leonardBang commented on a change in pull request #14708: URL: https://github.com/apache/flink/pull/14708#discussion_r563445932
########## File path: flink-table/flink-table-runtime-blink/src/main/java/org/apache/flink/table/runtime/operators/window/slicing/SlicingWindowOperator.java ########## @@ -0,0 +1,326 @@ +/* + * 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.flink.table.runtime.operators.window.slicing; + +import org.apache.flink.annotation.Internal; +import org.apache.flink.annotation.VisibleForTesting; +import org.apache.flink.api.common.functions.RuntimeContext; +import org.apache.flink.metrics.Counter; +import org.apache.flink.metrics.Gauge; +import org.apache.flink.metrics.Meter; +import org.apache.flink.metrics.MeterView; +import org.apache.flink.runtime.memory.MemoryManager; +import org.apache.flink.runtime.state.KeyedStateBackend; +import org.apache.flink.streaming.api.operators.ChainingStrategy; +import org.apache.flink.streaming.api.operators.InternalTimer; +import org.apache.flink.streaming.api.operators.InternalTimerService; +import org.apache.flink.streaming.api.operators.KeyContext; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.api.operators.Output; +import org.apache.flink.streaming.api.operators.TimestampedCollector; +import org.apache.flink.streaming.api.operators.Triggerable; +import org.apache.flink.streaming.api.watermark.Watermark; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +import org.apache.flink.table.data.RowData; +import org.apache.flink.table.data.binary.BinaryRowData; +import org.apache.flink.table.runtime.operators.TableStreamOperator; +import org.apache.flink.table.runtime.operators.aggregate.window.processors.SliceSharedWindowAggProcessor; + +import static org.apache.flink.util.Preconditions.checkNotNull; + +/** + * The {@link SlicingWindowOperator} implements an optimized processing for aligned windows which + * can apply the slicing optimization. The core idea of slicing optimization is to divide all + * elements from a data stream into a finite number of non-overlapping chunks (a.k.a. slices). + * + * <h3>Concept of Aligned Window and Unaligned Window</h3> + * + * <p>We divide windows into 2 categories: Aligned Windows and Unaligned Windows. + * + * <p>Aligned Windows are windows have predetermined window boundaries and windows can be divided + * into finite number of non-overlapping chunks. The boundary of an aligned window is determined + * independently from the time characteristic of the data stream, or messages it receives. For + * example, hopping (sliding) window is an aligned window as the window boundaries are predetermined + * based on the window size and slide. Aligned windows include tumbling, hopping, cumulative + * windows. + * + * <p>Unaligned Windows are windows determined dynamically based on elements. For example, session + * window is an unaligned window as the window boundaries are determined based on the messages + * timestamps and their correlations. Currently, unaligned windows include session window only. + * + * <p>Because aligned windows can be divided into finite number of non-overlapping chunks (a.k.a. + * slices), which can apply efficient processing to share intermediate results. + * + * <h3>Concept of Slice</h3> + * + * <p>Dividing a window of aligned windows into a finite number of non-overlapping chunks, where the + * chunks are slices. It has the following properties: + * + * <ul> + * <li>An element must only belong to a single slice. + * <li>Slices are non-overlapping, i.e. S_i and S_j should not have any shared elements if i != j. + * <li>A window is consist of a finite number of slices. + * </ul> + * + * <h3>Abstraction of Slicing Window Operator</h3> + * + * <p>A slicing window operator is a simple wrap of {@link SlicingWindowProcessor}. It delegates all + * the important methods to the underlying processor, where the processor can have different + * implementation for aggregate and topk or others. + * + * <p>A {@link SlicingWindowProcessor} usually leverages the {@link SliceAssigner} to assign slices + * and calculate based on the slices. See {@link SliceSharedWindowAggProcessor} as an example. + * + * <p>Note: since {@link SlicingWindowProcessor} leverages slicing optimization for aligned windows, + * therefore, it doesn't support unaligned windows, e.g. session window. + * + * <p>Note: currently, {@link SlicingWindowOperator} doesn't support early-fire and late-arrival. + * Thus late elements (elements belong to emitted windows) will be simply dropped. + */ +@Internal +public final class SlicingWindowOperator<K, W> extends TableStreamOperator<RowData> + implements OneInputStreamOperator<RowData, RowData>, Triggerable<K, W>, KeyContext { + + private static final long serialVersionUID = 1L; + + private static final String LATE_ELEMENTS_DROPPED_METRIC_NAME = "numLateRecordsDropped"; + private static final String LATE_ELEMENTS_DROPPED_RATE_METRIC_NAME = "lateRecordsDroppedRate"; + private static final String WATERMARK_LATENCY_METRIC_NAME = "watermarkLatency"; + + /** The concrete window operator implementation. */ + private final SlicingWindowProcessor<W> windowProcessor; + + // ------------------------------------------------------------------------ + + /** This is used for emitting elements with a given timestamp. */ + protected transient TimestampedCollector<RowData> collector; + + /** Flag to prevent duplicate function.close() calls in close() and dispose(). */ + private transient boolean functionsClosed = false; + + /** The service to register timers. */ + private transient InternalTimerService<W> internalTimerService; + + /** The tracked processing time triggered last time. */ + private transient long lastTriggeredProcessingTime; + + // ------------------------------------------------------------------------ + // Metrics + // ------------------------------------------------------------------------ + + private transient Counter numLateRecordsDropped; + private transient Meter lateRecordsDroppedRate; + private transient Gauge<Long> watermarkLatency; + + public SlicingWindowOperator(SlicingWindowProcessor<W> windowProcessor) { + this.windowProcessor = windowProcessor; + setChainingStrategy(ChainingStrategy.ALWAYS); + } + + @Override + public void open() throws Exception { + super.open(); + functionsClosed = false; + + lastTriggeredProcessingTime = Long.MIN_VALUE; + collector = new TimestampedCollector<>(output); + collector.eraseTimestamp(); + + internalTimerService = + getInternalTimerService( + "window-timers", windowProcessor.createWindowSerializer(), this); + + windowProcessor.open( + new WindowProcessorContext<>( + getContainingTask(), + getContainingTask().getEnvironment().getMemoryManager(), + computeMemorySize(), + internalTimerService, + getKeyedStateBackend(), + collector, + getRuntimeContext())); + + // metrics + this.numLateRecordsDropped = metrics.counter(LATE_ELEMENTS_DROPPED_METRIC_NAME); + this.lateRecordsDroppedRate = + metrics.meter( + LATE_ELEMENTS_DROPPED_RATE_METRIC_NAME, + new MeterView(numLateRecordsDropped)); + this.watermarkLatency = + metrics.gauge( + WATERMARK_LATENCY_METRIC_NAME, + () -> { + long watermark = internalTimerService.currentWatermark(); + if (watermark < 0) { + return 0L; + } else { + return internalTimerService.currentProcessingTime() - watermark; + } + }); + } + + @Override + public void close() throws Exception { + super.close(); + collector = null; + functionsClosed = true; + windowProcessor.close(); + } + + @Override + public void dispose() throws Exception { + super.dispose(); + collector = null; + if (!functionsClosed) { + functionsClosed = true; + windowProcessor.close(); + } + } + + @Override + public void processElement(StreamRecord<RowData> element) throws Exception { + RowData inputRow = element.getValue(); + BinaryRowData currentKey = (BinaryRowData) getCurrentKey(); + boolean isElementDropped = windowProcessor.processElement(currentKey, inputRow); + if (isElementDropped) { + // markEvent will increase numLateRecordsDropped + lateRecordsDroppedRate.markEvent(); + } + } + + @Override + public void processWatermark(Watermark mark) throws Exception { + windowProcessor.advanceProgress(mark.getTimestamp()); + super.processWatermark(mark); + } + + @Override + public void onEventTime(InternalTimer<K, W> timer) throws Exception { + onTimer(timer); + } + + @Override + public void onProcessingTime(InternalTimer<K, W> timer) throws Exception { + long timestamp = timer.getTimestamp(); + if (timestamp > lastTriggeredProcessingTime) { + // similar to the watermark advance, + // we need to notify WindowProcessor first to flush buffer into state + lastTriggeredProcessingTime = timestamp; + windowProcessor.advanceProgress(timestamp); + // register timer again, because we need to skip current timer to avoid duplicate output + setCurrentKey(timer.getKey()); + internalTimerService.registerProcessingTimeTimer(timer.getNamespace(), timestamp); Review comment: I delete the processing timer in `CombineRecordsFunction` at the same time ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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