How Can I Find the Next Activity from Group B Following Group A Activities in PostgreSQL Using Window Functions?

Conditional Lead/Lag Function in PostgreSQL

Your task is to generate a query that retrieves specific activity sequences for users from a given table. You want to determine the next activity from group B (that always occurs after a group A activity) for each user.

Problem Definition

Consider the following table:

Name	activity	time
user1	A1	12:00
user1	E3	12:01
user1	A2	12:02
user2	A1	10:05
user2	A2	10:06
user2	A3	10:07
user2	M6	10:07
user2	B1	10:08
user3	A1	14:15
user3	B2	14:20
user3	D1	14:25
user3	D2	14:30

The desired output for this table is:

Name	activity	next_activity
user1	A2	NULL
user2	A3	B1
user3	A1	B2

Solution

You can solve this problem by leveraging the DISTINCT ON and CASE statements in conjunction with window functions:

SELECT name
     , CASE WHEN a2 LIKE 'B%' THEN a1 ELSE a2 END AS activity
     , CASE WHEN a2 LIKE 'B%' THEN a2 END AS next_activity
FROM  (
   SELECT DISTINCT ON (name)
          name
        , lead(activity) OVER (PARTITION BY name ORDER BY time DESC) AS a1
        , activity AS a2
   FROM   t
   WHERE (activity LIKE 'A%' OR activity LIKE 'B%')
   ORDER  BY name, time DESC
   ) sub;

Explanation

• The subquery identifies the latest activity from group A and the following activity from group B (if any) for each user using the DISTINCT ON and window function lead() with an ORDER BY time DESC.
• The CASE statements handle the desired output: the latest activity from group A, and the next activity from group B (if it exists).

Conditional Window Functions

While PostgreSQL does not support conditional window functions directly (e.g., lead(activity) FILTER (WHERE activity LIKE 'A%')), you can utilize the FILTER clause with aggregate functions and use them as window functions:

lead(activity) FILTER (WHERE activity LIKE 'A%') OVER () AS activity

However, this approach is inefficient and impractical for large datasets. Instead, the solution presented above is recommended for both small and large datasets.

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