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One can refer to the inputs to a join as the "outer" and "inner" join operands, or "left" and "right", respectively. In the case of nested loops, for example, the database system will scan the entire inner relation for each row of the outer relation. One can classify query-plans involving joins as follows: [12] left-deep
Conjunctive queries without distinguished variables are called boolean conjunctive queries.Conjunctive queries where all variables are distinguished (and no variables are bound) are called equi-join queries, [1] because they are the equivalent, in the relational calculus, of the equi-join queries in the relational algebra (when selecting all columns of the result).
Queries can be nested so that the results of one query can be used in another query via a relational operator or aggregation function. A nested query is also known as a subquery. While joins and other table operations provide computationally superior (i.e. faster) alternatives in many cases, the use of subqueries introduces a hierarchy in ...
The recursive join is an operation used in relational databases, also sometimes called a "fixed-point join". It is a compound operation that involves repeating the join operation, typically accumulating more records each time, until a repetition makes no change to the results (as compared to the results of the previous iteration).
Associative tables are colloquially known under many names, including association table, bridge table, cross-reference table, crosswalk, intermediary table, intersection table, join table, junction table, link table, linking table, many-to-many resolver, map table, mapping table, pairing table, pivot table (as used in Laravel—not to be ...
Formally, a query q over a schema R is monotonic if and only if for every two instances I, J of R, () (q must be a monotonic function). [1] An example of a monotonic query is a select-project-join query containing only conditions of equality (also known as conjunctive queries). Examples of non-monotonic queries are aggregation queries, or ...
For example, think of A as Authors, and B as Books. An Author can write several Books, and a Book can be written by several Authors. In a relational database management system, such relationships are usually implemented by means of an associative table (also known as join table, junction table or cross-reference table), say, AB with two one-to-many relationships A → AB and B → AB.
Covering indexes are each for a specific table. Queries which JOIN/ access across multiple tables, may potentially consider covering indexes on more than one of these tables. [7] A covering index can dramatically speed up data retrieval but may itself be large due to the additional keys, which slow down data insertion and update.