Java tutorial
/* * 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.beam.runners.flink.translation.functions; import com.google.common.collect.Iterables; import java.util.ArrayList; import java.util.Collection; import java.util.Collections; import java.util.HashMap; import java.util.HashSet; import java.util.Iterator; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.beam.runners.core.SideInputReader; import org.apache.beam.sdk.options.PipelineOptions; import org.apache.beam.sdk.transforms.windowing.BoundedWindow; import org.apache.beam.sdk.transforms.windowing.PaneInfo; import org.apache.beam.sdk.transforms.windowing.TimestampCombiner; import org.apache.beam.sdk.transforms.windowing.WindowFn; import org.apache.beam.sdk.util.WindowedValue; import org.apache.beam.sdk.values.KV; import org.apache.beam.sdk.values.WindowingStrategy; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.util.Collector; import org.joda.time.Instant; /** * A Flink combine runner that builds a map of merged windows and produces output after * seeing all input. This is similar to what{@link org.apache.beam.runners.core.ReduceFnRunner} * does. */ public class HashingFlinkCombineRunner<K, InputT, AccumT, OutputT, W extends BoundedWindow> extends AbstractFlinkCombineRunner<K, InputT, AccumT, OutputT, W> { @Override public void combine(FlinkCombiner<K, InputT, AccumT, OutputT> flinkCombiner, WindowingStrategy<Object, W> windowingStrategy, SideInputReader sideInputReader, PipelineOptions options, Iterable<WindowedValue<KV<K, InputT>>> elements, Collector<WindowedValue<KV<K, OutputT>>> out) throws Exception { @SuppressWarnings("unchecked") TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner(); WindowFn<Object, W> windowFn = windowingStrategy.getWindowFn(); // Flink Iterable can be iterated over only once. List<WindowedValue<KV<K, InputT>>> inputs = new ArrayList<>(); Iterables.addAll(inputs, elements); Set<W> windows = collectWindows(inputs); Map<W, W> windowToMergeResult = mergeWindows(windowingStrategy, windows); // Combine all windowedValues into map Map<W, Tuple2<AccumT, Instant>> mapState = new HashMap<>(); Iterator<WindowedValue<KV<K, InputT>>> iterator = inputs.iterator(); WindowedValue<KV<K, InputT>> currentValue = iterator.next(); K key = currentValue.getValue().getKey(); do { for (BoundedWindow w : currentValue.getWindows()) { @SuppressWarnings("unchecked") W currentWindow = (W) w; W mergedWindow = windowToMergeResult.get(currentWindow); mergedWindow = mergedWindow == null ? currentWindow : mergedWindow; Set<W> singletonW = Collections.singleton(mergedWindow); Tuple2<AccumT, Instant> accumAndInstant = mapState.get(mergedWindow); if (accumAndInstant == null) { AccumT accumT = flinkCombiner.firstInput(key, currentValue.getValue().getValue(), options, sideInputReader, singletonW); Instant windowTimestamp = timestampCombiner.assign(mergedWindow, windowFn.getOutputTime(currentValue.getTimestamp(), mergedWindow)); accumAndInstant = new Tuple2<>(accumT, windowTimestamp); mapState.put(mergedWindow, accumAndInstant); } else { accumAndInstant.f0 = flinkCombiner.addInput(key, accumAndInstant.f0, currentValue.getValue().getValue(), options, sideInputReader, singletonW); accumAndInstant.f1 = timestampCombiner.combine(accumAndInstant.f1, timestampCombiner.assign(mergedWindow, windowingStrategy.getWindowFn() .getOutputTime(currentValue.getTimestamp(), mergedWindow))); } } if (iterator.hasNext()) { currentValue = iterator.next(); } else { break; } } while (true); // Output the final value of combiners for (Map.Entry<W, Tuple2<AccumT, Instant>> entry : mapState.entrySet()) { AccumT accumulator = entry.getValue().f0; Instant windowTimestamp = entry.getValue().f1; out.collect(WindowedValue.of( KV.of(key, flinkCombiner.extractOutput(key, accumulator, options, sideInputReader, Collections.singleton(entry.getKey()))), windowTimestamp, entry.getKey(), PaneInfo.NO_FIRING)); } } private Map<W, W> mergeWindows(WindowingStrategy<Object, W> windowingStrategy, Set<W> windows) throws Exception { WindowFn<Object, W> windowFn = windowingStrategy.getWindowFn(); if (windowingStrategy.getWindowFn().isNonMerging()) { // Return an empty map, indicating that every window is not merged. return Collections.emptyMap(); } Map<W, W> windowToMergeResult = new HashMap<>(); windowFn.mergeWindows(new MergeContextImpl(windowFn, windows, windowToMergeResult)); return windowToMergeResult; } private class MergeContextImpl extends WindowFn<Object, W>.MergeContext { private Set<W> windows; private Map<W, W> windowToMergeResult; MergeContextImpl(WindowFn<Object, W> windowFn, Set<W> windows, Map<W, W> windowToMergeResult) { windowFn.super(); this.windows = windows; this.windowToMergeResult = windowToMergeResult; } @Override public Collection<W> windows() { return windows; } @Override public void merge(Collection<W> toBeMerged, W mergeResult) throws Exception { for (W w : toBeMerged) { windowToMergeResult.put(w, mergeResult); } } } private Set<W> collectWindows(Iterable<WindowedValue<KV<K, InputT>>> values) { Set<W> windows = new HashSet<>(); for (WindowedValue<?> value : values) { for (BoundedWindow untypedWindow : value.getWindows()) { @SuppressWarnings("unchecked") W window = (W) untypedWindow; windows.add(window); } } return windows; } }