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Join Processing for Graph Patterns: An Old Dog with New Tricks

RelationalAI

01 January 2015

less than a minute read

Join Processing for Graph Patterns: An Old Dog with New Tricks

This paper asks if new join algorithms allow relational engines to close the performance gap with graph engines?

Dung Nguyen, Molham Aref, Martin Bravenboer, George Kollias, Hung Q. Ngo, Christopher Re´, Atri Rudra. 2015.

In Proceedings of the GRADES ‘15 (GRADES ‘15).

Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines. Recently, new join algorithms have been discovered that achieve optimal worst-case run times for any join or even so-called beyond worst-case (or instance optimal) run time guarantees for specialized classes of joins. These new algorithms match or improve on those used in specialized graph-processing systems. This paper asks can these new join algorithms allow relational engines to close the performance gap with graph engines?

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