How to Determine User Active Dates in Spark SQL Using Window Functions?

Find user active dates using complex window functions in Spark SQL

Question:

A DataFrame containing records of users logging into the website. You need to determine when a user is active and consider a period of activity. If the user logs in again after this period, their active date will be reset.

Proposed method:

Using a window function with hysteresis and recursion, identify the first or most recent login within the activity period to determine the activity date.

Spark native solution (>= 3.2):

Spark 3.2 and higher supports session windows. See the official documentation for usage examples.

Legacy solution (Spark < 3.2):

1. Import function:

   • Window is used to define windows
   • coalesce, datediff, lag, lit, min, sum

2. Definition window:

   • userWindow Partitioned by user_name and sorted by login_date
   • userSessionWindow Partition user_name by session and

3. Find the start of a new session:

   • Use datediff and lag to compare login dates and check if there is a gap that is larger than the active period.
   • Use cast to convert the result to bigint.
   • Use userWindow on sum to accumulate new session starts.

4. Find the earliest date for each session:

   • Use withColumn to add session columns.
   • Use userSessionWindow on min to find the earliest login_date for each session.
   • Delete the session column.

5. Example:

    val df = Seq(
      ("SirChillingtonIV", "2012-01-04"), ("Booooooo99900098", "2012-01-04"),
      ("Booooooo99900098", "2012-01-06"), ("OprahWinfreyJr", "2012-01-10"),
      ("SirChillingtonIV", "2012-01-11"), ("SirChillingtonIV", "2012-01-14"),
      ("SirChillingtonIV", "2012-08-11")
    ).toDF("user_name", "login_date")
   
    val result = sessionized //sessionized is assumed to be defined elsewhere, this is a crucial part missing from the original
      .withColumn("became_active", min($"login_date").over(userSessionWindow))
      .drop("session")
   
    df.show(5)
    result.show(5)

Note that the definition of sessionized is missing from the example code, which is a key part to completing this solution. The session column needs to be calculated based on activity period and login date. This usually requires a custom function or more complex window function logic. A complete solution requires adding this missing piece of code.

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