Aggregate user logs into 30-minute sessions

You are given a CSV file with columns:

user_id | log_datetime | topic

Example input rows (already in time order for the same user):

• 001 | 2025-03-01 00:01:00 | pricing
• 001 | 2025-03-01 00:02:00 | hotel
• 001 | 2025-03-01 00:03:00 | pricing
• 001 | 2025-03-01 01:30:00 | restaurant
• 001 | 2025-03-01 02:30:00 | restaurant

Task (Python):

1. For each user, sort events by log_datetime and split them into sessions: a new session starts when the gap from the previous event is more than 30 minutes.
2. For each session output:
   • user_id
   • session_start (timestamp of first event)
   • session_end
     • if the session has 2+ events, use the timestamp of the last event
     • if the session has only 1 event, set session_end = session_start + 30 minutes (treating 30 minutes as the maximum assumed duration)
   • count_topic: number of distinct topics in the session
   • count_time: number of events (rows) in the session

Expected output for the example:

• 001 | 2025-03-01 00:01:00 | 2025-03-01 00:03:00 | 2 | 3
• 001 | 2025-03-01 01:30:00 | 2025-03-01 02:00:00 | 1 | 1
• 001 | 2025-03-01 02:30:00 | 2025-03-01 03:00:00 | 1 | 1

Implement a function/program that reads the CSV and produces these session aggregates.

Aggregate User Logs into 30-Minute Sessions

Constraints

• 0 <= number of log rows <= 200000
• Each row has the format user_id,log_datetime,topic
• Timestamps are valid and use the format YYYY-MM-DD HH:MM:SS
• A gap of exactly 30 minutes stays in the same session; only gaps greater than 30 minutes start a new session

Hints

1. Group rows by user first, then sort each user's events by timestamp before building sessions.
2. When scanning a user's events, keep track of the current session start time, last event time, event count, and a set of distinct topics.