Running Sum in SQL: A Deep Dive into Snowflake and Partitioning

Introduction

Calculating a running sum of one column with respect to another, partitioning over a third column, can be achieved using various methods. In this article, we will explore the different approaches, including recursive Common Table Expressions (CTEs), window functions, and partitioned joins.

Firstly, let’s understand what each component means:

• Running sum: This refers to the cumulative total of a series of numbers.

• **Partitioning**: In SQL, partitioning is the process of dividing a dataset into smaller parts based on one or more columns. Each partition can be processed independently.
  

• SQL: Structured Query Language (SQL) is a standard language for managing and manipulating data in relational databases.

Common Table Expressions (CTEs)

Common Table Expressions are temporary result sets that can be referenced within the execution of a SQL statement, as well as other statements. CTEs can be used to simplify complex queries or to perform recursive operations.

In Snowflake, you can use recursive CTEs for window functions. However, as mentioned in the original question, Snowflake does not support window functions in recursive CTEs.

Window Functions

Window functions are a class of SQL functions that allow us to perform calculations across rows in a result set that aren’t directly related to the individual row’s value. In other words, we can calculate values based on one or more columns for each row in a query result.

In Snowflake, you can use window functions like SUM, AVG, and others over a partitioned set of rows with the OVER clause. The OVER clause specifies the type of window function to apply.

For our running sum example, we’ll be using the following SQL query:

SELECT t.*,
       SUM(flag) OVER (PARTITION BY id_1 ORDER BY date_1) AS FLAG_RUNNING_SUM
FROM t;

This works as follows:

• SUM(flag) calculates the cumulative total of the values in column flag.
• OVER (PARTITION BY id_1 ORDER BY date_1) specifies that we want to calculate this running sum across rows with the same value in column id_1, and sort them by values in column date_1. This ensures that each row has a valid previous value in our running sum.

Note: This is not possible with Snowflake SQL. You can use window functions but they cannot be used within Recursive CTEs.

Partitioned Joins

Partitioned joins allow us to perform joins across multiple partitions of the same table, which can significantly improve performance for large datasets. In a partitioned join, the database optimizes the join by connecting rows from different partitions that satisfy the join condition.

For our running sum example, we could use partitioned joins instead of recursive CTEs or window functions. We’d need to rewrite our query as follows:

SELECT p1.id_1,
       p1.date_1,
       p1.flag,
       p2.flag_running_sum
FROM t p1
LEFT JOIN (
    SELECT id_1,
           DATE_1 AS date_1,
           SUM(flag) OVER (PARTITION BY id_1 ORDER BY DATE_1) AS flag_running_sum
   FROM t
) p2 ON p1.id_1 = p2.id_1 AND p1.date_1 = p2.date_1;

This query works as follows:

• We join the table t with a derived table (a CTE in SQL terminology), which calculates our running sum.
• The inner query is similar to our original window function query. It partitions rows by id_1, sorts them by date_1, and calculates the cumulative total of values in column flag.
• We use a LEFT JOIN to ensure that all rows from t are included in the result set, even if there’s no matching row in the running sum CTE.

Recursive Joins

Recursive joins work similarly to partitioned joins but allow us to join a table with itself. This can be used for complex queries that require self-referential data.

However, using recursive joins for our running sum example is less efficient than using window functions or partitioned joins.

SELECT t1.id_1,
       t2.date_1,
       t1.flag,
       t2.flag_running_sum
FROM (
    SELECT id_1,
           DATE_1 AS date_1,
           flag,
           SUM(flag) OVER (PARTITION BY id_1 ORDER BY DATE_1) AS flag_running_sum
   FROM t
) t1
LEFT JOIN (
    SELECT id_1,
           date_1,
           flag,
           SUM(flag) OVER (PARTITION BY id_1 ORDER BY date_1) AS flag_running_sum
   FROM t
) t2 ON t1.id_1 = t2.id_1 AND t1.date_1 < t2.date_1;

This query works as follows:

• We join the table t with itself, selecting all columns.
• The inner queries are similar to our original window function queries. They partition rows by id_1, sort them by date_1, and calculate the cumulative total of values in column flag.
• We use a LEFT JOIN to ensure that all rows from t are included in the result set, even if there’s no matching row in the self-referential table.

Conclusion

Calculating a running sum with respect to another column, partitioning over a third column, can be achieved using various methods, including recursive CTEs, window functions, and partitioned joins. In this article, we’ve explored each approach and provided examples of how they can be used to solve similar problems.

When deciding which method to use, consider the following factors:

• Performance: Recursive joins are generally less efficient than window functions or partitioned joins.
• Data complexity: If your data is complex with self-referential relationships, recursive joins might be a better choice. Otherwise, window functions are usually the most efficient option.

By understanding how to calculate running sums in SQL, you can process large datasets more efficiently and extract valuable insights from your data.

Last modified on 2025-04-19