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A query optimizer will not always choose the most efficient query plan for a given query. In some databases the query plan can be reviewed, problems found, and then the query optimizer gives hints on how to improve it. In other databases, alternatives to express the same query (other queries that return the same results) can be tried.
Query plans for nested SQL queries can also be chosen using the same dynamic programming algorithm as used for join ordering, but this can lead to an enormous escalation in query optimization time. So some database management systems use an alternative rule-based approach that uses a query graph model.
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.).
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.
The main application of query containment is in query optimization: Deciding whether two queries are equivalent is possible by simply checking mutual containment. The query containment problem is undecidable for relational algebra and SQL but is decidable and NP-complete for conjunctive queries.
Whenever a query or an update addresses an ordinary view's virtual table, the DBMS converts these into queries or updates against the underlying base tables. A materialized view takes a different approach: the query result is cached as a concrete ("materialized") table (rather than a view as such) that may be updated from the original base ...
Identifying whether a query is monotonic can be crucial for query optimization, especially in view maintenance and data stream management. Since the answer set for a monotonic query can only grow as more tuples are added to the database, query processing may be optimized by executing only the new portions of the database and adding the new ...
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