Learn Window Functions for Product Metrics | A/B Test Analysis and Window Functions

Window functions are a powerful feature in SQL that allow you to perform calculations across sets of rows related to the current row, without collapsing your results into groups. Unlike aggregate functions that summarize data and return one row per group, window functions let you keep the original rows while adding new calculated columns. This is especially useful in analytics, where you often need to compare each row to others in its group or over a certain range. With window functions, you can rank users, calculate running totals, and analyze event sequences—all while preserving the detail of your dataset. These capabilities make window functions essential for advanced product metrics and A/B test analysis.


              1234567891011
            
SELECT
  u.user_id,
  u.ab_group,
  COUNT(e.event_id) AS event_count,
  ROW_NUMBER() OVER (
    PARTITION BY u.ab_group
    ORDER BY COUNT(e.event_id) DESC
  ) AS ab_group_rank
FROM users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.user_id, u.ab_group;

The ROW_NUMBER() window function assigns a unique sequential number to each row within a partition—in this case, each A/B group. By using PARTITION BY u.ab_group, you ensure that ranking starts over for each group, so users in group 'A' are ranked separately from those in group 'B'. The ORDER BY COUNT(e.event_id) DESC clause ranks the users within each group based on how many events they have, with the highest event counts getting the lowest row numbers. This is different from a simple aggregate, because ROW_NUMBER() provides a rank for each user in the context of their group, not just a summary.


              12345678910111213
            
SELECT
  e.user_id,
  e.event_date,
  e.event_type,
  SUM(
    CASE WHEN e.event_type = 'purchase' THEN 1 ELSE 0 END
  ) OVER (
    PARTITION BY e.user_id
    ORDER BY e.event_date
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS running_purchase_count
FROM events e
ORDER BY e.user_id, e.event_date;

Window functions such as SUM() OVER() are extremely valuable for calculating running totals or cumulative metrics. In product analytics, this helps you see how user behavior evolves over time. For instance, you might want to know how many purchases a user has made up to each event, or how engagement builds week by week. Window functions enable these insights without losing the granularity of your event data, making them ideal for tracking user journeys, comparing cohorts, or measuring incremental effects in A/B tests.

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Other window functions like RANK() and DENSE_RANK() are also useful for analytics. RANK() assigns the same rank to tied values but leaves gaps, while DENSE_RANK() does not leave gaps. These can help when you need to handle ties in user scores or event counts.

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Section 4. Chapter 3

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              1234567891011
            
SELECT
  u.user_id,
  u.ab_group,
  COUNT(e.event_id) AS event_count,
  ROW_NUMBER() OVER (
    PARTITION BY u.ab_group
    ORDER BY COUNT(e.event_id) DESC
  ) AS ab_group_rank
FROM users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.user_id, u.ab_group;


              12345678910111213
            
SELECT
  e.user_id,
  e.event_date,
  e.event_type,
  SUM(
    CASE WHEN e.event_type = 'purchase' THEN 1 ELSE 0 END
  ) OVER (
    PARTITION BY e.user_id
    ORDER BY e.event_date
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS running_purchase_count
FROM events e
ORDER BY e.user_id, e.event_date;

Study More

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

Window Functions for Product Metrics

1. What is a window function in SQL?

2. How does ROW_NUMBER() differ from COUNT()?

3. Why are window functions useful for product metrics?

Window Functions for Product Metrics

1. What is a window function in SQL?

2. How does ROW_NUMBER() differ from COUNT()?

3. Why are window functions useful for product metrics?