Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Data Compression Support in Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Database tuning advisor for microsoft SQL server 2005: demo
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Integrating compression and execution in column-oriented database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient index compression in DB2 LUW
Proceedings of the VLDB Endowment
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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.