We propose a novel Distributed Column-Oriented Database Engine (DCODE) for efficient analytic query processing that combines advantages of both column storage and parallel processing. In DCODE, we enhance an existing open-source columnar database engine by adding the capability for handling queries over a cluster. Specifically, we studied parallel query execution and optimization techniques such as horizontal partitioning, exchange operator allocation, query operator scheduling, operator push-down, and materialization strategies, etc. The experiments over the TPC-H dataset verified the effectiveness of our system.
CITATION STYLE
Liu, Y., Cao, F., Mortazavi, M., Chen, M., Yan, N., Ku, C., … Fang, F. (2015). DCODE: A distributed column-oriented database engine for big data analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9357, pp. 289–299). Springer Verlag. https://doi.org/10.1007/978-3-319-24315-3_30
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