How Can Spark SQL Window Functions Identify User Activity Periods Based on Complex Temporal Conditions?

Use Spark SQL window functions to identify user activity cycles based on complex time conditions

Spark SQL’s window functions provide a powerful mechanism for performing calculations on a set of rows within a specified time range or partition. A common application is to determine the start of a user activity cycle based on specific conditions.

Definition window

To do this, we define two windows:

• userWindow: Partitioned by user_name and sorted by login_date.
• userSessionWindow: Partitioned by user_name and session to be determined later.

Identifies the start of a new session

The key to determining when a new session starts is to compare the login dates of consecutive rows. If the difference between two consecutive login dates is greater than 5 days, a new session is identified. We capture this using the following code:

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

Assign session ID

Now we can assign a session ID to each row by summing the userWindow values ​​on newSession:

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

Confirm active date

Finally, we determine the userSessionWindow date for each session by finding the smallest login_date within each became_active:

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

Example

Use the provided sample data:

<code class="language-scala">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")</code>

The result will be:

<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>

This demonstrates how to use window functions in Spark SQL to efficiently determine complex conditions on temporal data.

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