/*
* 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.spark.translation;
import com.google.common.base.Function;
import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import javax.annotation.Nullable;
import org.apache.beam.runners.spark.util.SideInputBroadcast;
import org.apache.beam.sdk.transforms.CombineWithContext;
import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
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.TupleTag;
import org.apache.beam.sdk.values.WindowingStrategy;
import org.joda.time.Instant;
/**
* A {@link org.apache.beam.sdk.transforms.CombineFnBase.GlobalCombineFn}
* with a {@link CombineWithContext.Context} for the SparkRunner.
*/
public class SparkGlobalCombineFn<InputT, AccumT, OutputT> extends SparkAbstractCombineFn {
private final CombineWithContext.CombineFnWithContext<InputT, AccumT, OutputT> combineFn;
public SparkGlobalCombineFn(
CombineWithContext.CombineFnWithContext<InputT, AccumT, OutputT> combineFn,
SparkRuntimeContext runtimeContext,
Map<TupleTag<?>, KV<WindowingStrategy<?, ?>, SideInputBroadcast<?>>> sideInputs,
WindowingStrategy<?, ?> windowingStrategy) {
super(runtimeContext, sideInputs, windowingStrategy);
this.combineFn = combineFn;
}
/**
* Implements Spark's zeroValue function in:
* <p>
* {@link org.apache.spark.api.java.JavaRDD#aggregate}.
* </p>
*/
Iterable<WindowedValue<AccumT>> zeroValue() {
return Lists.newArrayList();
}
private Iterable<WindowedValue<AccumT>> createAccumulator(WindowedValue<InputT> input) {
// sort exploded inputs.
Iterable<WindowedValue<InputT>> sortedInputs = sortByWindows(input.explodeWindows());
TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner();
WindowFn<?, BoundedWindow> windowFn = windowingStrategy.getWindowFn();
//--- inputs iterator, by window order.
final Iterator<WindowedValue<InputT>> iterator = sortedInputs.iterator();
WindowedValue<InputT> currentInput = iterator.next();
BoundedWindow currentWindow = Iterables.getFirst(currentInput.getWindows(), null);
// first create the accumulator and accumulate first input.
AccumT accumulator = combineFn.createAccumulator(ctxtForInput(currentInput));
accumulator = combineFn.addInput(accumulator, currentInput.getValue(),
ctxtForInput(currentInput));
// keep track of the timestamps assigned by the TimestampCombiner.
Instant windowTimestamp =
timestampCombiner.assign(
currentWindow,
windowingStrategy
.getWindowFn()
.getOutputTime(currentInput.getTimestamp(), currentWindow));
// accumulate the next windows, or output.
List<WindowedValue<AccumT>> output = Lists.newArrayList();
// if merging, merge overlapping windows, e.g. Sessions.
final boolean merging = !windowingStrategy.getWindowFn().isNonMerging();
while (iterator.hasNext()) {
WindowedValue<InputT> nextValue = iterator.next();
BoundedWindow nextWindow = Iterables.getOnlyElement(nextValue.getWindows());
boolean mergingAndIntersecting = merging
&& isIntersecting((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
if (mergingAndIntersecting || nextWindow.equals(currentWindow)) {
if (mergingAndIntersecting) {
// merge intersecting windows.
currentWindow = merge((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
}
// keep accumulating and carry on ;-)
accumulator = combineFn.addInput(accumulator, nextValue.getValue(),
ctxtForInput(nextValue));
windowTimestamp =
timestampCombiner.merge(
currentWindow,
windowTimestamp,
windowingStrategy
.getWindowFn()
.getOutputTime(nextValue.getTimestamp(), currentWindow));
} else {
// moving to the next window, first add the current accumulation to output
// and initialize the accumulator.
output.add(WindowedValue.of(accumulator, windowTimestamp, currentWindow,
PaneInfo.NO_FIRING));
// re-init accumulator, window and timestamp.
accumulator = combineFn.createAccumulator(ctxtForInput(nextValue));
accumulator = combineFn.addInput(accumulator, nextValue.getValue(),
ctxtForInput(nextValue));
currentWindow = nextWindow;
windowTimestamp = timestampCombiner.assign(currentWindow,
windowFn.getOutputTime(nextValue.getTimestamp(), currentWindow));
}
}
// add last accumulator to the output.
output.add(WindowedValue.of(accumulator, windowTimestamp, currentWindow, PaneInfo.NO_FIRING));
return output;
}
/**
* Implement Spark's seqOp function in:
* <p>
* {@link org.apache.spark.api.java.JavaRDD#aggregate}.
* </p>
*/
Iterable<WindowedValue<AccumT>> seqOp(Iterable<WindowedValue<AccumT>> accum,
WindowedValue<InputT> input) {
return combOp(accum, createAccumulator(input));
}
/**
* Implement Spark's combOp function in:
* <p>
* {@link org.apache.spark.api.java.JavaRDD#aggregate}.
* </p>
*/
Iterable<WindowedValue<AccumT>> combOp(Iterable<WindowedValue<AccumT>> a1,
Iterable<WindowedValue<AccumT>> a2) {
// concatenate accumulators.
Iterable<WindowedValue<AccumT>> accumulators = Iterables.concat(a1, a2);
// if empty, return an empty accumulators iterable.
if (!accumulators.iterator().hasNext()) {
return Lists.newArrayList();
}
// sort accumulators, no need to explode since inputs were exploded.
Iterable<WindowedValue<AccumT>> sortedAccumulators = sortByWindows(accumulators);
@SuppressWarnings("unchecked")
TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner();
//--- accumulators iterator, by window order.
final Iterator<WindowedValue<AccumT>> iterator = sortedAccumulators.iterator();
// get the first accumulator and assign it to the current window's accumulators.
WindowedValue<AccumT> currentValue = iterator.next();
BoundedWindow currentWindow = Iterables.getFirst(currentValue.getWindows(), null);
List<AccumT> currentWindowAccumulators = Lists.newArrayList();
currentWindowAccumulators.add(currentValue.getValue());
// keep track of the timestamps assigned by the TimestampCombiner,
// in createCombiner we already merge the timestamps assigned
// to individual elements, here we will just merge them.
List<Instant> windowTimestamps = Lists.newArrayList();
windowTimestamps.add(currentValue.getTimestamp());
// accumulate the next windows, or output.
List<WindowedValue<AccumT>> output = Lists.newArrayList();
// if merging, merge overlapping windows, e.g. Sessions.
final boolean merging = !windowingStrategy.getWindowFn().isNonMerging();
while (iterator.hasNext()) {
WindowedValue<AccumT> nextValue = iterator.next();
BoundedWindow nextWindow = Iterables.getOnlyElement(nextValue.getWindows());
boolean mergingAndIntersecting = merging
&& isIntersecting((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
if (mergingAndIntersecting || nextWindow.equals(currentWindow)) {
if (mergingAndIntersecting) {
// merge intersecting windows.
currentWindow = merge((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
}
// add to window accumulators.
currentWindowAccumulators.add(nextValue.getValue());
windowTimestamps.add(nextValue.getTimestamp());
} else {
// before moving to the next window,
// add the current accumulation to the output and initialize the accumulation.
// merge the timestamps of all accumulators to merge.
Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
// merge accumulators.
// transforming a KV<K, Iterable<AccumT>> into a KV<K, Iterable<AccumT>>.
// for the (possibly merged) window.
Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
WindowedValue<Iterable<AccumT>> preMergeWindowedValue = WindowedValue.of(
accumsToMerge, mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
// applying the actual combiner onto the accumulators.
AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge,
ctxtForInput(preMergeWindowedValue));
WindowedValue<AccumT> postMergeWindowedValue = preMergeWindowedValue.withValue(accumulated);
// emit the accumulated output.
output.add(postMergeWindowedValue);
// re-init accumulator, window and timestamps.
currentWindowAccumulators.clear();
currentWindowAccumulators.add(nextValue.getValue());
currentWindow = nextWindow;
windowTimestamps.clear();
windowTimestamps.add(nextValue.getTimestamp());
}
}
// merge the last chunk of accumulators.
Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
WindowedValue<Iterable<AccumT>> preMergeWindowedValue = WindowedValue.of(
accumsToMerge, mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge,
ctxtForInput(preMergeWindowedValue));
WindowedValue<AccumT> postMergeWindowedValue = preMergeWindowedValue.withValue(accumulated);
output.add(postMergeWindowedValue);
return output;
}
Iterable<WindowedValue<OutputT>> extractOutput(Iterable<WindowedValue<AccumT>> wvas) {
return Iterables.transform(wvas,
new Function<WindowedValue<AccumT>, WindowedValue<OutputT>>() {
@Nullable
@Override
public WindowedValue<OutputT> apply(@Nullable WindowedValue<AccumT> wva) {
if (wva == null) {
return null;
}
return wva.withValue(combineFn.extractOutput(wva.getValue(), ctxtForInput(wva)));
}
});
}
}