Time window
A bounded temporal interval or event scope over which observations or events are grouped, evaluated, or processed.
Kafka Streams in Action, Second Edition
Window is a Kafka Streams API type representing the time interval associated with a windowed record or aggregation.
- How it is used
- Segments KStream aggregations into time-bounded results that make event patterns and operational decisions visible.
- How deep it goes
- Explains window construction, overlap, alignment, output keys, serialization choices, and result inspection; defers grace behavior.
- Examples
- Uses pizza-shop sales, IoT heat-sensor averages, and windowed aggregation output carrying start and end times.
- What you can do after
- Apply it via Kafka Streams TimeWindows to build hopping or tumbling aggregations and analyze their interval-bound results.
Read how Kafka Streams in Action, Second Edition explains time window →
Logs and Telemetry
An event window is a defined scope of multiple stream events over which a query computes filtering or aggregate results.
- How it is used
- Enables stream-processor aggregations that turn event occurrences into metrics, filters, or generated events.
- How deep it goes
- Explains multi-event aggregation, hopping-window syntax, duration choices, buffering limits, and timestamp constraints; only contrasts tumbling windows.
- Examples
- Demonstrates a 4-second hopping sum advanced every 2 seconds with dummy events valued at 2.
- What you can do after
- Apply it via Fluent Bit stream-processor SQL to aggregate tagged events across configured time scopes.
Apache Kafka in Action
A time window is a bounded temporal interval over which stream records are aggregated, correlated, or otherwise processed.
- How it is used
- Bounds aggregation and correlation over continuous streams so recent records can be counted or joined.
- How deep it goes
- Defines four window types and discusses timestamp choice and late arrivals, but leaves lateness configuration at a high level.
- Examples
- Uses a one-minute product-price-and-sale join and a one-minute count of authentication attempts for fraud detection.
- What you can do after
- Apply it via Kafka Streams or Flink SQL to count authentication attempts or correlate price changes with sales.
Machine Learning System Design
The number of observations included in a time-series training or testing window.
- How it is used
- Aligns the test-set span with forecast distance and the expected interval before retraining.
- How deep it goes
- Relates test-window sizing to forecast and retraining needs, and tunes candidate granularities with a model-wise learning curve.
- Examples
- Compares window sequences such as 7/14/21 and 30/60/90, and relates test size to forecast distance.
- What you can do after
- Evaluate candidate window granularities with a model-wise learning curve and align test spans with forecast and retraining needs.
Read how Machine Learning System Design explains time window →
MongoDB 8.0 in Action, Third Edition
A time window partitions streaming data into a bounded set of records delimited by time for stateful processing.
- How it is used
- Partitions continuous streams into bounded sets for stateful aggregation and time-based monitoring.
- How deep it goes
- Defines tumbling and hopping behavior and shows a windowed processor; does not detail window-closing or late-event handling.
- Examples
- Uses hourly sales totals, rolling averages, anomaly monitoring, and overlapping IoT temperature averages.
- What you can do after
- Apply it via Atlas Stream Processing to select one window stage and aggregate records within its intervals.
Read how MongoDB 8.0 in Action, Third Edition explains time window →
Kafka for Architects
A time window is a bounded interval of event or processing time over which a stream processor groups and evaluates records.
- How it is used
- Supports time-bounded aggregations in stream-processing applications instead of whole-stream totals.
- How deep it goes
- Defines four window types, timestamp assignment, grace periods, and per-key behavior without detailing implementation internals.
- Examples
- Uses daily order counts, five-minute login counts, and ten-minute inactivity gaps for web sessions.
- What you can do after
- Apply it via Kafka Streams to define time boundaries for keyed aggregations and control late-record handling.
Related concepts
- hopping windowincludes · A hopping window is a fixed-duration time window that advances at regular intervals and may overlap with other windows.
- tumbling windowincludes · A tumbling window is a fixed-duration, non-overlapping stream-processing interval in which each event belongs to exactly one window.
- sliding windowincludes · A sliding window is a bounded region over an ordered sequence or time range that advances, updating which elements fall within it.
- TimeWindowsproduced by · TimeWindows is a Kafka Streams class that defines fixed-size time-based windows with a configurable advance interval.
- training datasetconfigures · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
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