How can Spark SQL Window Functions Determine User Activation Dates with Session-Based Expiry?

Spark SQL window functions and complex conditions

Suppose you have a DataFrame containing user login details, and you want to add a column to indicate their activation date on the website. However, there is a caveat: a user's activity period expires after a certain period of time, and logging in again will reset their activation date.

This problem can be solved using window functions in Spark SQL. Here's one way:

Step 1: Define the window

<code>import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

val userWindow = Window.partitionBy("user_name").orderBy("login_date")
val userSessionWindow = Window.partitionBy("user_name", "session")</code>

Step 2: Detect the start of a new session

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

val sessionized = df.withColumn("session", sum(newSession).over(userWindow))</code>

Step 3: Find the earliest date for each session

<code>val result = sessionized
  .withColumn("became_active", min($"login_date").over(userSessionWindow))
  .drop("session")</code>

This method uses a sliding window to partition the data by user and sort it by login date. Then define the session window by grouping rows with the same session ID. The desired result can be achieved by detecting when a new session starts (newSession) and calculating the earliest login date in each session (became_active).

Latest Spark improvements

For Spark 3.2 and above, session windows are natively supported, making the above solution even simpler. See the official documentation for details.

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