D. Nguyen, M. Aref, Martin Bravenboer, G. Kollias, H. Ngo, C. Ré, A. Rudra
{"title":"Join Processing for Graph Patterns: An Old Dog with New Tricks","authors":"D. Nguyen, M. Aref, Martin Bravenboer, G. Kollias, H. Ngo, C. Ré, A. Rudra","doi":"10.1145/2764947.2764948","DOIUrl":null,"url":null,"abstract":"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 sub-optimality 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? We examine this question for graph-pattern queries or join queries. We find that classical relational databases like Postgres and MonetDB or newer graph databases/stores like Virtuoso and Neo4j may be orders of magnitude slower than these new approaches compared to a fully featured RDBMS, LogicBlox, using these new ideas. Our results demonstrate that an RDBMS with such new algorithms can perform as well as specialized engines like GraphLab -- while retaining a high-level interface. We hope our work adds to the ongoing debate of the role of graph accelerators, new graph systems, and relational systems in modern workloads.","PeriodicalId":144860,"journal":{"name":"Proceedings of the GRADES'15","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the GRADES'15","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2764947.2764948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 57
Abstract
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 sub-optimality 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? We examine this question for graph-pattern queries or join queries. We find that classical relational databases like Postgres and MonetDB or newer graph databases/stores like Virtuoso and Neo4j may be orders of magnitude slower than these new approaches compared to a fully featured RDBMS, LogicBlox, using these new ideas. Our results demonstrate that an RDBMS with such new algorithms can perform as well as specialized engines like GraphLab -- while retaining a high-level interface. We hope our work adds to the ongoing debate of the role of graph accelerators, new graph systems, and relational systems in modern workloads.