ableegoldman commented on a change in pull request #9039: URL: https://github.com/apache/kafka/pull/9039#discussion_r466050097
########## File path: streams/src/main/java/org/apache/kafka/streams/kstream/internals/KStreamSlidingWindowAggregate.java ########## @@ -0,0 +1,428 @@ +/* + * 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.time.Instant; +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 (reverseIteratorImplemented) { + processReverse(key, value); + } else { + processInOrder(key, value); + } + } + + public void processReverse(final K key, final V value) { + if (key == null) { + log.warn( + "Skipping record due to null key. value=[{}] topic=[{}] partition=[{}] offset=[{}]", + value, context().topic(), context().partition(), context().offset() + ); + droppedRecordsSensor.record(); + return; + } + + 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; + + // flag to help determine if out-of-order record’s right window is non-empty + boolean foundLeftFirst = false; + //if current record's left/right windows already exist + boolean leftWinExists = false; + boolean rightWindowExists = false; + //to determine if we're creating the previous record's right window, helps with determining empty windows + boolean prevRightWinExists = false; + boolean prevRightWinAlreadyCreated = false; + + try ( + //Fetch all the windows that have a start time between timestamp and timestamp+timeDifference + final KeyValueIterator<Windowed<K>, ValueAndTimestamp<Agg>> iterator = windowStore.fetch(key, + key, + Instant.ofEpochMilli(timestamp - 2 * windows.timeDifference()), + Instant.ofEpochMilli(timestamp + 1)) + ) { + KeyValue<Windowed<K>, ValueAndTimestamp<Agg>> next; + //if we've already seen the window with the closest start time to the record + boolean foundFirst = false; + //if we've already seen the window with the closest end time to the record + boolean foundFirstEndTime = false; + while (iterator.hasNext()) { + next = iterator.next(); + windowStartTimes.add(next.key.window().start()); + + //determine if current record's right window exists + if (next.key.window().start() == timestamp + 1) { + rightWindowExists = true; + continue; Review comment: Well, if the first window isn't the right window then we just wouldn't call `iterator.next` again, right? So we wouldn't have to do anything at all ---------------------------------------------------------------- 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
