Table of Contents
Conditional Lead/Lag Function in PostgreSQL
Conditional Window Functions
Logical Implication and Solution
Performance Considerations
Potential Optimizations
Home Database Mysql Tutorial How to Efficiently Extract the Last 'A' and Subsequent 'B' Activities per User in PostgreSQL?

How to Efficiently Extract the Last 'A' and Subsequent 'B' Activities per User in PostgreSQL?

Dec 31, 2024 am 02:14 AM

How to Efficiently Extract the Last 'A' and Subsequent 'B' Activities per User in PostgreSQL?

Conditional Lead/Lag Function in PostgreSQL

In a PostgreSQL table where activities are grouped into types A and B, such that B activities always follow A activities, users seek a solution to extract the last A activity and the subsequent B activity for each user. While the lead() function initially seemed like a promising approach, it proved ineffective.

Conditional Window Functions

Unfortunately, PostgreSQL does not currently support conditional window functions. The FILTER clause, which could provide conditional filtering for window functions, is only available for aggregate functions.

Logical Implication and Solution

The key insight lies in the logical implication of the problem statement: for each user, there is at most one B activity after one or more A activities. This suggests a solution using a single window function with DISTINCT ON and CASE statements.

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;
Copy after login

Performance Considerations

For a small number of users and activities, the query above will likely perform adequately without an index. However, as the number of rows and users increases, alternative techniques may be necessary to optimize performance.

Potential Optimizations

For high-volume data, consider using a more tailored approach:

  • If time allows NULL values, add NULLS LAST to the ORDER BY clause.
  • Use the pattern matching expression activity ~ '^[AB]' instead of activity LIKE 'A%' OR activity LIKE 'B%'.
  • Explore techniques for selecting the first row in each group, such as the one described in this article: [Select first row in each GROUP BY group?](https://stackoverflow.com/questions/18923181/select-first-row-in-each-group-by-group)
  • Investigate more advanced techniques for optimizing GROUP BY queries, especially when dealing with a high number of rows per user: [Optimize GROUP BY query to retrieve latest row per user](https://dba.stackexchange.com/questions/55252/optimize-group-by-query-to-retrieve-latest-row-per-user)

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