The LOD2 FP7 ends at the end of August, 2014. This post begins a series that will crown the project with a grand finale, another decisive step towards the project’s chief goal of giving RDF and linked data performance parity with SQL systems.
In a nutshell, LOD2 went like this:
Triples were done right, taking the best of the column store world and adapting it to RDF. This is now in widespread use.
SQL was done right, as I have described in detail in the TPC-H series. This is generally available as open source in v7fasttrack. SQL is the senior science and a runner-up like sem-tech will not carry the day without mastering this.
RDF is now breaking free of the triple store. RDF is a very general, minimalistic way of talking about things. It is not a prescription on how to do database. Confusing these two things has given rise to RDF’s relative cost against alternatives. To cap off LOD2, we will have the flexibility of triples with the speed of the best SQL.
In this post we will look at accomplishments so far and outline what is to follow during August. We will also look at what in fact constitutes the RDF overhead, why this is presently so, and why this does not have to stay thus.
This series will be of special interest to anybody concerned with RDF efficiency and scalability.
At the beginning of LOD2, I wrote a blog post discussing the RDF technology and its planned revolution in terms of the legend of Perseus. The classics give us exemplars and archetypes, but actual histories seldom follow them one-to-one; rather, events may have a fractal nature where subplots reproduce the overall scheme of the containing story.
So it is also with LOD2: The Promethean pattern of fetching the fire (state of the art of the column store) from the gods (the DB world) and bringing it to fuel the campfires of the primitive semantic tribes is one phase, but it is not the totality. This is successfully concluded, and Virtuoso 7 is widely used at present. Space efficiency gains are about 3x over the previous, with performance gains anywhere from 3 to 100x. As pointed out in the Star Schema Benchmark series (part 1 and part 2), in the good case one can run circles in SPARQL around anything but the best SQL analytics databases.
In the larger scheme of things, this is just preparation. In the classical pattern, there is the call or the crisis: Presently this is that having done triples about as right as they can be done, the mediocre in SQL can be vanquished, but the best cannot. Then there is the actual preparation: Perseus talking to Athena and receiving the shield of polished brass and the winged sandals. In the present case, this is my second pilgrimage to Mount Database, consisting of the TPC-H series. Now, the incense has been burned and libations offered at each of the 22 stations. This is not reading papers, but personally making one of the best-ever implementations of this foundational workload. This establishes Virtuoso as one of the top-of-the-line SQL analytics engines. The RDF public, which is anyway the principal Virtuoso constituency today, may ask what this does for them.
Well, without this step, the LOD2 goal of performance parity with SQL would be both meaningless and unattainable. The goal of parity is worth something only if you compare the RDF contestant to the very best SQL. And the comparison cannot possibly be successful unless it incorporates the very same hard core of down-to-the-metal competence the SQL world has been pursuing now for over forty years.
It is now time to cut the Gorgon’s head. The knowledge and prerequisite conditions exist.
The epic story is mostly about principles. If it is about personal combat, the persons stand for values and principles rather than for individuals. Here the enemy is actually an illusion, an error of perception, that has kept RDF in chains all this time. Yes, RDF is defined as a data model with triples in named graphs, i.e., quads. If nothing else is said, an RDF Store is a thing that can take arbitrary triples and retrieve them with SPARQL. The naïve implementation is to store things as rows in a quad table, indexed in any number of ways. There have been other approaches suggested, such as property tables or materialized views of some joins, but these tend to flush the baby with the bathwater: If RDF is used in the first place, it is used for its schema-less-ness and for having global identifiers. In some cases, there is also some inference, but the matter of schema-less-ness and identifiers predominates.
We need to go beyond a triple table and a dictionary of URI names while maintaining the present semantics and flexibility. Nobody said that physical structure needs to follow this. Everybody just implements things this way because this is the minimum that will in any case be required. Combining this with a SQL database for some other part of the data/workload hits basically insoluble problems of impedance mismatch between the SQL and SPARQL type systems, maybe using multiple servers for different parts of a query, etc. But if you own one of the hottest SQL racers in DB city and can make it do anything you want, most of these problems fall away.
The idea is simple: Put the de facto rectangular part of RDF data into tables; do not naively index everything in places where an index gives no benefit; keep the irregular or sparse part of the data as quads. Optimize queries according to the table-like structure, as that is where the volume is and where getting the best plan is a make or break matter, as we saw in the TPC-H series. Then, execute in a way where the details of the physical plan track the data; i.e., sometimes the operator is on a table, sometimes on triples, for the long tail of exceptions.
In the next articles we will look at how this works and what the gains are.
These experiments will for the first time showcase the adaptive schema features of the Virtuoso RDF store. Some of these features will be commercial only, but the interested will be able to reproduce the single server experiments themselves using the v7fasttrack open source preview. This will be updated around the second week of September to give a preview of this with BSBM and possibly some other datasets, e.g., Uniprot. Performance gains for regular datasets will be very large.
To be continued...
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Author: Virtuoso Data Space Bot
Published: 08/18/2014 16:55 GMT-0500
08/29/2014 12:50 GMT-0500
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