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Enables optimal performance in OLAP environments DB2 Query Optimization combines the functionality of three modules to provide performance optimizations for complex queries, data clustering for OLAP applications, and high performance for machines with multiple processors. This features consists of Materialized query tables (MQT), Multi-Dimensional Clustering (MDC) and Query Parallelism to deliver a complete solution for high performance environments.
MQT are tables whose definition is based on the result of a query. Queries executing against the MQT can help you obtain the results faster than you otherwise would. Multidimensional Clustering (MDC) provides an elegant method for clustering data in tables along multiple dimensions in a flexible, continuous, and automatic way. MDC can significantly improve query performance. Query Parallelism allows for the simultaneous processing of parts of a single query by multiple processors, dramatically improving overall performance on multi-processor machines.
Improve query response :
- Queries running against the MQT can fetch results faster
- Arbitrary queries can be satisfied if they match some part of the pre-computed query
Improve system performance:
- An MQT involving nicknames makes remote data available locally, thus saving network hops
- Query performance can be markedly improved using parallelism and clustering
Integrate with other applications:
- Caching data can be set up to use MQT for better performance
- Query optimizers can make cost-based decision to use MQT to satisfy queries
- MQT can be used in a federated system (for instance, with WebSphere Information Integrator) for better throughput
Available for
* DB2 Workgroup 9
* (included in DB2 Enterprise 9) Licencing metrics
* Value Unit
* Authorized user
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