Kaskade: Graph Views for Efficient Graph Analytics
Name
1906.05162.pdf
Description
Submitted version
Size
863.95 KB
Format
Adobe PDF
Checksum (MD5)
dabf7fe099b20b6d6b290b46b4be2c52
Author(s)
da Trindade, Joana MF
Karanasos, Konstantinos
Curino, Carlo
Madden, Samuel
Shun, Julian
Date Issued
2020
Journal
Proceedings - International Conference on Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2020 IEEE. Graphs are a natural way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive sub-graph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. In this work, we leverage structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present Kaskade, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. Kaskade employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query set and to select the best query evaluation plan given a set of materialized views. We evaluate its performance over real-world graphs, including the provenance graph that we maintain at Microsoft to enable auditing, service analytics, and advanced system optimizations. Our results show that Kaskade substantially reduces the effective graph size and yields significant performance speedups (up to 50X), in some cases making otherwise intractable queries possible.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICDE48307.2020.00024
