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How Can I Find the Subsequent Activity from Group B After the Last Activity from Group A in PostgreSQL?

Barbara Streisand
Release: 2024-12-28 05:31:10
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How Can I Find the Subsequent Activity from Group B After the Last Activity from Group A in PostgreSQL?

Conditional Lead/Lag Function in PostgreSQL

You have a table containing activities for different users and time stamps. You wish to identify the last activity from group A for each user and then determine the subsequent activity from group B, if any.

Using Lead Function

Initially, you attempted to use the lead() function to solve this problem. However, the lead() function by itself is not sufficient for this task because it simply retrieves the next value in sequence, regardless of any conditions.

Conditional Window Functions (FILTER)

To effectively apply conditions to window functions, PostgreSQL offers the FILTER clause. Unfortunately, this clause is currently not implemented for true window functions like lead() and lag(), as it is only available for aggregate functions.

Alternative Solution Using DISTINCT ON

To address this challenge without FILTER, we can utilize a combination of DISTINCT ON and CASE statements along with a subquery:

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;
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This approach leverages the DISTINCT ON clause to group rows by user and select the last qualifying activity from group A for each user. The CASE statements are used to determine the appropriate activity and subsequent activity based on the conditions specified.

Optimal Solution for Large Datasets

For cases involving a large number of rows per user, alternative techniques may be more efficient. These techniques typically involve creating temporary tables or using advanced indexing strategies. However, the optimal approach may vary depending on the specific characteristics of your data and workload.

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