aDFS: An Almost Depth-First-Search Distributed Graph-Querying System


Vasileios Trigonakis and Jean-Pierre Lozi, Oracle Labs; Tomáš Faltín, Oracle Labs and Charles University; Nicholas P. Roth, KUNGFU.AI; Iraklis Psaroudakis, Arnaud Delamare, Vlad Haprian, Călin Iorgulescu, Petr Koupy, Jinsoo Lee, Sungpack Hong, and Hassan Chafi, Oracle Labs


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 graph-querying 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.

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@inproceedings {273939,
author = {Vasileios Trigonakis and Jean-Pierre Lozi and Tom{\'a}{\v s} Falt{\'\i}n and Nicholas P. Roth and Iraklis Psaroudakis and Arnaud Delamare and Vlad Haprian and Calin Iorgulescu and Petr Koupy and Jinsoo Lee and Sungpack Hong and Hassan Chafi},
title = {{aDFS}: An Almost {Depth-First-Search} Distributed {Graph-Querying} System},
booktitle = {2021 USENIX Annual Technical Conference (USENIX ATC 21)},
year = {2021},
isbn = {978-1-939133-23-6},
pages = {209--224},
url = {},
publisher = {USENIX Association},
month = jul

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