{"title":"负载感知混合分区","authors":"Trupti Padiya, Jai Jai Kanwar, Minal Bhise","doi":"10.1145/2998476.2998479","DOIUrl":null,"url":null,"abstract":"Real life databases exhibit highly skewed access patterns. These skewed access patterns can be exploited to partition the data considering the query workload. The presented work proposes Workload Aware Hybrid Partitioning (WAHP). WAHP identifies clusters of attributes which are queried together. It identifies workload aware clusters for the actual query workload using a hybrid combination of horizontal and vertical partitioning. The paper demonstrates WAHP experiment using TPC-C benchmark, where 9% of the actual TPC-C data in workload aware clusters, is able to answer 73% of hottest query-workload with an average execution time gain of 37% against original database.","PeriodicalId":171399,"journal":{"name":"Proceedings of the 9th Annual ACM India Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Workload Aware Hybrid Partitioning\",\"authors\":\"Trupti Padiya, Jai Jai Kanwar, Minal Bhise\",\"doi\":\"10.1145/2998476.2998479\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real life databases exhibit highly skewed access patterns. These skewed access patterns can be exploited to partition the data considering the query workload. The presented work proposes Workload Aware Hybrid Partitioning (WAHP). WAHP identifies clusters of attributes which are queried together. It identifies workload aware clusters for the actual query workload using a hybrid combination of horizontal and vertical partitioning. The paper demonstrates WAHP experiment using TPC-C benchmark, where 9% of the actual TPC-C data in workload aware clusters, is able to answer 73% of hottest query-workload with an average execution time gain of 37% against original database.\",\"PeriodicalId\":171399,\"journal\":{\"name\":\"Proceedings of the 9th Annual ACM India Conference\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th Annual ACM India Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2998476.2998479\",\"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 of the 9th Annual ACM India Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2998476.2998479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real life databases exhibit highly skewed access patterns. These skewed access patterns can be exploited to partition the data considering the query workload. The presented work proposes Workload Aware Hybrid Partitioning (WAHP). WAHP identifies clusters of attributes which are queried together. It identifies workload aware clusters for the actual query workload using a hybrid combination of horizontal and vertical partitioning. The paper demonstrates WAHP experiment using TPC-C benchmark, where 9% of the actual TPC-C data in workload aware clusters, is able to answer 73% of hottest query-workload with an average execution time gain of 37% against original database.
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