{"title":"SQL-On-Hadoop查询处理中的统一调优问题","authors":"Edson Ramiro Lucas Filho","doi":"10.1145/3055167.3055172","DOIUrl":null,"url":null,"abstract":"SQL-On-Hadoop systems translate a given query into several MapReduce jobs. Each job executes a different set of query operators over different input data sets, which leads to distinct resource consumption patterns. Once each job has a different resource consumption pattern they should receive tailor made tuning setup. However, SQL-On-Hadoop systems propagate the same tuning to every job in the query plan because they are not able to apply a specific tuning setup per job. Propagating the same tuning through the query plan is a problem because it drives the query to sub-optimal performance and drives tuning advisors to re-profile similar jobs several times. In our research we characterize this problem and propose a solution. Preliminary results show that our approach can reduce the number of profiles required by tuning advisors in 67% for TPC-H.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":"1 1","pages":"22-24"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Uniform Tuning Problem on SQL-On-Hadoop Query Processing\",\"authors\":\"Edson Ramiro Lucas Filho\",\"doi\":\"10.1145/3055167.3055172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SQL-On-Hadoop systems translate a given query into several MapReduce jobs. Each job executes a different set of query operators over different input data sets, which leads to distinct resource consumption patterns. Once each job has a different resource consumption pattern they should receive tailor made tuning setup. However, SQL-On-Hadoop systems propagate the same tuning to every job in the query plan because they are not able to apply a specific tuning setup per job. Propagating the same tuning through the query plan is a problem because it drives the query to sub-optimal performance and drives tuning advisors to re-profile similar jobs several times. In our research we characterize this problem and propose a solution. Preliminary results show that our approach can reduce the number of profiles required by tuning advisors in 67% for TPC-H.\",\"PeriodicalId\":87344,\"journal\":{\"name\":\"Proceedings. ACM-SIGMOD International Conference on Management of Data\",\"volume\":\"1 1\",\"pages\":\"22-24\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. ACM-SIGMOD International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3055167.3055172\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. ACM-SIGMOD International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3055167.3055172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Uniform Tuning Problem on SQL-On-Hadoop Query Processing
SQL-On-Hadoop systems translate a given query into several MapReduce jobs. Each job executes a different set of query operators over different input data sets, which leads to distinct resource consumption patterns. Once each job has a different resource consumption pattern they should receive tailor made tuning setup. However, SQL-On-Hadoop systems propagate the same tuning to every job in the query plan because they are not able to apply a specific tuning setup per job. Propagating the same tuning through the query plan is a problem because it drives the query to sub-optimal performance and drives tuning advisors to re-profile similar jobs several times. In our research we characterize this problem and propose a solution. Preliminary results show that our approach can reduce the number of profiles required by tuning advisors in 67% for TPC-H.
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