Count cumulative total in PostgreSQL

Refining and Optimizing

The above is an elegant starting point, but actual data often throws in complications. Let's tackle those next.

De-duplicating daily data

When you want to count unique daily items such as users, use the DISTINCT clause:

SELECT date, 
       COUNT(DISTINCT email) OVER (ORDER BY date) AS unique_email_count
FROM user_table;


This lets John Doe feel super special each day by being counted only once 😎.

Remove pesky overall duplicates

To ensure uniqueness throughout all your data, we can transform our query using DISTINCT ON to ensure each user is counted once throughout:

SELECT date, 
       COUNT(email) OVER (ORDER BY date) AS cumulative_unique_emails
FROM (
  SELECT DISTINCT ON (email) email, date
  FROM user_table
  ORDER BY email, date
) as unique_emails;


This makes sure John Doe remains the star, even if he loves spamming your logs. 🌟

Don't forget, for larger datasets, an index on (email, date) will make your query run as fast as Usain Bolt. ⏱

Handling date gaps through date series generation

If you want to capture data for days without user logins, then generate a series and LEFT JOIN:

WITH date_series AS (
  SELECT generate_series(MIN(date)::date, MAX(date)::date, '1 day'::interval)::date as date
  FROM user_table
)
SELECT ds.date, 
       COALESCE(COUNT(ut.email), 0) OVER (ORDER BY ds.date) AS cumulative_total
FROM date_series ds
LEFT JOIN user_table ut ON ds.date = ut.date
GROUP BY ds.date
ORDER BY ds.date;


Now, even on the quietest of days, your data would be cooler than a silent monk. 🧘‍♂️

Remember, always ensure your PostgreSQL version supports window functions and use appropriate data types to avoid any surprises.

Advanced Techniques

It's always good to have some extra tools in your PostgreSQL toolbox for more complex scenarios.

Using ROLLUP for subtotals

ROLLUP generates a result set that is similar to the one generated by UNION, but with total aggregates for hierarchical levels:

SELECT date, 
       value, 
       SUM(value) OVER (ORDER BY date) AS cumulative_total
FROM your_table
GROUP BY ROLLUP(date, value);


This works like an extra pair of glasses, helping you see total values by date and value. 🕶

Comparative cumulative counts with INNER JOIN

Let's say you're keen on comparing counts across different dates. Here's an approach using an INNER JOIN:

SELECT a.date, 
       COUNT(b.email) AS cumulative_count
FROM (SELECT DISTINCT date FROM user_table) a
INNER JOIN user_table b ON a.date >= b.date
GROUP BY a.date
ORDER BY a.date;


This architectural marvel will help you see how your user logins stack up over time. 🏗

Including empty dates

It's crucial to maintain data consistency and coherence, and include counts for days without any new logins. Here's how:

WITH recursive cte AS (
  SELECT MIN(date)::date as date FROM user_table
  UNION ALL
  SELECT date + 1 FROM cte WHERE date < CURRENT_DATE
)
SELECT cte.date,
       COALESCE(SUM(value), 0) OVER (ORDER BY cte.date) AS cumulative_total
FROM cte
LEFT JOIN user_table ut ON cte.date = ut.date
GROUP BY cte.date;


A recursive CTE(WITH RECURSIVE) generates a continuous range of dates, keeping a consistently accurate track of cumulative totals.