably out next week.</p> </div>
TPC H as Linked Data (Updated 2)
http://www.openlinksw.com/weblog/oerling/?date=2008-03-06#1321
2008-03-06T16:22:03Z
2008-08-28T11:25:55-04:00
<p>We have a new demo online at <a href="http://demo.openlinksw.com/tpc-h" id="link-id1829c9a0">http://demo.openlinksw.com/tpc-h</a>. This takes the industry standard <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xeb7e460">TPC-H</a> benchmark <a href="http://dbpedia.org/resource/Data" id="link-id0xb40fcb8">data</a> and presents it as <a href="http://dbpedia.org/resource/Linked_Data" id="link-id0x9edbd128">linked data</a> with a <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xf566a50">SPARQL</a> end point and dereferenceable URIs. </p> <p>This is an example of using <a href="http://virtuoso.openlinksw.com" id="link-id0x11e59f80">Virtuoso</a>'s relational-to-<a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xfc93c70">RDF</a> mapping for publishing business data, for browsing using the linked data principles and opening it to analytics queries in SPARQL.</p> <p> As noted before, we have extended SPARQL with aggregation and nested queries, thus making it a viable <a href="http://dbpedia.org/resource/SQL" id="link-id0xffe4520">SQL</a> substitute for decision support queries. </p> <p>The article at <a href="http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSTPCHLinkedData" id="link-id10799d10">http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSTPCHLinkedData</a> gives details and the source code for the implementation.</p> <p> We are still working on some aspects of the more complex TPC-H queries, thus the demo is not complete with all the 22 queries. This is however enough to see a representative sample of how analytics queries work with SPARQL and Virtuoso's SQL-to-RDF mapping. The demo will be part of the next Virtuoso Open Source download, probably out next week.</p>
LUBM results with Virtuoso 6.0
http://www.openlinksw.com/blog/vdb/blog/?date=2008-02-04#1309
2008-02-04T10:26:17Z
2008-08-28T12:06:11-04:00
<div> <div style="display:none;">LUBM results with Virtuoso 6.0</div> <p>We have now run the LUBM benchmark on <a href="http://virtuoso.openlinksw.com" id="link-id0x1c2197e8">Virtuoso</a> v6, with the same configuration <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1302" id="link-id107f0238">as discussed last Friday</a>.</p> <p>We had a database of 8000 universities, and we ran 8 clients on slices of 100, 1000 and 8000 universities — same <a href="http://dbpedia.org/resource/Data" id="link-id0x7d0e498">data</a> but different sizes of working set.</p> <blockquote> <pre> 100 universities: 35.3 qps 1000 universities: 26.3 qps 8000 universities: 13.1 qps</pre></blockquote> <p>The 100 universities slice is about the same as with v5.0.5 (35.3 vs 33.1 qps). <br />The 8000 universities set is almost 3x better (13.1 vs. 4.8 qps).</p> <p>This comes from the fact that the v6 database takes half of the space of the v5.0.5 one. Further, this is with 64-bit IDs for everything. If the 5.5 database were with 64-bit IDs, we'd have a difference of over 3x. This is worth something if it lets you get by with only 1 terabyte of RAM for the 100 billion triple application, instead of 3 TB.</p> <p> <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1358" id="link-id15fb4d38">In a few more days</a>, we'll give the results for Virtuoso v6 Cluster.</p> </div>
LUBM results with Virtuoso 6.0
http://www.openlinksw.com/weblog/oerling/?date=2008-02-04#1308
2008-02-04T09:58:03Z
2008-08-28T12:06:04-04:00
<p>We have now run the LUBM benchmark on <a href="http://virtuoso.openlinksw.com" id="link-id0x1a6cb3c8">Virtuoso</a> v6, with the same configuration <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1302" id="link-id107f0238">as discussed last Friday</a>.</p> <p>We had a database of 8000 universities, and we ran 8 clients on slices of 100, 1000 and 8000 universities — same <a href="http://dbpedia.org/resource/Data" id="link-id0x12ac6cc8">data</a> but different sizes of working set.</p> <blockquote> <pre> 100 universities: 35.3 qps 1000 universities: 26.3 qps 8000 universities: 13.1 qps</pre></blockquote> <p>The 100 universities slice is about the same as with v5.0.5 (35.3 vs 33.1 qps). <br />The 8000 universities set is almost 3x better (13.1 vs. 4.8 qps).</p> <p>This comes from the fact that the v6 database takes half of the space of the v5.0.5 one. Further, this is with 64-bit IDs for everything. If the 5.5 database were with 64-bit IDs, we'd have a difference of over 3x. This is worth something if it lets you get by with only 1 terabyte of RAM for the 100 billion triple application, instead of 3 TB.</p> <p> <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1358" id="link-id15fb4d38">In a few more days</a>, we'll give the results for Virtuoso v6 Cluster.</p>
Latest LUBM Benchmark results for Virtuoso
http://www.openlinksw.com/blog/vdb/blog/?date=2008-02-01#1305
2008-02-01T15:16:59Z
2008-08-28T12:06:08-04:00
<div> <div style="display:none;">Latest LUBM Benchmark results for Virtuoso</div> <p>We have now taken a close look at the query side of the LUBM benchmark, <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1296" id="link-id10a98120">as promised a couple of blog posts ago.</a> </p> <p>We load 8000 universities and run a query mix consisting of the 14 LUBM queries with different numbers of clients against different portions of the database.</p> <p>When it is all in memory, we get 33 queries per second with 8 concurrent clients; when it is so I/O bound that 7.7 of 8 threads wait for disk, we get 5 qps. This was run in 8G RAM with 2 Xeon 5130.</p> <p>We adapted some of the queries so that they do not run over the whole database. In terms of retrieving triples per second, this would be about 330000 for the rate of 33 qps, with 4 cores at 2GHz. This is a combination of random access and linear scans and bitmap merge intersections; lookups for non-found triples are not counted. The rate of random lookups alone based on known G, S, P, O, without any query logic, is about 250000 random lookups per core per second.</p> <p>The article <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VOSArticleLUBMBenchmark" id="link-id10237708">LUBM and Virtuoso</a> gives the details.</p> <p>In the process of going through the workload we made some cost model adjustments and optimized the bitmap intersection join. In this way we can quickly determine which subjects are, for example, professors holding a degree from a given university. So the benchmark served us well in that it provided an incentive to further optimize some things.</p> <p>Now, what has been said about <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x12cba3d8">RDF</a> benchmarking previously still holds. What does it mean to do so many LUBM queries per second? What does this say about the capacity to run an online site off RDF <a href="http://dbpedia.org/resource/Data" id="link-id0x9e043f38">data</a>? Or about <a href="http://dbpedia.org/resource/Information" id="link-id0x10c1a4f8">information</a> integration? Not very much. But then this was not the aim of the authors either.</p> <p>So we still need to make a benchmark for online queries and search, and another for E-science and business intelligence. But we are getting there.</p> <p>In the immediate future, we have the general availability of <a href="http://virtuoso.openlinksw.com" id="link-id0x9ec5e620">Virtuoso</a> Open Source 5.0.5 early next week. This comes with a LUBM test driver and a test suite running against the LUBM qualification database.</p> <p>After this we will give some numbers for the cluster edition with LUBM and <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xcd7ec48">TPC-H</a>.</p> </div>
Latest LUBM Benchmark results for Virtuoso
http://www.openlinksw.com/weblog/oerling/?date=2008-02-01#1304
2008-02-01T14:39:04Z
2008-08-28T12:06:01-04:00
<p>We have now taken a close look at the query side of the LUBM benchmark, <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1296" id="link-id10a98120">as promised a couple of blog posts ago.</a> </p> <p>We load 8000 universities and run a query mix consisting of the 14 LUBM queries with different numbers of clients against different portions of the database.</p> <p>When it is all in memory, we get 33 queries per second with 8 concurrent clients; when it is so I/O bound that 7.7 of 8 threads wait for disk, we get 5 qps. This was run in 8G RAM with 2 Xeon 5130.</p> <p>We adapted some of the queries so that they do not run over the whole database. In terms of retrieving triples per second, this would be about 330000 for the rate of 33 qps, with 4 cores at 2GHz. This is a combination of random access and linear scans and bitmap merge intersections; lookups for non-found triples are not counted. The rate of random lookups alone based on known G, S, P, O, without any query logic, is about 250000 random lookups per core per second.</p> <p>The article <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VOSArticleLUBMBenchmark" id="link-id10237708">LUBM and Virtuoso</a> gives the details.</p> <p>In the process of going through the workload we made some cost model adjustments and optimized the bitmap intersection join. In this way we can quickly determine which subjects are, for example, professors holding a degree from a given university. So the benchmark served us well in that it provided an incentive to further optimize some things.</p> <p>Now, what has been said about <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x104257c0">RDF</a> benchmarking previously still holds. What does it mean to do so many LUBM queries per second? What does this say about the capacity to run an online site off RDF <a href="http://dbpedia.org/resource/Data" id="link-id0x7376478">data</a>? Or about <a href="http://dbpedia.org/resource/Information" id="link-id0x13fd3f30">information</a> integration? Not very much. But then this was not the aim of the authors either.</p> <p>So we still need to make a benchmark for online queries and search, and another for E-science and business intelligence. But we are getting there.</p> <p>In the immediate future, we have the general availability of <a href="http://virtuoso.openlinksw.com" id="link-id0x193509e8">Virtuoso</a> Open Source 5.0.5 early next week. This comes with a LUBM test driver and a test suite running against the LUBM qualification database.</p> <p>After this we will give some numbers for the cluster edition with LUBM and <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1b8d1348">TPC-H</a>.</p>
LUBM and Virtuoso 5.5
http://www.openlinksw.com/weblog/oerling/?date=2008-02-01#1302
2008-02-01T12:37:53Z
2008-03-25T14:43:30.000004-04:00
<p>We have now taken a close look at the query side of the LUBM benchmark, as promised a couple of blog posts ago.</p> <p>We load 8000 universities and run a query mix consisting of the 14 LUBM queries with different numbers of clients against different portions of the database.</p> <p>When it is all in memory, we get 33 queries per second with 8 concurrent clients; when it is so I/O bound that 7.7 of 8 threads wait for disk, we get 5 qps. This was run in 8G RAM with 2 Xeon 5130.</p> <p>We adapted some of the queries so that they do not run over the whole database. In terms of retrieving triples per second, this would be about 330000 for the rate of 33 qps, with 4 cores at 2GHz. This is a combination of random access and linear scans and bitmap merge intersections; lookups for non-found triples are not counted. The rate of random lookups alone based on known G, S, P, O, without any query logic, is about 250000 random lookups per core per second.</p> <p>The article <a href="http://virtuoso.openlinksw.com/wiki/main/Main/VOSArticleLUBMBenchmark" id="link-id121f54d8">LUBM and Virtuoso</a> gives the details.</p> <p>In the process of going through the workload, we made some cost model adjustments and optimized the bitmap intersection join. In this way we can quickly determine which subjects are, for example, professors holding a degree from a given university. So the benchmark served us well in that it provided an incentive to further optimize some things.</p> <p>Now, what has been said about <a href="http://dbpedia.org/resource/RDF" id="link-id0x186bcec0">RDF</a> benchmarking previously still holds. What does it mean to do so many LUBM queries per second? What does this say about the capacity to run an online site off <a href="http://dbpedia.org/resource/RDF" id="link-id0xa1e11918">RDF</a> data? Or about information integration? Not very much. But then this was not the aim of the authors either.</p> <p>So we still need to make a benchmark for online queries and search, and another for E-science and business intelligence. But we are getting there.</p> <p>In the immediate future, we have the general availability of <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x189b11b8">Virtuoso</a> Open Source 5.0.5 early next week. This comes with a LUBM test driver and a test suite running against the LUBM qualification database.</p> <p>After this we will give some numbers for the cluster edition with LUBM and <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1b948dd8">TPC</a> <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x189e58a8">H</a>.</p>
SPARQL Extensions for Subqueries
http://www.openlinksw.com/blog/vdb/blog/?date=2008-01-16#1297
2008-01-16T15:47:09Z
2008-04-14T14:02:43-04:00
<div> <div style="display:none;">SPARQL Extensions for Subqueries</div> <p>Last time I said we had extended <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1c6616d0">SPARQL</a> for sub-queries. As a preview of the new functionality, let us look at a query from <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x17ac7450">TPC-H</a>.</p> <p>Below is the <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x1b0ec750">Virtuoso</a> SPARQL version of Q2.</p> <blockquote> <pre> sparql define sql:signal-void-variables 1 prefix tpcd: <http://www.openlinksw.com/schemas/tpcd#> prefix oplsioc: <http://www.openlinksw.com/schemas/oplsioc#> prefix sioc: <http://rdfs.org/sioc/ns#> prefix foaf: <http://xmlns.com/foaf/0.1/> select ?supp+>tpcd:acctbal, ?supp+>tpcd:name, ?supp+>tpcd:has_nation+>tpcd:name as ?nation_name, ?part+>tpcd:partkey, ?part+>tpcd:mfgr, ?supp+>tpcd:address, ?supp+>tpcd:phone, ?supp+>tpcd:comment from <http://example.com/tpcd> where { ?ps a tpcd:partsupp ; tpcd:has_supplier ?supp ; tpcd:has_part ?part . ?supp+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' . ?part tpcd:size 15 . ?ps tpcd:supplycost ?minsc . { select ?p min(?ps+>tpcd:supplycost) as ?minsc where { ?ps a tpcd:partsupp ; tpcd:has_part ?p ; tpcd:has_supplier ?ms . ?ms+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' . } } filter (?part+>tpcd:type like '%BRASS') } order by desc (?supp+>tpcd:acctbal) ?supp+>tpcd:has_nation+>tpcd:name ?supp+>tpcd:name ?part+>tpcd:partkey ; </pre></blockquote> <p>Note the pattern <code>{ ?ms+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' }</code> which is a shorthand for <code>{ ?ms tpcd:has_nation ?t1 . ?t1 tpcd:has-region ?t2 . ?t2 tpcd:has_region ?t3 . ?t3 tpcd:name "EUROPE" } </code> </p> <p>Also note a sub-query is used for determining the lowest supply cost for a part. </p> <p>The SQL text of the query can be found in the TPC-H benchmark specification, reproduced below: </p> <blockquote> <pre>select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment from part, supplier, partsupp, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and p_size = 15 and p_type like '%BRASS' and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE' and ps_supplycost = ( select min(ps_supplycost) from partsupp, supplier, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE') order by s_acctbal desc, n_name, s_name, p_partkey; </pre></blockquote> <p> For brevity we have omitted the declarations for mapping the TPC-H schema to its <a href="http://dbpedia.org/resource/RDF" id="link-id0xd044708">RDF</a> equivalent. The mapping is straightforward, with each column mapping to a predicate and each table to a class.</p> <p> This is now part of the next Virtuoso Open Source cut, due around next week. </p> <p> As of this writing we are going through the TPC-H query by query and testing with mapping going to Virtuoso and Oracle databases. </p> <p> Also we have been busy measuring Virtuoso 6. Even after switching from 32-bit to 64-bit IDs for IRIs and objects, the new databases are about half the size of the same Virtuoso 5.0.2 databases. This does not include any stream compression like gzip for disk pages. The load and query speeds are higher because of better working set. For all in memory, they are about even with 5.0.2. So now on an 8G box, we load 1067 million LUBM triples at 39.7 Kt/s instead of 29 Kt/s with 5.0.2. Right now we experimenting with clusters at Amazon EC2. We'll write about that in a bit. </p> </div>
SPARQL Extensions for Subqueries
http://www.openlinksw.com/weblog/oerling/?date=2008-01-16#1296
2008-01-16T15:11:00Z
2008-03-25T14:43:28.000001-04:00
<p>Last time I said we had extended <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1c668bb0">SPARQL</a> for sub-queries. As a preview of the new functionality, let us look at a query from <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xd469258">TPC</a> <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1c68fe58">H</a>.</p> <p>Below is the <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0xcabe1f0">Virtuoso</a> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1d3ba3d0">SPARQL</a> version of Q2.</p> <blockquote> <pre> <a href="http://dbpedia.org/resource/SPARQL" id="link-id0xd45b8c0">sparql</a> define sql:signal-void-variables 1 prefix tpcd: <http://www.openlinksw.com/schemas/tpcd#> prefix oplsioc: <http://www.openlinksw.com/schemas/oplsioc#> prefix sioc: <http://rdfs.org/sioc/ns#> prefix foaf: <http://xmlns.com/foaf/0.1/> select ?supp+>tpcd:acctbal, ?supp+>tpcd:name, ?supp+>tpcd:has_nation+>tpcd:name as ?nation_name, ?part+>tpcd:partkey, ?part+>tpcd:mfgr, ?supp+>tpcd:address, ?supp+>tpcd:phone, ?supp+>tpcd:comment from <http://example.com/tpcd> where { ?ps a tpcd:partsupp ; tpcd:has_supplier ?supp ; tpcd:has_part ?part . ?supp+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' . ?part tpcd:size 15 . ?ps tpcd:supplycost ?minsc . { select ?p min(?ps+>tpcd:supplycost) as ?minsc where { ?ps a tpcd:partsupp ; tpcd:has_part ?p ; tpcd:has_supplier ?ms . ?ms+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' . } } filter (?part+>tpcd:type like '%BRASS') } order by desc (?supp+>tpcd:acctbal) ?supp+>tpcd:has_nation+>tpcd:name ?supp+>tpcd:name ?part+>tpcd:partkey ; </pre></blockquote> <p>Note the pattern <code>{ ?ms+>tpcd:has_nation+>tpcd:has_region tpcd:name 'EUROPE' }</code> which is a shorthand for <code>{ ?ms tpcd:has_nation ?t1 . ?t1 tpcd:has-region ?t2 . ?t2 tpcd:has_region ?t3 . ?t3 tpcd:name "EUROPE" } </code> </p> <p>Also note a sub-query is used for determining the lowest supply cost for a part. </p> <p>The SQL text of the query can be found in the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xb945ed8">TPC</a> <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1c832038">H</a> benchmark specification, reproduced below: </p> <blockquote> <pre>select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment from part, supplier, partsupp, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and p_size = 15 and p_type like '%BRASS' and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE' and ps_supplycost = ( select min(ps_supplycost) from partsupp, supplier, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE') order by s_acctbal desc, n_name, s_name, p_partkey; </pre></blockquote> <p> For brevity we have omitted the declarations for mapping the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1d365d90">TPC</a> <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x17b6f668">H</a> schema to its <a href="http://dbpedia.org/resource/RDF" id="link-id0x1898eaf0">RDF</a> equivalent. The mapping is straightforward, with each column mapping to a predicate and each table to a class.</p> <p> This is now part of the next <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x1aaaa3f0">Virtuoso</a> Open Source cut, due around next week. </p> <p> As of this writing we are going through the <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1950f1c0">TPC</a> <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xd0d8bc0">H</a> query by query and testing with mapping going to <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x18b88f08">Virtuoso</a> and Oracle databases. </p> <p> Also we have been busy measuring <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x17b3fbb0">Virtuoso</a> 6. Even after switching from 32-bit to 64-bit IDs for IRIs and objects, the new databases are about half the size of the same <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0xbe2ec80">Virtuoso</a> 5.0.2 databases. This does not include any stream compression like gzip for disk pages. The load and query speeds are higher because of better working set. For all in memory, they are about even with 5.0.2. So now on an 8G box, we load 1067 million LUBM triples at 39.7 Kt/s instead of 29 Kt/s with 5.0.2. Right now we experimenting with clusters at Amazon EC2. We'll write about that in a bit. </p>
Retrospective and Outlook for 2008
http://www.openlinksw.com/blog/vdb/blog/?date=2007-12-18#1287
2007-12-18T12:22:21Z
2007-12-18T07:22:21.000006-05:00
<div><div style="display:none;">Retrospective and Outlook for 2008</div> <p>At this close of the year, I'll give a little recap over the past year in terms of <a href="http://virtuoso.openlinksw.com/" id="link-id103d2cb0">Virtuoso</a> development and a look at where we are headed for 2008.  </p> <p>A year ago, I was in the middle of redoing the Virtuoso database engine for better SMP performance. We redid the way traversal of index structures and cache buffers was serialized for SMP and generally compared Virtuoso and Oracle engines function by function. We had just returned from the ISWC 2006 in Athens, Georgia and the Virtuoso database was becoming a usable triple store.</p> <p>Soon thereafter, we comfirmed that all this worked when we put out the first cut of <a href="http://dbpedia.org/" id="link-idf8b4728">Dbpedia</a> with Chris Bizer et al and were working with Alan Ruttenberg on what would become <a href="http://esw.w3.org/topic/HCLS/Banff2007Demo" id="link-id10397930">the Banff health care and life sciences demo</a>.</p> <p>The <a href="http://www2007.org/" id="link-id10335160">WWW 2007 conference in Banff</a>, Canada, was a sort of kick-off for the <a href="http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData" id="link-id102cc0c8">Linking Open Data</a> movement, which started as a community project under <a href="http://www.w3.org/2001/sw/sweo/" id="link-idfbe9cb8">SWEO</a>, the W3C interest group for Semantic Web Education and Outreach, and has gained a life of its own since.</p> <p>Right after WWW 2007 the Virtuoso development effort split on two tracks, one for enhancing the then new 5.0 release and one for building a new generation of Virtuoso, notably featuring clustering and double storage density for RDF.</p> <p>The first track produced constant improvements to the relational to RDF mapping functionality, SPARQL enhancements, Redland, Jena and Sesame compatible client libraries with Virtuoso as as a triple store. These things have been out with testers for a while and are all generally available as of this writing.  </p> <p>The second track started with adding key compression to the storage engine, specifically with regard to RDF, even though there are some gains in relational applications as well. With RDF, the space consumption drops to about half, all without recourse to any non-random access compatible compression like gzip. Since the start of August, we turned to clustering and are now code complete, pretty much with all the tricks one would expect, of course full function SQL and taking advantage of colocated joins and doing aggregation and generally all possible processing where the data is. I have covered details of this along the way in previous posts. The key ppoint is that now the thing is written and works with test cases. </p> <p>In late October, we were at the W3C workshop for mapping relational data to RDF. For us, this confirmed the importance of mapping and scalability in general. Ivan Herman proposed forming a W3C incubator group on benchmarking. Also a W3C incubator group of relational to RDF mapping is being formed. </p> <p>Now, scalability has two sides. One is dealing with volume and the other is dealing with complexity. Volume alone will not help if interesting queries cannot be formulated. Hence we recently extended SPARQL with subqueries so that we can now express at least any SQL workloads, which was previously not the case. It is sort of a contradiction in terms to say that SPARQL is the universal language for information integration while not being able to express for example the <a href="http://www.tpc.org/tpch/" id="link-idf7ac7e8">TPC H</a> queries. Well, we fixed this. A separate post will jhighlight how. The W3C process will eventually follow, as the necessity of these things is undeniable, on the unimpeachable authority of the whole SQL world. Anyway, for now, SPARQL as it is ought to become a recommendation and extensions can be addressed later.</p> <p>For now, the only RDF benchmark that seems to be out there is the loading part of the LUBM. We did a couple of enhancements of our own for that just recently but much bigger things are on the way. Also, the billion triples challenge is an interesting initiative in the area. We all recognize that loading any number of triples is a finite problem with known solutions. The challenge is running interesting queries on large volumes. </p> <p>Our present emphasis is demonstrating both RDF data warehousing and RDF mapping with complex queries and large data. We start with the <a href="http://www.tpc.org/tpch/" id="link-idfcf8470">TPC H</a> benchmark and doing the queries both through mapping to SQL against any RDBMS, Oracle, DB2, Virtuoso or other, and by querying the physical RDF rendition of the data in Virtuoso. From there, we move to querying a collection of RDBMS's hosting similar data. </p> <p>Doing this with performance at the level of direct SQL in the case of mappping and not very much slower with physical triples is an important milestone on the way to real world enterprise data web. Real life has harder and more unexpected issues than a benchmark but at any rate doing the benchmark without breaking a sweat is a step on the way. We sent a paper to ESWC 2008 about that but it was rather incomplete. By the time of the VLDB submissions deadline in March we'll have more meat.</p> <p>Another tack soon to start is a rearchitecting of <a href="http://zitgist.com/" id="link-id104024b8">Zitgist</a> around clustered Virtuoso. Aside matters of scale, we will make a number of qualitatively new things possible. Again, more will be released in the first quarter of 08.</p> <p>Beyond these short and mid-term goals we have the introduction of entirely dynamic and demand driven partitioning, a la Google Bigtable or Amazon Dynamo. Now, regular partitioning will do for a while yet but this is the future when we move the the vision of linked dataeverywhere.</p> <p>In conclusion, this year we have built the basis and the next year is about deployment. The bulk of really new development is behind us and now we start applying. Also, the community will find adoption easier due to our recent support of the common RDF API's. </p> <a href="index.vspx?tag=benchmarking" rel="tag" style="display:none;">benchmarking</a><a href="index.vspx?tag=sparql" rel="tag" style="display:none;">sparql</a><a href="index.vspx?tag=rdf" rel="tag" style="display:none;">rdf</a><a href="index.vspx?tag=sem web" rel="tag" style="display:none;">sem web</a><a href="index.vspx?tag=semantic web" rel="tag" style="display:none;">semantic web</a><a href="index.vspx?tag=database" rel="tag" style="display:none;">database</a><a href="index.vspx?tag=databases" rel="tag" style="display:none;">databases</a></div>
Retrospective and Outlook for 2008
http://www.openlinksw.com/weblog/oerling/?date=2007-12-18#1286
2007-12-18T10:53:40Z
2008-04-14T14:02:31.000004-04:00
<p>At this close of the year, I'll give a little recap of the past year in terms of <a href="http://virtuoso.openlinksw.com/" id="link-idfe5ba58">Virtuoso</a> development, and take a look at where we are headed for 2008.</p> <p>A year ago, I was in the middle of redoing the <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this">Virtuoso</a> database engine for better SMP performance. We redid the way traversal of index structures and cache buffers was serialized for SMP, and generally compared Virtuoso and Oracle engines function by function. We had just returned from the ISWC 2006 in Athens, Georgia, and the Virtuoso database was becoming a usable triple store.</p> <p>Soon thereafter, we confirmed that all this worked when we put out the first cut of <a href="http://dbpedia.org/" id="link-id149dcff8">Dbpedia</a> with Chris Bizer, et al, and were working with Alan Ruttenberg on what would become <a href="http://esw.w3.org/topic/HCLS/Banff2007Demo" id="link-id10c25b50">the Banff health care and life sciences demo</a>.</p> <p>The <a href="http://www2007.org/" id="link-idfdbd0e0">WWW 2007 conference in Banff</a>, Canada, was a sort of kick-off for the <a href="http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData" id="link-id10e54940">Linking Open Data</a> movement, which started as a community project under <a href="http://www.w3.org/2001/sw/sweo/" id="link-idfd99988">SWEO</a>, the W3C interest group for Semantic Web Education and Outreach, and has gained a life of its own since.</p> <p>Right after WWW 2007, the Virtuoso development effort split onto two tracks: one for enhancing the then new 5.0 release; and one for building a new generation of Virtuoso, notably featuring clustering and double storage density for <a href="http://dbpedia.org/resource/RDF">RDF</a>.</p> <p>The first track produced constant improvements to the relational-to-RDF mapping functionality, <a href="http://dbpedia.org/resource/SPARQL">SPARQL</a> enhancements, and Redland-, Jena- and Sesame-compatible client libraries with Virtuoso as as a triple store. These things have been out with testers for a while and are all generally available as of this writing.</p> <p>The second track started with adding key compression to the storage engine, specifically with regard to RDF, even though there are some gains in relational applications as well. With RDF, the space consumption drops to about half, all without recourse to any non-random access compatible compression like gzip. Since the start of August, we turned to clustering and are now code complete, pretty much with all the tricks one would expect, of course full function SQL and taking advantage of co-located joins and doing aggregation and generally all possible processing where the data is. I have covered details of this along the way in previous posts. The key point is that now the thing is written and works with test cases. </p> <p>In late October, we were at the W3C workshop for mapping relational data to RDF. For us, this confirmed the importance of mapping and scalability in general. Ivan Herman proposed forming a W3C incubator group on benchmarking. Also a W3C incubator group of relational to RDF mapping is being formed. </p> <p>Now, scalability has two sides. One is dealing with volume, and the other is dealing with complexity. Volume alone will not help if interesting queries cannot be formulated. Hence, we recently extended SPARQL with sub-queries so that we can now express at least any SQL workloads, which was previously not the case. It is sort of a contradiction in terms to say that SPARQL is the universal language for information integration while not being able to express, for example, the <a href="http://dbpedia.org/resource/TPC-H">TPC-H</a> queries. Well, we fixed this. A separate post will highlight how. The W3C process will eventually follow, as the necessity of these things is undeniable, on the unimpeachable authority of the whole SQL world. Anyway, for now, SPARQL as it is ought to become a recommendation and extensions can be addressed later.</p> <p>For now, the only RDF benchmark that seems to be out there is the loading part of the LUBM. We did a couple of enhancements of our own for that just recently, but much bigger things are on the way. Also, the billion triples challenge is an interesting initiative in the area. We all recognize that loading any number of triples is a finite problem with known solutions. The challenge is running interesting queries on large volumes. </p> <p>Our present emphasis is demonstrating both RDF data warehousing and RDF mapping with complex queries and large data. We start with the TPC-H benchmark and doing the queries both through mapping to SQL against any RDBMS â Oracle, DB2, Virtuoso or other â and by querying the physical RDF rendition of the data in Virtuoso. From there, we move to querying a collection of RDBMS hosting similar data. </p> <p>Doing this with performance at the level of direct SQL in the case of mapping and not very much slower with physical triples is an important milestone on the way to a real world enterprise data web. Real life has harder and more unexpected issues than a benchmark, but at any rate doing the benchmark without breaking a sweat is a step on the way. We sent a paper to ESWC 2008 about that but it was rather incomplete. By the time of the VLDB submissions deadline in March we'll have more meat.</p> <p>Another tack soon to start is a re-architecting of <a href="http://zitgist.com/" id="link-id10e8b9f0">Zitgist</a> around clustered Virtuoso. Aside matters of scale, we will make a number of qualitatively new things possible. Again, more will be released in the first quarter of 2008.</p> <p>Beyond these short and mid-term goals we have the introduction of entirely dynamic and demand driven partitioning, <i>a la</i> Google Bigtable or Amazon Dynamo. Now, regular partitioning will do for a while yet but this is the future when we move the the vision of linked data everywhere.</p> <p>In conclusion, this year we have built the basis and the next year is about deployment. The bulk of really new development is behind us and now we start applying. Also, the community will find adoption easier due to our recent support of the common RDF APIs. </p> <a href="index.vspx?tag=database" rel="tag" style="display:none;">database</a><a href="index.vspx?tag=databases" rel="tag" style="display:none;">databases</a><a href="index.vspx?tag=lubm" rel="tag" style="display:none;">lubm</a><a href="index.vspx?tag=benchmarking" rel="tag" style="display:none;">benchmarking</a><a href="index.vspx?tag=semanticweb" rel="tag" style="display:none;">semanticweb</a><a href="index.vspx?tag=sparql" rel="tag" style="display:none;">sparql</a>
Virtuoso LUBM Load Update
http://www.openlinksw.com/blog/vdb/blog/?date=2007-12-07#1285
2007-12-07T08:45:07Z
2008-04-14T14:02:41-04:00
<div> <div style="display:none;">Virtuoso LUBM Load Update</div> <p>As part of the recent conversation on benchmarking <a href="http://dbpedia.org/resource/RDF" id="link-id0x17fd3fd0">RDF</a> stores, we re-ran the LUBM 8000 load test (1067 million triples) with the current <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x1c8a6730">Virtuoso</a>.</p> <p>We did it on two different machines, one with 2 Xeon 5130 2Ghz and 8G RAM and one with 2 Xeon 5330 2GHZ and 16G RAM. Both had 6 x 7800 rpm SATA-2 drives. The load rate on the 16G configuration was 36.8 Ktriples per second. The load rate on the 8G configuration was 29.7 Ktriples per second. Both loads were made using 6 concurrent load streams. Some small changes to the numbers may be released later as a result of changing tuning.</p> <p>The Virtuoso version was 5.0, in the update to be released on the week of Dec 10, 2007. This is an incremental release of Virtuoso 5.0 and has the same engine as the prior 5.0s, with some optimizations for RDF loading and diverse bug fixes, notably in RDF mapping of relational data. This release will be further described in a separate post.</p> <p>The load does not include forward chaining but then Virtuoso supports sub-class and sub-property without materializing the entailed triples.</p> <p>Most of the LUBM entailed triples represent sub-classes and sub-properties. The LUBM query and forward chaining side deserves a separate treatment but this is for another time.</p> <p>Most recent posts on this blog refer to Virtuoso 6, which is presently under development. We will publish results with the 6.0 engine later. Also, further enhancements to triple store performance will take place on the Virtuoso 6 platform.</p> </div>
Virtuoso LUBM Load Update
http://www.openlinksw.com/weblog/oerling/?date=2007-12-06#1284
2007-12-06T13:35:21Z
2008-04-14T14:02:31-04:00
<p>As part of the recent conversation on benchmarking <a href="http://dbpedia.org/resource/RDF" id="link-id0x17cd8cf8">RDF</a> stores, we re-ran the LUBM 8000 load test (1067 million triples) with the current <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0x17581ae0">Virtuoso</a>.</p> <p>We did it on two different machines, one with 2 Xeon 5130 2Ghz and 8G RAM and one with 2 Xeon 5330 2GHZ and 16G RAM. Both had 6 x 7800 rpm SATA-2 drives. The load rate on the 16G configuration was 36.8 Ktriples per second. The load rate on the 8G configuration was 29.7 Ktriples per second. Both loads were made using 6 concurrent load streams. Some small changes to the numbers may be released later as a result of changing tuning.</p> <p>The Virtuoso version was 5.0, in the update to be released on the week of Dec 10, 2007. This is an incremental release of Virtuoso 5.0 and has the same engine as the prior 5.0s, with some optimizations for RDF loading and diverse bug fixes, notably in RDF mapping of relational data. This release will be further described in a separate post.</p> <p>The load does not include forward chaining but then Virtuoso supports sub-class and sub-property without materializing the entailed triples.</p> <p>Most of the LUBM entailed triples represent sub-classes and sub-properties. The LUBM query and forward chaining side deserves a separate treatment but this is for another time.</p> <p>Most recent posts on this blog refer to Virtuoso 6, which is presently under development. We will publish results with the 6.0 engine later. Also, further enhancements to triple store performance will take place on the Virtuoso 6 platform.</p>
Virtuoso Cluster, RDF, and SQL Map-Reduce
http://www.openlinksw.com/blog/vdb/blog/?date=2007-11-21#1273
2007-11-21T13:07:05Z
2008-04-14T14:42:28.000008-04:00
<div> <div style="display:none;">Virtuoso Cluster, RDF, and SQL Map-Reduce</div> <p>We have now arrived at the <a href="http://dbpedia.org/resource/RDF" id="link-id0x19280810">RDF</a> specific parts of the <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0xc70b540">Virtuoso</a> cluster effort.</p> <p>This has to do with special tricks for dealing with the mapping of IRIs and object values and their internal IDs. A quad will refer to the graph, subject, predicate, and object by an internal ID. The object, if short enough, can be inlined so that no join is needed to get its external form in most cases.</p> <p>Now this is old stuff.</p> <p>Clustering does not change this, except that now the tables for the mappings between IDs and their external form are partitioned. So more often than not, getting the string for the ID involves an RPC. The most recently used mappings are of course cached outside of the table but still, having a network round trip each time an IRI is returned for the first time in a while is no good.</p> <p>The solution is the same as always, namely doing one round trip per partition, with all the IDs concerned in a single message. The same applies to reading documents, where strings are translated to IDs and new IDs are made, only now we have a distributed read-write transaction.</p> <p>In terms of programming model we have a general purpose partitioned pipe. One might think of this in terms of a single control structure combining map and reduce.</p> <p>Now consider returning results for a text search:</p> <p> <code>SELECT doc_id, summary (doc_id, 'search pattern') FROM (SELECT TOP 10 doc_id FROM docs WHERE CONTAINS (text, 'search pattern', score) ORDER BY relevance (doc_id, score, 'search pattern') DESC) f;</code> </p> <p>The <code>summary</code> and <code>relevance</code> are each a function of the <code>doc_id</code> and <code>search pattern</code>. The <code>relevance</code> function would typically access some precomputed site rank and combine this with a hit score that is a function of the word frequencies in the individual hit.</p> <p>Now we are not specifically in the business of text search but this well known example will serve to make a more general point.</p> <p>How does a query like this work when everything is partitioned?</p> <p>The text index (inverted file from word to positions in each document) can be split by word or by document ID or both. In either case, it will produce the hits fairly quickly, partitioning or not. The question is sorting the million hits according to criteria that involve joining with document or site metadata. To produce fast response, this loop must run in parallel but then each score is independent, so no problem. Finally, summaries have to be made only for the items actually returned and again each summary is independent.</p> <p>A text search engine does not depend on a general purpose query processor for scheduling things in this way.</p> <p>The case is different with RDF where we must do basically the same things with ad-hoc queries. As it happens, the above sample query will run as described if only the summary and relevance functions are properly declared.</p> <p>So we have a special declaration for a partitioned function call. Further, the partitioned function call (in this case summary and relevance) will dispatch according to a given index, thus going to run on a node hosting the actual data. This is like the map part of map-reduce. But this is not all. The functions can return either a final value or a next step. This can be regarded as a second map or in some cases a reduce step. The next step is another partitioned function that gets the output of the previous one as its input and may use the same or different partitioning key.</p> <p>Now the functions can be large or small, including very small, like a single index lookup, where the RPC delay is an order of magnitude greater than the time to perform the function. The partitioned pipe manages batching these together and overlapping processing on all nodes. Plus the work can be transactional, all bound in a single distributed transaction or it can be each task for itself with error retries etc, as in the usual map-reduce situation where relatively large tasks are sent around.</p> <p>At present, we have all this implemented and we are running tests with large RDF data sets on clustered Virtuoso.</p> </div>
RDBMS to RDF Mapping Workshop, and Benchmarks
http://www.openlinksw.com/blog/vdb/blog/?date=2007-11-21#1271
2007-11-21T13:07:03Z
2008-04-25T16:29:53-04:00
<div> <div style="display:none;">RDBMS to RDF Mapping Workshop, and Benchmarks</div> <p>I was recently in Boston for the <a href="http://www.w3.org/2007/03/RdfRDB/" id="link-id10f990b0">Mapping Relational Data to RDF workshop</a> of the W3C.</p> <p>The common feeling was that mapping everything to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1c343278">RDF</a> and querying it in terms of a generic domain ontology, mapped on demand into whatever line of business systems, would be very good if it only could be done. However, since this is not so easily done, the next best is to extract the <a href="http://dbpedia.org/resource/Data" id="link-id0xb6f01d0">data</a> and then warehouse it as RDF.</p> <p>The obstacles perceived were of the following types:</p> <ul> <li> <p>Lack of quality in the data. The different line of business systems do not in and of themselves hold enough semantics. If the meaning of data columns in relational tables were really known and explicit, these could be meaningfully used for joining across systems. But this is more complex than just mapping the metal <i>lead</i> to the chemical symbol <i>Pb</i> and back.</p> </li> <li> <p>Lack of performance in RDF storage. Data sets even in the tens-of-millions of triples do not run very well in some stores. Well, we had the Banff life sciences demo with 450M triples in a small server box running <a href="http://virtuoso.openlinksw.com" id="link-id0x1ca1c488">Virtuoso</a>, so this is not universal, plus of course we are coming up with a whole different order of magnitude, as often discussed on this <a href="http://dbpedia.org/resource/Blog" id="link-id0xb4dc850">blog</a>.</p> </li> <li> <p>Lack of functionality in mapping and possibly lack of pushing through enough of the query processing to the underlying data stores.</p> </li> </ul> <p>Personally, I am quite aware of what to do with regard to performance of mapping and storage, and see these as eminently solvable issues. After all, we have a great investment of talent in databases in general and it can be well deployed towards RDF, as we have been doing these past couple of years. So we talk about the promise of a 360-degree view of <a href="http://dbpedia.org/resource/Information" id="link-id0x1ae64448">information</a>, with RDF being the top layer. Everybody agrees that this is a nice concept. But this is a nice concept especially when it can do the things that are the most common baseline expectation of any regular DBMS, i.e., aggregation, grouping, sub-queries, VIEWs. Now, I would not go sell a DBMS that has no <code>COUNT</code> operator to a data warehousing shop.</p> <p>The fact that OpenLink and <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1aa10fd8">Oracle</a> allow RDF inside <a href="http://dbpedia.org/resource/SQL" id="link-id0xa26d330">SQL</a>, and OpenLink even adds native aggregates and grouping to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1d81d990">SPARQL</a>, fixes the problem with regard to specific products, but leaves the standardization issue open. Of course, any vendor will solve these questions one way or another because a database with no aggregation is a non-starter.</p> <p>I talked to Lee Feigenbaum, chair of the W3C DAWG, about the question of aggregates and general BI capabilities in SPARQL. He told me that, prior to his time with the DAWG, these were left out because they conflicted with the <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id0x1be3ab98">open-world</a> assumption around RDF: You cannot count a set because by definition you do not know that you have all the members, the world being open and all that.</p> <p>Say what? Talk about the road to hell being paved with good intentions. Now, this is in no way Lee's or the present day DAWG's fault; as a member myself, I can attest to the good work and would under no circumstances wish any delays or revisions to SPARQL at this point. I am just pointing out a matter that all implementations should address, as a sort of precondition of entry into the real world IS space. If this can be done interoperably, so much the better.</p> <p>Now, out of the deliberations at the Boston workshop arose at least two ideas for follow-up activity.</p> <p>The first was an incubator group for RDF store and mapping benchmarking. This is very appropriate in order to dispel the bad name RDF storage and querying performance has been saddled with. As a first step in this direction, I will outline a <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1269" id="link-id10306200">social web oriented benchmark</a> on this blog.</p> <p>The second activity was an <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id10150a58">incubator group for preparing standardization of mapping methodologies from relational schemas to RDF</a>. We will be active on this as well.</p> <p>The two offshoots appear logically separate but are not necessarily so in practice. A benchmark is after all something that is supposed to promote a technology to a user base. The user base seems to wish to put all online systems and data warehouses under a common top level RDF model and then query away, introducing no further replication of data or performance cost or ETL latencies.</p> <p>Updating would also be nice but even query only would be very good. Personally, I'd say the RDF strength is all on the query side. Transactions are taken care of well enough by what there already is, RDF stands out in integration and the ad-hoc and discovery side of the matter. Given this, we expect the value to be consumed in a heterogeneous, multi-database, federated environment. Thus a benchmark should measure this aspect of the use-case. With the right mapping and queries, we could probably demonstrate the added cost of RDF to be very low, as long as we could push all queries that can be answered by a single source to the responsible DBMS. For distributed joins, we are back at the question of optimizing distributed queries but this is a familiar one and RDF is not the principal cost factor.</p> <p>The subject does become quite complex at this point. We would have to take supposedly representative synthetic OLTP and BI data sets (like the ones in TPC-D, TPC-E, and <a href="http://dbpedia.org/resource/TPC-H" id="link-id0xb576e78">TPC-H</a>), and invent queries across them that would both make sense and be implementable in SPARQL extended with aggregates and sub-queries. Reliance on SPARQL extensions is simply unavoidable. Setting up the test systems would be non-trivial, even though there is a lot of industry experience in these matters on the database side.</p> <p>So, while this is probably the benchmark most relevant to the target audience, we may have to start with a simpler one. I will next <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1269" id="link-id10fa7a50">outline something to the effect</a>.</p> </div>
Virtuoso Cluster, RDF, and SQL Map-Reduce
http://www.openlinksw.com/weblog/oerling/?date=2007-11-20#1270
2007-11-20T17:09:45Z
2008-04-14T14:42:26.000002-04:00
<p>We have now arrived at the <a href="http://dbpedia.org/resource/RDF" id="link-id0x189c3778">RDF</a> specific parts of the <a href="http://data.openlinksw.com/oplweb/product_family/virtuoso#this" id="link-id0xa20ce710">Virtuoso</a> cluster effort.</p> <p>This has to do with special tricks for dealing with the mapping of IRIs and object values and their internal IDs. A quad will refer to the graph, subject, predicate, and object by an internal ID. The object, if short enough, can be inlined so that no join is needed to get its external form in most cases.</p> <p>Now this is old stuff.</p> <p>Clustering does not change this, except that now the tables for the mappings between IDs and their external form are partitioned. So more often than not, getting the string for the ID involves an RPC. The most recently used mappings are of course cached outside of the table but still, having a network round trip each time an IRI is returned for the first time in a while is no good.</p> <p>The solution is the same as always, namely doing one round trip per partition, with all the IDs concerned in a single message. The same applies to reading documents, where strings are translated to IDs and new IDs are made, only now we have a distributed read-write transaction.</p> <p>In terms of programming model we have a general purpose partitioned pipe. One might think of this in terms of a single control structure combining map and reduce.</p> <p>Now consider returning results for a text search:</p> <p> <code>SELECT doc_id, summary (doc_id, 'search pattern') FROM (SELECT TOP 10 doc_id FROM docs WHERE CONTAINS (text, 'search pattern', score) ORDER BY relevance (doc_id, score, 'search pattern') DESC) f;</code> </p> <p>The <code>summary</code> and <code>relevance</code> are each a function of the <code>doc_id</code> and <code>search pattern</code>. The <code>relevance</code> function would typically access some precomputed site rank and combine this with a hit score that is a function of the word frequencies in the individual hit.</p> <p>Now we are not specifically in the business of text search but this well known example will serve to make a more general point.</p> <p>How does a query like this work when everything is partitioned?</p> <p>The text index (inverted file from word to positions in each document) can be split by word or by document ID or both. In either case, it will produce the hits fairly quickly, partitioning or not. The question is sorting the million hits according to criteria that involve joining with document or site metadata. To produce fast response, this loop must run in parallel but then each score is independent, so no problem. Finally, summaries have to be made only for the items actually returned and again each summary is independent.</p> <p>A text search engine does not depend on a general purpose query processor for scheduling things in this way.</p> <p>The case is different with RDF where we must do basically the same things with ad-hoc queries. As it happens, the above sample query will run as described if only the summary and relevance functions are properly declared.</p> <p>So we have a special declaration for a partitioned function call. Further, the partitioned function call (in this case summary and relevance) will dispatch according to a given index, thus going to run on a node hosting the actual data. This is like the map part of map-reduce. But this is not all. The functions can return either a final value or a next step. This can be regarded as a second map or in some cases a reduce step. The next step is another partitioned function that gets the output of the previous one as its input and may use the same or different partitioning key.</p> <p>Now the functions can be large or small, including very small, like a single index lookup, where the RPC delay is an order of magnitude greater than the time to perform the function. The partitioned pipe manages batching these together and overlapping processing on all nodes. Plus the work can be transactional, all bound in a single distributed transaction or it can be each task for itself with error retries etc, as in the usual map-reduce situation where relatively large tasks are sent around.</p> <p>At present, we have all this implemented and we are running tests with large RDF data sets on clustered Virtuoso.</p>
RDBMS to RDF Mapping Workshop, and Benchmarks
http://www.openlinksw.com/weblog/oerling/?date=2007-11-08#1268
2007-11-08T12:51:44Z
2008-04-25T16:29:42-04:00
<p>I was recently in Boston for the <a href="http://www.w3.org/2007/03/RdfRDB/" id="link-id10f990b0">Mapping Relational Data to RDF workshop</a> of the W3C.</p> <p>The common feeling was that mapping everything to <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xb5de0b8">RDF</a> and querying it in terms of a generic domain ontology, mapped on demand into whatever line of business systems, would be very good if it only could be done. However, since this is not so easily done, the next best is to extract the <a href="http://dbpedia.org/resource/Data" id="link-id0xa05278e0">data</a> and then warehouse it as RDF.</p> <p>The obstacles perceived were of the following types:</p> <ul> <li> <p>Lack of quality in the data. The different line of business systems do not in and of themselves hold enough semantics. If the meaning of data columns in relational tables were really known and explicit, these could be meaningfully used for joining across systems. But this is more complex than just mapping the metal <i>lead</i> to the chemical symbol <i>Pb</i> and back.</p> </li> <li> <p>Lack of performance in RDF storage. Data sets even in the tens-of-millions of triples do not run very well in some stores. Well, we had the Banff life sciences demo with 450M triples in a small server box running <a href="http://virtuoso.openlinksw.com" id="link-id0x1b47d218">Virtuoso</a>, so this is not universal, plus of course we are coming up with a whole different order of magnitude, as often discussed on this <a href="http://dbpedia.org/resource/Blog" id="link-id0xb6e3410">blog</a>.</p> </li> <li> <p>Lack of functionality in mapping and possibly lack of pushing through enough of the query processing to the underlying data stores.</p> </li> </ul> <p>Personally, I am quite aware of what to do with regard to performance of mapping and storage, and see these as eminently solvable issues. After all, we have a great investment of talent in databases in general and it can be well deployed towards RDF, as we have been doing these past couple of years. So we talk about the promise of a 360-degree view of <a href="http://dbpedia.org/resource/Information" id="link-id0x1be90ae0">information</a>, with RDF being the top layer. Everybody agrees that this is a nice concept. But this is a nice concept especially when it can do the things that are the most common baseline expectation of any regular DBMS, i.e., aggregation, grouping, sub-queries, VIEWs. Now, I would not go sell a DBMS that has no <code>COUNT</code> operator to a data warehousing shop.</p> <p>The fact that OpenLink and <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1a882490">Oracle</a> allow RDF inside <a href="http://dbpedia.org/resource/SQL" id="link-id0x1c350498">SQL</a>, and OpenLink even adds native aggregates and grouping to <a href="http://dbpedia.org/resource/SPARQL" id="link-id0x1a82e880">SPARQL</a>, fixes the problem with regard to specific products, but leaves the standardization issue open. Of course, any vendor will solve these questions one way or another because a database with no aggregation is a non-starter.</p> <p>I talked to Lee Feigenbaum, chair of the W3C DAWG, about the question of aggregates and general BI capabilities in SPARQL. He told me that, prior to his time with the DAWG, these were left out because they conflicted with the <a href="http://dbpedia.org/resource/Open_world_assumption" id="link-id0x9f7ce7d8">open-world</a> assumption around RDF: You cannot count a set because by definition you do not know that you have all the members, the world being open and all that.</p> <p>Say what? Talk about the road to hell being paved with good intentions. Now, this is in no way Lee's or the present day DAWG's fault; as a member myself, I can attest to the good work and would under no circumstances wish any delays or revisions to SPARQL at this point. I am just pointing out a matter that all implementations should address, as a sort of precondition of entry into the real world IS space. If this can be done interoperably, so much the better.</p> <p>Now, out of the deliberations at the Boston workshop arose at least two ideas for follow-up activity.</p> <p>The first was an incubator group for RDF store and mapping benchmarking. This is very appropriate in order to dispel the bad name RDF storage and querying performance has been saddled with. As a first step in this direction, I will outline a <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1269" id="link-id10306200">social web oriented benchmark</a> on this blog.</p> <p>The second activity was an <a href="http://www.w3.org/2005/Incubator/rdb2rdf/" id="link-id10150a58">incubator group for preparing standardization of mapping methodologies from relational schemas to RDF</a>. We will be active on this as well.</p> <p>The two offshoots appear logically separate but are not necessarily so in practice. A benchmark is after all something that is supposed to promote a technology to a user base. The user base seems to wish to put all online systems and data warehouses under a common top level RDF model and then query away, introducing no further replication of data or performance cost or ETL latencies.</p> <p>Updating would also be nice but even query only would be very good. Personally, I'd say the RDF strength is all on the query side. Transactions are taken care of well enough by what there already is, RDF stands out in integration and the ad-hoc and discovery side of the matter. Given this, we expect the value to be consumed in a heterogeneous, multi-database, federated environment. Thus a benchmark should measure this aspect of the use-case. With the right mapping and queries, we could probably demonstrate the added cost of RDF to be very low, as long as we could push all queries that can be answered by a single source to the responsible DBMS. For distributed joins, we are back at the question of optimizing distributed queries but this is a familiar one and RDF is not the principal cost factor.</p> <p>The subject does become quite complex at this point. We would have to take supposedly representative synthetic OLTP and BI data sets (like the ones in TPC-D, TPC-E, and <a href="http://dbpedia.org/resource/TPC-H" id="link-id0x1de16b90">TPC-H</a>), and invent queries across them that would both make sense and be implementable in SPARQL extended with aggregates and sub-queries. Reliance on SPARQL extensions is simply unavoidable. Setting up the test systems would be non-trivial, even though there is a lot of industry experience in these matters on the database side.</p> <p>So, while this is probably the benchmark most relevant to the target audience, we may have to start with a simpler one. I will next <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1269" id="link-id10fa7a50">outline something to the effect</a>.</p>
Virtuoso Cluster Stage 1
http://www.openlinksw.com/blog/vdb/blog/?date=2007-09-06#1251
2007-09-06T11:11:52Z
2008-04-25T15:57:25-04:00
<div> <div style="display:none;">Virtuoso Cluster Stage 1</div> <p>I recall a quote from a stock car racing movie.</p> <p>"What is the necessary prerequisite for winning a race?" asked the racing team boss.</p> <p>"Being the fastest," answered the hotshot driver, after yet another wrecked engine.</p> <p>"No. It is finishing the race."</p> <p>In the interest of finishing, we'll now leave optimizing the cluster traffic and scheduling and move to completing functionality. Our next stop is TPC-D. After this <a href="http://dbpedia.org/resource/TPC-C" id="link-id0x1c3d9cb8">TPC-C</a>, which adds the requirement of handling distributed deadlocks. After this we add <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0x1be00c10">RDF</a>-specific optimizations.</p> <p>This will be <a href="http://virtuoso.openlinksw.com" id="link-id0x1f91c098">Virtuoso</a> 6 with the first stage of clustering support. This is with fixed partitions, which is just like a single database, except it runs on multiple machines. The stage after this is Virtuoso Cloud, the database with all the space filling properties of foam, expanding and contracting to keep an even <a href="http://dbpedia.org/resource/Data" id="link-id0x1e1aeff8">data</a> density as load and resource availability change.</p> <p>Right now, we have a pretty good idea of the final form of evaluating loop joins in a cluster, which after all is the main function of the thing. It makes sense to tune this to a point before going further. You want the pipes and pumps and turbines to have known properties and fittings before building a power plant.</p> <p>To test this, we took a table of a million short rows and made one copy partitioned over 4 databases and one copy with all rows in one database. We ran all the instances in a 4 core Xeon box. We used Unix sockets for communication.</p> <p>We joined the table to itself, like <code><b>SELECT COUNT (*) FROM ct a, ct b WHERE b.row_no = a.row_no + 3</b></code>. The <code><b>+ 3</b></code> causes the joined rows never to be on the same partition.</p> <p>With cluster, the single operation takes 3s and with a single process it takes 4s. The overall CPU time for cluster is about 30% higher, some of which is inevitable since it must combine results, serialize them, and so forth. Some real time is gained by doing multiple iterations of the inner loop (getting the row for b) in parallel. This can be further optimized to maybe 2x better with cluster but this can wait a little.</p> <p>Then we make a stream of 10 such queries. The stream with cluster is 14s; with the single process, it is 22s. Then we run 4 streams in parallel. The time with cluster is 39s and with a single process 36s. With 16 streams in parallel, cluster gets 2m51 and single process 3m21.</p> <p>The conclusion is that clustering overhead is not significant in a CPU-bound situation. Note that all the runs were at 4 cores at 98-100%, except for the first, single-client run, which had one process at 98% and 3 at 32%.</p> <p>The SMP single process loses by having more contention for mutexes serializing index access. Each wait carries an entirely ridiculous penalty of up to 6µs or so, <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1229" id="link-id106dca10">as discussed earlier on this blog</a>. The cluster wins by less contention due to distributed data and loses due to having to process messages and remember larger intermediate results. These balance out, or close enough.</p> <p>For the case with a single client, we can cut down on the coordination overhead by simply optimizing the code some more. This is quite possible, so we could get one process at 100% and 3 at 50%.</p> <p>The numbers are only relevant as ballpark figures and the percentages will vary between different queries. The point is to prove that we actually win and do not jump from the frying pan into the fire by splitting queries across processes. As a point of comparison, running the query clustered just as one would run it locally took 53s.</p> <p>We will later look at <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1336" id="link-id108d9868">the effects of different networks</a>, as we get to revisit the theme with some real benchmarks.</p> </div>
Virtuoso Cluster Stage 1
http://www.openlinksw.com/weblog/oerling/?date=2007-09-06#1250
2007-09-06T10:38:31Z
2008-04-25T15:57:20-04:00
<p>I recall a quote from a stock car racing movie.</p> <p>"What is the necessary prerequisite for winning a race?" asked the racing team boss.</p> <p>"Being the fastest," answered the hotshot driver, after yet another wrecked engine.</p> <p>"No. It is finishing the race."</p> <p>In the interest of finishing, we'll now leave optimizing the cluster traffic and scheduling and move to completing functionality. Our next stop is TPC-D. After this <a href="http://dbpedia.org/resource/TPC-C" id="link-id0x1b4d9898">TPC-C</a>, which adds the requirement of handling distributed deadlocks. After this we add <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xa57ade08">RDF</a>-specific optimizations.</p> <p>This will be <a href="http://virtuoso.openlinksw.com" id="link-id0x9fc61850">Virtuoso</a> 6 with the first stage of clustering support. This is with fixed partitions, which is just like a single database, except it runs on multiple machines. The stage after this is Virtuoso Cloud, the database with all the space filling properties of foam, expanding and contracting to keep an even <a href="http://dbpedia.org/resource/Data" id="link-id0x1de20610">data</a> density as load and resource availability change.</p> <p>Right now, we have a pretty good idea of the final form of evaluating loop joins in a cluster, which after all is the main function of the thing. It makes sense to tune this to a point before going further. You want the pipes and pumps and turbines to have known properties and fittings before building a power plant.</p> <p>To test this, we took a table of a million short rows and made one copy partitioned over 4 databases and one copy with all rows in one database. We ran all the instances in a 4 core Xeon box. We used Unix sockets for communication.</p> <p>We joined the table to itself, like <code><b>SELECT COUNT (*) FROM ct a, ct b WHERE b.row_no = a.row_no + 3</b></code>. The <code><b>+ 3</b></code> causes the joined rows never to be on the same partition.</p> <p>With cluster, the single operation takes 3s and with a single process it takes 4s. The overall CPU time for cluster is about 30% higher, some of which is inevitable since it must combine results, serialize them, and so forth. Some real time is gained by doing multiple iterations of the inner loop (getting the row for b) in parallel. This can be further optimized to maybe 2x better with cluster but this can wait a little.</p> <p>Then we make a stream of 10 such queries. The stream with cluster is 14s; with the single process, it is 22s. Then we run 4 streams in parallel. The time with cluster is 39s and with a single process 36s. With 16 streams in parallel, cluster gets 2m51 and single process 3m21.</p> <p>The conclusion is that clustering overhead is not significant in a CPU-bound situation. Note that all the runs were at 4 cores at 98-100%, except for the first, single-client run, which had one process at 98% and 3 at 32%.</p> <p>The SMP single process loses by having more contention for mutexes serializing index access. Each wait carries an entirely ridiculous penalty of up to 6µs or so, <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1229" id="link-id106dca10">as discussed earlier on this blog</a>. The cluster wins by less contention due to distributed data and loses due to having to process messages and remember larger intermediate results. These balance out, or close enough.</p> <p>For the case with a single client, we can cut down on the coordination overhead by simply optimizing the code some more. This is quite possible, so we could get one process at 100% and 3 at 50%.</p> <p>The numbers are only relevant as ballpark figures and the percentages will vary between different queries. The point is to prove that we actually win and do not jump from the frying pan into the fire by splitting queries across processes. As a point of comparison, running the query clustered just as one would run it locally took 53s.</p> <p>We will later look at <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1336" id="link-id108d9868">the effects of different networks</a>, as we get to revisit the theme with some real benchmarks.</p>
Virtuoso and cluster capacity allocation
http://www.openlinksw.com/blog/vdb/blog/?date=2007-08-28#1248
2007-08-28T11:54:30Z
2008-04-30T15:08:20-04:00
<div> <div style="display:none;">Virtuoso and cluster capacity allocation</div> <p>I just read <a href="http://labs.google.com/papers/bigtable.html" id="link-id140a68b0">Google's Bigtable</a> paper. It is relevant here because it talks about keeping petabyte scale (1024TB) tables on a variable size cluster of machines.</p> <p>I have talked about partitioning versus distributed cache in the <a href="http://www.openlinksw.com/weblog/oerling/?id=1229" id="link-id13f70dc8">second to last post</a>. The problem in short is that you do not expect a DBA to really know how to partition things, and even if the indices are correctly partitioned initially, repartitioning them is so bad that doing it online can be a problem. And repartitioning is needed whenever adding machines, unless the size increment is a doubling, which it will never be.</p> <p>So <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0xa2957e38">Oracle</a> has really elegantly stepped around the whole problem by not partitioning for clustering in the first place. So incremental capacity change does not require repartitioning. Oracle has partitioning for other purposes but this is not tied to their cluster proposition.</p> <p>I did not go the cache fusion route because I could not figure a way to know with near certainty where to send a request for a given key value. In the case we are interested in, the job simply must go to the <a href="http://dbpedia.org/resource/Data" id="link-id0xa20ce8e0">data</a> and not the other way around. Besides, not being totally dependent on a microsecond latency interconnect and a SAN for performance enhances deployment options. Sending large batches of functions tolerates latency better than cache consistency messages which are a page at a time, unless of course you kill yourself with extra trickery for batching these too.</p> <p>So how to adapt to capacity change? Well, by making the unit of capacity allocation much smaller than a machine, of course.</p> <p>Google has done this in Bigtable by a scheme of dynamic range partitioning. The partition size is in the tens to hundreds of megabytes, something that can be moved around within reason. When the partition, called a tablet, gets too big, it splits. Just like a Btree index. The tree top must be common <a href="http://dbpedia.org/resource/Knowledge" id="link-id0xa20cebb0">knowledge</a>, as well as the allocation of partitions to servers but these can be cached here and there and do not change all the time.</p> <p>So how could we do something of the sort here? I know for an experiential fact that when people cannot change the server memory pool size, let alone correctly set up disk striping, they simply cannot be expected to deal with partitioning. Besides, even if you know exactly what you are doing and why, configuring and refilling large numbers of partitions by hand is error prone, tedious, time consuming, and will run out of disk and require restoring backups and all sorts of DBA activity that will have everything down for a long time, unless of course you have MIS staff such as is not easily found.</p> <p>The solution is not so complex. We start with a set number of machines and make a file group on each. A file group has a bunch of disk stripes and a log file and can be laid out on the local file system in the usual manner. The data goes into the file group, partitioned as defined. You still specify partitioning columns but not where each partition goes. The system will decide this by itself. When a server's file group gets too big, it splits. One half of each key's partition in the original stays where it was and the other half goes to the copy. The copies will hold rows that no longer belong there but these can be removed in the background. The new file group will be managed by the same server process and the partitioning <a href="http://dbpedia.org/resource/Information" id="link-id0xc803148">information</a> on all servers gets updated to reflect the existence of the new file group and the range of hash values that belong there.</p> <p>If a file group is kept at some reasonable size, under a few GB, these can be moved around between servers, even dynamically. </p> <p>If data is kept replicated, then the replicas have to split at the same time and the system will have to make sure that the replicas are kept on separate machines.</p> <p>So what happens to disk locality when file groups split? Nothing much. Firstly, partitioning will be set up so that consecutive values go to the same hash value, so that key compression is not ruined. Thus, consecutive numbers will be on the same page. Imagine an integer key partitioned two ways on bits 10-20. Values 0-1K go together, values 1K-2K go another way, values 2K-3K go the first way etc. </p> <p>Now let us suppose the first partition, the even K's splits. It could split so that multiples of 4 go one way and the rest another way. Now we'd have 0-1K in place, 2K-3K in the new partition, 4K-5K in place and so on. A sequential disk read, with some read ahead, would scan the partitions in parallel but the disk access would be made sequential by the read ahead logic â remember that these are controlled by the same server process.</p> <p>For purposes of sending functions, the file group would be the recipient, not the host, per se. The allocation of file groups to hosts could change. </p> <p>Now picture a transaction that touches multiple file groups. The requests going to collocated file groups can travel in the same batch and the recipient server process can run them sequentially or with a thread per file group, as may be convenient. Multiple threads per query on the same index make contention and needless thread switches. But since distinct file groups have their distinct mutexes there is less interference.</p> <p>For purposes of transactions, we might view a file group as deserving a its own branch. In this way we would not have to abort transactions if file groups moved. A file group split would probably have to kill all uncommitted transactions on it so as not to have to split one branch in two or deal with uncommitted data in the split. This is hardly a problem, the event being rare. For purposes of checkpoints, logging, log archival, recovery, and such, a file group is its own unit. The Bigtable paper had some ideas about combining transaction logs and such, all quite straightforward and intuitive.</p> <p>Writing the clustering logic with the file group, not the database process, as the main unit of location is a good idea and an entirely trivial change. This will make it possible to adjust capacity in almost real time without bringing everything to a halt by re-inserting terabytes of data in system wide repartitioning runs.</p> <p>Implementing this on the current <a href="http://virtuoso.openlinksw.com" id="link-id0xa21de0e0">Virtuoso</a> is not a real difficulty. There is already a concept of file group, although we use only two, one for the data and one for temp. Using multiple ones is not a big deal.</p> <p>Supporting capacity allocation at the file group level instead of the server level can be introduced towards the middle of the clustering effort and will not greatly impact timetables.</p> </div>
Virtuoso and cluster capacity allocation
http://www.openlinksw.com/weblog/oerling/?date=2007-08-28#1246
2007-08-28T10:08:25Z
2008-04-25T12:39:16.000002-04:00
<p>I just read <a href="http://labs.google.com/papers/bigtable.html" id="link-id10967a78">Google's Bigtable</a> paper. It is relevant here because it talks about keeping petabyte scale (1024TB) tables on a variable size cluster of machines.</p> <p>I have talked about partitioning versus distributed cache in the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1229" id="link-id10913318">second to last post</a>. The problem in short is that you do not expect a DBA to really know how to partition things, and even if the indices are correctly partitioned initially, repartitioning them is so bad that doing it online can be a problem. And repartitioning is needed whenever adding machines, unless the size increment is a doubling, which it will never be.</p> <p>So <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1c4caaa0">Oracle</a> has really elegantly stepped around the whole problem by not partitioning for clustering in the first place. So incremental capacity change does not require repartitioning. Oracle has partitioning for other purposes but this is not tied to their cluster proposition.</p> <p>I did not go the cache fusion route because I could not figure a way to know with near certainty where to send a request for a given key value. In the case we are interested in, the job simply must go to the <a href="http://dbpedia.org/resource/Data" id="link-id0xa1b52ab8">data</a> and not the other way around. Besides, not being totally dependent on a microsecond latency interconnect and a SAN for performance enhances deployment options. Sending large batches of functions tolerates latency better than cache consistency messages which are a page at a time, unless of course you kill yourself with extra trickery for batching these too.</p> <p>So how to adapt to capacity change? Well, by making the unit of capacity allocation much smaller than a machine, of course.</p> <p>Google has done this in Bigtable by a scheme of dynamic range partitioning. The partition size is in the tens to hundreds of megabytes, something that can be moved around within reason. When the partition, called a tablet, gets too big, it splits. Just like a Btree index. The tree top must be common <a href="http://dbpedia.org/resource/Knowledge" id="link-id0x9f94a5f8">knowledge</a>, as well as the allocation of partitions to servers but these can be cached here and there and do not change all the time.</p> <p>So how could we do something of the sort here? I know for an experiential fact that when people cannot change the server memory pool size, let alone correctly set up disk striping, they simply cannot be expected to deal with partitioning. Besides, even if you know exactly what you are doing and why, configuring and refilling large numbers of partitions by hand is error prone, tedious, time consuming, and will run out of disk and require restoring backups and all sorts of DBA activity that will have everything down for a long time, unless of course you have MIS staff such as is not easily found.</p> <p>The solution is not so complex. We start with a set number of machines and make a file group on each. A file group has a bunch of disk stripes and a log file and can be laid out on the local file system in the usual manner. The data goes into the file group, partitioned as defined. You still specify partitioning columns but not where each partition goes. The system will decide this by itself. When a server's file group gets too big, it splits. One half of each key's partition in the original stays where it was and the other half goes to the copy. The copies will hold rows that no longer belong there but these can be removed in the background. The new file group will be managed by the same server process and the partitioning <a href="http://dbpedia.org/resource/Information" id="link-id0x1a3e17a0">information</a> on all servers gets updated to reflect the existence of the new file group and the range of hash values that belong there.</p> <p>If a file group is kept at some reasonable size, under a few GB, these can be moved around between servers, even dynamically. </p> <p>If data is kept replicated, then the replicas have to split at the same time and the system will have to make sure that the replicas are kept on separate machines.</p> <p>So what happens to disk locality when file groups split? Nothing much. Firstly, partitioning will be set up so that consecutive values go to the same hash value, so that key compression is not ruined. Thus, consecutive numbers will be on the same page. Imagine an integer key partitioned two ways on bits 10-20. Values 0-1K go together, values 1K-2K go another way, values 2K-3K go the first way etc. </p> <p>Now let us suppose the first partition, the even K's splits. It could split so that multiples of 4 go one way and the rest another way. Now we'd have 0-1K in place, 2-3K in the new partition, 4K-5K in place and so on. A sequential disk read, with some read ahead, would scan the partitions in parallel but the disk access would be made sequential by the read ahead logic â remember that these are controlled by the same server process.</p> <p>For purposes of sending functions, the file group would be the recipient, not the host, per se. The allocation of file groups to hosts could change. </p> <p>Now picture a transaction that touches multiple file groups. The requests going to collocated file groups can travel in the same batch and the recipient server process can run them sequentially or with a thread per file group, as may be convenient. Multiple threads per query on the same index make contention and needless thread switches. But since distinct file groups have their distinct mutexes there is less interference.</p> <p>For purposes of transactions, we might view a file group as deserving a its own branch. In this way we would not have to abort transactions if file groups moved. A file group split would probably have to kill all uncommitted transactions on it so as not to have to split one branch in two or deal with uncommitted data in the split. This is hardly a problem, the event being rare. For purposes of checkpoints, logging, log archival, recovery, and such, a file group is its own unit. The Bigtable paper had some ideas about combining transaction logs and such, all quite straightforward and intuitive.</p> <p>Writing the clustering logic with the file group, not the database process, as the main unit of location is a good idea and an entirely trivial change. This will make it possible to adjust capacity in almost real time without bringing everything to a halt by re-inserting terabytes of data in system wide repartitioning runs.</p> <p>Implementing this on the current <a href="http://virtuoso.openlinksw.com" id="link-id0x1a35c638">Virtuoso</a> is not a real difficulty. There is already a concept of file group, although we use only two, one for the data and one for temp. Using multiple ones is not a big deal.</p> <p>Supporting capacity allocation at the file group level instead of the server level can be introduced towards the middle of the clustering effort and will not greatly impact timetables.</p> Â
Virtuoso Cluster Preview
http://www.openlinksw.com/blog/vdb/blog/?date=2007-08-27#1245
2007-08-27T09:51:45Z
2008-04-25T11:59:31-04:00
<div> <div style="display:none;">Virtuoso Cluster Preview</div> <p> <b><i>I wrote the basics of the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1229" id="link-id1383c310">Virtuoso clustering support</a> over the past three weeks. It can now manage connections, decide where things go, do two phase commits, insert and select <a href="http://dbpedia.org/resource/Data" id="link-id0xad603e0">data</a> from tables partitioned over multiple <a href="http://virtuoso.openlinksw.com" id="link-id0xbc64f48">Virtuoso</a> instances. It works about enough to be measured, of which I will <a href="http://dbpedia.org/resource/Blog" id="link-id0xb958e90">blog</a> more over the next two weeks.</i> </b> </p> <p> <b><i>I will in the following give a features preview of what will be in the Virtuoso clustering support when it is released in the fall of this year (2007).</i> </b> </p> <h3>Data Partitioning</h3> <p>A Virtuoso database consists of indices only, so that the row of a table is stored together with the primary key. Blobs are stored on separate pages when they do not fit inline within the row. With clustering, partitioning can be specified index by index. Partitioning means that values of specific columns are used for determining where the containing index entry will be stored. Virtuoso partitions by hash and allows specifying what parts of partitioning columns are used for the hash, for example bits 14-6 of an integer or the first 5 characters of a string. Like this, key compression gains are not lost by storing consecutive values on different partitions.</p> <p>Once the partitioning is specified, we specify which set of cluster nodes stores this index. Not every index has to be split evenly across all nodes. Also, all nodes do not have to have equal slices of the partitioned index, accommodating differences in capacity between cluster nodes.</p> <p>Each Virtuoso instance can manage up to 32TB of data. A cluster has no definite size limit.</p> <h3>Load Balancing and Fault Tolerance</h3> <p>When data is partitioned, an operation on the data goes where the data is.  This provides a certain natural parallelism but we will discuss this further below.</p> <p>Some data may be stored multiple times in the cluster, either for fail-over or for splitting read load. Some data, such as database schema, is replicated on all nodes. When specifying a set of nodes for storing the partitions of a key, it is possible to specify multiple nodes for the same partition. If this is the case, updates go to all nodes and reads go to a randomly picked node from the group.</p> <p>If one of the nodes in the group fails, operation can resume with the surviving node.  The failed node can be brought back online from the transaction logs of the surviving nodes. A few transactions may be rolled back at the time of failure and again at the time of the failed node rejoining the cluster but these are aborts as in the case of deadlock and lose no committed data.</p> <h3>Shared Nothing</h3> <p>The Virtuoso architecture does not require a SAN for disk sharing across nodes. This is reasonable since a few disks on a local controller can easily provide 300MB/s of read and passing this over an interconnect fabric that would also have to carry inter-node messages could saturate even a fast network. </p> <h3>Client View</h3> <p>A <a href="http://dbpedia.org/resource/SQL" id="link-id0xbb32b58">SQL</a> or <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0xaa4e2b8">HTTP</a> client can connect to any node of the cluster and get an identical view of all data with full transactional semantics. DDL operations like table creation and package installation are limited to one node, though.</p> <p>Applications such as <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x1bc18300">ODS</a> will run unmodified. They are installed on all nodes with a single install command. After this, the data partitioning must be declared, which is a one time operation to be done cluster by cluster. The only application change is specifying the partitioning columns for each index. The gain is optional redundant storage and capacity not limited to a single machine. The penalty is that single operations may take a little longer when not all data is managed by the same process but then the parallel throughput is increased.  We note that the main ODS performance factor is web page logic and not database access.  Thus splitting the web server logic over multiple nodes gives basically linear scaling.</p> <h3>Parallel Query Execution</h3> <p>Message latency is the principal performance factor in a clustered database. Due to this, Virtuoso packs the maximum number of operations in a single message. For example, when doing a loop join that reads one table sequentially and retrieves a row of another table for each row of the outer table, a large number of the join of the inner loop are run in parallel. So, if there is a join of five tables that gets one row from each table and all rows are on different nodes, the time will be spent on message latency. If each step of the join gets 10 rows, for a total of 100000 results, the message latency is not a significant factor and the cluster will clearly outperform a single node.</p> <p>Also, if the workload consists of large numbers of concurrent short updates or queries, the message latencies will even out and throughput will scale up even if doing a single transaction were faster on a single node.</p> <h3>Parallel SQL</h3> <p>There are SQL extensions for stored procedures allowing parallelizing operations.  For example, if a procedure has a loop doing inserts, the inserted rows can be buffered until a sufficient number is available, at which point they are sent in batches to the nodes concerned.  Transactional semantics are kept but error detection is deferred to the actual execution.</p> <h3>Transactions</h3> <p>Each transaction is owned by one node of the cluster, the node to which the client is connected. When more than one node besides the owner of the transaction is updated, two phase commit is used. This is transparent to the application code. No external transaction monitor is required, the Virtuoso instances perform these functions internally. There is a distributed deadlock detection scheme based on the nodes periodically sharing transaction waiting <a href="http://dbpedia.org/resource/Information" id="link-id0xb78b5c0">information</a>.</p> <p>Since read transactions can operate without locks, reading the last committed state of uncommitted updated rows, waiting for locks is not very common.</p> <h3>Interconnect and Threading</h3> <p>Virtuoso uses TCP to connect between instances. A single instance can have multiple listeners at different network interfaces for cluster activity. The interfaces will be used in a round-robin fashion by the peers, spreading the load over all network interfaces. A separate thread is created for monitoring each interface. Long messages, such as transfers of blobs are done on a separate thread, thus allowing normal service on the cluster node while the transfer is proceeding.</p> <p>We will have to test the performance of TCP over <i>Infiniband</i> to see if there is clear gain in going to a lower level interface like <i>MPI</i>. The Virtuoso architecture is based on streams connecting cluster nodes point to point. The design does not per se gain from remote DMA or other features provided by MPI. Typically, messages are quite short, under 100K.  Flow control for transfer of blobs is however nice to have but can be written at the application level if needed. We will get real data on the performance of different interconnects in the next weeks. </p> <h3>Deployment and Management</h3> <p>Configuring is quite simple, with each process sharing a copy of the same configuration file.  One line in the file differs from host to host, telling it which one it is. Otherwise the database configuration files are individual per host, accommodating different file system layouts etc.  Setting up a node requires copying the executable and two configuration files, no more.  All functionality is contained in a single process. There are no installers to be run or such.</p> <p>Changing the number or network interface of cluster nodes requires a cluster restart. Changing data partitioning requires copying the data into a new table and renaming this over the old one. This is time consuming and does not mix well with updates. Splitting an existing cluster node requires no copying with repartitioning but shifting data between partitions does.</p> <p>A consolidated status report shows the general state and level of intra-cluster traffic as count of messages and count of bytes.</p> <p>Start, shutdown, backup, and package installation commands can only be issued from a single master node. Otherwise all is symmetrical.</p> <h3>Present State and Next Developments</h3> <p>The basics are now in place. Some code remains to be written for such things as distributed deadlock detection, 2-phase commit recovery cycle, management functions, etc. Some SQL operations like text index, statistics sampling, and index intersection need special support, yet to be written.</p> <p>The <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xbca4e90">RDF</a> capabilities are not specifically affected by clustering except in a couple of places. Loading will be slightly revised to use larger batches of rows to minimize latency, for example.</p> <p>There is a pretty much infinite world of SQL optimizations for splitting aggregates, taking advantage of co-located joins etc. These will be added gradually. These are however not really central to the first application of RDF storage but are quite important for business intelligence, for example.</p> <p>We will run some benchmarks for comparing single host and clustered Virtuoso instances over the next weeks. Some of this will be with real data, giving an estimate on when we can move some of the RDF data we presently host to the new platform. We will benchmark against <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0xa9cc1b8">Oracle</a> and <a href="http://dbpedia.org/resource/IBM_DB2" id="link-id0x1be5abb0">DB2</a> later but first we get things to work and compare against ourselves.</p> <p>We roughly expect a halving in space consumption and a significant increase in single query performance and linearly scaling parallel throughput through addition of cluster nodes.</p> <p> <i>The <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1246" id="link-id106de430">next update</a> will be on this blog within two weeks.</i> </p> </div>
Virtuoso Cluster Preview
http://www.openlinksw.com/weblog/oerling/?date=2007-08-27#1244
2007-08-27T09:44:40Z
2008-04-25T11:59:21.000003-04:00
<p> <b><i>I wrote the basics of the <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1229" id="link-id1383c310">Virtuoso clustering support</a> over the past three weeks. It can now manage connections, decide where things go, do two phase commits, insert and select <a href="http://dbpedia.org/resource/Data" id="link-id0xbbf5988">data</a> from tables partitioned over multiple <a href="http://virtuoso.openlinksw.com" id="link-id0x1da47d98">Virtuoso</a> instances. It works about enough to be measured, of which I will <a href="http://dbpedia.org/resource/Blog" id="link-id0xabf4a10">blog</a> more over the next two weeks.</i> </b> </p> <p> <b><i>I will in the following give a features preview of what will be in the Virtuoso clustering support when it is released in the fall of this year (2007).</i> </b> </p> <h3>Data Partitioning</h3> <p>A Virtuoso database consists of indices only, so that the row of a table is stored together with the primary key. Blobs are stored on separate pages when they do not fit inline within the row. With clustering, partitioning can be specified index by index. Partitioning means that values of specific columns are used for determining where the containing index entry will be stored. Virtuoso partitions by hash and allows specifying what parts of partitioning columns are used for the hash, for example bits 14-6 of an integer or the first 5 characters of a string. Like this, key compression gains are not lost by storing consecutive values on different partitions.</p> <p>Once the partitioning is specified, we specify which set of cluster nodes stores this index. Not every index has to be split evenly across all nodes. Also, all nodes do not have to have equal slices of the partitioned index, accommodating differences in capacity between cluster nodes.</p> <p>Each Virtuoso instance can manage up to 32TB of data. A cluster has no definite size limit.</p> <h3>Load Balancing and Fault Tolerance</h3> <p>When data is partitioned, an operation on the data goes where the data is.  This provides a certain natural parallelism but we will discuss this further below.</p> <p>Some data may be stored multiple times in the cluster, either for fail-over or for splitting read load. Some data, such as database schema, is replicated on all nodes. When specifying a set of nodes for storing the partitions of a key, it is possible to specify multiple nodes for the same partition. If this is the case, updates go to all nodes and reads go to a randomly picked node from the group.</p> <p>If one of the nodes in the group fails, operation can resume with the surviving node.  The failed node can be brought back online from the transaction logs of the surviving nodes. A few transactions may be rolled back at the time of failure and again at the time of the failed node rejoining the cluster but these are aborts as in the case of deadlock and lose no committed data.</p> <h3>Shared Nothing</h3> <p>The Virtuoso architecture does not require a SAN for disk sharing across nodes. This is reasonable since a few disks on a local controller can easily provide 300MB/s of read and passing this over an interconnect fabric that would also have to carry inter-node messages could saturate even a fast network. </p> <h3>Client View</h3> <p>A <a href="http://dbpedia.org/resource/SQL" id="link-id0x9fc302a0">SQL</a> or <a href="http://dbpedia.org/resource/Hypertext_Transfer_Protocol" id="link-id0x19faa348">HTTP</a> client can connect to any node of the cluster and get an identical view of all data with full transactional semantics. DDL operations like table creation and package installation are limited to one node, though.</p> <p>Applications such as <a href="http://dbpedia.org/resource/OpenLink_Data_Spaces" id="link-id0x20cd9e98">ODS</a> will run unmodified. They are installed on all nodes with a single install command. After this, the data partitioning must be declared, which is a one time operation to be done cluster by cluster. The only application change is specifying the partitioning columns for each index. The gain is optional redundant storage and capacity not limited to a single machine. The penalty is that single operations may take a little longer when not all data is managed by the same process but then the parallel throughput is increased.  We note that the main ODS performance factor is web page logic and not database access.  Thus splitting the web server logic over multiple nodes gives basically linear scaling.</p> <h3>Parallel Query Execution</h3> <p>Message latency is the principal performance factor in a clustered database. Due to this, Virtuoso packs the maximum number of operations in a single message. For example, when doing a loop join that reads one table sequentially and retrieves a row of another table for each row of the outer table, a large number of the join of the inner loop are run in parallel. So, if there is a join of five tables that gets one row from each table and all rows are on different nodes, the time will be spent on message latency. If each step of the join gets 10 rows, for a total of 100000 results, the message latency is not a significant factor and the cluster will clearly outperform a single node.</p> <p>Also, if the workload consists of large numbers of concurrent short updates or queries, the message latencies will even out and throughput will scale up even if doing a single transaction were faster on a single node.</p> <h3>Parallel SQL</h3> <p>There are SQL extensions for stored procedures allowing parallelizing operations.  For example, if a procedure has a loop doing inserts, the inserted rows can be buffered until a sufficient number is available, at which point they are sent in batches to the nodes concerned.  Transactional semantics are kept but error detection is deferred to the actual execution.</p> <h3>Transactions</h3> <p>Each transaction is owned by one node of the cluster, the node to which the client is connected. When more than one node besides the owner of the transaction is updated, two phase commit is used. This is transparent to the application code. No external transaction monitor is required, the Virtuoso instances perform these functions internally. There is a distributed deadlock detection scheme based on the nodes periodically sharing transaction waiting <a href="http://dbpedia.org/resource/Information" id="link-id0xbcc0a50">information</a>.</p> <p>Since read transactions can operate without locks, reading the last committed state of uncommitted updated rows, waiting for locks is not very common.</p> <h3>Interconnect and Threading</h3> <p>Virtuoso uses TCP to connect between instances. A single instance can have multiple listeners at different network interfaces for cluster activity. The interfaces will be used in a round-robin fashion by the peers, spreading the load over all network interfaces. A separate thread is created for monitoring each interface. Long messages, such as transfers of blobs are done on a separate thread, thus allowing normal service on the cluster node while the transfer is proceeding.</p> <p>We will have to test the performance of TCP over <i>Infiniband</i> to see if there is clear gain in going to a lower level interface like <i>MPI</i>. The Virtuoso architecture is based on streams connecting cluster nodes point to point. The design does not per se gain from remote DMA or other features provided by MPI. Typically, messages are quite short, under 100K.  Flow control for transfer of blobs is however nice to have but can be written at the application level if needed. We will get real data on the performance of different interconnects in the next weeks. </p> <h3>Deployment and Management</h3> <p>Configuring is quite simple, with each process sharing a copy of the same configuration file.  One line in the file differs from host to host, telling it which one it is. Otherwise the database configuration files are individual per host, accommodating different file system layouts etc.  Setting up a node requires copying the executable and two configuration files, no more.  All functionality is contained in a single process. There are no installers to be run or such.</p> <p>Changing the number or network interface of cluster nodes requires a cluster restart. Changing data partitioning requires copying the data into a new table and renaming this over the old one. This is time consuming and does not mix well with updates. Splitting an existing cluster node requires no copying with repartitioning but shifting data between partitions does.</p> <p>A consolidated status report shows the general state and level of intra-cluster traffic as count of messages and count of bytes.</p> <p>Start, shutdown, backup, and package installation commands can only be issued from a single master node. Otherwise all is symmetrical.</p> <h3>Present State and Next Developments</h3> <p>The basics are now in place. Some code remains to be written for such things as distributed deadlock detection, 2-phase commit recovery cycle, management functions, etc. Some SQL operations like text index, statistics sampling, and index intersection need special support, yet to be written.</p> <p>The <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xb919200">RDF</a> capabilities are not specifically affected by clustering except in a couple of places. Loading will be slightly revised to use larger batches of rows to minimize latency, for example.</p> <p>There is a pretty much infinite world of SQL optimizations for splitting aggregates, taking advantage of co-located joins etc. These will be added gradually. These are however not really central to the first application of RDF storage but are quite important for business intelligence, for example.</p> <p>We will run some benchmarks for comparing single host and clustered Virtuoso instances over the next weeks. Some of this will be with real data, giving an estimate on when we can move some of the RDF data we presently host to the new platform. We will benchmark against <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x1ddf8288">Oracle</a> and <a href="http://dbpedia.org/resource/IBM_DB2" id="link-id0xa04b6ae8">DB2</a> later but first we get things to work and compare against ourselves.</p> <p>We roughly expect a halving in space consumption and a significant increase in single query performance and linearly scaling parallel throughput through addition of cluster nodes.</p> <p> <i>The <a href="http://www.openlinksw.com/dataspace/oerling/weblog/Orri%20Erling%27s%20Blog/1246" id="link-id106de430">next update</a> will be on this blog within two weeks.</i> </p>
RDF, Clustered Databases, and Partitioning Vs. Cache Fusion
http://www.openlinksw.com/blog/vdb/blog/?date=2007-07-19#1230
2007-07-19T12:29:12Z
2008-04-25T11:59:26-04:00
<div> <div style="display:none;">RDF, Clustered Databases, and Partitioning Vs. Cache Fusion</div> <p>I recently read <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0xb279258">Oracle</a>'s papers about RAC, Real Application Clusters. This is relevant as we are presently working on the <a href="http://virtuoso.openlinksw.com" id="link-id0xba1d4e0">Virtuoso</a> equivalent.</p> <p>Caveat: The following is quite technical and not the final word on the matter.</p> <p>Oracle's claim is roughly as follows: Take a number of machines with access to a shared pool of disks and get scalability in processing power and memory without having to explicitly partition the <a href="http://dbpedia.org/resource/Data" id="link-id0x1a9a7ab8">data</a> or perform other complicated configuration.</p> <p>This works through implementing a cache consistency protocol between the participating boxes and by parallelizing queries just as one would do on a shared memory SMP box. Each disk page has a box assigned to keep track of it and the responsibility migrates so that the box most often needing the page gets to be the page's guardian, so as not to have to ask anybody else for permission to write the page.</p> <p>This is a compelling proposition. Surely, it must be unrealistic to expect people to manually partition databases. This would require some understanding of first principles which is scarce out there.</p> <p>So, should we implement clustering a la Oracle?</p> <p>Let's look at some basics. If we have an OLTP workload like <a href="http://dbpedia.org/resource/TPC-C" id="link-id0xbaa89d8">TPC-C</a>, we usually have affinity between clients and the data they access. This will make each client's pages migrate to be managed by the box the client is connected to. This will work pretty well, no worse than with a single box. If two clients are updating the same data but are connected to two different boxes, this is quite bad since the box that does not have responsibility for the page must ask the other box for write access. This is a round trip, at least tens of microseconds (µs). Consider in comparison that finding a row out of a million takes some 3µs.</p> <p>Would it not be better to have each partition in a known place and leave all processing to that place? The write contention would be resolved in the box owning the partition and there would be a message but now for requesting the update, not dealing with cache consistency. At what level should one communicate between cluster nodes? Talk about disk pages or about logical operations? If there is complete affinity between boxes and data, the RAC style shared cache needs no messages, each box ends up managing the pages of its clients and all works just as with a local situation. If on the other hand any client will update any page at random, most updates must request the write permission from another node. I will here presume that index tree tops get eventually cached on all nodes. If this were not so, even index lookups would have to most often request each index page from a remote box. Never forget that with a tree index, it takes about 1µs to descend one level and 50µs for a message round trip between processes, not counting any transport latency.</p> <p>What of <a href="http://dbpedia.org/resource/Resource_Description_Framework" id="link-id0xbe1bd98">RDF</a> query workloads? After all, we are in the first instance concerned with winning the RDF storage race and after this the TPC ones. We design for both but do RDF first since this is our chosen specialty.</p> <p>The disadvantage of having to specify partitioning is less weighty with RDF since there are only a few big tables and they will be at default settings, pretty much always. We do not expect the application developer to ever change these settings although it is in principle possible.</p> <p>What about queries? The RDF workload is mostly random access and loop joins. How would these run on RAC? For now, let's make a thought experiment and compare cache fusion to a hash partitioned cluster. In the following, I do not describe how Oracle actually works but will just describe how I would do things if I implemented a RAC style clustering. With a RAC style cluster, I'd split the outer loop into equal partitions and run them in parallel on different boxes. Each would build a working set for its part of the query and pages that were needed by more than one box would be read once from disk and a second time from the node that had them in cache. The top nodes of index trees would end up cached on all boxes. It would seem that all boxes would fill their cache with the same data. Now it may be that RAC makes it so that a page is cached only on one box and other boxes wanting the page must go to that box to get access to the page. But this would be a disaster in index lookups. It is less than a microsecond per local index tree level, but if there is a round trip, it would be at least 50µs per level of the index tree. I don't know for sure about Oracle but if I did RAC, I'd have to allow duplicate read content in caches. This would have the effect that the aggregate cache size would be closer to the single cache size than to the sum of the cache sizes. A physically partitioned database would not ship pages so caches would not overlap and the aggregate cache would indeed be the sum of the sizes. Now this is good only insofar all boxes participate but with evenly distributed data this is a good possibility.</p> <p>Of course, if RAC knew to split queries so that data and nodes had real affinity then the problem would be less. For indices one would need a map of key values to boxes. A little like a top level index shared among all nodes. The key value would give the node, just like with partitioning.</p> <p>This would make partitioning on the fly. Joins that were made the most frequently would cause migration that would make these joins co-located.</p> <p>We must optimize the number of messages needed to execute long series of loop joins. For parallelizing single queries, the most obvious approach would be to partition the first/outermost loop that is more than one iteration into equal size chunks. With RDF data, the join keys will mostly begin GS or GO with a possible P appended. If GO or GS specify the partition, partitioning by hash will yield the node that will provide the result.</p> <p>The number of messages can be reduced to a minimum of the number of join steps times number of boxes minus one if the loops are short enough and multiple operations are carried by one message.</p> <p>With RAC style clustering, each index lookup would have to be sent to the node most likely to hold the answer. If pages have to be fetched from other nodes, we have disastrous performance, at least 50µs for each non-local page. If there are two non-local pages in a lookup, the overhead will exceed the overhead of delegating the single lookup. Index page access in lookups cannot be easily batched, the way index lookups going to the same node can be batched. Batching multiple hopefully long operations into a single message is the only way to defeat the extreme cost of sending messages. An index lookup does not know what page it will need until it needs it. A way of batching these would be to run multiple lookups in parallel and to combine remote page requests grouping them by destination. This would not be impossible, simply we would have to run 100 index lookups in parallel on a thread, 100 first levels, 100 second levels and so forth. Suppose an outer loop that gives 100 rows and then an inner loop that retrieves 1 row for each. A query to get the email address of Mary would do this, supposing 100 Marys in the db. <code>{?person firstName "Mary" . ?person email ?m .}</code>. Suppose a cluster of 10 nodes. The first node gets the 100 rows of the outer loop, splits these into 10x10, 10 on each node and then each node does 10 lookups in parallel, meaning 10 first levels, 10 second levels, 10 third levels. The index tree would be 4 deep, branching 300 ways. Running the query a second time would find all data in memory and run with only 18 messages after getting the 100 rows of the first loop. The first run would send lots of messages, almost two per page, for about 800 messages after getting the 100 rows of the first loop.</p> <p>With partitioning, the situation would be sending 18 messages constant. 9 batches of 10 index lookups and their replies. The latency is 50µs and the lookup is 4µs. We would in fact gain in real time, counting 50µs for messages and 4µs per lookup, the time through the whole exercise of 10x10 random lookups would be 90µs.</p> <p>If I did RAC style clustering, I'd have to allow replicating the tops of index trees to all caches, and I'd have to batch page request messages from index lookups, effectively doing the lookup vector processing style, meaning 100 first levels, 100 second levels etc. Given a key beginning, I'd have to know what node to send this to, meaning pretty much doing the first levels of the lookup before deciding where to send the lookup, only to have the lookup redone by the box ending up with the lookup. Doing things this way would make Oracle RAC style clustering work with the use case.</p> <p>Given this, it appears that hash partitioning is easier to implement. Cache fusion clustering without the above mentioned gimmicks would be easiest of all but it would have a disastrous number of messages or it would fill all the caches with the same data. Avoiding this is possible but hard, as described above.</p> <p>We will have to experiment with Oracle RAC itself a bit farther down the road. Deciding to use partitioning instead of cache fusion does bring along conversion cost and a very high cost for repartitioning.</p> <p>Now let us look at the issue of co-location of joins. In a loop join this means that the node that holds the row from the outer loop also holds the row in the inner loop. For example, if order and order line are partitioned on order id, joining them on order id will be a co-located join. Such joins do not involve necessary messages in partitioned clusters. In RAC, they do not involve messages if the pages have migrated to be managed by the node doing the join, otherwise they do, up to 20 or so for the worst case.</p> <p>Do we get any benefit from co-location with RDF? Supposing joins that go from S to O to S (e.g., population of the city where Mary lives), we do not get much guarantee of co-location.</p> <p>Suppose the indices GSPO partitioned of GS and OGPS on OG, we know the box with Marys and then we'd know the box where the residence of each Mary was, based on GS. given the city as S, we would again know the box that had the population. All the three triples could be on different boxes. This cannot be helped at design time. At run time this can be helped by batching messages that go to the same node. Let's see how this fans out. 100 Marys from the first node. To get the city, we get 10 batches of 10 messages. We get the 100 cities and then we get their populations, again 10 batches of 10. In this scenario, the scheduling is centrally done by one thread. Suppose it were done by the 10 batches of 10 for getting the city of each Mary. For 10 cities, we'd get 10 lookups for population, each potentially to a different node. For this case, managing the execution by one thread instead of several makes bigger batches and less messages, as one would expect.</p> <p>It seems that with the RDF case, one may as well forget co-location. In the relational case, one must take advantage of it when there is co-location and when not, try to compensate with longer batches of function shipping.</p> <p>Excellent as some of the RAC claims are, it still seems that making it work well for an RDF workload would take such magical heuristics of location choice that implementing them would be hard and the result not altogether certain. I could get it to work eventually but hash partitioning seems by far the more predictable route. Also hash partitioning will work in shared nothing scenarios whereas RAC requires shared disks. Shared nothing will not require a SAN, which may make it somewhat lower cost. Also, if messages are grouped in large batches, the performance of the interconnect is not so critical, meaning that maybe even gigabit ethernet might do in cases. RAC style cache maintenance is more sensitive to interconnect latency than batched function shipping. Batched cache consistency is conceivable as discussed above but tough to do.</p> <p>For recovery and hot software updates, things can be arranged if there is non-local disk access or if partitions are mirrored. A RAC type cluster could use a SAN with internal mirroring. A hash partitioned system could mirror partitions to more than one box with local disk, thus using no mirrored disks. Repartitioning remains the bane of partitioning and not much can be done about that, it seems. The only easy repartitioning is doubling the cluster size. So it seems.</p> </div>
RDF, Clustered Databases, and Partitioning Vs. Cache Fusion
http://www.openlinksw.com/weblog/oerling/?date=2007-07-19#1229
2007-07-19T12:23:45Z
2008-04-25T11:59:16-04:00
<p>I recently read <a href="http://dbpedia.org/resource/Oracle_Database" id="link-id0x19e2dac0">Oracle</a>'s papers about RAC, Real Application Clusters. This is relevant as we are presently working on the <a href="http://virtuoso.openlinksw.com" id="link-id0x19f7c0d8">Virtuoso</a> equivalent.</p> <p>Caveat: The following is quite technical and not the final word on the matter.</p> <p>Oracle's claim is roughly as follows: Take a number of machines with access to a shared pool of disks and get scalability in processing power and memory without having to explicitly partition the <a href="http://dbpedia.org/resource/Data" id="link-id0x1c95f0c0">data</a> or perform other complicated configuration.</p> <p>This works through implementing a cache consistency protocol between the participating boxes and by parallelizing queries just as one would do on a shared memory SMP box. Each disk page has a box assigned to keep track of it and the responsibility migrates so that the box most often needing the page gets to be the page's guardian, so as not to have to ask anybody else for permission to write the page.</p> <p>This is a compelling proposition. Surely, it must be unrealistic to expect people to manually partition databases. This would require some understanding of first principles which is scarce out there.</p> <p>So, should we implement clustering a la Oracle?</p> <p>Let's look at some basics. If we have an OLTP workload like <a href="http://dbpedia.org/resource/TPC-C" id="link-id0xad88978">TPC-C</a>, we usually have affinity between clients and the data they access. This will make each client's pages migrate to be managed by the box the client is connected to. This will work pretty well, no worse than with a single box. If two clients are updating the same data but are connected to two different boxes, this is quite bad since the box that does not have responsibility for the page must ask the other box for write access. This is a round trip, at least tens of microseconds (µs). Consider in comparison that finding a row out of a million takes some 3µs.</p> <p>Would it not be better to have each partition in a known place and leave all processing to that place? The write contention would be resolved in the box owning the partition and there would be a message but now for requesting the update, not dealing with cache consistency. At what level should one communicate between cluster nodes? Talk about disk pages or about logical operations? If there is complete affinity between boxes and data, the RAC style shared cache needs no messages, each box ends up managing the pages of its clients and all works just as with a local situation. If on the other hand any client will update