Fast In-Memory SQL Analytics on Graphs
Chunbin Lin, Benjamin Mandel, Yannis Papakonstantinou, Matthias, Springer

TL;DR
This paper introduces GQ-Fast, an in-memory SQL engine optimized for graph relationship queries, achieving significant speedups over traditional databases in biomedical OLAP scenarios.
Contribution
GQ-Fast combines a novel data organization, pipelined execution, and specialized compression to efficiently process complex graph relationship queries in-memory.
Findings
GQ-Fast outperforms Postgres by 2-4 orders of magnitude.
GQ-Fast outperforms MonetDB and Neo4j by 1-3 orders of magnitude.
GQ-Fast reduces memory usage through dense compression techniques.
Abstract
We study a class of graph analytics SQL queries, which we call relationship queries. Relationship queries are a wide superset of fixed-length graph reachability queries and of tree pattern queries. Intuitively, it discovers target entities that are reachable from source entities specified by the query. It usually also finds aggregated scores, which correspond to the target entities and are calculated by applying aggregation functions on measure attributes, which are found on the target entities, the source entities and the paths from the sources to the targets. We present real-world OLAP scenarios, where efficient relationship queries are needed. However, row stores, column stores and graph databases are unacceptably slow in such OLAP scenarios. We briefly comment on the straightforward extension of relationship queries that allows accessing arbitrary schemas. The GQ-Fast in-memory…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Semantic Web and Ontologies · Data Management and Algorithms
