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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.
If a query scans millions of rows, consider breaking it into multiple queries. Query time versus number of rows increases exponentially, not linearly. Indexes - Check the documentation on MediaWiki wiki for each database table. There is a list of indexes. Pay attention to what those are, since you'll want to try to search only those fields.
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
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.).
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
A bitmap index is a special kind of indexing that stores the bulk of its data as bit arrays (bitmaps) and answers most queries by performing bitwise logical operations on these bitmaps. The most commonly used indexes, such as B+ trees , are most efficient if the values they index do not repeat or repeat a small number of times.
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.
Query-level features or query features, which depend only on the query. For example, the number of words in a query. Some examples of features, which were used in the well-known LETOR dataset: TF, TF-IDF, BM25, and language modeling scores of document's zones (title, body, anchors text, URL) for a given query; Lengths and IDF sums of document's ...