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How PARTITION BY Works in Snowflake, and SQL in general

   August 7, 2024
Snowflake

In Snowflake, and SQL in general, the PARTITION BY clause is used as part of window functions to divide a result set into partitions where the window function is applied independently. This is useful when you need to perform calculations across a set of rows that are somehow related to each other (e.g., all rows having the same customer ID or the same order date).

How PARTITION BY Works

The PARTITION BY clause specifies the columns that will be used to split the data into partitions. Each partition will have the window function applied to it independently of other partitions. This means that for each partition, the window function will start over with its calculation.

Here’s a general syntax for using a window function with PARTITION BY:

SELECT column1, column2, ..., window_function(columnX) OVER (PARTITION BY columnY [ORDER BY columnZ])
FROM table_name;

Example Usage
Suppose you have a sales table with the following columns: SaleID, ProductID, SaleDate, and Amount. If you want to calculate the running total of sales amounts for each product separately, you can use the SUM() window function with PARTITION BY:

SELECT
  SaleID,
  ProductID,
  SaleDate,
  Amount,
  SUM(Amount) OVER (PARTITION BY ProductID ORDER BY SaleDate) AS RunningTotal
FROM Sales;

In this query:

The data in the Sales table is partitioned by ProductID, so each product’s sales are considered a separate group.
The SUM(Amount) window function is applied to calculate the running total of sales for each product.
The ORDER BY SaleDate within the OVER clause specifies that the running total should be calculated in the order of the sale date.
This setup allows you to view each sale alongside the cumulative total of sales for that product up until that sale, recalculated for each product independently.

Using PARTITION BY effectively can significantly enhance data analysis capabilities by allowing you to perform complex computations grouped by certain fields directly in your SQL queries.

Sample Sales Data

Imagine a table named Sales with the following data:

SaleID ProductID SaleDate Amount
1 A 2023-01-01 100
2 A 2023-01-02 150
3 B 2023-01-01 200
4 B 2023-01-03 300
5 A 2023-01-03 120
6 C 2023-01-01 450

SQL Query with PARTITION BY

Here’s the SQL query again that calculates the running total of sales for each product separately:

SELECT
  SaleID,
  ProductID,
  SaleDate,
  Amount,
  SUM(Amount) OVER (PARTITION BY ProductID ORDER BY SaleDate) AS RunningTotal
FROM Sales;

Resulting Output

When you run the query above on this sample data, the output will look like this:

SaleID ProductID SaleDate Amount RunningTotal
1 A 2023-01-01 100 100
2 A 2023-01-02 150 250
5 A 2023-01-03 120 370
3 B 2023-01-01 200 200
4 B 2023-01-03 300 500
6 C 2023-01-01 450 450

Explanation

  • Product A: The RunningTotal starts at 100, then adds 150 for a total of 250, and finally adds 120 for a total of 370.
  • Product B: The RunningTotal starts at 200, then adds 300 for a total of 500 after the sale on January 3rd.
  • Product C: Only one sale, so the running total is simply the amount of that sale, which is 450.

This breakdown shows how the window function SUM() with PARTITION BY is applied to calculate the running totals within each group defined by ProductID, independent of the other products.

When using the ROW_NUMBER() window function combined with PARTITION BY, the function assigns a unique row number to each row within the partition of the result set. The row number starts at 1 for the first row in each partition and increases by one for each subsequent row.

Updated SQL Query

The query you’ve provided now calculates the row number for each sale within each product, ordered by the sale date:

SELECT
  SaleID,
  ProductID,
  SaleDate,
  Amount,
  ROW_NUMBER() OVER (PARTITION BY ProductID ORDER BY SaleDate) AS RowNumber
FROM Sales;

Sample Sales Data (Recap)

Using the same sample data as before:

SaleID ProductID SaleDate Amount
1 A 2023-01-01 100
2 A 2023-01-02 150
3 B 2023-01-01 200
4 B 2023-01-03 300
5 A 2023-01-03 120
6 C 2023-01-01 450

Resulting Output with ROW_NUMBER()

Here is what the output will look like when using ROW_NUMBER():

SaleID ProductID SaleDate Amount RowNumber
1 A 2023-01-01 100 1
2 A 2023-01-02 150 2
5 A 2023-01-03 120 3
3 B 2023-01-01 200 1
4 B 2023-01-03 300 2
6 C 2023-01-01 450 1

Explanation

  • Product A: The row numbers are assigned sequentially from 1 to 3, based on the order of the sale dates.
  • Product B: The row numbers are assigned from 1 to 2, reflecting the chronological order of sales.
  • Product C: Since there is only one sale, it receives a row number of 1.

The ROW_NUMBER() function is useful when you need to uniquely identify rows within partitions to perform further operations, like selecting the first or last sale per product or deleting duplicates while retaining the first occurrence.

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