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The purpose of query optimization, which is an automated process, is to find the way to process a given query in minimum time. The large possible variance in time justifies performing query optimization, though finding the exact optimal query plan, among all possibilities, is typically very complex, time-consuming by itself, may be too costly ...
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]
Since SQL is declarative, there are typically many alternative ways to execute a given query, with widely varying performance. When a query is submitted to the database, the query optimizer evaluates some of the different, correct possible plans for executing the query and returns what it considers the best option. Because query optimizers are ...
Lecture Notes: Relational Algebra – A quick tutorial to adapt SQL queries into relational algebra; Relational – A graphic implementation of the relational algebra; Query Optimization This paper is an introduction into the use of the relational algebra in optimizing queries, and includes numerous citations for more in-depth study.
Query optimization Somewhat important Recent databases DV Very important Latest version databases Scriptability With some effort AM Formalized, automated for everything Views and triggers Mentioned, hand-made and case-by-case AM Formalized, automated for everything Assumptions Built to last Needs assumptions AM Built to change No assumptions
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.
The skyline operator is the subject of an optimization problem and computes the Pareto optimum on tuples with multiple dimensions.. This operator is an extension to SQL proposed by Börzsönyi et al. [1] to filter results from a database to keep only those objects that are not worse in multiple dimensions than any other.
Presto (including PrestoDB, and PrestoSQL which was re-branded to Trino) is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, [1] and allows use of multiple data sources within a query.