Abstract
OLAP servers based on relational backends typically exploit materialized aggregate tables to improve response times of complex analytical queries. One of the key problems in this context is the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper, we present a system that continuously monitors the workload and raises a quantified alert, when a better configuration is available. We address the tasks of query monitoring and view selection at the OLAP level instead of the SQL level, which simplifies the containment checks as well as rewriting and in this way helps to reduce the complexity of the backend system. At the demo we plan to show how our system works, i.e., how the system reacts upon arbitrary (interactive) workloads and how the user is alerted that a better configuration is available.
- S. Agrawal, S. Chaudhuri, and V. Narasayya. Automated Selection of Materialized Views and Indexes in SQL Databases. In VLDB '00, pages 496--505, 2000. Google ScholarDigital Library
- N. Bruno and S. Chaudhuri. To Tune or not to Tune? A Lightweight Physical Design Alerter. In VLDB 06, pages 499--510, 2006. Google ScholarDigital Library
- N. Bruno and S. Chaudhuri. An Online Approach to Physical Design Tuning. In ICDE '07, pages 826--835, 2007.Google ScholarCross Ref
- S. Chaudhuri and V. Narasayya. An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. In VLDB '97, pages 146--155, 1997. Google ScholarDigital Library
- S. Chaudhuri and G. Weikum. Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System. In VLDB '00, pages 1--10, 2000. Google ScholarDigital Library
- P. Corporation. Pentaho Analysis Services: Mondrian Project. http://mondrian.pentaho.org, 2007.Google Scholar
- V. Harinarayan, A. Rajaraman, and J. Ullman. Implementing Data Cubes Efficiently. SIGMOD '96, pages 205--216, 1996. Google ScholarDigital Library
- M. Lee and J. Hammer. Speeding Up Materialized View Selection in Data Warehouses Using a Randomized Algorithm. Int. J. Cooperative Inf. Syst., 10(3):327--353, 2001.Google ScholarCross Ref
- M. Lühring, K. Sattler, K. Schmidt, and E. Schallehn. Autonomous Tuning with Soft Indexes. In SMDB '07, pages 450--458, 2007. Google ScholarDigital Library
- K. Sattler, I. Geist, and E. Schallehn. QUIET: Continuous Query-driven Index Tuning. In VLDB '03, pages 1129--1132, 2003. Google ScholarDigital Library
- C. Zhang and J. Yang. Genetic Algorithm for Materialized View Selection in Data Warehouse Environments. In DaWaK '99, pages 116--125, 1999. Google ScholarDigital Library
- D. Zilio, J. Rao, S. Lightstone, G. Lohman, A. Storm, C. Garcia-Arellano, and S. Fadden. DB2 Design Advisor: Integrated Automatic Physical Database Design. In VLDB '04, pages 1087--1097, 2004. Google ScholarDigital Library
Index Terms
- When is it time to rethink the aggregate configuration of your OLAP server?
Comments