[
https://issues.apache.org/jira/browse/BEAM-7574?focusedWorklogId=283366&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-283366
]
ASF GitHub Bot logged work on BEAM-7574:
----------------------------------------
Author: ASF GitHub Bot
Created on: 26/Jul/19 13:45
Start Date: 26/Jul/19 13:45
Worklog Time Spent: 10m
Work Description: je-ik commented on pull request #8946: [BEAM-7574] fix
Combine performance for SparkRunner
URL: https://github.com/apache/beam/pull/8946#discussion_r307739518
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkCombineFn.java
##########
@@ -0,0 +1,760 @@
+/*
+ * 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 java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.TreeMap;
+import java.util.function.BiConsumer;
+import java.util.function.BiFunction;
+import java.util.stream.Collectors;
+import java.util.stream.StreamSupport;
+import javax.annotation.Nullable;
+import org.apache.beam.runners.core.construction.SerializablePipelineOptions;
+import org.apache.beam.runners.spark.util.SideInputBroadcast;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.coders.CoderException;
+import org.apache.beam.sdk.coders.IterableCoder;
+import org.apache.beam.sdk.transforms.CombineWithContext;
+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.UserCodeException;
+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.TypeDescriptor;
+import org.apache.beam.sdk.values.WindowingStrategy;
+import
org.apache.beam.vendor.guava.v26_0_jre.com.google.common.annotations.VisibleForTesting;
+import
org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterables;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Lists;
+import org.apache.spark.api.java.function.Function;
+import org.joda.time.Instant;
+
+/**
+ * A {@link org.apache.beam.sdk.transforms.CombineFnBase.GlobalCombineFn} with
a {@link
+ * org.apache.beam.sdk.transforms.CombineWithContext.Context} for the
SparkRunner.
+ */
+public class SparkCombineFn<InputT, ValueT, AccumT, OutputT> extends
SparkAbstractCombineFn {
+
+ /** Accumulator of WindowedValues holding values for different windows. */
+ public interface WindowedAccumulator<
+ InputT, ValueT, AccumT, ImplT extends WindowedAccumulator<InputT,
ValueT, AccumT, ImplT>> {
+
+ /**
+ * Type of the accumulator. The type depends (mostly) on {@link
WindowingStrategy}, and more
+ * specialized versions enable more optimizations.
+ */
+ enum Type {
+ MERGING,
+ NON_MERGING,
+ EXPLODE_WINDOWS,
+ SINGLE_WINDOW;
+
+ boolean isMapBased() {
+ return this == NON_MERGING || this == MERGING;
+ }
+ }
+
+ /**
+ * Check if this accumulator is empty.
+ *
+ * @return {@code true} if this accumulator is empty
+ */
+ boolean isEmpty();
+
+ /** Add value with unexploded windows into the accumulator. */
+ void add(WindowedValue<InputT> value, SparkCombineFn<InputT, ValueT,
AccumT, ?> context)
+ throws Exception;
+
+ /**
+ * Merge other acccumulator into this one.
+ *
+ * @param other the other accumulator to merge
+ */
+ void merge(ImplT other, SparkCombineFn<?, ?, AccumT, ?> context) throws
Exception;
+
+ /** Extract output. */
+ Collection<WindowedValue<AccumT>> extractOutput();
+
+ /** Create concrete accumulator for given type. */
+ static <InputT, ValueT, AccumT> WindowedAccumulator<InputT, ValueT,
AccumT, ?> create(
+ SparkCombineFn<InputT, ValueT, AccumT, ?> context,
+ Function<InputT, ValueT> toValue,
+ WindowingStrategy<?, ?> windowingStrategy,
+ Comparator<BoundedWindow> windowComparator) {
+ return create(toValue, context.getType(windowingStrategy),
windowComparator);
+ }
+
+ static <InputT, ValueT, AccumT> WindowedAccumulator<InputT, ValueT,
AccumT, ?> create(
+ Function<InputT, ValueT> toValue, Type type, Comparator<BoundedWindow>
windowComparator) {
+ switch (type) {
+ case MERGING:
+ return MergingWindowedAccumulator.create(toValue, windowComparator);
+ case NON_MERGING:
+ return NonMergingWindowedAccumulator.create(toValue);
+ case SINGLE_WINDOW:
+ case EXPLODE_WINDOWS:
+ return SingleWindowWindowedAccumulator.create(toValue);
+ default:
+ throw new IllegalArgumentException("Unknown type: " + type);
+ }
+ }
+
+ /** Create concrete accumulator for given type. */
+ static <InputT, ValueT, AccumT> WindowedAccumulator<InputT, ValueT,
AccumT, ?> create(
+ Function<InputT, ValueT> toValue,
+ Type type,
+ Iterable<WindowedValue<AccumT>> values,
+ Comparator<BoundedWindow> windowComparator) {
+ switch (type) {
+ case MERGING:
+ return MergingWindowedAccumulator.from(toValue, values,
windowComparator);
+ case NON_MERGING:
+ return NonMergingWindowedAccumulator.from(toValue, values);
+ case SINGLE_WINDOW:
+ case EXPLODE_WINDOWS:
+ Iterator<WindowedValue<AccumT>> iter = values.iterator();
+ if (iter.hasNext()) {
+ return SingleWindowWindowedAccumulator.create(toValue,
iter.next());
+ }
+ return SingleWindowWindowedAccumulator.create(toValue);
+ default:
+ throw new IllegalArgumentException("Unknown type: " + type);
+ }
+ }
+ }
+
+ /**
+ * Accumulator for {@link WindowFn WindowFns} which create only single
window per key, and
+ * therefore don't need any map to hold different windows per key.
+ *
+ * @param <AccumT> type of accumulator
+ */
+ static class SingleWindowWindowedAccumulator<InputT, ValueT, AccumT>
+ implements WindowedAccumulator<
+ InputT, ValueT, AccumT, SingleWindowWindowedAccumulator<InputT,
ValueT, AccumT>> {
+
+ static <InputT, ValueT, AccumT> SingleWindowWindowedAccumulator<InputT,
ValueT, AccumT> create(
+ Function<InputT, ValueT> toValue) {
+ return new SingleWindowWindowedAccumulator<>(toValue);
+ }
+
+ static <InputT, ValueT, AccumT> WindowedAccumulator<InputT, ValueT,
AccumT, ?> create(
+ Function<InputT, ValueT> toValue, WindowedValue<AccumT> accumulator) {
+ return new SingleWindowWindowedAccumulator<>(toValue, accumulator);
+ }
+
+ final Function<InputT, ValueT> toValue;
+ AccumT windowAccumulator = null;
+ Instant accTimestamp = null;
+ BoundedWindow accWindow = null;
+
+ SingleWindowWindowedAccumulator(Function<InputT, ValueT> toValue) {
+ this.toValue = toValue;
+ }
+
+ SingleWindowWindowedAccumulator(
+ Function<InputT, ValueT> toValue, WindowedValue<AccumT> accumulator) {
+ this.toValue = toValue;
+ this.windowAccumulator = accumulator.getValue();
+ this.accTimestamp =
+ accumulator.getTimestamp().equals(BoundedWindow.TIMESTAMP_MIN_VALUE)
+ ? null
+ : accumulator.getTimestamp();
+ this.accWindow = getWindow(accumulator);
+ }
+
+ @Override
+ public void add(WindowedValue<InputT> value, SparkCombineFn<InputT,
ValueT, AccumT, ?> context)
+ throws Exception {
+ BoundedWindow window = getWindow(value);
+ SparkCombineContext ctx = context.ctxtForValue(value);
+ TimestampCombiner combiner =
context.windowingStrategy.getTimestampCombiner();
+ Instant windowTimestamp =
+ combiner.assign(
+ window,
+
context.windowingStrategy.getWindowFn().getOutputTime(value.getTimestamp(),
window));
+ final AccumT acc;
+ final Instant timestamp;
+ if (windowAccumulator == null) {
+ acc = context.combineFn.createAccumulator(ctx);
+ timestamp = windowTimestamp;
+ } else {
+ acc = windowAccumulator;
+ timestamp = accTimestamp;
+ }
+ AccumT result = context.combineFn.addInput(acc, toValue(value), ctx);
+ Instant timestampCombined = combiner.combine(windowTimestamp, timestamp);
+ windowAccumulator = result;
+ accTimestamp = timestampCombined;
+ accWindow = window;
+ }
+
+ @Override
+ public void merge(
+ SingleWindowWindowedAccumulator<InputT, ValueT, AccumT> other,
+ SparkCombineFn<?, ?, AccumT, ?> context) {
+ if (windowAccumulator != null && other.windowAccumulator != null) {
+ List<AccumT> accumulators = Arrays.asList(windowAccumulator,
other.windowAccumulator);
+ AccumT merged =
+ context.combineFn.mergeAccumulators(
+ accumulators,
context.ctxtForWindows(Arrays.asList(accWindow)));
+ Instant combined =
+ context
+ .windowingStrategy
+ .getTimestampCombiner()
+ .combine(accTimestamp, other.accTimestamp);
+ windowAccumulator = merged;
+ accTimestamp = combined;
+ } else if (windowAccumulator == null) {
+ windowAccumulator = other.windowAccumulator;
+ accTimestamp = other.accTimestamp;
+ accWindow = other.accWindow;
+ }
+ }
+
+ @Override
+ public Collection<WindowedValue<AccumT>> extractOutput() {
+ if (windowAccumulator != null) {
+ return Arrays.asList(
+ WindowedValue.of(
+ windowAccumulator, accTimestamp, accWindow,
PaneInfo.ON_TIME_AND_ONLY_FIRING));
+ }
+ return Collections.emptyList();
+ }
+
+ ValueT toValue(WindowedValue<InputT> input) {
+ try {
+ return toValue.call(input.getValue());
+ } catch (Exception ex) {
+ throw UserCodeException.wrap(ex);
+ }
+ }
+
+ @Override
+ public boolean isEmpty() {
+ return windowAccumulator == null;
+ }
+ }
+
+ /**
+ * Abstract base class for {@link WindowedAccumulator WindowedAccumulators}
which hold
+ * accumulators in map.
+ *
+ * @param <AccumT> type of accumulator
+ * @param <ImplT> type of final subclass
+ */
+ abstract static class MapBasedWindowedAccumulator<
+ InputT,
+ ValueT,
+ AccumT,
+ ImplT extends MapBasedWindowedAccumulator<InputT, ValueT, AccumT,
ImplT>>
+ implements WindowedAccumulator<InputT, ValueT, AccumT, ImplT> {
+
+ final Function<InputT, ValueT> toValue;
+ final Map<BoundedWindow, WindowedValue<AccumT>> map;
+
+ MapBasedWindowedAccumulator(
+ Function<InputT, ValueT> toValue, Map<BoundedWindow,
WindowedValue<AccumT>> map) {
+ this.toValue = toValue;
+ this.map = map;
+ }
+
+ @Override
+ public void add(WindowedValue<InputT> value, SparkCombineFn<InputT,
ValueT, AccumT, ?> context)
+ throws Exception {
+ for (WindowedValue<InputT> v : value.explodeWindows()) {
+ SparkCombineContext ctx = context.ctxtForValue(v);
+ BoundedWindow window = getWindow(v);
+ TimestampCombiner combiner =
context.windowingStrategy.getTimestampCombiner();
+ Instant windowTimestamp =
+ combiner.assign(
+ window,
+
context.windowingStrategy.getWindowFn().getOutputTime(v.getTimestamp(),
window));
+ map.compute(
+ window,
+ (w, windowAccumulator) -> {
+ final AccumT acc;
+ final Instant timestamp;
+ if (windowAccumulator == null) {
+ acc = context.combineFn.createAccumulator(ctx);
+ timestamp = windowTimestamp;
+ } else {
+ acc = windowAccumulator.getValue();
+ timestamp = windowAccumulator.getTimestamp();
+ }
+ AccumT result = context.combineFn.addInput(acc, toValue(v), ctx);
+ Instant timestampCombined = combiner.combine(windowTimestamp,
timestamp);
+ return WindowedValue.of(result, timestampCombined, window,
PaneInfo.NO_FIRING);
+ });
+ }
+ mergeWindows(context);
+ }
+
+ @Override
+ public void merge(ImplT other, SparkCombineFn<?, ?, AccumT, ?> context)
throws Exception {
+ other.map.forEach(
+ (window, acc) -> {
+ WindowedValue<AccumT> thisAcc = this.map.get(window);
+ if (thisAcc == null) {
+ // just copy
+ this.map.put(window, acc);
+ } else {
+ // merge
+ this.map.put(
+ window,
+ WindowedValue.of(
+ context.combineFn.mergeAccumulators(
+ Lists.newArrayList(thisAcc.getValue(),
acc.getValue()),
+ context.ctxtForValue(acc)),
+ context
+ .windowingStrategy
+ .getTimestampCombiner()
+ .combine(acc.getTimestamp(), thisAcc.getTimestamp()),
+ window,
+ PaneInfo.NO_FIRING));
+ }
+ });
+ mergeWindows(context);
+ }
+
+ @Override
+ public Collection<WindowedValue<AccumT>> extractOutput() {
+ return map.values();
+ }
+
+ @Override
+ public boolean isEmpty() {
+ return map.isEmpty();
+ }
+
+ void mergeWindows(SparkCombineFn<?, ?, AccumT, ?> fn) throws Exception {}
+
+ private ValueT toValue(WindowedValue<InputT> value) {
+ try {
+ return toValue.call(value.getValue());
+ } catch (Exception ex) {
+ throw UserCodeException.wrap(ex);
+ }
+ }
+ }
+
+ /** Accumulator for non-merging windows. */
+ static class NonMergingWindowedAccumulator<InputT, ValueT, AccumT>
+ extends MapBasedWindowedAccumulator<
+ InputT, ValueT, AccumT, NonMergingWindowedAccumulator<InputT,
ValueT, AccumT>> {
+
+ static <InputT, ValueT, AccumT> NonMergingWindowedAccumulator<InputT,
ValueT, AccumT> create(
+ Function<InputT, ValueT> toValue) {
+ return new NonMergingWindowedAccumulator<>(toValue);
+ }
+
+ static <InputT, ValueT, AccumT> NonMergingWindowedAccumulator<InputT,
ValueT, AccumT> from(
+ Function<InputT, ValueT> toValue, Iterable<WindowedValue<AccumT>>
values) {
+ return new NonMergingWindowedAccumulator<>(toValue, values);
+ }
+
+ @SuppressWarnings("unchecked")
+ private NonMergingWindowedAccumulator(Function<InputT, ValueT> toValue) {
+ super(toValue, new HashMap<>());
+ }
+
+ private NonMergingWindowedAccumulator(
+ Function<InputT, ValueT> toValue, Iterable<WindowedValue<AccumT>>
values) {
+ super(toValue, asMap(values, new HashMap<>()));
+ }
+ }
+
+ /** Accumulator for merging windows. */
+ static class MergingWindowedAccumulator<InputT, ValueT, AccumT>
+ extends MapBasedWindowedAccumulator<
+ InputT, ValueT, AccumT, MergingWindowedAccumulator<InputT, ValueT,
AccumT>> {
+
+ static <InputT, ValueT, AccumT> MergingWindowedAccumulator<InputT, ValueT,
AccumT> create(
+ Function<InputT, ValueT> toValue, Comparator<BoundedWindow>
windowComparator) {
+ return new MergingWindowedAccumulator<>(toValue, windowComparator);
+ }
+
+ static <InputT, ValueT, AccumT> MergingWindowedAccumulator<InputT, ValueT,
AccumT> from(
+ Function<InputT, ValueT> toValue,
+ Iterable<WindowedValue<AccumT>> values,
+ Comparator<BoundedWindow> windowComparator) {
+ return new MergingWindowedAccumulator<>(toValue, values,
windowComparator);
+ }
+
+ @SuppressWarnings("unchecked")
+ private MergingWindowedAccumulator(
+ Function<InputT, ValueT> toValue, Comparator<BoundedWindow>
windowComparator) {
+ super(toValue, new TreeMap<>(windowComparator));
+ }
+
+ private MergingWindowedAccumulator(
+ Function<InputT, ValueT> toValue,
+ Iterable<WindowedValue<AccumT>> values,
+ Comparator<BoundedWindow> windowComparator) {
+ super(toValue, asMap(values, new TreeMap<>(windowComparator)));
+ }
+
+ @Override
+ void mergeWindows(SparkCombineFn<?, ?, AccumT, ?> fn) throws Exception {
+
+ SparkCombineContext ctx = fn.ctxtForWindows(this.map.keySet());
+
+ @SuppressWarnings("unchecked")
+ WindowFn<Object, BoundedWindow> windowFn = (WindowFn)
fn.windowingStrategy.getWindowFn();
+ windowFn.mergeWindows(
+ asMergeContext(
+ windowFn,
+ (a, b) -> fn.combineFn.mergeAccumulators(Lists.newArrayList(a,
b), ctx),
+ (toBeMerged, mergeResult) -> {
+ Instant mergedInstant =
+ fn.windowingStrategy
+ .getTimestampCombiner()
+ .merge(
+ mergeResult.getKey(),
+ toBeMerged.stream()
+ .map(w -> map.get(w).getTimestamp())
+ .collect(Collectors.toList()));
+ toBeMerged.forEach(this.map::remove);
+ this.map.put(
+ mergeResult.getKey(),
+ WindowedValue.of(
+ mergeResult.getValue(),
+ mergedInstant,
+ mergeResult.getKey(),
+ PaneInfo.NO_FIRING));
+ },
+ map));
+ }
+
+ private WindowFn<Object, BoundedWindow>.MergeContext asMergeContext(
+ WindowFn<Object, BoundedWindow> windowFn,
+ BiFunction<AccumT, AccumT, AccumT> mergeFn,
+ BiConsumer<Collection<BoundedWindow>, KV<BoundedWindow, AccumT>>
afterMerge,
+ Map<BoundedWindow, WindowedValue<AccumT>> map) {
+
+ return windowFn.new MergeContext() {
+
+ @Override
+ public Collection<BoundedWindow> windows() {
+ return map.keySet();
+ }
+
+ @Override
+ public void merge(Collection<BoundedWindow> toBeMerged, BoundedWindow
mergeResult) {
+ AccumT accumulator = null;
+ for (BoundedWindow w : toBeMerged) {
+ WindowedValue<AccumT> windowAccumulator =
Objects.requireNonNull(map.get(w));
+ if (accumulator == null) {
+ accumulator = windowAccumulator.getValue();
+ } else {
+ accumulator = mergeFn.apply(accumulator,
windowAccumulator.getValue());
+ }
+ }
+ afterMerge.accept(toBeMerged, KV.of(mergeResult, accumulator));
+ }
+ };
+ }
+
+ @Override
+ public String toString() {
+ return "MergingWindowedAccumulator(" + this.map + ")";
+ }
+ }
+
+ static class WindowedAccumulatorCoder<InputT, ValueT, AccumT>
+ extends Coder<WindowedAccumulator<InputT, ValueT, AccumT, ?>> {
+
+ private final Function<InputT, ValueT> toValue;
+ private final IterableCoder<WindowedValue<AccumT>> wrap;
+ private final Coder<WindowedValue<AccumT>> accumCoder;
+ private final Comparator<BoundedWindow> windowComparator;
+ private final WindowedAccumulator.Type type;
+
+ @SuppressWarnings("unchecked")
+ WindowedAccumulatorCoder(
+ Function<InputT, ValueT> toValue,
+ Coder<BoundedWindow> windowCoder,
+ Comparator<BoundedWindow> windowComparator,
+ Coder<AccumT> accumCoder,
+ WindowedAccumulator.Type type) {
+
+ this.toValue = toValue;
+ this.accumCoder = WindowedValue.FullWindowedValueCoder.of(accumCoder,
windowCoder);
+ this.windowComparator = windowComparator;
+ this.wrap = IterableCoder.of(this.accumCoder);
+ this.type = type;
+ }
+
+ @Override
+ public void encode(WindowedAccumulator<InputT, ValueT, AccumT, ?> value,
OutputStream outStream)
+ throws CoderException, IOException {
+ if (type.isMapBased()) {
+ wrap.encode(((MapBasedWindowedAccumulator<?, ?, AccumT, ?>)
value).map.values(), outStream);
+ } else {
+ SingleWindowWindowedAccumulator<?, ?, AccumT> swwa =
+ (SingleWindowWindowedAccumulator<?, ?, AccumT>) value;
+ if (swwa.isEmpty()) {
+ outStream.write(0);
+ } else {
+ outStream.write(1);
+ accumCoder.encode(
+ WindowedValue.of(
+ swwa.windowAccumulator, swwa.accTimestamp, swwa.accWindow,
PaneInfo.NO_FIRING),
+ outStream);
+ }
+ }
+ }
+
+ @Override
+ public WindowedAccumulator<InputT, ValueT, AccumT, ?> decode(InputStream
inStream)
+ throws CoderException, IOException {
+ if (type.isMapBased()) {
+ return WindowedAccumulator.create(toValue, type,
wrap.decode(inStream), windowComparator);
+ }
+ boolean empty = inStream.read() == 0;
+ if (empty) {
+ return WindowedAccumulator.create(toValue, type, windowComparator);
+ }
+ return WindowedAccumulator.create(
+ toValue, type, Arrays.asList(accumCoder.decode(inStream)),
windowComparator);
+ }
+
+ @Override
+ public List<? extends Coder<?>> getCoderArguments() {
+ return wrap.getComponents();
+ }
+
+ @Override
+ public void verifyDeterministic() throws NonDeterministicException {}
+ }
+
+ @VisibleForTesting
+ static <K, V, AccumT, OutputT> SparkCombineFn<KV<K, V>, V, AccumT, OutputT>
keyed(
+ CombineWithContext.CombineFnWithContext<V, AccumT, OutputT> combineFn,
+ SerializablePipelineOptions options,
+ Map<TupleTag<?>, KV<WindowingStrategy<?, ?>, SideInputBroadcast<?>>>
sideInputs,
+ WindowingStrategy<?, ?> windowingStrategy,
+ WindowedAccumulator.Type nonMergingStrategy) {
+ return new SparkCombineFn<>(
+ false, KV::getValue, combineFn, options, sideInputs,
windowingStrategy, nonMergingStrategy);
+ }
+
+ public static <K, V, AccumT, OutputT> SparkCombineFn<KV<K, V>, V, AccumT,
OutputT> keyed(
+ CombineWithContext.CombineFnWithContext<V, AccumT, OutputT> combineFn,
+ SerializablePipelineOptions options,
+ Map<TupleTag<?>, KV<WindowingStrategy<?, ?>, SideInputBroadcast<?>>>
sideInputs,
+ WindowingStrategy<?, ?> windowingStrategy) {
+ return new SparkCombineFn<>(
+ false, KV::getValue, combineFn, options, sideInputs,
windowingStrategy);
+ }
+
+ public static <InputT, AccumT, OutputT> SparkCombineFn<InputT, InputT,
AccumT, OutputT> globally(
+ CombineWithContext.CombineFnWithContext<InputT, AccumT, OutputT>
combineFn,
+ SerializablePipelineOptions options,
+ Map<TupleTag<?>, KV<WindowingStrategy<?, ?>, SideInputBroadcast<?>>>
sideInputs,
+ WindowingStrategy<?, ?> windowingStrategy) {
+ return new SparkCombineFn<>(true, e -> e, combineFn, options, sideInputs,
windowingStrategy);
+ }
+
+ private static <T> Map<BoundedWindow, WindowedValue<T>> asMap(
+ Iterable<WindowedValue<T>> values, Map<BoundedWindow, WindowedValue<T>>
res) {
+ for (WindowedValue<T> v : values) {
+ res.put(getWindow(v), v);
+ }
+ return res;
+ }
+
+ /**
+ * Create comparator for given type descriptor. Note that the returned
{@link Comparator} has to
+ * be {@link Serializable}.
+ */
+ @SuppressWarnings({"unchecked", "rawtypes"})
+ private static Comparator<BoundedWindow> asWindowComparator(
+ @Nullable TypeDescriptor<?> windowType) {
+ final Comparator<BoundedWindow> comparator;
+ if (windowType != null
+ && StreamSupport.stream(windowType.getInterfaces().spliterator(),
false)
+ .anyMatch(t ->
t.isSubtypeOf(TypeDescriptor.of(Comparable.class)))) {
+ comparator = (Comparator) Comparator.naturalOrder();
+ } else {
+ java.util.function.Function<BoundedWindow, ?> keyExtractor =
Review comment:
Fixed, thanks!
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Issue Time Tracking
-------------------
Worklog Id: (was: 283366)
Time Spent: 6h 50m (was: 6h 40m)
> Spark runner: Combine.perKey performance issues
> -----------------------------------------------
>
> Key: BEAM-7574
> URL: https://issues.apache.org/jira/browse/BEAM-7574
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.13.0
> Reporter: Jan Lukavský
> Assignee: Jan Lukavský
> Priority: Major
> Time Spent: 6h 50m
> Remaining Estimate: 0h
>
> Combine.perKey on current implementation uses technique of creating an
> accumulator for each input key and then merge all these accumulators
> together. Optimize this by:
> * changing accumulator from Iterable to Map, and using addInput as much as
> possible
> * try to move the window explode to pre-shuffle (add window label to key for
> non-merging windows), measure the impact, and if the impact is substantial,
> implement that for at leasit window functions assigning to single (global)
> window or single window per element (tumbling windows)
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