Tumbling window
A tumbling window is a fixed-duration, non-overlapping stream-processing interval in which each event belongs to exactly one window.
MongoDB 8.0 in Action, Third Edition
$tumblingWindow is an Atlas Stream Processing aggregation pipeline stage that partitions a data stream into fixed, nonoverlapping time windows.
- How it is used
- Bounds continuous stream data into distinct intervals for aggregations such as periodic totals and summaries.
- How deep it goes
- Explains nonoverlapping membership and works through interval configuration and nested aggregation; does not detail window-closing mechanics.
- Examples
- Uses daily sales totals and a ten-second solar-device aggregation with sampled window start and end times.
- What you can do after
- Build it from scratch in mongosh by nesting $group in a configured $tumblingWindow and writing results with $merge.
Read how MongoDB 8.0 in Action, Third Edition explains tumbling window →
Kafka Streams in Action, Second Edition
A tumbling window is a fixed-duration, non-overlapping stream-processing window whose boundaries advance by the full window duration.
- How it is used
- Calculates non-overlapping time-based aggregations for regularly arriving events such as IoT sensor readings.
- How deep it goes
- Explains window sizing, default advancement, output keys, serialization, and epoch alignment, but does not detail broker internals.
- Examples
- Demonstrates one-minute IoT temperature aggregation, Windowed keys, key unwrapping, and epoch-aligned intervals.
- What you can do after
- Build it from scratch with Kafka Streams using TimeWindows, aggregation, materialization, and appropriate output serdes.
Read how Kafka Streams in Action, Second Edition explains tumbling window →
Logs and Telemetry
A tumbling window is a non-overlapping time window in stream processing whose successive intervals begin immediately after the preceding intervals end.
- How it is used
- Aggregates stream events into discrete intervals for trend analysis, metrics, and threshold-triggered follow-up events.
- How deep it goes
- Defines non-overlap, contrasts hopping behavior, and demonstrates Fluent Bit SQL configuration with timing limitations.
- Examples
- Demonstrates four-second sums of random values and five-second counted streams routed by count thresholds.
- What you can do after
- Apply it via Fluent Bit SQL stream tasks to calculate non-overlapping interval aggregates and route resulting events.
Related concepts
- time windowkind of · A bounded temporal interval or event scope over which observations or events are grouped, evaluated, or processed.
- hopping windowalternative to · A hopping window is a fixed-duration time window that advances at regular intervals and may overlap with other windows.
- $grouphas part · $group is a MongoDB aggregation pipeline stage that groups documents by an expression and computes aggregate results for each group.
- windowingkind of · Windowing is the division of data or events into bounded subsets, often based on time or other constraints, for separate processing and result association.
- trendaddresses · A trend is a developing or persistent direction, pattern, or movement in observations, conditions, behavior, or attention.
- session windowcontrasts with · A session window is a variable-length data-stream window whose boundaries are separated by a configured period of inactivity.
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