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- W2892057745 abstract "Currently, Spark SQL cannot optimize the multi-query tasks: tasks provided by batch processing are translated into different Spark jobs, and these jobs cannot share input data. To solve this problem, this paper explores the optimization of translating an SQL query into Spark jobs via two strategies: 1) A Middle Storage Layer is added between the persistent file system and the Spark core to address the data sharing problem among multiple queries. 2) When loading data into the Middle Storage Layer, the use of a selection strategy based on the cost model, in which the input data are distributed to multiple proper nodes for processing to achieve efficient utilization of cluster resources, is proposed. Based on this exploration, we develop a system called QOMS(Query Optimization approach with Middle Storage layer for Spark SQL), which offers better performance in improving query speed compared to Spark SQL with respect to the TPC-H benchmark." @default.
- W2892057745 created "2018-09-27" @default.
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- W2892057745 date "2018-05-01" @default.
- W2892057745 modified "2023-09-25" @default.
- W2892057745 title "Query Optimization Approach with Middle Storage Layer for Spark SQL" @default.
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- W2892057745 doi "https://doi.org/10.1109/cscwd.2018.8465226" @default.
- W2892057745 hasPublicationYear "2018" @default.
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