Window Functions for Aggregate Calculations with Conditional Summation

When working with data that has multiple sequences or patterns, it can be challenging to apply aggregate calculations like summing values while accounting for non-sequential rows. In this article, we’ll explore how to use window functions in SQL to achieve this type of calculation.

Introduction to Window Functions

Window functions are a set of functions that allow you to perform calculations across a set of rows that are related to the current row. They’re similar to aggregate functions like SUM or AVG, but instead of grouping all rows together, window functions group only the rows that meet certain conditions.

In our case, we want to calculate the sum of values in the pop column for each row, but only if the sequence is intact (i.e., consecutive numbers). If the sequence is broken, we don’t want to include those rows in the calculation. This requires a combination of window functions and conditional logic.

The Problem with Subquery-Based Solutions

The original code snippet attempts to use a subquery-based solution:

select id, state, num, pop,
       sum(pop) over (partition by id, state order by num ) as new_population
from table;

However, this approach has several issues:

• It uses order by num without any constraint, which can lead to unexpected results if the data is not in the correct order.
• It partitions by both id and state, but ignores the sequence aspect. This means that even if two rows have a broken sequence, they’ll still be included in the same partition.

A Better Approach with Row Numbering

The answer provided uses row numbering to solve this problem:

select t.*,
       sum(pop) over (partition by state, num - seqnum) as new_population
from (select t.*,
             row_number() over (partition by state order by num) as seqnum
      from t
     ) t;

Here’s how it works:

1. We use a subquery to number each row within the state and num columns, using row_number() with an order by num clause.
2. We then subtract the sequence number (seqnum) from the original num value to create a new column that indicates whether the sequence is intact or broken.
3. In the outer query, we partition by both state and the new column (i.e., num - seqnum). This ensures that only rows with an intact sequence are included in each partition.

How it Works

Let’s take our original table as an example:

+----+--------+------+------+
| id | state  | num  | pop  |
+----+--------+------+------+
| 1  | ny     | 1    | 100  |
| 1  | ny     | 2    | 200  |
| 1  | ny     | 3    | 600  |
| 1  | ny     | 6    | 400  |
| 1  | ny     | 7    | 300  |
| 1  | ny     | 14   | 1000 |
| 2  | nj     | 3    | 250  |
+----+--------+------+------+

If we apply the row numbering solution, we get:

+----+--------+-----+-------+
| id | state  | num | seqnum|
+----+--------+-----+-------+
| 1  | ny     | 1   | 1     |
| 1  | ny     | 2   | 2     |
| 1  | ny     | 3   | 3     |
| 1  | ny     | 6   | 4     |
| 1  | ny     | 7   | 5     |
| 1  | ny     | 14  | 6     |
| 2  | nj     | 3   | 1     |
+----+--------+-----+-------+

Now, let’s calculate the sum of pop for each partition (i.e., state and intact sequence):

+----+--------+-----+-------+---------------+
| id | state  | num | seqnum | new_population|
+----+--------+-----+-------+---------------+
| 1  | ny     | 1   | 1     | 100           |
| 1  | ny     | 2   | 2     | 300           |
| 1  | ny     | 3   | 3     | 900           |
| 1  | ny     | 6   | 4     | 700           |
| 1  | ny     | 7   | 5     | 700           |
| 1  | ny     | 14  | 6     | 1000          |
| 2  | nj     | 3   | 1     | 250           |
+----+--------+-----+-------+---------------+

The resulting table shows the original columns (id, state, and num) alongside a new column (new_population) that contains the sum of pop values for each row with an intact sequence.

Conclusion

Window functions offer a powerful way to perform aggregate calculations while accounting for conditional logic. By using row numbering and partitioning by multiple columns, we can isolate rows with an intact sequence and calculate the desired sums. This approach provides more accurate results than traditional subquery-based solutions and is more efficient to maintain in large datasets.

In this example, we’ve demonstrated how to use window functions to solve a common problem: calculating the sum of values for each row while ignoring broken sequences. The technique can be applied to various scenarios where conditional logic plays a crucial role.

Last modified on 2025-01-23