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Title Authors ----- ----- SQL Examples and Guide 4 The Joy of SQL 1 An Introduction to SQL 2 Pitfalls of SQL 1 Under the precondition that isbn is the only common column name of the two tables and that a column named title only exists in the Book table, one could re-write the query above in the following form:
all rows for which the predicate in the WHERE clause is True are affected (or returned) by the SQL DML statement or query. Rows for which the predicate evaluates to False or Unknown are unaffected by the DML statement or query. The following query returns only those rows from table mytable where the value in column mycol is greater than 100.
An example of a data table column with low-cardinality would be a CUSTOMER table with a column named NEW_CUSTOMER. This column would contain only two distinct values: Y or N, denoting whether the customer was new or not. Since there are only two possible values held in this column, its cardinality type would be referred to as low-cardinality. [2]
The following example EXCEPT query returns all rows from the Orders table where Quantity is between 1 and 49, and those with a Quantity between 76 and 100. Worded another way; the query returns all rows where the Quantity is between 1 and 100, apart from rows where the quantity is between 50 and 75.
The expressions are evaluated and the results are used for the sorting, i.e., the values stored in the column or the results of the function call. ORDER BY is the only way to sort the rows in the result set. Without this clause, the relational database system may return the rows in any order.
Title Authors ----- ----- SQL Examples and Guide 4 The Joy of SQL 1 An Introduction to SQL 2 Pitfalls of SQL 1 Under the precondition that isbn is the only common column name of the two tables and that a column named title only exists in the Book table, one could re-write the query above in the following form:
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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.