Abstract | ||
---|---|---|
Graph processing is an invaluable tool for data analytics. In particular, pattern-matching queries enable flexible graph exploration and analysis, similar to what SQL provides for relational databases. Graph queries focus on following connections in the data; they are a challenging workload because even seemingly trivial queries can easily produce billions of intermediate results and irregular data access patterns.In this paper, we introduce aDFS: A distributed graphquerying system that can process practically any query fully in memory, while maintaining bounded runtime memory consumption. To achieve this behavior, aDFS relies on (i) almost depth-first (aDFS) graph exploration with some breadth-first characteristics for performance, and (ii) non-blocking dispatching of intermediate results to remote edges. We evaluate aDFS against state-of-the-art graph-querying (Neo4J and GraphFrames for Apache Spark), graph-mining (G-Miner, Fractal, and Peregrine), as well as dataflow joins (BiGJoin), and show that aDFS significantly outperforms prior work on a diverse selection of workloads. |
Year | Venue | DocType |
---|---|---|
2021 | PROCEEDINGS OF THE 2021 USENIX ANNUAL TECHNICAL CONFERENCE | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
12 |
Name | Order | Citations | PageRank |
---|---|---|---|
Vasileios Trigonakis | 1 | 116 | 6.43 |
Jean-Pierre Lozi | 2 | 111 | 7.13 |
Tomás Faltín | 3 | 0 | 0.34 |
Nicholas P. Roth | 4 | 0 | 0.34 |
Iraklis Psaroudakis | 5 | 0 | 0.34 |
Arnaud Delamare | 6 | 0 | 0.34 |
Vlad Haprian | 7 | 0 | 0.34 |
Calin Iorgulescu | 8 | 0 | 0.34 |
Petr Koupy | 9 | 0 | 0.34 |
Jinsoo Lee | 10 | 1 | 1.77 |
Sungpack Hong | 11 | 0 | 0.34 |
Hassan Chafi | 12 | 1118 | 61.11 |