In SQL databases, LAST_VALUE() is a window function that returns the last value in an ordered set of values. It allows us to get a value from the last row of a query result set or partition.
You may need to explicitly set the window frame if you want LAST_VALUE() to return the actual last value from the partition or result set. That’s because in many/most DBMSs, the default window frame ends with the current row.
In SQL databases, the FIRST_VALUE() function is a window function that returns the first value in an ordered set of values.
The exact syntax will probably depend on your DBMS, but it will usually require an OVER clause to determine how the rowset is partitioned and ordered before the window function is applied.
Many relational database management systems (RDBMSs) provide a DENSE_RANK() function that we can use in our SQL queries. The SQL DENSE_RANK() function is a window function that returns the rank of the current row within its partition, without gaps.
The “without gaps” part is what distinguishes the DENSE_RANK() function from the RANK() function.
DENSE_RANK() returns contiguous rank numbers whenever there are ties, whereas RANK() will leave a gap between the tie and the next rank, resulting in noncontiguous rank numbers.
When we create a window function in SQL, we have the option of defining it directly in the OVER clause or in a named window. When we define it in a named window, we can refer to that window name in our OVER clause, which saves us from having to define it directly in the OVER clause.
In SQL databases, the RANK() function is a window function that returns the rank of the current row within its partition, with gaps.
By “with gaps” I mean that if there are any ties for a given rank, there will be a gap between that rank value and the next rank value.
If you don’t want such gaps, use the DENSE_RANK() function instead, as it returns the rank without gaps.
Many relational database management systems (RDBMSs) provide us with a range of window functions.
The CUME_DIST() function is a window function that’s commonly implemented in RDBMSs for the purpose of calculating the cumulative distribution across a data set. In other words, it calculates the relative position of a specified value in a group of values.
Most of the major RDBMSs allow us to create window functions in SQL queries.
A window function is a special aggregate-like function that enables partitioning and ordering of data within a result set. Unlike regular aggregate functions, which collapse multiple rows into a single result, window functions retain individual rows in the output, augmenting them with additional computed values.
The following example demonstrates how to create a window function in SQL.
Continue readingWhile it’s true that we can use the SQL MAX() function to get the maximum value in a column, what if we want to return the value from another column instead of the actual maximum value itself? And what if we want it partitioned by category, so that it’s based on the maximum value from each category? And what if we want all values listed out, including those that aren’t the most populous?
In the following example, we use a window function to solve this problem.
Continue readingThe WINDOW clause is an optional clause that we can use in our SQL queries to create a named window. The named window can then be used as part of a window function.
When creating a window function, a SQL developer will often define it directly in the OVER clause. But that’s not the only way to do it. We can alternatively use the WINDOW clause to define it in a named window, and then refer to that named window in the OVER clause.
When we have a query that returns a column with numerical data such as prices or population, we can use the following methods to automatically calculate the totals and subtotals within a category.
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