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首页> 外文期刊>PeerJ Computer Science >A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method
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A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

机译：MapReduce框架并行查询优化技术和基于语义的聚类方法

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.
机译：查询优化是识别最佳查询执行计划（QEP）的过程。基于最小资源使用情况，查询优化器会为给定查询生成接近最佳QEP。问题是，对于给定查询，存在有大量不同的等效执行计划，每个执行计划具有相应的执行成本。为了产生有效的查询计划，需要检查大量替代计划。访问计划推荐是数据库查询优化的替代技术，它重用了先前生成的QEPS以执行新查询。在此技术中，查询优化器使用群集方法来识别类似查询的组。然而，由于巨大的处理时间，群集这种大型数据集对传统聚类算法具有挑战。已经介绍了许多基于云的平台，为处理Padoop，Hive，PIG等的分布式查询的处理提供了低成本解决方案。本文已经应用并测试了使用MapReduce的大型查询数据集的聚类变体大小模型。结果展示了查询工作负载集群并行实现的有效性，以实现良好的可扩展性。

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