ableegoldman commented on a change in pull request #9039: URL: https://github.com/apache/kafka/pull/9039#discussion_r478715664
########## File path: streams/src/main/java/org/apache/kafka/streams/kstream/internals/KStreamSlidingWindowAggregate.java ########## @@ -0,0 +1,391 @@ +/* + * 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 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; + private boolean reverseIteratorImplemented = false; + + @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 + ); + //catch unsupported operation error + 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; + } + if (reverseIteratorImplemented) { + processReverse(key, value); + } else { + processInOrder(key, value); + } + } + + public void processReverse(final K key, final V value) { + + final long timestamp = context().timestamp(); + observedStreamTime = Math.max(observedStreamTime, timestamp); + final long closeTime = observedStreamTime - windows.gracePeriodMs(); + //store start times of windows we find + final HashSet<Long> windowStartTimes = new HashSet<Long>(); + // 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; + + try ( + final KeyValueIterator<Windowed<K>, ValueAndTimestamp<Agg>> iterator = windowStore.fetch( + key, + key, + timestamp - 2 * windows.timeDifferenceMs(), + timestamp + 1) + ) { + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> next; + //if we've already seen the window with the closest start time to the record + boolean foundRightWinAgg = false; + //if we've already seen the window with the closest end time to the record + boolean foundLeftWinAgg = false; + while (iterator.hasNext()) { + next = iterator.next(); + windowStartTimes.add(next.key.window().start()); + + //determine if current record's right window exists, will only be true at most once, on the first pass + if (next.key.window().start() == timestamp + 1) { + rightWinAlreadyCreated = true; + continue; + } else if (next.key.window().end() > timestamp) { + if (!foundRightWinAgg) { + foundRightWinAgg = true; + rightWinAgg = next.value; + } + putAndForward(next.key.window(), next.value, key, value, closeTime, timestamp); + continue; + } else if (next.key.window().end() == timestamp) { + putAndForward(next.key.window(), next.value, key, value, closeTime, timestamp); + leftWinAlreadyCreated = true; + continue; + } else { + if (!foundLeftWinAgg) { + leftWinAgg = next.value; + foundLeftWinAgg = true; + } + //If it's a left window, there is a record at this window's end time who may need a corresponding right window + if (isLeftWindow(next)) { + final long rightWinStart = next.key.window().end() + 1; + if (!windowStartTimes.contains(rightWinStart)) { + 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); + } + break; + } + } + } + } + //create the left window of the current record if it's not created + if (!leftWinAlreadyCreated) { + final ValueAndTimestamp<Agg> valueAndTime; + //confirms that the left window contains more than the current record + if (leftWinAgg.timestamp() < timestamp && leftWinAgg.timestamp() > timestamp - windows.timeDifferenceMs()) { + valueAndTime = ValueAndTimestamp.make(leftWinAgg.value(), timestamp); + } else { + //left window just contains the current record + valueAndTime = ValueAndTimestamp.make(initializer.apply(), timestamp); + } + final TimeWindow window = new TimeWindow(Math.max(0, timestamp - windows.timeDifferenceMs()), timestamp); + putAndForward(window, valueAndTime, key, value, closeTime, timestamp); + } + //create the right window for the current record, if need be + if (!rightWinAlreadyCreated && rightWinAgg != null && rightWinAgg.timestamp() > 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); + } + } + + public void processInOrder(final K key, final V value) { + + final long timestamp = context().timestamp(); + //don't process records that don't fall within a full sliding window + if (timestamp < windows.timeDifferenceMs()) { Review comment: No, the condition is correct. In this context "early" just means "within timeDifferenceMs of the zero timestamp". We need some special handling to cover this full range of all record timestamps due to the inability to store negative timestamps. This algorithm works correctly for all records outside of this regardless of "now" ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
