P. Terlecki, Hardik Bati, C. Galindo-Legaria, P. Zabback
{"title":"Filtered statistics","authors":"P. Terlecki, Hardik Bati, C. Galindo-Legaria, P. Zabback","doi":"10.1145/1559845.1559943","DOIUrl":null,"url":null,"abstract":"Column statistics are an important element of cardinality estimation frameworks. More accurate estimates allow the optimizer of a RDBMS to generate better plans and improve the overall system's efficiency. This paper introduces filtered statistics, which model value distribution over a set of rows restricted by a predicate. This feature, available in Microsoft SQL Server, can be used to handle column correlation, as well as focus on interesting data ranges. In particular, it fits well for scenarios with logical subtables, like flexible schema or multi-tenant applications. Integration with the existing cardinality estimation infrastructure is presented.","PeriodicalId":344093,"journal":{"name":"Proceedings of the 2009 ACM SIGMOD International Conference on Management of data","volume":"20 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2009 ACM SIGMOD International Conference on Management of data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1559845.1559943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Column statistics are an important element of cardinality estimation frameworks. More accurate estimates allow the optimizer of a RDBMS to generate better plans and improve the overall system's efficiency. This paper introduces filtered statistics, which model value distribution over a set of rows restricted by a predicate. This feature, available in Microsoft SQL Server, can be used to handle column correlation, as well as focus on interesting data ranges. In particular, it fits well for scenarios with logical subtables, like flexible schema or multi-tenant applications. Integration with the existing cardinality estimation infrastructure is presented.
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