Stardog Performance: SP2B Benchmark

As you may already know, we’re working hard on Stardog, our upcoming RDF database. It’s presently in closed alpha testing (55 testers), at version 0.5.3, and progressing rapidly. The overriding goals for Stardog, which we’ve repeated often, are:

  1. insanely fast performance on complex SPARQL queries in the out-of-the-box, untuned configuration
  2. feature-rich: logical inference, statistical inference, transactions, store procedures, etc.
  3. lightweight and pure Java

We think we’re on track to meet those goals for a 1.0 release in Q3, 2011.

In advance of Mike Grove’s talk next week at Semantic Technology Conference 2011, and since the Dydra peeps are publishing SP2B benchmark numbers, too, I thought we’d say a bit about Stardog performance.

SP2B Benchmark Performance

In short, it’s pretty awesome. We’re reporting SP2B—the leading SPARQL benchmark for complex, real-world performance—results that are noteworthy.

As you can see, we’re reporting SP2B benchmarks for six dataset sizes: 10k, 50k, 250k, 1M, 5M, and 25M. We’re not aware of any RDF database previously reporting any numbers for 25M SP2B dataset size. Note, too, that the y-axis is logarithmic in milliseconds.

With the exception of query Q5a—about which more below—the performance numbers are quite good; the benchmark machine was quite modest (an iMac with 2 i7s using 8GB RAM), a bit under-powered in comparison to the average production machine these days.

In particular, note Q7, which Stardog evaluates for 1M dataset in less than 1 second; at 5M in about 5 seconds; and at 25M—which no other RDF database has reported any performance numbers—in about 12 seconds. For Q4, at 5M, Stardog completes in 45 seconds. The next fastest RDF database, for which SP2B results have been reported, completes Q4 for 1M dataset in 134 seconds.

State of the Art

A brief word about SP2B query Q5a; despite what Arto said recently, we don’t believe there’s any mistake in the SP2B benchmark for Q5a. It is simply a very hard query, requiring the detection of an implicit join for good performance.

We’re not aware of any RDF database that is able to detect this implicit join generally. Stardog has an optimization that detects it for Q5a and for similar queries, which will be available in a future release, pending some additional engineering. These benchmark results do not include that optimization.

Conclusion

It’s an exciting time to be a semantic technology vendor, especially in the RDF database market which is still seeing rapid innovation and maturation. We’re happy that the Dydra folks are using SP2B as a benchmark; we agree with them that SP2B is the “gold standard of SPARQL benchmarks” and will continue to develop Stardog with SP2B as one measure of progress among many.

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