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A query optimizer has two basic freedoms: Join order: Because it joins functions commutatively and associatively, the order in which the system joins tables does not change the final result set of the query. However, join-order could have an enormous impact on the cost of the join operation, so choosing the best join order becomes very important.
The following example of a SELECT query returns a list of expensive books. The query retrieves all rows from the Book table in which the price column contains a value greater than 100.00. The result is sorted in ascending order by title. The asterisk (*) in the select list indicates that all columns of the Book table should be included in the ...
If a query contains GROUP BY, rows from the tables are grouped and aggregated. After the aggregating operation, HAVING is applied, filtering out the rows that don't match the specified conditions. Therefore, WHERE applies to data read from tables, and HAVING should only apply to aggregated data, which isn't known in the initial stage of a query.
Two possible query plans for the triangle query R(A, B) ⋈ S(B, C) ⋈ T(A, C); the first joins S and T first and joins the result with R, the second joins R and S first and joins the result with T Relational database management systems often include a query optimizer which attempts to determine the most efficient way to execute a given query.
In a SQL database query, a correlated subquery (also known as a synchronized subquery) is a subquery (a query nested inside another query) that uses values from the outer query. This can have major impact on performance because the correlated subquery might get recomputed every time for each row of the outer query is processed.
WHERE clauses are not mandatory clauses of SQL DML statements, but can be used to limit the number of rows affected by a SQL DML statement or returned by a query. In brief SQL WHERE clause is used to extract only those results from a SQL statement, such as: SELECT, INSERT, UPDATE, or DELETE statement. [1]
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The idea was that you could do a JOIN to the DUAL table and create two rows in the result for every one row in your table. Then, by using GROUP BY, the resulting join could be summarized to show the amount of storage for the DATA extent and for the INDEX extent(s). The name, DUAL, seemed apt for the process of creating a pair of rows from just one.