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Creators/Authors contains: "Arnold, Jason"

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  1. ; ; (, IPDPS)
    We present HRDBMS, a novel distributed shared-nothing database system developed with the goal of improving scalability of MPP databases based on a principled combination of techniques from MPP and Big Data systems with novel communication and work-distribution techniques. HRDBMS runs on a custom distributed and asynchronous execution engine that features highly parallelized operator implementations. The system features a cost-based optimization framework, user-defined data partitioning, locality-aware query execution, a non-blocking and hierarchical shuffle, and data skipping based on caching predicate matches. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS’s scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases (Greenplum). 
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