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Query optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases.The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans.
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]
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.).
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
She played a fundamental role in the development of System R, a pioneering relational database implementation, and wrote the canonical paper on relational query optimization. [2] She is a pioneer in relational database management and inventor of the technique of cost-based query optimization.
Furthermore, there are also cost-based optimization techniques like in DBMS, where a query plan with the lowest costs is chosen from different equivalent query plans. One example is to choose the order of two successive join operators. In DBMS this decision is mostly done by certain statistics of the involved databases.
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.
Normalization is a database design technique, which is used to design a relational database table up to higher normal form. [9] The process is progressive, and a higher level of database normalization cannot be achieved unless the previous levels have been satisfied.