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Query optimization is a feature of many relational database management ... The result of a query is generated by processing the rows in a database in a way that ...
Some query tools can generate embedded hints in the query, for use by the optimizer. Some databases - like Oracle - provide a plan table for query tuning. This plan table will return the cost and time for executing a query. Oracle offers two optimization approaches: CBO or Cost Based Optimization; RBO or Rule Based Optimization
Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). [1]
Given two queries and and a database schema, the query containment problem is the problem of deciding whether for all possible database instances over the input database schema, () (). The main application of query containment is in query optimization: Deciding whether two queries are equivalent is possible by simply checking mutual containment.
This is a form of caching the results of a query, similar to memoization of the value of a function in functional languages, and it is sometimes described as a form of precomputation. [2] [3] As with other forms of precomputation, database users typically use materialized views for performance reasons, i.e. as a form of optimization. [4]
Database tuning describes a group of activities used to optimize and homogenize the performance of a database. It usually overlaps with query tuning, but refers to design of the database files, selection of the database management system (DBMS) application, and configuration of the database's environment ( operating system , CPU , etc.).
However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer tremendously. Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use. HOLAP attempts to mix the best of ROLAP and MOLAP.
It was also the first system to demonstrate that a relational database management system could provide good transaction processing performance. Design decisions in System R, as well as some fundamental algorithm choices (such as the dynamic programming algorithm used in query optimization [2]), influenced many later relational systems.