Understanding Window Functions in PostgreSQL

Window functions are a powerful tool in SQL that allow you to perform calculations across rows that are related to the current row. In this article, we will explore how to use window functions to create a “step increasing” column in a table.

Introduction to Window Functions

A window function is a type of SQL function that performs an operation on a set of rows that are related to the current row. Unlike aggregate functions, which return a single value for a group of rows, window functions return a value for each row individually.

In PostgreSQL, window functions can be used in various contexts, such as with SELECT, UPDATE, and DELETE statements.

The Problem

The problem presented in the Stack Overflow question is to create a new column “b” that starts with the minimum value of column “a” and increments by 1 for each group of three consecutive values in column “a”. For example, if column “a” has the following values:

The desired output for column “b” would be:

Solution Using Window Functions

The solution presented in the Stack Overflow answer uses window functions to achieve the desired result.

SELECT a, 
       min(a) over (partition by ceiling(a / 3.0)) as b
FROM tab;

This query uses the min window function to find the minimum value of column “a” for each group of three consecutive values in column “a”. The partition by ceiling(a / 3.0) clause specifies that the groups should be created based on the ceiling of the division of column “a” by 3. This ensures that the groups are formed around the starting value of each group, rather than being evenly spaced.

However, this query has a limitation: it does not guarantee that the values in column “b” will never exceed the value in column “a”. To overcome this limitation, we need to use a more complex approach.

Solution Using Recursive CTE

One way to achieve the desired result is to use a recursive Common Table Expression (CTE). Here’s how it works:

WITH RECURSIVE tt AS (
  SELECT a, row_number() over (order by a) as seqnum
  FROM tab
),
cte AS (
  SELECT a, seqnum, a as grp
  FROM tt
  WHERE seqnum = 1
  UNION ALL
  SELECT tt.a, tt.seqnum,
         (CASE WHEN tt.a <= grp + 2 THEN grp ELSE tt.a END)
  FROM cte join tt ON tt.seqnum = cte.seqnum + 1
)
SELECT *
FROM cte;

This query first uses a recursive CTE to assign a row number to each value in column “a”. The outer CTE then joins the original table with the result of the inner CTE, using the row numbers to determine whether to increment or reset the value.

The CASE statement is used to ensure that the values in column “b” do not exceed the starting value of the group. If the current value is less than or equal to the sum of the previous two values plus one, then it increments the value; otherwise, it resets it to the current value.

Example Use Case

Here’s an example use case for this solution:

Suppose we have a table tab with the following values:

We can run the recursive CTE query to generate the desired output:

WITH RECURSIVE tt AS (
  SELECT a, row_number() over (order by a) as seqnum
  FROM tab
),
cte AS (
  SELECT a, seqnum, a as grp
  FROM tt
  WHERE seqnum = 1
  UNION ALL
  SELECT tt.a, tt.seqnum,
         (CASE WHEN tt.a <= grp + 2 THEN grp ELSE tt.a END)
  FROM cte join tt ON tt.seqnum = cte.seqnum + 1
)
SELECT a, b FROM (
  SELECT a, 
         min(a) over (partition by ceiling(a / 3.0)) as b
  FROM tab
) AS subquery;

The result will be:

As we can see, the values in column “b” have been correctly generated based on the starting value of each group.

Conclusion

In this article, we explored how to use window functions and recursive Common Table Expressions (CTEs) to create a new column with step-increasing values. We saw that using window functions alone is not enough to guarantee the desired result, but can be combined with CTEs to achieve the correct behavior.

Last modified on 2023-09-17