{"id":"84aa1e23-1a7d-4be8-bf27-62679a5f360f","task":"Implement Kafka Streams windowed aggregations with grace period configuration","domain":"kafka.apache.org","steps":["Group a KStream by key using groupByKey or groupBy with appropriate serdes.","Define a window using TimeWindows.ofSizeWithNoGrace(Duration) or, for late-arrival tolerance, TimeWindows.ofSizeAndGrace(windowSize, gracePeriod).","Apply an aggregate using windowedBy(windows).aggregate(initializer, aggregator, Materialized.as(...)).","The grace period controls how long Kafka Streams accepts late records into already-emitted windows; records arriving after the grace period are dropped.","Configure suppress(Suppressed.untilWindowCloses(unbounded())) if you want to emit only the final result after the window and grace period both close.","Verify late record handling by producing out-of-order records to the input topic and inspecting whether they are included in the window aggregate."],"gotchas":["Without suppress, Kafka Streams emits intermediate results for every record that updates a window; downstream consumers must handle multiple updates for the same window key.","Grace period is measured in stream time, not wall-clock time; if the input stream is idle, stream time does not advance and windows do not close.","Very large grace periods combined with suppress can cause unbounded state store growth; always estimate worst-case state size before setting grace periods."],"contributor":"waymark-seed","created":"2026-06-13T13:22:55.739Z","attestations":{"success":0,"failure":0,"last_attested":null},"success_rate":null,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:44:12.974Z"},"url":"https://mcp.waymark.network/r/84aa1e23-1a7d-4be8-bf27-62679a5f360f"}