Orri shares his findings from internal experimentation re. Virtuoso and bulk loading RDF content such as Wikpedia3 and Wordnet Data Sets:

Here is a dump of the post titled: Intermediate RDF Loading Results:

Following from the post about a new Multithreaded RDF Loader, here are some intermediate results and action plans based on my findings.

The experiments were made on a dual 1.6GHz Sun SPARC with 4G RAM and 2 SCSI disks. The data sets were the 48M triple Wikipedia data set and the 1.9M triple Wordnet data set. 100% CPU means one CPU constantly active. 100% disk means one thread blocked on the read system call at all times.

Starting with an empty database, loading the Wikipedia set took 315 minutes, amounting to about 2500 triples per second. After this, loading the Wordnet data set with cold cache and 48M triples already in the table took 4 minutes 12 seconds, amounting to 6838 triples per second. Loading the Wikipedia data had CPU usage up to 180% but over the whole run CPU usage was around 50% with disk I/O around 170%. Loading the larger data set was significantly I/O bound while loading the smaller set was more CPU bound, yet was not at full 200% CPU.

The RDF quad table was indexed on GSPO and PGOS. As one would expect, the bulk of I/O was on the PGOS index. We note that the pages of this index were on the average only 60% full. Thus the most relevant optimization seems to be to fill the pages closer to 90%. This will directly cut about a third of all I/O plus will have an additional windfall benefit in the form of better disk cache hit rates resulting from a smaller database.

The most practical way of having full index pages in the case of unpredictable random insert order will be to take sets of adjacent index leaf pages and compact the rows so that the last page of the set goes empty. Since this is basically an I/O optimization, this should be done when preparing to write the pages to disk, hence concerning mostly old dirty pages. Insert and update times will not be affected since these operations will not concern themselves with compaction. Thus the CPU cost of background compaction will be negligible in comparison with writing the pages to disk. Naturally this will benefit any relational application as well as free text indexing. RDF and free text will be the largest beneficiaries due to the large numbers of short rows inserted in random order.

Looking at the CPU usage of the tests, locating the place in the index where to insert, which by rights should be the bulk of the time cost, was not very significant, only about 15%. Thus there are many unused possibilities for optimization,for example writing some parts of the loader current done as stored procedures in C. Also the thread usage of the loader, with one thread parsing and mapping IRI strings to IRI IDs and 6 threads sharing the inserting could be refined for better balance, as we have noted that the parser thread sometimes forms a bottleneck. Doing the updating of the IRI name to IRI id mapping on the insert thread pool would produce some benefit.

Anyway, since the most important test was I/O bound, we will first implement some background index compaction and then revisit the experiment. We expect to be able to double the throughput of the Wikipedia data set loading.