Hopping window
A hopping window is a fixed-duration time window that advances at regular intervals and may overlap with other windows.
MongoDB 8.0 in Action, Third Edition
$hoppingWindow is an Atlas Stream Processing aggregation pipeline stage that creates overlapping time windows separated by a configured hop interval.
- How it is used
- Provides overlapping views of streaming events for rolling aggregates and continuous monitoring.
- How deep it goes
- Defines duration and hop interval and walks through a sensor aggregation; does not detail window-stage internals.
- Examples
- Uses five-minute moving averages and a Kafka-fed sensor pipeline averaging temperatures in 30-second windows every 10 seconds.
- What you can do after
- Build it from scratch in an Atlas Stream Processing pipeline to calculate overlapping aggregates and store their results.
Read how MongoDB 8.0 in Action, Third Edition explains hopping window →
Logs and Telemetry
A hopping window is a stream-processing time window whose successive intervals advance by a hop duration that can be shorter than the window duration, allowing overlap.
- How it is used
- Supports near-real-time anomaly detection by smoothing aggregated network-traffic and application-workload fluctuations.
- How deep it goes
- Explains refresh behavior, smoothing effects, configuration syntax, runnable input, output, and computational trade-offs against tumbling windows.
- Examples
- Demonstrates a 4-second window advancing every 2 seconds over dummy rand_value events emitted once per second.
- What you can do after
- Apply it via Fluent Bit stream-task configuration to calculate smoothed near-real-time aggregates.
Kafka Streams in Action, Second Edition
A hopping window is a fixed-size time window whose successive windows may overlap because they advance by an interval smaller than their size.
- How it is used
- Calculates continuously updated aggregations over overlapping fixed-size intervals, such as recent sensor averages reported every few seconds.
- How deep it goes
- Explains window size, advance interval, epoch alignment, wrapped keys, and output Serdes through Java aggregation examples.
- Examples
- Uses one-minute IoT temperature windows advancing every ten seconds, with aggregation output sent to a Kafka topic.
- What you can do after
- Build it from scratch with Kafka Streams TimeWindows, advanceBy, aggregation, and compatible Windowed key Serdes.
Read how Kafka Streams in Action, Second Edition explains hopping window →
Related concepts
- time windowkind of · A bounded temporal interval or event scope over which observations or events are grouped, evaluated, or processed.
- tumbling windowalternative to · A tumbling window is a fixed-duration, non-overlapping stream-processing interval in which each event belongs to exactly one window.
- 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.
- sliding windowalternative to · A sliding window is a bounded region over an ordered sequence or time range that advances, updating which elements fall within it.
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