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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.
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.
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]
RBO is slowly being deprecated. For CBO to be used, all the tables referenced by the query must be analyzed. To analyze a table, a DBA can launch code from the DBMS_STATS package. Other tools for query optimization include: SQL Trace [1] Oracle Trace and TKPROF [2] Microsoft SMS (SQL) Execution Plan [3] Tableau Performance Recording (all DB) [4
As mentioned above, each non-recursive Datalog rule corresponds precisely to a conjunctive query. Therefore, many of the techniques from database theory used to speed up conjunctive queries are applicable to bottom-up evaluation of Datalog, such as Index selection [10] Query optimization, especially join order [11] [12] Join algorithms
Query optimization consists in determining from a query the most efficient manner (or manners) to execute it. Query optimization can be formalized as translating a relational calculus expression delivering an answer A into efficient relational algebraic expressions delivering the same answer A.
She was a key member of the original System R team that created the first relational database research prototype. [3] [4] [5] The dynamic programming algorithm for determining join order proposed in that paper still forms the basis for most of the query optimizers used in modern relational systems. She also established and led IBM’s Database ...
Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data sets. Array models provide natural indexing.