lct45 commented on a change in pull request #9157: URL: https://github.com/apache/kafka/pull/9157#discussion_r480194412
########## File path: streams/src/main/java/org/apache/kafka/streams/kstream/internals/KStreamSlidingWindowAggregate.java ########## @@ -0,0 +1,386 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.kafka.streams.kstream.internals; + +import org.apache.kafka.clients.consumer.ConsumerRecord; +import org.apache.kafka.common.metrics.Sensor; +import org.apache.kafka.streams.KeyValue; +import org.apache.kafka.streams.kstream.Aggregator; +import org.apache.kafka.streams.kstream.Initializer; +import org.apache.kafka.streams.kstream.Window; +import org.apache.kafka.streams.kstream.Windowed; +import org.apache.kafka.streams.kstream.SlidingWindows; +import org.apache.kafka.streams.processor.AbstractProcessor; +import org.apache.kafka.streams.processor.Processor; +import org.apache.kafka.streams.processor.ProcessorContext; +import org.apache.kafka.streams.processor.internals.InternalProcessorContext; +import org.apache.kafka.streams.processor.internals.metrics.StreamsMetricsImpl; +import org.apache.kafka.streams.state.KeyValueIterator; +import org.apache.kafka.streams.state.TimestampedWindowStore; +import org.apache.kafka.streams.state.ValueAndTimestamp; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import java.util.HashSet; +import java.util.Set; + +import static org.apache.kafka.streams.processor.internals.metrics.TaskMetrics.droppedRecordsSensorOrLateRecordDropSensor; +import static org.apache.kafka.streams.processor.internals.metrics.TaskMetrics.droppedRecordsSensorOrSkippedRecordsSensor; +import static org.apache.kafka.streams.state.ValueAndTimestamp.getValueOrNull; + +public class KStreamSlidingWindowAggregate<K, V, Agg> implements KStreamAggProcessorSupplier<K, Windowed<K>, V, Agg> { + private final Logger log = LoggerFactory.getLogger(getClass()); + + private final String storeName; + private final SlidingWindows windows; + private final Initializer<Agg> initializer; + private final Aggregator<? super K, ? super V, Agg> aggregator; + + private boolean sendOldValues = false; + + public KStreamSlidingWindowAggregate(final SlidingWindows windows, + final String storeName, + final Initializer<Agg> initializer, + final Aggregator<? super K, ? super V, Agg> aggregator) { + this.windows = windows; + this.storeName = storeName; + this.initializer = initializer; + this.aggregator = aggregator; + } + + @Override + public Processor<K, V> get() { + return new KStreamSlidingWindowAggregateProcessor(); + } + + public SlidingWindows windows() { + return windows; + } + + @Override + public void enableSendingOldValues() { + sendOldValues = true; + } + + private class KStreamSlidingWindowAggregateProcessor extends AbstractProcessor<K, V> { + private TimestampedWindowStore<K, Agg> windowStore; + private TimestampedTupleForwarder<Windowed<K>, Agg> tupleForwarder; + private StreamsMetricsImpl metrics; + private InternalProcessorContext internalProcessorContext; + private Sensor lateRecordDropSensor; + private Sensor droppedRecordsSensor; + private long observedStreamTime = ConsumerRecord.NO_TIMESTAMP; + + @SuppressWarnings("unchecked") + @Override + public void init(final ProcessorContext context) { + super.init(context); + internalProcessorContext = (InternalProcessorContext) context; + metrics = internalProcessorContext.metrics(); + final String threadId = Thread.currentThread().getName(); + lateRecordDropSensor = droppedRecordsSensorOrLateRecordDropSensor( + threadId, + context.taskId().toString(), + internalProcessorContext.currentNode().name(), + metrics + ); + droppedRecordsSensor = droppedRecordsSensorOrSkippedRecordsSensor(threadId, context.taskId().toString(), metrics); + windowStore = (TimestampedWindowStore<K, Agg>) context.getStateStore(storeName); + tupleForwarder = new TimestampedTupleForwarder<>( + windowStore, + context, + new TimestampedCacheFlushListener<>(context), + sendOldValues); + } + + @Override + public void process(final K key, final V value) { + if (key == null || value == null) { + log.warn( + "Skipping record due to null key or value. value=[{}] topic=[{}] partition=[{}] offset=[{}]", + value, context().topic(), context().partition(), context().offset() + ); + droppedRecordsSensor.record(); + return; + } + + final long timestamp = context().timestamp(); + processInOrder(key, value, timestamp); + } + + public void processInOrder(final K key, final V value, final long timestamp) { + + observedStreamTime = Math.max(observedStreamTime, timestamp); + final long closeTime = observedStreamTime - windows.gracePeriodMs(); + + if (timestamp < windows.timeDifferenceMs()) { + processEarly(key, value, timestamp, closeTime); + return; + } + + final Set<Long> windowStartTimes = new HashSet<>(); + + // aggregate that will go in the current record’s left/right window (if needed) + ValueAndTimestamp<Agg> leftWinAgg = null; + ValueAndTimestamp<Agg> rightWinAgg = null; + + //if current record's left/right windows already exist + boolean leftWinAlreadyCreated = false; + boolean rightWinAlreadyCreated = false; + + // keep the left type window closest to the record + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> latestLeftTypeWindow = null; + try ( + final KeyValueIterator<Windowed<K>, ValueAndTimestamp<Agg>> iterator = windowStore.fetch( + key, + key, + Math.max(0, timestamp - 2 * windows.timeDifferenceMs()), + // to catch the current record's right window, if it exists, without more calls to the store + timestamp + 1) + ) { + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> next; + while (iterator.hasNext()) { + next = iterator.next(); + windowStartTimes.add(next.key.window().start()); + final long startTime = next.key.window().start(); + final long endTime = startTime + windows.timeDifferenceMs(); + + if (endTime < timestamp) { + leftWinAgg = next.value; + // store the combined window (window from [0,timeDifference] that contains any early records) + // if it is found so that a right window can be created for the combined window's max record, as needed + if (isLeftWindow(next) || endTime == windows.timeDifferenceMs()) { + latestLeftTypeWindow = next; + } + } else if (endTime == timestamp) { + leftWinAlreadyCreated = true; + // if current record's left window is the combined window, need to check later if there is a + // record that needs a right window within the combined window + if (endTime == windows.timeDifferenceMs()) { + latestLeftTypeWindow = next; + } + putAndForward(next.key.window(), next.value, key, value, closeTime, timestamp); + } else if (endTime > timestamp && startTime <= timestamp) { + rightWinAgg = next.value; + putAndForward(next.key.window(), next.value, key, value, closeTime, timestamp); + } else { + rightWinAlreadyCreated = true; + } + } + } + //create right window for previous record + if (latestLeftTypeWindow != null) { + final long previousWindowEnd = latestLeftTypeWindow.key.window().end(); + final long rightWinStart = previousWindowEnd == windows.timeDifferenceMs() ? latestLeftTypeWindow.value.timestamp() + 1 : previousWindowEnd + 1; + if (rightWindowNecessaryAndPossible(windowStartTimes, rightWinStart, timestamp)) { + final TimeWindow window = new TimeWindow(rightWinStart, rightWinStart + windows.timeDifferenceMs()); + final ValueAndTimestamp<Agg> valueAndTime = ValueAndTimestamp.make(initializer.apply(), timestamp); + putAndForward(window, valueAndTime, key, value, closeTime, timestamp); + } + } + + //create left window for new record + if (!leftWinAlreadyCreated) { + final ValueAndTimestamp<Agg> valueAndTime; + // if there's a right window that the new record could create && max record falls within left window --> new record's left window is not empty + if (latestLeftTypeWindow != null && recordFallsWithinCurrentRecordsLeftWindow(latestLeftTypeWindow.value.timestamp(), timestamp)) { + valueAndTime = ValueAndTimestamp.make(leftWinAgg.value(), timestamp); + } else { + valueAndTime = ValueAndTimestamp.make(initializer.apply(), timestamp); + } + final TimeWindow window = new TimeWindow(timestamp - windows.timeDifferenceMs(), timestamp); + putAndForward(window, valueAndTime, key, value, closeTime, timestamp); + } + //create right window for new record + if (!rightWinAlreadyCreated && rightWindowIsNotEmpty(rightWinAgg, timestamp)) { + final TimeWindow window = new TimeWindow(timestamp + 1, timestamp + 1 + windows.timeDifferenceMs()); + final ValueAndTimestamp<Agg> valueAndTime = ValueAndTimestamp.make(getValueOrNull(rightWinAgg), Math.max(rightWinAgg.timestamp(), timestamp)); + putAndForward(window, valueAndTime, key, value, closeTime, timestamp); + } + } + + /** + * Created to handle records that have a timestamp > 0 but < timeDifference. These records would create + * windows with negative start times, which is not supported. Instead, they will fall within the [0, timeDifference] + * window, and we will update their right windows as new records come in later + */ + private void processEarly(final K key, final V value, final long timestamp, final long closeTime) { + ValueAndTimestamp<Agg> rightWinAgg = null; + //window from [0,timeDifference] that holds all early records + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> combinedWindow = null; + boolean rightWinAlreadyCreated = false; + final Set<Long> windowStartTimes = new HashSet<>(); + + try ( + final KeyValueIterator<Windowed<K>, ValueAndTimestamp<Agg>> iterator = windowStore.fetch( + key, + key, + Math.max(0, timestamp - 2 * windows.timeDifferenceMs()), + // to catch the current record's right window, if it exists, without more calls to the store + timestamp + 1) + ) { + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> next; + while (iterator.hasNext()) { + next = iterator.next(); + windowStartTimes.add(next.key.window().start()); + final long startTime = next.key.window().start(); + final long endTime = startTime + windows.timeDifferenceMs(); + + if (startTime == 0) { + combinedWindow = next; + } else if (startTime <= timestamp) { + rightWinAgg = next.value; + putAndForward(next.key.window(), next.value, key, value, closeTime, timestamp); + } else if (startTime == timestamp + 1) { + rightWinAlreadyCreated = true; + } + } + } + + // if there wasn't a right window agg found and we need a right window for our new record, + // the current aggregate in the combined window will go in the new record's right window + if (rightWinAgg == null && combinedWindow != null && combinedWindow.value.timestamp() > timestamp) { + rightWinAgg = combinedWindow.value; + } + + if (combinedWindow == null) { + final TimeWindow window = new TimeWindow(0, windows.timeDifferenceMs()); + final ValueAndTimestamp<Agg> valueAndTime = ValueAndTimestamp.make(initializer.apply(), timestamp); + putAndForward(window, valueAndTime, key, value, closeTime, timestamp); + + } else { + //create the right window for the combined window's max record before the current record was added + final long maxRightWindowStart = combinedWindow.value.timestamp() + 1; Review comment: Yeah I see what you mean, I think it makes sense to utilize previous record for both `processInOrder` and `processEarly` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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