Optimizing Navigational Graph Queries
For practitioners working with graph databases, this work provides the first practical evaluation solution for complex navigational queries, addressing a known performance bottleneck.
The paper tackles the optimization of navigational graph queries combining recursive and pattern-matching fragments, presenting novel optimization techniques that constrain intermediate results. The proposed solution achieves several orders of magnitude improvement in query evaluation performance over state-of-the-art techniques on diverse datasets.
We study the optimization of navigational graph queries, i.e., queries which combine recursive and pattern-matching fragments. Current approaches to their evaluation are not effective in practice. Towards addressing this, we present a number of novel powerful optimization techniques which aim to constrain the intermediate results during query evaluation. We show how these techniques can be planned effectively and executed efficiently towards the first practical evaluation solution for complex navigational queries on real-world workloads. Indeed, our experimental results show several orders of magnitude improvement in query evaluation performance over state-of-the-art techniques on a wide range of queries on diverse datasets.