[
https://issues.apache.org/jira/browse/BEAM-3708?focusedWorklogId=118339&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-118339
]
ASF GitHub Bot logged work on BEAM-3708:
----------------------------------------
Author: ASF GitHub Bot
Created on: 02/Jul/18 18:33
Start Date: 02/Jul/18 18:33
Worklog Time Spent: 10m
Work Description: lukecwik closed pull request #5795: [BEAM-3708] Adding
grouping table to Precombine step.
URL: https://github.com/apache/beam/pull/5795
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git
a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/CombineRunners.java
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/CombineRunners.java
index 35399b92e4c..178c4c1c0d8 100644
---
a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/CombineRunners.java
+++
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/CombineRunners.java
@@ -18,20 +18,40 @@
package org.apache.beam.fn.harness;
import com.google.auto.service.AutoService;
+import com.google.common.annotations.VisibleForTesting;
import com.google.common.collect.ImmutableMap;
+import com.google.common.collect.Iterables;
+import com.google.common.collect.Multimap;
import java.io.IOException;
+import java.util.Collection;
import java.util.Map;
+import java.util.function.Consumer;
+import java.util.function.Supplier;
+import org.apache.beam.fn.harness.control.BundleSplitListener;
+import org.apache.beam.fn.harness.data.BeamFnDataClient;
+import org.apache.beam.fn.harness.data.MultiplexingFnDataReceiver;
+import org.apache.beam.fn.harness.state.BeamFnStateClient;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
import org.apache.beam.model.pipeline.v1.RunnerApi.CombinePayload;
+import org.apache.beam.model.pipeline.v1.RunnerApi.PCollection;
import org.apache.beam.model.pipeline.v1.RunnerApi.PTransform;
import org.apache.beam.model.pipeline.v1.RunnerApi.StandardPTransforms;
import org.apache.beam.runners.core.construction.BeamUrns;
+import org.apache.beam.runners.core.construction.RehydratedComponents;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.fn.data.FnDataReceiver;
import org.apache.beam.sdk.fn.function.ThrowingFunction;
+import org.apache.beam.sdk.fn.function.ThrowingRunnable;
+import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.transforms.Combine.CombineFn;
import org.apache.beam.sdk.util.SerializableUtils;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.util.WindowedValue.WindowedValueCoder;
import org.apache.beam.sdk.values.KV;
/** Executes different components of Combine PTransforms. */
-public class CombineRunners<InputT, OutputT> {
+public class CombineRunners {
/** A registrar which provides a factory to handle combine component
PTransforms. */
@AutoService(PTransformRunnerFactory.Registrar.class)
@@ -41,7 +61,7 @@
public Map<String, PTransformRunnerFactory> getPTransformRunnerFactories()
{
return ImmutableMap.of(
BeamUrns.getUrn(StandardPTransforms.CombineComponents.COMBINE_PER_KEY_PRECOMBINE),
-
MapFnRunners.forValueMapFnFactory(CombineRunners::createPrecombineMapFunction),
+ new PrecombineFactory(),
BeamUrns.getUrn(StandardPTransforms.CombineComponents.COMBINE_PER_KEY_MERGE_ACCUMULATORS),
MapFnRunners.forValueMapFnFactory(CombineRunners::createMergeAccumulatorsMapFunction),
BeamUrns.getUrn(StandardPTransforms.CombineComponents.COMBINE_PER_KEY_EXTRACT_OUTPUTS),
@@ -51,17 +71,120 @@
}
}
- static <KeyT, InputT, AccumT>
- ThrowingFunction<KV<KeyT, InputT>, KV<KeyT, AccumT>>
createPrecombineMapFunction(
- String pTransformId, PTransform pTransform) throws IOException {
- CombinePayload combinePayload =
CombinePayload.parseFrom(pTransform.getSpec().getPayload());
- CombineFn<InputT, AccumT, ?> combineFn =
- (CombineFn)
- SerializableUtils.deserializeFromByteArray(
-
combinePayload.getCombineFn().getSpec().getPayload().toByteArray(),
"CombineFn");
+ private static class PrecombineRunner<KeyT, InputT, AccumT> {
+ private PipelineOptions options;
+ private CombineFn<InputT, AccumT, ?> combineFn;
+ private FnDataReceiver<WindowedValue<KV<KeyT, AccumT>>> output;
+ private Coder<KeyT> keyCoder;
+ private GroupingTable<WindowedValue<KeyT>, InputT, AccumT> groupingTable;
+ private Coder<AccumT> accumCoder;
+
+ PrecombineRunner(
+ PipelineOptions options,
+ CombineFn<InputT, AccumT, ?> combineFn,
+ FnDataReceiver<WindowedValue<KV<KeyT, AccumT>>> output,
+ Coder<KeyT> keyCoder,
+ Coder<AccumT> accumCoder) {
+ this.options = options;
+ this.combineFn = combineFn;
+ this.output = output;
+ this.keyCoder = keyCoder;
+ this.accumCoder = accumCoder;
+ }
+
+ void startBundle() {
+ groupingTable =
+ PrecombineGroupingTable.combiningAndSampling(
+ options, combineFn, keyCoder, accumCoder, 0.001
/*sizeEstimatorSampleRate*/);
+ }
+
+ void processElement(WindowedValue<KV<KeyT, InputT>> elem) throws Exception
{
+ groupingTable.put(
+ elem, (Object outputElem) -> output.accept((WindowedValue<KV<KeyT,
AccumT>>) outputElem));
+ }
+
+ void finishBundle() throws Exception {
+ groupingTable.flush(
+ (Object outputElem) -> output.accept((WindowedValue<KV<KeyT,
AccumT>>) outputElem));
+ }
+ }
+
+ /** A factory for {@link PrecombineRunner}s. */
+ @VisibleForTesting
+ public static class PrecombineFactory<KeyT, InputT, AccumT>
+ implements PTransformRunnerFactory<PrecombineRunner<KeyT, InputT,
AccumT>> {
+
+ @Override
+ public PrecombineRunner<KeyT, InputT, AccumT> createRunnerForPTransform(
+ PipelineOptions pipelineOptions,
+ BeamFnDataClient beamFnDataClient,
+ BeamFnStateClient beamFnStateClient,
+ String pTransformId,
+ PTransform pTransform,
+ Supplier<String> processBundleInstructionId,
+ Map<String, PCollection> pCollections,
+ Map<String, RunnerApi.Coder> coders,
+ Map<String, RunnerApi.WindowingStrategy> windowingStrategies,
+ Multimap<String, FnDataReceiver<WindowedValue<?>>>
pCollectionIdsToConsumers,
+ Consumer<ThrowingRunnable> addStartFunction,
+ Consumer<ThrowingRunnable> addFinishFunction,
+ BundleSplitListener splitListener)
+ throws IOException {
+ // Get objects needed to create the runner.
+ RehydratedComponents rehydratedComponents =
+ RehydratedComponents.forComponents(
+ RunnerApi.Components.newBuilder()
+ .putAllCoders(coders)
+ .putAllWindowingStrategies(windowingStrategies)
+ .build());
+ String mainInputTag =
Iterables.getOnlyElement(pTransform.getInputsMap().keySet());
+ RunnerApi.PCollection mainInput =
pCollections.get(pTransform.getInputsOrThrow(mainInputTag));
+
+ // Input coder may sometimes be WindowedValueCoder depending on runner,
instead of the
+ // expected KvCoder.
+ Coder<?> uncastInputCoder =
rehydratedComponents.getCoder(mainInput.getCoderId());
+ KvCoder<KeyT, InputT> inputCoder;
+ if (uncastInputCoder instanceof WindowedValueCoder) {
+ inputCoder =
+ (KvCoder<KeyT, InputT>)
+ ((WindowedValueCoder<KV<KeyT, InputT>>)
uncastInputCoder).getValueCoder();
+ } else {
+ inputCoder = (KvCoder<KeyT, InputT>)
rehydratedComponents.getCoder(mainInput.getCoderId());
+ }
+ Coder<KeyT> keyCoder = inputCoder.getKeyCoder();
+
+ CombinePayload combinePayload =
CombinePayload.parseFrom(pTransform.getSpec().getPayload());
+ CombineFn<InputT, AccumT, ?> combineFn =
+ (CombineFn)
+ SerializableUtils.deserializeFromByteArray(
+
combinePayload.getCombineFn().getSpec().getPayload().toByteArray(),
"CombineFn");
+ Coder<AccumT> accumCoder =
+ (Coder<AccumT>)
rehydratedComponents.getCoder(combinePayload.getAccumulatorCoderId());
+
+ Collection<FnDataReceiver<WindowedValue<KV<KeyT, AccumT>>>> consumers =
+ (Collection)
+ pCollectionIdsToConsumers.get(
+
Iterables.getOnlyElement(pTransform.getOutputsMap().values()));
- return (KV<KeyT, InputT> input) ->
- KV.of(input.getKey(),
combineFn.addInput(combineFn.createAccumulator(), input.getValue()));
+ // Create the runner.
+ PrecombineRunner<KeyT, InputT, AccumT> runner =
+ new PrecombineRunner<>(
+ pipelineOptions,
+ combineFn,
+ MultiplexingFnDataReceiver.forConsumers(consumers),
+ keyCoder,
+ accumCoder);
+
+ // Register the appropriate handlers.
+ addStartFunction.accept(runner::startBundle);
+ pCollectionIdsToConsumers.put(
+ Iterables.getOnlyElement(pTransform.getInputsMap().values()),
+ (FnDataReceiver)
+ (FnDataReceiver<WindowedValue<KV<KeyT, InputT>>>)
runner::processElement);
+ addFinishFunction.accept(runner::finishBundle);
+
+ return runner;
+ }
}
static <KeyT, AccumT>
diff --git
a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/GroupingTable.java
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/GroupingTable.java
new file mode 100644
index 00000000000..5fdd46f2be0
--- /dev/null
+++
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/GroupingTable.java
@@ -0,0 +1,35 @@
+/*
+ * 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.fn.harness;
+
+/** An interface that groups inputs to an accumulator and flushes the output.
*/
+public interface GroupingTable<K, InputT, AccumT> {
+
+ /** Abstract interface of things that accept inputs one at a time via
process(). */
+ interface Receiver {
+ /** Processes the element. */
+ void process(Object outputElem) throws Exception;
+ }
+
+ /** Adds a pair to this table, possibly flushing some entries to output if
the table is full. */
+ void put(Object pair, Receiver receiver) throws Exception;
+
+ /** Flushes all entries in this table to output. */
+ void flush(Receiver output) throws Exception;
+}
diff --git
a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/PrecombineGroupingTable.java
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/PrecombineGroupingTable.java
new file mode 100644
index 00000000000..060b0e5efd4
--- /dev/null
+++
b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/PrecombineGroupingTable.java
@@ -0,0 +1,568 @@
+/*
+ * 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.fn.harness;
+
+import com.google.common.annotations.VisibleForTesting;
+import com.google.common.io.ByteStreams;
+import com.google.common.io.CountingOutputStream;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.Random;
+import org.apache.beam.runners.core.GlobalCombineFnRunner;
+import org.apache.beam.runners.core.GlobalCombineFnRunners;
+import org.apache.beam.runners.core.NullSideInputReader;
+import org.apache.beam.runners.core.SideInputReader;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.transforms.Combine.CombineFn;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.util.common.ElementByteSizeObserver;
+import org.apache.beam.sdk.values.KV;
+import org.joda.time.Instant;
+
+/** Static utility methods that provide {@link GroupingTable} implementations.
*/
+public class PrecombineGroupingTable<K, InputT, AccumT>
+ implements GroupingTable<K, InputT, AccumT> {
+ /** Returns a {@link GroupingTable} that combines inputs into a accumulator.
*/
+ public static <K, InputT, AccumT> GroupingTable<WindowedValue<K>, InputT,
AccumT> combining(
+ PipelineOptions options,
+ CombineFn<InputT, AccumT, ?> combineFn,
+ Coder<K> keyCoder,
+ Coder<? super AccumT> accumulatorCoder) {
+ Combiner<WindowedValue<K>, InputT, AccumT, ?> valueCombiner =
+ new ValueCombiner<>(
+ GlobalCombineFnRunners.create(combineFn),
NullSideInputReader.empty(), options);
+ return new PrecombineGroupingTable<>(
+ DEFAULT_MAX_GROUPING_TABLE_BYTES,
+ new WindowingCoderGroupingKeyCreator<>(keyCoder),
+ WindowedPairInfo.create(),
+ valueCombiner,
+ new CoderSizeEstimator<>(WindowedValue.getValueOnlyCoder(keyCoder)),
+ new CoderSizeEstimator<>(accumulatorCoder));
+ }
+
+ /**
+ * Returns a {@link GroupingTable} that combines inputs into a accumulator
with sampling {@link
+ * SizeEstimator SizeEstimators}.
+ */
+ public static <K, InputT, AccumT>
+ GroupingTable<WindowedValue<K>, InputT, AccumT> combiningAndSampling(
+ PipelineOptions options,
+ CombineFn<InputT, AccumT, ?> combineFn,
+ Coder<K> keyCoder,
+ Coder<? super AccumT> accumulatorCoder,
+ double sizeEstimatorSampleRate) {
+ Combiner<WindowedValue<K>, InputT, AccumT, ?> valueCombiner =
+ new ValueCombiner<>(
+ GlobalCombineFnRunners.create(combineFn),
NullSideInputReader.empty(), options);
+ return new PrecombineGroupingTable<>(
+ DEFAULT_MAX_GROUPING_TABLE_BYTES,
+ new WindowingCoderGroupingKeyCreator<>(keyCoder),
+ WindowedPairInfo.create(),
+ valueCombiner,
+ new SamplingSizeEstimator<>(
+ new
CoderSizeEstimator<>(WindowedValue.getValueOnlyCoder(keyCoder)),
+ sizeEstimatorSampleRate,
+ 1.0),
+ new SamplingSizeEstimator<>(
+ new CoderSizeEstimator<>(accumulatorCoder),
sizeEstimatorSampleRate, 1.0));
+ }
+
+ /** Provides client-specific operations for grouping keys. */
+ public interface GroupingKeyCreator<K> {
+ Object createGroupingKey(K key) throws Exception;
+ }
+
+ /** Implements Precombine GroupingKeyCreator via Coder. */
+ public static class WindowingCoderGroupingKeyCreator<K>
+ implements GroupingKeyCreator<WindowedValue<K>> {
+
+ private static final Instant ignored = BoundedWindow.TIMESTAMP_MIN_VALUE;
+
+ private final Coder<K> coder;
+
+ WindowingCoderGroupingKeyCreator(Coder<K> coder) {
+ this.coder = coder;
+ }
+
+ @Override
+ public Object createGroupingKey(WindowedValue<K> key) {
+ // Ignore timestamp for grouping purposes.
+ // The Precombine output will inherit the timestamp of one of its inputs.
+ return WindowedValue.of(
+ coder.structuralValue(key.getValue()), ignored, key.getWindows(),
key.getPane());
+ }
+ }
+
+ /** Provides client-specific operations for size estimates. */
+ public interface SizeEstimator<T> {
+ long estimateSize(T element) throws Exception;
+ }
+
+ /** Implements SizeEstimator via Coder. */
+ public static class CoderSizeEstimator<T> implements SizeEstimator<T> {
+ /** Basic implementation of {@link ElementByteSizeObserver} for use in
size estimation. */
+ private static class Observer extends ElementByteSizeObserver {
+ private long observedSize = 0;
+
+ @Override
+ protected void reportElementSize(long elementSize) {
+ observedSize += elementSize;
+ }
+ }
+
+ final Coder<T> coder;
+
+ CoderSizeEstimator(Coder<T> coder) {
+ this.coder = coder;
+ }
+
+ @Override
+ public long estimateSize(T value) throws Exception {
+ // First try using byte size observer
+ CoderSizeEstimator.Observer observer = new CoderSizeEstimator.Observer();
+ coder.registerByteSizeObserver(value, observer);
+
+ if (!observer.getIsLazy()) {
+ observer.advance();
+ return observer.observedSize;
+ } else {
+ // Coder byte size observation is lazy (requires iteration for
observation) so fall back to
+ // counting output stream
+ CountingOutputStream os = new
CountingOutputStream(ByteStreams.nullOutputStream());
+ coder.encode(value, os);
+ return os.getCount();
+ }
+ }
+ }
+
+ /**
+ * Provides client-specific operations for working with elements that are
key/value or key/values
+ * pairs.
+ */
+ public interface PairInfo {
+ Object getKeyFromInputPair(Object pair);
+
+ Object getValueFromInputPair(Object pair);
+
+ Object makeOutputPair(Object key, Object value);
+ }
+
+ /** Implements Precombine PairInfo via KVs. */
+ public static class WindowedPairInfo implements PairInfo {
+ private static WindowedPairInfo theInstance = new WindowedPairInfo();
+
+ public static WindowedPairInfo create() {
+ return theInstance;
+ }
+
+ private WindowedPairInfo() {}
+
+ @Override
+ public Object getKeyFromInputPair(Object pair) {
+ @SuppressWarnings("unchecked")
+ WindowedValue<KV<?, ?>> windowedKv = (WindowedValue<KV<?, ?>>) pair;
+ return windowedKv.withValue(windowedKv.getValue().getKey());
+ }
+
+ @Override
+ public Object getValueFromInputPair(Object pair) {
+ @SuppressWarnings("unchecked")
+ WindowedValue<KV<?, ?>> windowedKv = (WindowedValue<KV<?, ?>>) pair;
+ return windowedKv.getValue().getValue();
+ }
+
+ @Override
+ public Object makeOutputPair(Object key, Object values) {
+ WindowedValue<?> windowedKey = (WindowedValue<?>) key;
+ return windowedKey.withValue(KV.of(windowedKey.getValue(), values));
+ }
+ }
+
+ /** Provides client-specific operations for combining values. */
+ public interface Combiner<K, InputT, AccumT, OutputT> {
+ AccumT createAccumulator(K key);
+
+ AccumT add(K key, AccumT accumulator, InputT value);
+
+ AccumT merge(K key, Iterable<AccumT> accumulators);
+
+ AccumT compact(K key, AccumT accumulator);
+
+ OutputT extract(K key, AccumT accumulator);
+ }
+
+ /** Implements Precombine Combiner via Combine.KeyedCombineFn. */
+ public static class ValueCombiner<K, InputT, AccumT, OutputT>
+ implements Combiner<WindowedValue<K>, InputT, AccumT, OutputT> {
+ private final GlobalCombineFnRunner<InputT, AccumT, OutputT> combineFn;
+ private final SideInputReader sideInputReader;
+ private final PipelineOptions options;
+
+ private ValueCombiner(
+ GlobalCombineFnRunner<InputT, AccumT, OutputT> combineFn,
+ SideInputReader sideInputReader,
+ PipelineOptions options) {
+ this.combineFn = combineFn;
+ this.sideInputReader = sideInputReader;
+ this.options = options;
+ }
+
+ @Override
+ public AccumT createAccumulator(WindowedValue<K> windowedKey) {
+ return this.combineFn.createAccumulator(options, sideInputReader,
windowedKey.getWindows());
+ }
+
+ @Override
+ public AccumT add(WindowedValue<K> windowedKey, AccumT accumulator, InputT
value) {
+ return this.combineFn.addInput(
+ accumulator, value, options, sideInputReader,
windowedKey.getWindows());
+ }
+
+ @Override
+ public AccumT merge(WindowedValue<K> windowedKey, Iterable<AccumT>
accumulators) {
+ return this.combineFn.mergeAccumulators(
+ accumulators, options, sideInputReader, windowedKey.getWindows());
+ }
+
+ @Override
+ public AccumT compact(WindowedValue<K> windowedKey, AccumT accumulator) {
+ return this.combineFn.compact(
+ accumulator, options, sideInputReader, windowedKey.getWindows());
+ }
+
+ @Override
+ public OutputT extract(WindowedValue<K> windowedKey, AccumT accumulator) {
+ return this.combineFn.extractOutput(
+ accumulator, options, sideInputReader, windowedKey.getWindows());
+ }
+ }
+
+ // By default, how many bytes we allow the grouping table to consume before
+ // it has to be flushed.
+ private static final long DEFAULT_MAX_GROUPING_TABLE_BYTES = 100_000_000L;
+
+ // How many bytes a word in the JVM has.
+ private static final int BYTES_PER_JVM_WORD = getBytesPerJvmWord();
+ /**
+ * The number of bytes of overhead to store an entry in the grouping table
(a {@code
+ * HashMap<StructuralByteArray, KeyAndValues>}), ignoring the actual number
of bytes in the keys
+ * and values:
+ *
+ * <ul>
+ * <li>an array element (1 word),
+ * <li>a HashMap.Entry (4 words),
+ * <li>a StructuralByteArray (1 words),
+ * <li>a backing array (guessed at 1 word for the length),
+ * <li>a KeyAndValues (2 words),
+ * <li>an ArrayList (2 words),
+ * <li>a backing array (1 word),
+ * <li>per-object overhead (JVM-specific, guessed at 2 words * 6 objects).
+ * </ul>
+ */
+ private static final int PER_KEY_OVERHEAD = 24 * BYTES_PER_JVM_WORD;
+
+ /** A {@link GroupingTable} that uses the given combiner to combine values
in place. */
+ // Keep the table relatively full to increase the chance of collisions.
+ private static final double TARGET_LOAD = 0.9;
+
+ private long maxSize;
+ private final GroupingKeyCreator<? super K> groupingKeyCreator;
+ private final PairInfo pairInfo;
+ private final Combiner<? super K, InputT, AccumT, ?> combiner;
+ private final SizeEstimator<? super K> keySizer;
+ private final SizeEstimator<? super AccumT> accumulatorSizer;
+
+ private long size = 0;
+ private Map<Object, GroupingTableEntry<K, InputT, AccumT>> table;
+
+ PrecombineGroupingTable(
+ long maxSize,
+ GroupingKeyCreator<? super K> groupingKeyCreator,
+ PairInfo pairInfo,
+ Combiner<? super K, InputT, AccumT, ?> combineFn,
+ SizeEstimator<? super K> keySizer,
+ SizeEstimator<? super AccumT> accumulatorSizer) {
+ this.maxSize = maxSize;
+ this.groupingKeyCreator = groupingKeyCreator;
+ this.pairInfo = pairInfo;
+ this.combiner = combineFn;
+ this.keySizer = keySizer;
+ this.accumulatorSizer = accumulatorSizer;
+ this.table = new HashMap<>();
+ }
+
+ interface GroupingTableEntry<K, InputT, AccumT> {
+ K getKey();
+
+ AccumT getValue();
+
+ void add(InputT value) throws Exception;
+
+ long getSize();
+
+ void compact() throws Exception;
+ }
+
+ private GroupingTableEntry<K, InputT, AccumT> createTableEntry(final K key)
throws Exception {
+ return new GroupingTableEntry<K, InputT, AccumT>() {
+ final long keySize = keySizer.estimateSize(key);
+ AccumT accumulator = combiner.createAccumulator(key);
+ long accumulatorSize = 0; // never used before a value is added...
+
+ @Override
+ public K getKey() {
+ return key;
+ }
+
+ @Override
+ public AccumT getValue() {
+ return accumulator;
+ }
+
+ @Override
+ public long getSize() {
+ return keySize + accumulatorSize;
+ }
+
+ @Override
+ public void compact() throws Exception {
+ AccumT newAccumulator = combiner.compact(key, accumulator);
+ if (newAccumulator != accumulator) {
+ accumulator = newAccumulator;
+ accumulatorSize = accumulatorSizer.estimateSize(newAccumulator);
+ }
+ }
+
+ @Override
+ public void add(InputT value) throws Exception {
+ accumulator = combiner.add(key, accumulator, value);
+ accumulatorSize = accumulatorSizer.estimateSize(accumulator);
+ }
+ };
+ }
+
+ /** Adds a pair to this table, possibly flushing some entries to output if
the table is full. */
+ @SuppressWarnings("unchecked")
+ @Override
+ public void put(Object pair, Receiver receiver) throws Exception {
+ put(
+ (K) pairInfo.getKeyFromInputPair(pair),
+ (InputT) pairInfo.getValueFromInputPair(pair),
+ receiver);
+ }
+
+ /**
+ * Adds the key and value to this table, possibly flushing some entries to
output if the table is
+ * full.
+ */
+ public void put(K key, InputT value, Receiver receiver) throws Exception {
+ Object groupingKey = groupingKeyCreator.createGroupingKey(key);
+ GroupingTableEntry<K, InputT, AccumT> entry = table.get(groupingKey);
+ if (entry == null) {
+ entry = createTableEntry(key);
+ table.put(groupingKey, entry);
+ size += PER_KEY_OVERHEAD;
+ } else {
+ size -= entry.getSize();
+ }
+ entry.add(value);
+ size += entry.getSize();
+
+ if (size >= maxSize) {
+ long targetSize = (long) (TARGET_LOAD * maxSize);
+ Iterator<GroupingTableEntry<K, InputT, AccumT>> entries =
table.values().iterator();
+ while (size >= targetSize) {
+ if (!entries.hasNext()) {
+ // Should never happen, but sizes may be estimates...
+ size = 0;
+ break;
+ }
+ GroupingTableEntry<K, InputT, AccumT> toFlush = entries.next();
+ entries.remove();
+ size -= toFlush.getSize() + PER_KEY_OVERHEAD;
+ output(toFlush, receiver);
+ }
+ }
+ }
+
+ /**
+ * Output the given entry. Does not actually remove it from the table or
update this table's size.
+ */
+ private void output(GroupingTableEntry<K, InputT, AccumT> entry, Receiver
receiver)
+ throws Exception {
+ entry.compact();
+ receiver.process(pairInfo.makeOutputPair(entry.getKey(),
entry.getValue()));
+ }
+
+ /** Flushes all entries in this table to output. */
+ @Override
+ public void flush(Receiver output) throws Exception {
+ for (GroupingTableEntry<K, InputT, AccumT> entry : table.values()) {
+ output(entry, output);
+ }
+ table.clear();
+ size = 0;
+ }
+
+ @VisibleForTesting
+ public void setMaxSize(long maxSize) {
+ this.maxSize = maxSize;
+ }
+
+ @VisibleForTesting
+ public long size() {
+ return size;
+ }
+
+ /** Returns the number of bytes in a JVM word. In case we failed to find the
answer, returns 8. */
+ private static int getBytesPerJvmWord() {
+ String wordSizeInBits = System.getProperty("sun.arch.data.model");
+ try {
+ return Integer.parseInt(wordSizeInBits) / 8;
+ } catch (NumberFormatException e) {
+ // The JVM word size is unknown. Assume 64-bit.
+ return 8;
+ }
+ }
+
+ ////////////////////////////////////////////////////////////////////////////
+ // Size sampling.
+
+ /**
+ * Implements size estimation by adaptively delegating to an underlying
(potentially more
+ * expensive) estimator for some elements and returning the average value
for others.
+ */
+ @VisibleForTesting
+ static class SamplingSizeEstimator<T> implements SizeEstimator<T> {
+
+ /**
+ * The degree of confidence required in our expected value predictions
before we allow
+ * under-sampling.
+ *
+ * <p>The value of 3.0 is a confidence interval of about 99.7% for a a
high-degree-of-freedom
+ * t-distribution.
+ */
+ static final double CONFIDENCE_INTERVAL_SIGMA = 3;
+
+ /**
+ * The desired size of our confidence interval (relative to the measured
expected value).
+ *
+ * <p>The value of 0.25 is plus or minus 25%.
+ */
+ static final double CONFIDENCE_INTERVAL_SIZE = 0.25;
+
+ /** Default number of elements that must be measured before elements are
skipped. */
+ static final long DEFAULT_MIN_SAMPLED = 20;
+
+ private final SizeEstimator<T> underlying;
+ private final double minSampleRate;
+ private final double maxSampleRate;
+ private final long minSampled;
+ private final Random random;
+
+ private long totalElements = 0;
+ private long sampledElements = 0;
+ private long sampledSum = 0;
+ private double sampledSumSquares = 0;
+ private long estimate;
+
+ private long nextSample = 0;
+
+ private SamplingSizeEstimator(
+ SizeEstimator<T> underlying, double minSampleRate, double
maxSampleRate) {
+ this(underlying, minSampleRate, maxSampleRate, DEFAULT_MIN_SAMPLED, new
Random());
+ }
+
+ @VisibleForTesting
+ SamplingSizeEstimator(
+ SizeEstimator<T> underlying,
+ double minSampleRate,
+ double maxSampleRate,
+ long minSampled,
+ Random random) {
+ this.underlying = underlying;
+ this.minSampleRate = minSampleRate;
+ this.maxSampleRate = maxSampleRate;
+ this.minSampled = minSampled;
+ this.random = random;
+ }
+
+ @Override
+ public long estimateSize(T element) throws Exception {
+ if (sampleNow()) {
+ return recordSample(underlying.estimateSize(element));
+ } else {
+ return estimate;
+ }
+ }
+
+ private boolean sampleNow() {
+ totalElements++;
+ return --nextSample < 0;
+ }
+
+ private long recordSample(long value) {
+ sampledElements += 1;
+ sampledSum += value;
+ sampledSumSquares += value * value;
+ estimate = (long) Math.ceil((double) sampledSum / sampledElements);
+ long target = desiredSampleSize();
+ if (sampledElements < minSampled || sampledElements < target) {
+ // Sample immediately.
+ nextSample = 0;
+ } else {
+ double rate =
+ cap(
+ minSampleRate,
+ maxSampleRate,
+ Math.max(
+ 1.0 / (totalElements - minSampled + 1), // slowly ramp down
+ target / (double) totalElements)); // "future" target
+ // Uses the geometric distribution to return the likely distance
between
+ // successive independent trials of a fixed probability p. This gives
the
+ // same uniform distribution of branching on Math.random() < p, but
with
+ // one random number generation per success rather than one
+ // per test, which can be a significant savings if p is small.
+ nextSample =
+ rate == 1.0 ? 0 : (long) Math.floor(Math.log(random.nextDouble())
/ Math.log(1 - rate));
+ }
+ return value;
+ }
+
+ private static double cap(double min, double max, double value) {
+ return Math.min(max, Math.max(min, value));
+ }
+
+ private long desiredSampleSize() {
+ // We have no a-priori information on the actual distribution of data
+ // sizes, so compute our desired sample as if it were normal.
+ // Yes this formula is unstable for small stddev, but we only care about
large stddev.
+ double mean = sampledSum / (double) sampledElements;
+ double sumSquareDiff =
+ (sampledSumSquares - (2 * mean * sampledSum) + (sampledElements *
mean * mean));
+ double stddev = Math.sqrt(sumSquareDiff / (sampledElements - 1));
+ double sqrtDesiredSamples =
+ (CONFIDENCE_INTERVAL_SIGMA * stddev) / (CONFIDENCE_INTERVAL_SIZE *
mean);
+ return (long) Math.ceil(sqrtDesiredSamples * sqrtDesiredSamples);
+ }
+ }
+}
diff --git
a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CombineRunnersTest.java
b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CombineRunnersTest.java
index aac4476e09c..b55acdefaef 100644
---
a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CombineRunnersTest.java
+++
b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CombineRunnersTest.java
@@ -93,6 +93,7 @@ public Integer extractOutput(Integer accum) {
private RunnerApi.PTransform pTransform;
private String inputPCollectionId;
private String outputPCollectionId;
+ private RunnerApi.Pipeline pProto;
@Before
public void createPipeline() throws Exception {
@@ -109,7 +110,7 @@ public void createPipeline() throws Exception {
// Create FnApi protos needed for the runner.
SdkComponents sdkComponents = SdkComponents.create();
- RunnerApi.Pipeline pProto = PipelineTranslation.toProto(p, sdkComponents);
+ pProto = PipelineTranslation.toProto(p, sdkComponents);
inputPCollectionId = sdkComponents.registerPCollection(inputPCollection);
outputPCollectionId = sdkComponents.registerPCollection(outputPCollection);
pTransform = pProto.getComponents().getTransformsOrThrow(TEST_COMBINE_ID);
@@ -133,7 +134,7 @@ public void testPrecombine() throws Exception {
List<ThrowingRunnable> finishFunctions = new ArrayList<>();
// Create runner.
-
MapFnRunners.forValueMapFnFactory(CombineRunners::createPrecombineMapFunction)
+ new CombineRunners.PrecombineFactory<>()
.createRunnerForPTransform(
PipelineOptionsFactory.create(),
null,
@@ -141,16 +142,15 @@ public void testPrecombine() throws Exception {
TEST_COMBINE_ID,
pTransform,
null,
- Collections.emptyMap(),
- Collections.emptyMap(),
- Collections.emptyMap(),
+ pProto.getComponents().getPcollectionsMap(),
+ pProto.getComponents().getCodersMap(),
+ pProto.getComponents().getWindowingStrategiesMap(),
consumers,
startFunctions::add,
finishFunctions::add,
null);
- assertThat(startFunctions, empty());
- assertThat(finishFunctions, empty());
+ Iterables.getOnlyElement(startFunctions).run();
// Send elements to runner and check outputs.
mainOutputValues.clear();
@@ -164,18 +164,23 @@ public void testPrecombine() throws Exception {
input.accept(valueInGlobalWindow(KV.of("B", "2")));
input.accept(valueInGlobalWindow(KV.of("C", "3")));
+ Iterables.getOnlyElement(finishFunctions).run();
+
// Check that all values for "A" were converted to accumulators regardless
of how they were
// combined by the Precombine optimization.
Integer sum = 0;
- while ("A".equals(mainOutputValues.getFirst().getValue().getKey())) {
- sum += mainOutputValues.getFirst().getValue().getValue();
- mainOutputValues.removeFirst();
+ for (WindowedValue<KV<String, Integer>> outputValue : mainOutputValues) {
+ if ("A".equals(outputValue.getValue().getKey())) {
+ sum += outputValue.getValue().getValue();
+ }
}
assertThat(sum, equalTo(9));
+ // Check that elements for "B" and "C" are present as well.
+ mainOutputValues.removeIf(elem -> "A".equals(elem.getValue().getKey()));
assertThat(
mainOutputValues,
- contains(valueInGlobalWindow(KV.of("B", 2)),
valueInGlobalWindow(KV.of("C", 3))));
+ containsInAnyOrder(valueInGlobalWindow(KV.of("B", 2)),
valueInGlobalWindow(KV.of("C", 3))));
}
/**
diff --git
a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/PrecombineGroupingTableTest.java
b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/PrecombineGroupingTableTest.java
new file mode 100644
index 00000000000..1fd26f071a0
--- /dev/null
+++
b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/PrecombineGroupingTableTest.java
@@ -0,0 +1,304 @@
+/*
+ * 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.fn.harness;
+
+import static org.hamcrest.Matchers.anyOf;
+import static org.hamcrest.Matchers.empty;
+import static org.hamcrest.Matchers.hasItem;
+import static org.hamcrest.Matchers.isIn;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertThat;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.beam.fn.harness.GroupingTable.Receiver;
+import org.apache.beam.fn.harness.PrecombineGroupingTable.Combiner;
+import org.apache.beam.fn.harness.PrecombineGroupingTable.GroupingKeyCreator;
+import
org.apache.beam.fn.harness.PrecombineGroupingTable.SamplingSizeEstimator;
+import org.apache.beam.fn.harness.PrecombineGroupingTable.SizeEstimator;
+import org.apache.beam.sdk.values.KV;
+import org.hamcrest.Description;
+import org.hamcrest.TypeSafeDiagnosingMatcher;
+import org.hamcrest.collection.IsIterableContainingInAnyOrder;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Unit tests for {@link PrecombineGroupingTable}. */
+@RunWith(JUnit4.class)
+public class PrecombineGroupingTableTest {
+
+ private static class TestOutputReceiver implements Receiver {
+ final List<Object> outputElems = new ArrayList<>();
+
+ @Override
+ public void process(Object elem) {
+ outputElems.add(elem);
+ }
+ }
+
+ @Test
+ public void testCombiningGroupingTable() throws Exception {
+ Combiner<Object, Integer, Long, Long> summingCombineFn =
+ new Combiner<Object, Integer, Long, Long>() {
+
+ @Override
+ public Long createAccumulator(Object key) {
+ return 0L;
+ }
+
+ @Override
+ public Long add(Object key, Long accumulator, Integer value) {
+ return accumulator + value;
+ }
+
+ @Override
+ public Long merge(Object key, Iterable<Long> accumulators) {
+ long sum = 0;
+ for (Long part : accumulators) {
+ sum += part;
+ }
+ return sum;
+ }
+
+ @Override
+ public Long compact(Object key, Long accumulator) {
+ return accumulator;
+ }
+
+ @Override
+ public Long extract(Object key, Long accumulator) {
+ return accumulator;
+ }
+ };
+
+ PrecombineGroupingTable<String, Integer, Long> table =
+ new PrecombineGroupingTable<>(
+ 100_000_000L,
+ new IdentityGroupingKeyCreator(),
+ new KvPairInfo(),
+ summingCombineFn,
+ new StringPowerSizeEstimator(),
+ new IdentitySizeEstimator());
+ table.setMaxSize(1000);
+
+ TestOutputReceiver receiver = new TestOutputReceiver();
+
+ table.put("A", 1, receiver);
+ table.put("B", 2, receiver);
+ table.put("B", 3, receiver);
+ table.put("C", 4, receiver);
+ assertThat(receiver.outputElems, empty());
+
+ table.put("C", 5000, receiver);
+ assertThat(receiver.outputElems, hasItem((Object) KV.of("C", 5004L)));
+
+ table.put("DDDD", 6, receiver);
+ assertThat(receiver.outputElems, hasItem((Object) KV.of("DDDD", 6L)));
+
+ table.flush(receiver);
+ assertThat(
+ receiver.outputElems,
+ IsIterableContainingInAnyOrder.containsInAnyOrder(
+ KV.of("A", 1L), KV.of("B", 2L + 3), KV.of("C", 5000L + 4),
KV.of("DDDD", 6L)));
+ }
+
+ ////////////////////////////////////////////////////////////////////////////
+ // Tests for the sampling size estimator.
+
+ @Test
+ public void testSampleFlatSizes() throws Exception {
+ IdentitySizeEstimator underlying = new IdentitySizeEstimator();
+ SizeEstimator<Long> estimator =
+ new SamplingSizeEstimator<>(underlying, 0.05, 1.0, 10, new Random(1));
+ // First 10 elements are always sampled.
+ for (int k = 0; k < 10; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ assertEquals(k + 1, underlying.calls);
+ }
+ // Next 10 are sometimes sampled.
+ for (int k = 10; k < 20; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ }
+ assertThat(underlying.calls, between(11, 19));
+ int initialCalls = underlying.calls;
+ // Next 1000 are sampled at about 5%.
+ for (int k = 20; k < 1020; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ }
+ assertThat(underlying.calls - initialCalls, between(40, 60));
+ }
+
+ @Test
+ public void testSampleBoringSizes() throws Exception {
+ IdentitySizeEstimator underlying = new IdentitySizeEstimator();
+ SizeEstimator<Long> estimator =
+ new SamplingSizeEstimator<>(underlying, 0.05, 1.0, 10, new Random(1));
+ // First 10 elements are always sampled.
+ for (int k = 0; k < 10; k += 2) {
+ assertEquals(100, estimator.estimateSize(100L));
+ assertEquals(102, estimator.estimateSize(102L));
+ assertEquals(k + 2, underlying.calls);
+ }
+ // Next 10 are sometimes sampled.
+ for (int k = 10; k < 20; k += 2) {
+ assertThat(estimator.estimateSize(100L), between(100L, 102L));
+ assertThat(estimator.estimateSize(102L), between(100L, 102L));
+ }
+ assertThat(underlying.calls, between(11, 19));
+ int initialCalls = underlying.calls;
+ // Next 1000 are sampled at about 5%.
+ for (int k = 20; k < 1020; k += 2) {
+ assertThat(estimator.estimateSize(100L), between(100L, 102L));
+ assertThat(estimator.estimateSize(102L), between(100L, 102L));
+ }
+ assertThat(underlying.calls - initialCalls, between(40, 60));
+ }
+
+ @Test
+ public void testSampleHighVarianceSizes() throws Exception {
+ // The largest element is much larger than the average.
+ List<Long> sizes = Arrays.asList(1L, 10L, 100L, 1000L);
+ IdentitySizeEstimator underlying = new IdentitySizeEstimator();
+ SizeEstimator<Long> estimator =
+ new SamplingSizeEstimator<>(underlying, 0.1, 0.2, 10, new Random(1));
+ // First 10 elements are always sampled.
+ for (int k = 0; k < 10; k++) {
+ long size = sizes.get(k % sizes.size());
+ assertEquals(size, estimator.estimateSize(size));
+ assertEquals(k + 1, underlying.calls);
+ }
+ // We're still not out of the woods; sample every element.
+ for (int k = 10; k < 20; k++) {
+ long size = sizes.get(k % sizes.size());
+ assertEquals(size, estimator.estimateSize(size));
+ assertEquals(k + 1, underlying.calls);
+ }
+ // Sample some more to let things settle down.
+ for (int k = 20; k < 500; k++) {
+ estimator.estimateSize(sizes.get(k % sizes.size()));
+ }
+ // Next 1000 are sampled at about 20% (maxSampleRate).
+ int initialCalls = underlying.calls;
+ for (int k = 500; k < 1500; k++) {
+ long size = sizes.get(k % sizes.size());
+ assertThat(estimator.estimateSize(size), anyOf(isIn(sizes),
between(250L, 350L)));
+ }
+ assertThat(underlying.calls - initialCalls, between(180, 220));
+ // Sample some more to let things settle down.
+ for (int k = 1500; k < 3000; k++) {
+ estimator.estimateSize(sizes.get(k % sizes.size()));
+ }
+ // Next 1000 are sampled at about 10% (minSampleRate).
+ initialCalls = underlying.calls;
+ for (int k = 3000; k < 4000; k++) {
+ long size = sizes.get(k % sizes.size());
+ assertThat(estimator.estimateSize(size), anyOf(isIn(sizes),
between(250L, 350L)));
+ }
+ assertThat(underlying.calls - initialCalls, between(90, 110));
+ }
+
+ @Test
+ public void testSampleChangingSizes() throws Exception {
+ IdentitySizeEstimator underlying = new IdentitySizeEstimator();
+ SizeEstimator<Long> estimator =
+ new SamplingSizeEstimator<>(underlying, 0.05, 1.0, 10, new Random(1));
+ // First 10 elements are always sampled.
+ for (int k = 0; k < 10; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ assertEquals(k + 1, underlying.calls);
+ }
+ // Next 10 are sometimes sampled.
+ for (int k = 10; k < 20; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ }
+ assertThat(underlying.calls, between(11, 19));
+ int initialCalls = underlying.calls;
+ // Next 1000 are sampled at about 5%.
+ for (int k = 20; k < 1020; k++) {
+ assertEquals(100, estimator.estimateSize(100L));
+ }
+ assertThat(underlying.calls - initialCalls, between(40, 60));
+ // Inject a big element until it is sampled.
+ while (estimator.estimateSize(1000000L) == 100) {}
+ // Check that we have started sampling more regularly again.
+ assertEquals(99, estimator.estimateSize(99L));
+ }
+
+ private static <T extends Comparable<T>> TypeSafeDiagnosingMatcher<T>
between(
+ final T min, final T max) {
+ return new TypeSafeDiagnosingMatcher<T>() {
+ @Override
+ public void describeTo(Description description) {
+ description.appendText("is between " + min + " and " + max);
+ }
+
+ @Override
+ protected boolean matchesSafely(T item, Description mismatchDescription)
{
+ return min.compareTo(item) <= 0 && item.compareTo(max) <= 0;
+ }
+ };
+ }
+
+ /** Return the key as its grouping key. */
+ private static class IdentityGroupingKeyCreator implements
GroupingKeyCreator<Object> {
+ @Override
+ public Object createGroupingKey(Object key) {
+ return key;
+ }
+ }
+
+ /** "Estimate" the size of longs by looking at their value. */
+ private static class IdentitySizeEstimator implements SizeEstimator<Long> {
+ int calls = 0;
+
+ @Override
+ public long estimateSize(Long element) {
+ calls++;
+ return element;
+ }
+ }
+
+ /** "Estimate" the size of strings by taking the tenth power of their
length. */
+ private static class StringPowerSizeEstimator implements
SizeEstimator<String> {
+ @Override
+ public long estimateSize(String element) {
+ return (long) Math.pow(10, element.length());
+ }
+ }
+
+ private static class KvPairInfo implements PrecombineGroupingTable.PairInfo {
+ @SuppressWarnings("unchecked")
+ @Override
+ public Object getKeyFromInputPair(Object pair) {
+ return ((KV<Object, ?>) pair).getKey();
+ }
+
+ @Override
+ public Object getValueFromInputPair(Object pair) {
+ return ((KV<?, ?>) pair).getValue();
+ }
+
+ @Override
+ public Object makeOutputPair(Object key, Object value) {
+ return KV.of(key, value);
+ }
+ }
+}
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Issue Time Tracking
-------------------
Worklog Id: (was: 118339)
Time Spent: 5h 50m (was: 5h 40m)
> Implement the portable lifted Combiner transforms in Java SDK
> -------------------------------------------------------------
>
> Key: BEAM-3708
> URL: https://issues.apache.org/jira/browse/BEAM-3708
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-java-core, sdk-java-harness
> Reporter: Daniel Oliveira
> Assignee: Daniel Oliveira
> Priority: Major
> Labels: portability
> Time Spent: 5h 50m
> Remaining Estimate: 0h
>
> Lifted combines are split into separate parts with different URNs. These
> parts need to be implemented in the Java SDK harness so that the SDK can
> actually execute them when receiving Combine transforms with the
> corresponding URNs.
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