Journal Paper
In this paper, we address these points by: (i) using the polyglot approach of the CloudMdsQL query language that allows native queries to be expressed as inline scripts and combined with SQL statements for ad-hoc integration and (ii) incorporating the approach within the LeanXcale distributed query engine, thus allowing for native scripts to be processed in parallel at data store shards. In addition, (iii) efficient optimization techniques, such as bind join, can take place to improve the performance of selective joins. We evaluate the performance benefits of exploiting parallelism in combination with high expressivity and optimization through our experimental validation.
Wednesday, 3 February, 2021 - 07:36
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