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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. To view the present condition formed by the GROUP BY clause, the HAVING ...
The WHERE clause eliminates all rows from the result set where the comparison predicate does not evaluate to True. The GROUP BY clause projects rows having common values into a smaller set of rows. [clarification needed] GROUP BY is often used in conjunction with SQL aggregation functions or to eliminate duplicate rows from a result set.
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity.
In standard SQL:1999 hierarchical queries are implemented by way of recursive common table expressions (CTEs). Unlike Oracle's earlier connect-by clause, recursive CTEs were designed with fixpoint semantics from the beginning. [1] Recursive CTEs from the standard were relatively close to the existing implementation in IBM DB2 version 2. [1]
This clause currently is supported by CA DATACOM/DB 11, IBM DB2, SAP SQL Anywhere, PostgreSQL, EffiProz, H2, HSQLDB version 2.0, Oracle 12c and Mimer SQL. Microsoft SQL Server 2008 and higher supports FETCH FIRST, but it is considered part of the ORDER BY clause. The ORDER BY, OFFSET, and FETCH FIRST clauses are all required for this usage.
For the special case of conjunctive queries in which all relations used are binary, this notion corresponds to the treewidth of the dependency graph of the variables in the query (i.e., the graph having the variables of the query as nodes and an undirected edge {,} between two variables if and only if there is an atomic formula (,) or (,) in ...
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.
Conversely, an inner join can result in disastrously slow performance or even a server crash when used in a large volume query in combination with database functions in an SQL Where clause. [2] [3] [4] A function in an SQL Where clause can result in the database ignoring relatively compact table indexes. The database may read and inner join the ...