Until now we have discussed several aspects of the Semantic Publishing Benchmark (SPB) such as the difference in performance between virtual and real servers configuration, how to choose an appropriate query mix for a benchmark run and our experience with using SPB in the development process of GraphDB for finding performance issues.

In this post we provide a step-by-step guide on how to run SPB using the Sesame RDF data store on a fresh install …

Sizing AWS Instances for the Semantic Publishing Benchmark


LDBC’s Semantic Publishing Benchmark (SPB) measures the performance of an RDF database in a load typical for metadata-based content publishing, such as the famous BBC Dynamic Semantic Publishing scenario. Such load combines tens of updates per second (e.g. adding metadata about new articles) with even higher volume of read requests (SPARQL queries collecting recent content and data to generate web page on a specific subject, e.g. Frank …

During the past six months we (the OWLIM Team at Ontotext) have integrated the LDBC Semantic Publishing Benchmark (LDBC-SPB) as a part of our development and release process.

First thing we’ve started using the LDBC-SPB for is to monitor the performance of our RDF Store when a new release is about to come out.

Initially we’ve decided to fix some of the benchmark parameters :

  • the dataset size - 50 million triples (LDBC-SPB50) * benchmark warmup …