Abstract
Graph algorithms (e.g., k-hop queries) are widely used to find the deep association of data in various real-world applications such as business recommendation and fraud detection. However, most of the data are still stored in relational database (i.e., RDBMS) and the performance is rather limited when processing graph queries on RDBMS due to the inherent hardness of complicated table join. In this paper, we propose a fast interactive engine SQLG+, which can be integrated to any RDBMS and enable them to process k-hop graph queries efficiently. Different from naive table-join implementations, SQLG+ caches important nodes with their adjacency lists in memory (i.e., graph cache) and generates a hybrid query plan which combines the ability of graph cache and RDBMS. Also, SQLG+ removes duplicates in the end of each hop (using AdaptiveSet) and expands the frontiers in different ways. Furthermore, dynamic BFS/DFS switch is adopted to achieve the balance between query performance and memory occupation. Extensive experiments show that SQLG+ outperforms the state-of-the-art RDBMS-based implementations by up to several orders of magnitude and is even comparable to the fastest graph databases.
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Zeng, L., Zhou, J., Qin, S., Cai, H., Zhao, R., Chen, X. (2022). SQLG+: Efficient k-hop Query Processing on RDBMS. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_37
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