{"title":"Run-time performance optimization of a BigData query language","authors":"Yanbin Liu, Parijat Dube, Scott Gray","doi":"10.1145/2568088.2576800","DOIUrl":null,"url":null,"abstract":"JAQL is a query language for large-scale data that connects BigData analytics and MapReduce framework together. Also an IBM product, JAQL's performance is critical for IBM InfoSphere BigInsights, a BigData analytics platform. In this paper, we report our work on improving JAQL performance from multiple perspectives. We explore the parallelism of JAQL, profile JAQL for performance analysis, identify I/O as the dominant performance bottleneck, and improve JAQL performance with an emphasis on reducing I/O data size and increasing (de)serialization efficiency. With TPCH benchmark on a simple Hadoop cluster, we report up to 2x performance improvements in JAQL with our optimization fixes.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2568088.2576800","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
JAQL is a query language for large-scale data that connects BigData analytics and MapReduce framework together. Also an IBM product, JAQL's performance is critical for IBM InfoSphere BigInsights, a BigData analytics platform. In this paper, we report our work on improving JAQL performance from multiple perspectives. We explore the parallelism of JAQL, profile JAQL for performance analysis, identify I/O as the dominant performance bottleneck, and improve JAQL performance with an emphasis on reducing I/O data size and increasing (de)serialization efficiency. With TPCH benchmark on a simple Hadoop cluster, we report up to 2x performance improvements in JAQL with our optimization fixes.
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