Stop Forcing Time Windows on Bursty User Behavior - Try This Instead

Most user activity happens in unpredictable bursts—but our analytics tools still assume time flows in clean, fixed intervals. It’s time for a smarter approach.

Effectively analyzing user behavior in streaming data requires selecting an appropriate windowing strategy. Traditional fixed-time windows, such as tumbling or hopping windows, divide time into uniform segments. However, these approaches often fail to capture the natural burstiness and irregularity inherent in real-world user activities. This challenge has led to the rise of Session Windows—a more adaptive windowing technique that groups events based on activity patterns rather than rigid time intervals.

Why Use Session Windows?

By forming windows based on actual activity separated by periods of inactivity, session windows provide a more accurate and insightful view of streaming data. Here are some key benefits:

• Flexible event grouping: Dynamically cluster events separated by inactivity gaps, accurately modeling real user or system behavior.
• Behavior-driven analytics: Ideal for use cases like app usage, clickstreams, IoT sensor data, and anomaly detection where activity is irregular.
• Simplified querying: Define session windows directly in SQL using familiar window function syntax, making complex event grouping straightforward.
• Enhanced insight: Capture meaningful patterns missed by fixed windows, providing deeper understanding of user engagement and system activity.

What Are Session Windows?

A session window groups together events that occur close to each other in time. If two events are separated by less than a specified gap duration, they belong to the same session. If the gap is too long, a new session starts.

The choice of gap duration is critical. A smaller gap creates more, shorter sessions—possibly fragmenting continuous activity—while a larger gap risks merging unrelated bursts. Finding the right balance depends on your specific application.

This windowing strategy is especially powerful for modeling user behavior—clickstreams, browsing sessions, app usage, etc.—where activity doesn’t follow a fixed interval, but rather comes and goes in bursts.

Recommended Use Cases

Session windows are well-suited for scenarios where events occur in bursts with variable gaps. Unlike fixed-time windows, they dynamically group events based on periods of activity and inactivity. Common use cases include:

• Web and app analytics: Accurately identify user sessions based on actual click or navigation behavior rather than arbitrary time slots.
• IoT sensor data: Group intermittent sensor readings into active operational periods, excluding idle or offline intervals.
• Anomaly detection: Detect unusual session durations or inactivity gaps indicative of system or user anomalies.
• Financial market data: Capture bursts of trading activity or order flow related to market events.

Use Session Windows in a Real System

RisingWave implements session window semantics through a specialized window function frame type: SESSION. This allows users to define and analyze sessions directly within the SQL query structure, leveraging standard window functions over dynamically determined windows.

Consider the following example table user_views tracking product views by users:

 user_id | product_id | viewed_at
---------+------------+---------------------
 1       | 1001       | 2022-07-01 22:00:00
 1       | 1002       | 2022-07-01 22:01:00
 1       | 1001       | 2022-07-01 22:03:00
 1       | 1003       | 2022-07-01 22:10:00 -- Gap > 5 mins from previous
 2       | 1003       | 2022-07-01 22:05:00
 2       | 1006       | 2022-07-01 22:05:30 -- Gap < 5 mins from previous

You can compute session windows using the following query:

SELECT
    user_id, product_id, viewed_at,
    first_value(viewed_at) OVER (
        PARTITION BY user_id ORDER BY viewed_at
        SESSION WITH GAP INTERVAL '5 MINUTES'
    ) AS window_start,
    last_value(viewed_at) OVER (
        PARTITION BY user_id ORDER BY viewed_at
        SESSION WITH GAP INTERVAL '5 MINUTES'
    ) AS window_end
FROM user_views;

This query assigns a session window to each row by computing its window_start and window_end. It works by partitioning events per user, sorting them by time, and applying a 5-minute session gap.

With a 5-minute gap setting, RisingWave will treat the first three rows for user_id = 1 as one session (since the gaps are within 5 minutes), while the last row starts a new session. For user_id = 2, the two events are within 30 seconds, so they belong to the same session.

The output clearly illustrates how session windows are formed:

 user_id | product_id | viewed_at           | window_start        | window_end
---------+------------+---------------------+---------------------+---------------------
 1       | 1001       | 2022-07-01 22:00:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
 1       | 1002       | 2022-07-01 22:01:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
 1       | 1001       | 2022-07-01 22:03:00 | 2022-07-01 22:00:00 | 2022-07-01 22:03:00
 1       | 1003       | 2022-07-01 22:10:00 | 2022-07-01 22:10:00 | 2022-07-01 22:10:00
 2       | 1003       | 2022-07-01 22:05:00 | 2022-07-01 22:05:00 | 2022-07-01 22:05:30
 2       | 1006       | 2022-07-01 22:05:30 | 2022-07-01 22:05:00 | 2022-07-01 22:05:30

Aggregate Data Within Session Windows

Beyond assigning session boundaries, RisingWave also supports aggregation over session windows. This allows you to compute metrics like counts, sums, or distinct values within each session group.

For example, to calculate the number of distinct products viewed per user session:

SELECT
    user_id, window_start,
    count(DISTINCT product_id) AS n_viewed_product
FROM (
    SELECT
        *,
        first_value(viewed_at) OVER (
            PARTITION BY user_id ORDER BY viewed_at
            SESSION WITH GAP INTERVAL '5 MINUTES'
        ) AS window_start
    FROM user_views
) AS session_data
GROUP BY user_id, window_start
ORDER BY user_id, window_start;

This produces the following result:

 user_id | window_start        | n_viewed_product
---------+---------------------+-----------------
 1       | 2022-07-01 22:00:00 | 2
 1       | 2022-07-01 22:10:00 | 1
 2       | 2022-07-01 22:05:00 | 2

This pattern makes it easy to extract session-level insights from raw event streams, such as user behavior or device activity.

Get Started with Session Windows

Session windows in RisingWave provide a flexible, behavior-driven windowing paradigm that better aligns with the bursty, irregular nature of real-world event streams. By defining dynamic windows based on inactivity gaps rather than fixed time intervals, RisingWave enables deeper insights into user and system activity.

Ready to leverage the power of session windows for your real-time event stream analysis？

• Try it Today: Explore RisingWave Cloud for a fully managed experience, or check out the open-source version to run it yourself.
• Dive Deeper: Explore the Time Windows documentation to understand the full capabilities of session windows and other windowing functions.