How to Implement Conditional Lead/Lag Functions in PostgreSQL?

Conditional Lead/Lag Function Postgres?

Many users are perplexed by how to implement conditional lead/lag functions in Postgres using windowing functions. In this example, we have a table with data like this:

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

We wish to transform this table into a report that shows the next activity of type B for each user where the previous activity was type A. Specifically, we aim to create a table like this:

Name	Activity	Next Activity
user1	A2	NULL
user2	A3	B1
user3	A1	B2

Conditional Window Functions

Traditionally, users may attempt to use the lead() function to solve this problem. However, due to the limitations of Postgres's window functions, conditional functions are not directly supported. Specifically, the FILTER clause cannot be applied to lead() or lag().

Solving the Problem

To solve this problem, we must abandon the notion of using a conditional lead function and instead use a more complex query strategy. One approach is to use DISTINCT ON and CASE statements to achieve the desired result. Here is a query that demonstrates this strategy:

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 selects distinct rows for each user, ordered by time descending.
• The lead() function returns the next activity in the sequence, which is used to determine the next activity of type B for each user.
• The CASE statements are used to determine the activity and next activity based on the type of activity.

Performance Considerations

For small datasets, the above query should perform sufficiently well. However, for large datasets, optimizing the query using techniques such as indexing or window functions may be necessary.

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