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In addition to basic equality and inequality conditions, SQL allows for more complex conditional logic through constructs such as CASE, COALESCE, and NULLIF.The CASE expression, for example, enables SQL to perform conditional branching within queries, providing a mechanism to return different values based on evaluated conditions.
In SQL the UNION clause combines the results of two SQL queries into a single table of all matching rows. The two queries must result in the same number of columns and compatible data types in order to unite. Any duplicate records are automatically removed unless UNION ALL is used. UNION can be useful in data warehouse applications where tables ...
However, SELECT COUNT(*) can't count the number of null columns. The query is unselective The number of return rows is too large and takes nearly 100% in the whole table. These rows are unselective. The table statistics does not update The number of rows in the table is higher than before, but table statistics haven't been updated yet. The ...
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.
Without an ORDER BY clause, the order of rows returned by an SQL query is undefined. The DISTINCT keyword [5] eliminates duplicate data. [6] 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
The OFFSET clause specifies the number of rows to skip before starting to return data. The FETCH FIRST clause specifies the number of rows to return. Some SQL databases instead have non-standard alternatives, e.g. LIMIT, TOP or ROWNUM. The clauses of a query have a particular order of execution, [5] which is denoted by the number on the right ...
Join method: Given two tables and a join condition, multiple algorithms can produce the result set of the join. Which algorithm runs most efficiently depends on the sizes of the input tables, the number of rows from each table that match the join condition, and the operations required by the rest of the query.
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 ...