Window Functions for Data Analysis with Postgres

SQL makes sense when it's working on a single row, or even when it's aggregating across multiple rows. But what happens when you want to compare between rows of something you've already calculated? Or make groups of data and query those? Enter window functions.

Window functions tend to confuse people - but they’re a pretty awesome tool in SQL for data analytics. The best part is that you don’t need charts, fancy BI tools or AI to get some actionable and useful data for your stakeholders. Window functions let you quickly:

Calculate running totals
Provide summary statistics for groups/partitions of data
Create rankings
Perform lag/lead analysis, ie comparing two separate sets of data with each other
Compute moving/rolling averages

In this post, I will show various types of window functions and how they can apply to certain situations. I’m using a super simple e-commerce schema for this to follow along with the kinds of queries I’m going to run with window functions.

This post is available as an interactive tutorial as well in our Postgres playground.

The OVER function

The OVER part of the Window function is what creates the window. Annoyingly the word window appears nowhere in any of the functions. 😂 Typically the OVER part is preambled by another function, either an aggregate or mathematical function. There’s also often a frame, to specify which rows you’re looking at like ROWS BETWEEN 6 PRECEDING AND CURRENT ROW.

Window functions vs where clauses

Window functions kind of feel like a where clause at first, since they’re looking at a set of data. But they’re really different. Window functions are more for times when you need to look across sets of data or across groups of data. There are cases where you could use either. In general:

Use WHERE clause when you need to filter rows based on a condition.
Use window functions when you need to perform calculations across rows that remain after filtering, without removing any rows from the result set.

Running totals

Here’s a simple place to get started. Let’s ask for orders, customer data, order totals, and then a running total of orders. This will show us our total orders across a date range.

SELECT
    SUM(total_amount) OVER (ORDER BY order_date) AS running_total,
    order_date,
    order_id,
    customer_id,
    total_amount
FROM
    orders
ORDER BY
    order_date;

 running_total |     order_date      | order_id | customer_id | total_amount
---------------+---------------------+----------+-------------+--------------
        349.98 | 2024-08-21 10:00:00 |       21 |           1 |       349.98
       1249.96 | 2024-08-22 11:30:00 |       22 |           2 |       899.98
       1284.94 | 2024-08-23 09:15:00 |       23 |           3 |        34.98
       1374.93 | 2024-08-24 14:45:00 |       24 |           4 |        89.99
       1524.92 | 2024-08-25 08:25:00 |       25 |           5 |       149.99
       1589.90 | 2024-08-26 12:05:00 |       26 |           6 |        64.98

What's happening here is that each frame of data is the existing row plus the rows before. This sort of does calculations one one slice at a time, which you might see in the docs called a virtual table. Here's a diagram to get the general idea of how each frame of data is a set of rows, aggregated by the function with the SUM OVER.

First and last values

Window functions can look at groups of data, so say a specific customer ID and give you something like their first and last order, total, for your most recent 10 orders.

SELECT
    FIRST_VALUE(o.order_date) OVER (PARTITION BY o.customer_id ORDER BY o.order_date) AS first_order_date,
    LAST_VALUE(o.order_date) OVER (PARTITION BY o.customer_id ORDER BY o.order_date) AS last_order_date,
    o.order_id,
    o.customer_id,
    o.order_date,
    o.total_amount
FROM
    orders o
ORDER BY
    o.order_date DESC;

  first_order_date   |   last_order_date   | order_id | customer_id |     order_date      | total_amount
---------------------+---------------------+----------+-------------+---------------------+--------------
 2024-08-30 17:50:00 | 2024-09-19 18:50:00 |       50 |          10 | 2024-09-19 18:50:00 |       149.98
 2024-08-29 13:10:00 | 2024-09-18 14:10:00 |       49 |           9 | 2024-09-18 14:10:00 |       199.98
 2024-08-28 10:20:00 | 2024-09-17 11:20:00 |       48 |           8 | 2024-09-17 11:20:00 |       139.99
 2024-08-27 16:35:00 | 2024-09-16 17:35:00 |       47 |           7 | 2024-09-16 17:35:00 |       249.98
 2024-08-26 12:05:00 | 2024-09-15 13:05:00 |       46 |           6 | 2024-09-15 13:05:00 |        89.98

Using date_trunc GROUP BY CTEs with Window Functions

date_trunc is an incredibly handy Postgres function that summarizes units of time, hours, days, weeks, month. When combined with a GROUP BY in a CTE, you can create really easy summary statistics by day, month, week, year, etc.

When you combine the date_trunc GROUP BY partitions with window functions, some pretty magical stuff happens, and you can get ready-made summary statistics straight out of your database. In my opinion this is one of the most powerful features of Postgres window functions that really gets you to the next level.

Here’s an example query that starts with a CTE, calling a date_trunc to sum orders by daily totals. The second part of the query, the window function, ranks the sales in descending order with the best day of sales.

WITH DailySales AS (
    SELECT
        date_trunc('day', o.order_date) AS sales_date,
        SUM(o.total_amount) AS daily_total_sales
    FROM
        orders o
    GROUP BY
        date_trunc('day', o.order_date)
)
SELECT
    sales_date,
    daily_total_sales,
    RANK() OVER (
        ORDER BY daily_total_sales DESC
    ) AS sales_rank
FROM
    DailySales
ORDER BY
    sales_rank;

    sales_date      | daily_total_sales | sales_rank
---------------------+-------------------+------------
 2024-09-02 00:00:00 |           2419.97 |          1
 2024-09-01 00:00:00 |           1679.94 |          2
 2024-08-22 00:00:00 |            899.98 |          3
 2024-09-07 00:00:00 |            699.95 |          4
 2024-09-10 00:00:00 |            659.96 |          5
 2024-09-09 00:00:00 |            499.94 |          6
 2024-09-06 00:00:00 |            409.94 |          7
 2024-08-30 00:00:00 |            349.99 |          8

I should note that this uses one of the really helpful math functions, RANK in a Window function.

`LAG` Analysis

Let’s do more with our date_trunc CTEs. Now that we know we have our data by day, we can use a window function to calculate changes between these groups. For example we could look at the difference in sales from the prior day. In this example, LAG looks at the sales date and creates a comparison with the previous day.

WITH DailySales AS (
    SELECT
        date_trunc('day', o.order_date) AS sales_date,
        SUM(o.total_amount) AS daily_total_sales
    FROM
        orders o
    GROUP BY
        date_trunc('day', o.order_date)
)
SELECT
    sales_date,
    daily_total_sales,
    LAG(daily_total_sales) OVER (
        ORDER BY sales_date
    ) AS previous_day_sales,
    daily_total_sales - LAG(daily_total_sales) OVER (
        ORDER BY sales_date
    ) AS sales_difference
FROM
    DailySales
ORDER BY
    sales_date;


     sales_date      | daily_total_sales | previous_day_sales | sales_difference
---------------------+-------------------+--------------------+------------------
 2024-08-21 00:00:00 |            349.98 |                    |
 2024-08-22 00:00:00 |            899.98 |             349.98 |           550.00
 2024-08-23 00:00:00 |             34.98 |             899.98 |          -865.00
 2024-08-24 00:00:00 |             89.99 |              34.98 |            55.01
 2024-08-25 00:00:00 |            149.99 |              89.99 |            60.00
 2024-08-26 00:00:00 |             64.98 |             149.99 |           -85.01

LEAD works the same way, looking forward in the data set.

Rolling averages

Using our same day groups we can also make a rolling average. The AVG function takes an input ROWS BETWEEN 6 PRECEDING AND CURRENT ROW for a rolling 7 day sales average.

WITH DailySales AS (
    SELECT
        date_trunc('day', o.order_date) AS sales_date,
        SUM(o.total_amount) AS daily_total_sales
    FROM
        orders o
    GROUP BY
        date_trunc('day', o.order_date)
)
SELECT
    sales_date,
    daily_total_sales,
    AVG(daily_total_sales) OVER (
        ORDER BY sales_date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS rolling_average_7_days
FROM
    DailySales
ORDER BY
    sales_date
LIMIT 10;

     sales_date      | daily_total_sales | rolling_average_7_days
---------------------+-------------------+------------------------
 2024-08-21 00:00:00 |            349.98 |   349.9800000000000000
 2024-08-22 00:00:00 |            899.98 |   624.9800000000000000
 2024-08-23 00:00:00 |             34.98 |   428.3133333333333333
 2024-08-24 00:00:00 |             89.99 |   343.7325000000000000
 2024-08-25 00:00:00 |            149.99 |   304.9840000000000000
 2024-08-26 00:00:00 |             64.98 |   264.9833333333333333
 2024-08-27 00:00:00 |            249.98 |   262.8400000000000000
 2024-08-28 00:00:00 |            129.99 |   231.4128571428571429
 2024-08-29 00:00:00 |            179.98 |   128.5557142857142857
 2024-08-30 00:00:00 |            349.99 |   173.5571428571428571
(10 rows)

N-tiles with Window Functions

The NTILE function is a window function in SQL that is used to divide a result set into a specified number of roughly equal parts, known as tiles or buckets. By assigning a unique tile number to each row, n-tiles helps categorize and analyze data distribution within a dataset. This function is particularly useful in statistical and financial analysis for understanding how data is distributed across different segments, identifying trends, and making comparisons between groups with different characteristics.

For example, using NTILE(4) divides the data into four quartiles, ranking each row into one of four groups. The descending part here makes sure that quartile 1 is the top quarter and so on.

WITH DailySales AS (
    SELECT
        date_trunc('day', o.order_date) AS sales_date,
        SUM(o.total_amount) AS daily_total_sales
    FROM
        orders o
    GROUP BY
        date_trunc('day', o.order_date)
)
SELECT
    sales_date,
    daily_total_sales,
    NTILE(4) OVER (
        ORDER BY daily_total_sales DESC
    ) AS sales_quartile
FROM
    DailySales
ORDER BY
    sales_date;

     sales_date      | daily_total_sales | sales_quartile
---------------------+-------------------+----------------
 2024-09-06 00:00:00 |            409.94 |              1
 2024-08-30 00:00:00 |            349.99 |              1
 2024-08-21 00:00:00 |            349.98 |              2
 2024-09-08 00:00:00 |            349.96 |              2

Window functions resources

We’re big fans of Postgres functions at Crunchy Data, so check out our resources on Window functions in our Postgres Tutorials:

Latest Articles

Window Functions for Data Analysis with Postgres

Running totals

First and last values

Using date_trunc GROUP BY CTEs with Window Functions

`LAG` Analysis

Rolling averages

N-tiles with Window Functions

Window functions resources

Related Articles

Latest Articles

Window Functions for Data Analysis with Postgres

Running totals

First and last values

Using date_trunc GROUP BY CTEs with Window Functions

LAG Analysis

Rolling averages

N-tiles with Window Functions

Window functions resources

Related Articles

`LAG` Analysis