In the previous blog post, we saw how one could partition a time series log of web activities into web page-based sessions. Operating on the same original dataset, we’re going to generate sessions based on a different set of rules.

Rather than web page-based, sessions are defined with the following rules:

1. A session expires after inactivity of a timeout period (say tmo1), and,
2. An active session expires after a timeout period (say tmo2).

First, we assemble the original sample dataset used in the previous blog:

Let’s set the first timeout tmo1 to 15 minutes, and the second timeout tmo2 to 60 minutes.

The end result should look something like below:

Given the above session creation rules, it’s obvious that all programming logic is going to be centered around the timestamp alone, hence the omission of columns like page in the expected final result.

Generating sessions based on rule #1 is rather straight forward as computing the timestamp difference between consecutive rows is easy with Spark built-in Window functions. As for session creation rule #2, it requires dynamically identifying the start of the next session that depends on where the current session ends. Hence, even robust Window functions over, say, partitionBy(user).orderBy(timestamp).rangeBetween(0, tmo2) wouldn’t cut it.

The solution to be suggested involves using a UDF (user-defined fucntion) to leverage Scala’s feature-rich set of functions:

Note that the timestamp diff list tsDiffs is the main input being processed for generating sessions based on the tmo2 value (session create rule #2). The timestamp list tsList is being “passed thru” merely to be included in the output with each timestamp paired with the corresponding session ID.

Also note that the accumulator for foldLeft in the UDF is a Tuple of (ls, j, k), where:

• ls is the list of formatted session IDs to be returned
• j and k are for carrying over the conditionally changing timestamp value and session id number, respectively, to the next iteration

Now, let’s lay out the steps for carrying out the necessary transformations to generate the sessions:

1. Identify sessions (with 0 = start of a session) per user based on session creation rule #1
2. Group the dataset to assemble the timestamp diff list per user
3. Process the timestamp diff list via the above UDF to identify sessions based on rule #2 and generate all session IDs per user
4. Expand the processed dataset which consists of the timestamp paired with the corresponding session IDs

Step 1:

Steps 2-4: