Abstract
SummaryImpala system is an open source, analytic MPP database for Apache Hadoop. Impala system uses a query execution scheduling scheme that assigns near‐equal bytes retrieval tasks for different hosts to ensure system load balance. However, such “load balance” cannot guarantee a short response time for Impala system, when there are original loads in the system. Traditional query execution scheduling methods require either some assumptions or particular architecture, which cannot be directly used in Impala system. In this paper, we present a query execution scheduling scheme for Impala system. If the query fetches data from a single table, the scheme exploits the maximum flow algorithm. If the query fetches data from multiple tables, the scheme employs a cost‐based algorithm with heuristic pruning rules. In addition, we propose a cost model for Impala system, which considers parallel execution, communication cost, and cluster load. The performance of the proposed scheme is evaluated by the TPC‐DS benchmark, and experimental results show that the scheme can reduce the query response time by 10%‐30%.
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