Session Window

A Session Window is a type of data window used in stream processing that groups events based on periods of activity, separated by defined gaps of inactivity (timeouts). Unlike fixed-duration windows like Tumbling or Hopping windows, Session Windows have variable lengths that are determined by the data itself—specifically, by the arrival times of events.

Session Windows are particularly useful for analyzing user behavior, device activity, or any sequence of events where you want to group related events that occur closely together in time, followed by a period of silence.

Core Concepts

Session: A session is a burst of activity from a single entity (e.g., a user, a device) followed by a period of inactivity.
Inactivity Gap (or Session Timeout): This is a user-defined duration. If no new events arrive for a particular key (e.g., user ID) within this gap period, the current session for that key is considered closed.
Key-Based: Session Windows are typically applied to keyed streams (e.g., data partitioned by user_id, session_id, device_id). A separate session is tracked for each key.
Variable Duration: The length of a session window is not fixed beforehand. It starts when the first event for a key arrives after a period of inactivity and ends when the inactivity gap is exceeded.
Merging (Sometimes): Some systems might merge overlapping session windows if an event arrives that falls within the inactivity gap of two previously distinct sessions for the same key.

How it Works

When the first event for a specific key arrives, a new session window opens for that key.
As subsequent events for the same key arrive, they are included in the current open session window, and the window's end time is extended (effectively, the inactivity timer is reset).
If no event for that key arrives within the specified inactivity gap, the session window closes. Any aggregations or computations for that session are finalized and emitted.
The next event for that key will start a new session window.

Example

Imagine analyzing user clicks on a website with a session inactivity gap of 30 minutes:

User A clicks at 10:00 AM. (Session A starts)
User A clicks at 10:05 AM. (Session A continues)
User B clicks at 10:06 AM. (Session B starts for User B)
User A clicks at 10:25 AM. (Session A continues)
User B clicks at 10:10 AM. (Session B continues)
At 10:40 AM (10:10 AM + 30 mins), if User B has no more clicks, Session B closes.
At 10:55 AM (10:25 AM + 30 mins), if User A has no more clicks, Session A closes.

Use Cases

Web Analytics: Analyzing user sessions (e.g., pages visited per session, duration of sessions).
IoT/Sensor Data: Identifying periods of device activity versus inactivity.
Fraud Detection: Grouping related suspicious activities that occur in quick succession.
Network Monitoring: Analyzing traffic flows or connection periods.

Session Windows in RisingWave

RisingWave supports session windowing through its SQL interface, typically using functions like SESSION_START() and SESSION_END() in conjunction with GROUP BY clauses that include these session boundaries along with the key.

-- Example (Conceptual - actual syntax might vary slightly)
SELECT
    user_id,
    SESSION_START(event_timestamp, INTERVAL '30 minutes') AS session_start_time,
    COUNT(*) AS events_in_session
FROM
    user_clicks
GROUP BY
    user_id,
    SESSION(event_timestamp, INTERVAL '30 minutes'); -- Defines session based on event_timestamp and 30 min gap