{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:23:06Z","timestamp":1773886986821,"version":"3.50.1"},"reference-count":15,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2011,7]]},"abstract":"<jats:p>Modern RDBMSs support the ability to compress data using methods such as null suppression and dictionary encoding. Data compression offers the promise of significantly reducing storage requirements and improving I\/O performance for decision support queries. However, compression can also slow down update and query performance due to the CPU costs of compression and decompression. In this paper, we study how data compression affects choice of appropriate physical database design, such as indexes, for a given workload. We observe that approaches that decouple the decision of whether or not to choose an index from whether or not to compress the index can result in poor solutions. Thus, we focus on the novel problem of integrating compression into physical database design in a scalable manner. We have implemented our techniques by modifying Microsoft SQL Server and the Database Engine Tuning Advisor (DTA) physical design tool. Our techniques are general and are potentially applicable to DBMSs that support other compression methods. Our experimental results on real world as well as TPC-H benchmark workloads demonstrate the effectiveness of our techniques.<\/jats:p>","DOI":"10.14778\/2021017.2021023","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"657-668","source":"Crossref","is-referenced-by-count":13,"title":["Compression aware physical database design"],"prefix":"10.14778","volume":"4","author":[{"given":"Hideaki","family":"Kimura","sequence":"first","affiliation":[{"name":"Brown University, Providence, RI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivek","family":"Narasayya","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoj","family":"Syamala","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142548"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304207"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066292"},{"key":"e_1_2_1_4_1","volume-title":"Automated selection of materialized views and indexes in SQL databases. VLDB, 496--505","author":"Agrawal S.","year":"2000","unstructured":"Agrawal , S. , Chaudhuri , S. , and Narasayya , V . Automated selection of materialized views and indexes in SQL databases. VLDB, 496--505 , 2000 . Agrawal, S., Chaudhuri, S., and Narasayya, V. Automated selection of materialized views and indexes in SQL databases. VLDB, 496--505, 2000."},{"key":"e_1_2_1_5_1","volume-title":"et al. Efficient index compression in DB2 LUW. VLDB, 1462--1473","author":"Bhattacharjee B.","year":"2009","unstructured":"Bhattacharjee , B. , Lim , L. , Malkemus , T. et al. Efficient index compression in DB2 LUW. VLDB, 1462--1473 , 2009 . Bhattacharjee, B., Lim, L., Malkemus, T. et al. Efficient index compression in DB2 LUW. VLDB, 1462--1473, 2009."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/335168.335230"},{"key":"e_1_2_1_7_1","volume-title":"An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB, 146--155","author":"Chaudhuri S.","year":"1997","unstructured":"Chaudhuri , S. and Narasayya , V . An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB, 146--155 , 1997 . Chaudhuri, S. and Narasayya, V. An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB, 146--155, 1997."},{"key":"e_1_2_1_8_1","volume-title":"Index merging. ICDE, 296--303","author":"Chaudhuri S.","year":"1999","unstructured":"Chaudhuri , S. and Narasayya , V . Index merging. ICDE, 296--303 , 1999 . Chaudhuri, S. and Narasayya, V. Index merging. ICDE, 296--303, 1999."},{"key":"e_1_2_1_9_1","first-page":"54","article-title":"The variance of the product of K random variables","volume":"297","author":"Goodman L. A","year":"1962","unstructured":"Goodman , L. A . The variance of the product of K random variables . Journal of the American Statistical Association , 297 , 54 -- 60 , 1962 . Goodman, L. A. The variance of the product of K random variables. Journal of the American Statistical Association, 297, 54--60, 1962.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_2_1_10_1","unstructured":"http:\/\/msdn.microsoft.com\/en-us\/library\/cc280449.aspx. SQL Server 2008 R2 Books Online.  http:\/\/msdn.microsoft.com\/en-us\/library\/cc280449.aspx. SQL Server 2008 R2 Books Online ."},{"key":"e_1_2_1_11_1","volume-title":"Estimating the compression fraction of an index using sampling. ICDE, 441--444","author":"Idreos S.","year":"2010","unstructured":"Idreos , S. , Kaushik , R. , Narasayya , V. , and Ramamurthy , R . Estimating the compression fraction of an index using sampling. ICDE, 441--444 , 2010 . Idreos, S., Kaushik, R., Narasayya, V., and Ramamurthy, R. Estimating the compression fraction of an index using sampling. ICDE, 441--444, 2010."},{"key":"e_1_2_1_12_1","volume-title":"Data Compression support in databases. VLDB, 695--704","author":"Iyer B.","year":"1994","unstructured":"Iyer , B. and Wilhite , D . Data Compression support in databases. VLDB, 695--704 , 1994 . Iyer, B. and Wilhite, D. Data Compression support in databases. VLDB, 695--704, 1994."},{"key":"e_1_2_1_13_1","volume-title":"Capacity Planning and Best Practices. Microsoft","author":"Mishra S.","year":"2009","unstructured":"Mishra , S. Data Compression : Strategy , Capacity Planning and Best Practices. Microsoft , 2009 . Whitepaper. Mishra, S. Data Compression: Strategy, Capacity Planning and Best Practices. Microsoft, 2009. Whitepaper."},{"key":"e_1_2_1_14_1","volume-title":"VLDB, 937--947","author":"P\u00f6ss M.","year":"2003","unstructured":"P\u00f6ss , M. and Potapov , D . Data compression in Oracle , VLDB, 937--947 , 2003 . P\u00f6ss, M. and Potapov, D. Data compression in Oracle, VLDB, 937--947, 2003."},{"key":"e_1_2_1_15_1","volume-title":"DB2 design advisor: integrated automatic physical database design. VLDB, 1087--1097","author":"Zilio D.","year":"2004","unstructured":"Zilio , D. , Rao , J. , Lightstone , S. , Lohman , G. , Storm , A. , Arellano , C. , and Fadden , S . DB2 design advisor: integrated automatic physical database design. VLDB, 1087--1097 , 2004 . Zilio, D., Rao, J., Lightstone, S., Lohman, G., Storm, A., Arellano, C., and Fadden, S. DB2 design advisor: integrated automatic physical database design. VLDB, 1087--1097, 2004."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2021017.2021023","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:42:38Z","timestamp":1672220558000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2021017.2021023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,7]]},"references-count":15,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2011,7]]}},"alternative-id":["10.14778\/2021017.2021023"],"URL":"https:\/\/doi.org\/10.14778\/2021017.2021023","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2011,7]]}}}