The article outlines how to analyze web server logs to determine user sessions based on a 30-minute activity rule. It emphasizes using Spark's DataFrame API to process the logs, define user behavior, and calculate session IDs. By comparing timestamps within a user's actions, it allows for identifying when a session starts and stops, ultimately benefiting the product team's understanding of user engagement over time. The provided dataset serves as a framework for implementing this system efficiently.
To accurately assign session IDs based on user activity, we need to understand the time difference between consecutive actions. If the difference exceeds 30 minutes, a new session starts.
Using Spark's DataFrame API, we can effectively group user activities, calculate time differences, and assign session IDs, ensuring comprehensive insights into user interactions over time.
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