How Can Window Functions Identify User Activation Status with a 5-Day Active Period Reset?

Using Spark Window Functions to Define User Activity with Complex Conditions

This example demonstrates how to use Spark window functions to determine user activation status, considering a 5-day active period that resets upon subsequent logins. We're given a DataFrame of user logins and aim to add a column showing when each user became active.

Methodology: Leveraging Window Functions

Our approach uses window functions to identify login events triggering an active status reset. A window is created to order logins by user and date. Lagging this window enables comparison between current and previous login times.

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

val window = Window.partitionBy("user_name").orderBy("login_date")
val df2 = df.withColumn("previous_login", lag("login_date", 1).over(window))

Addressing Active Status Resets

The became_active date is determined by checking if the previous login ( previous_login ) falls within the 5-day active period. If previous_login is null (first login) or the time difference (login_date - previous_login) is less than 5 days, became_active is set to the current login_date. Otherwise, the process continues recursively until this condition is met.

Spark Implementation (Versions >= 3.2)

Spark 3.2 and later offer native session window support, simplifying this task (see official documentation for details).

Spark Implementation (Older Versions)

For older Spark versions, a workaround is necessary:

1. Define windows for user partitioning and session identification:

val userWindow = Window.partitionBy("user_name").orderBy("login_date")
val userSessionWindow = Window.partitionBy("user_name", "session")

2. Identify session start points based on login gaps:

val newSession = (coalesce(
  datediff($"login_date", lag($"login_date", 1).over(userWindow)),
  lit(0)
) > 5).cast("bigint")

3. Create a sessionized DataFrame and find the earliest login date within each session:

val sessionized = df.withColumn("session", sum(newSession).over(userWindow))
val result = sessionized
  .withColumn("became_active", min($"login_date").over(userSessionWindow))
  .drop("session")

Example Output

The following output demonstrates the result using a sample dataset:

<code>+----------------+----------+-------------+
|       user_name|login_date|became_active|
+----------------+----------+-------------+
|  OprahWinfreyJr|2012-01-10|    2012-01-10|
|SirChillingtonIV|2012-01-04|    2012-01-04|
|SirChillingtonIV|2012-01-11|    2012-01-11|
|SirChillingtonIV|2012-01-14|    2012-01-11|
|SirChillingtonIV|2012-08-11|    2012-08-11|
|Booooooo99900098|2012-01-04|    2012-01-04|
|Booooooo99900098|2012-01-06|    2012-01-04|
+----------------+----------+-------------+</code>

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