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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2023/01/02 09:28:49 UTC
[GitHub] [beam] je-ik commented on a diff in pull request #24837: Enable async processing for SDF on Spark runner #23852
je-ik commented on code in PR #24837:
URL: https://github.com/apache/beam/pull/24837#discussion_r1059907930
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/SparkCommonPipelineOptions.java:
##########
@@ -84,4 +84,11 @@ static void prepareFilesToStage(SparkCommonPipelineOptions options) {
PipelineResources.prepareFilesForStaging(options);
}
}
+
+ @Description(
+ "Enable/disable async output for operators with possibly large output ( such as splittable DoFn )")
+ @Default.Boolean(true)
+ Boolean getEnableAsyncOperatorOutput();
Review Comment:
Is this really what it does? Is it async? My understanding was, that it only uses two threads that pass data using BlockingQueue. Did I miss something?
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
Review Comment:
Should this check if the bundle is non-empty? I.e. call startBundle only there are elements to be processed?
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
+ isBundleStarted = true;
+ // call startBundle() before beginning to process the partition.
+ ctx.getDoFnRunner().startBundle();
+ }
+
+ while (true) {
+ if (outputIterator.hasNext()) {
+ return outputIterator.next();
+ }
+
+ outputManager.clear();
+ if (inputIterator.hasNext()) {
+ // grab the next element and process it.
+ ctx.getDoFnRunner().processElement(inputIterator.next());
+ outputIterator = outputManager.iterator();
+ } else if (ctx.getTimerDataIterator().hasNext()) {
+ fireTimer(ctx.getTimerDataIterator().next());
+ outputIterator = outputManager.iterator();
+ } else {
+ // no more input to consume, but finishBundle can produce more output
+ if (!isBundleFinished) {
+ isBundleFinished = true;
+ ctx.getDoFnRunner().finishBundle();
+ outputIterator = outputManager.iterator();
+ continue; // try to consume outputIterator from start of loop
+ }
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ return endOfData();
+ }
+ }
+ } catch (final RuntimeException re) {
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ throw re;
+ }
+ }
+
+ private void fireTimer(TimerInternals.TimerData timer) {
+ StateNamespace namespace = timer.getNamespace();
+ checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+ BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+ ctx.getDoFnRunner()
+ .onTimer(
+ timer.getTimerId(),
+ timer.getTimerFamilyId(),
+ ctx.getKey(),
+ window,
+ timer.getTimestamp(),
+ timer.getOutputTimestamp(),
+ timer.getDomain());
+ }
+ }
+}
+
+class AsyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final BlockingDoFnOutputManager outputManager = new BlockingDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new AsyncInOutIterator<>(input, ctx);
+ }
+
+ /**
+ * Output manager which can hold limited number of output elements. If capacity is reached, then
+ * attempt to output more elements will block until some elements are consumed.
+ */
+ private class BlockingDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final LinkedBlockingQueue<Tuple2<TupleTag<?>, WindowedValue<?>>> queue =
+ new LinkedBlockingQueue<>(500);
+ private volatile boolean stopped = false;
+
+ public void stop() {
+ stopped = true;
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ return new Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>>() {
+
+ @Override
+ public boolean hasNext() {
+ // expect elements appearing in queue until stop() is invoked.
+ // after that, no more inputs can arrive, so just drain the queue
+ while (true) {
+ if (queue.isEmpty()) {
+ try {
+ // Wait for a bit before checking again if more data is available
+ Thread.sleep(20);
+ if (stopped) {
+ return !queue.isEmpty();
+ }
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
+ }
+ } else {
+ return true;
+ }
+ }
+ }
+
+ @Override
+ public Tuple2<TupleTag<?>, WindowedValue<?>> next() {
+ Tuple2<TupleTag<?>, WindowedValue<?>> poll;
+ try {
+ poll = queue.poll(Long.MAX_VALUE, TimeUnit.MILLISECONDS);
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
+ throw new RuntimeException(e);
Review Comment:
Possibly `IllegalStateException`?
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/SparkCommonPipelineOptions.java:
##########
@@ -84,4 +84,11 @@ static void prepareFilesToStage(SparkCommonPipelineOptions options) {
PipelineResources.prepareFilesForStaging(options);
}
}
+
+ @Description(
+ "Enable/disable async output for operators with possibly large output ( such as splittable DoFn )")
Review Comment:
```suggestion
"Enable/disable async output for operators with possibly large output (such as splittable DoFn)")
```
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
Review Comment:
```suggestion
```
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
+ isBundleStarted = true;
+ // call startBundle() before beginning to process the partition.
+ ctx.getDoFnRunner().startBundle();
+ }
+
+ while (true) {
+ if (outputIterator.hasNext()) {
+ return outputIterator.next();
+ }
+
+ outputManager.clear();
+ if (inputIterator.hasNext()) {
+ // grab the next element and process it.
+ ctx.getDoFnRunner().processElement(inputIterator.next());
+ outputIterator = outputManager.iterator();
+ } else if (ctx.getTimerDataIterator().hasNext()) {
+ fireTimer(ctx.getTimerDataIterator().next());
+ outputIterator = outputManager.iterator();
+ } else {
+ // no more input to consume, but finishBundle can produce more output
+ if (!isBundleFinished) {
+ isBundleFinished = true;
+ ctx.getDoFnRunner().finishBundle();
+ outputIterator = outputManager.iterator();
+ continue; // try to consume outputIterator from start of loop
+ }
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ return endOfData();
+ }
+ }
+ } catch (final RuntimeException re) {
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ throw re;
+ }
+ }
+
+ private void fireTimer(TimerInternals.TimerData timer) {
+ StateNamespace namespace = timer.getNamespace();
+ checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+ BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+ ctx.getDoFnRunner()
+ .onTimer(
+ timer.getTimerId(),
+ timer.getTimerFamilyId(),
+ ctx.getKey(),
+ window,
+ timer.getTimestamp(),
+ timer.getOutputTimestamp(),
+ timer.getDomain());
+ }
+ }
+}
+
+class AsyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final BlockingDoFnOutputManager outputManager = new BlockingDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new AsyncInOutIterator<>(input, ctx);
+ }
+
+ /**
+ * Output manager which can hold limited number of output elements. If capacity is reached, then
+ * attempt to output more elements will block until some elements are consumed.
+ */
+ private class BlockingDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final LinkedBlockingQueue<Tuple2<TupleTag<?>, WindowedValue<?>>> queue =
+ new LinkedBlockingQueue<>(500);
+ private volatile boolean stopped = false;
+
+ public void stop() {
+ stopped = true;
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ return new Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>>() {
+
+ @Override
+ public boolean hasNext() {
+ // expect elements appearing in queue until stop() is invoked.
+ // after that, no more inputs can arrive, so just drain the queue
+ while (true) {
+ if (queue.isEmpty()) {
+ try {
+ // Wait for a bit before checking again if more data is available
+ Thread.sleep(20);
Review Comment:
Why to have fixed waiting? Could it do `queue.poll(timeout)`?
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
+ isBundleStarted = true;
+ // call startBundle() before beginning to process the partition.
+ ctx.getDoFnRunner().startBundle();
+ }
+
+ while (true) {
+ if (outputIterator.hasNext()) {
+ return outputIterator.next();
+ }
+
+ outputManager.clear();
+ if (inputIterator.hasNext()) {
+ // grab the next element and process it.
+ ctx.getDoFnRunner().processElement(inputIterator.next());
+ outputIterator = outputManager.iterator();
+ } else if (ctx.getTimerDataIterator().hasNext()) {
+ fireTimer(ctx.getTimerDataIterator().next());
+ outputIterator = outputManager.iterator();
+ } else {
+ // no more input to consume, but finishBundle can produce more output
+ if (!isBundleFinished) {
+ isBundleFinished = true;
+ ctx.getDoFnRunner().finishBundle();
+ outputIterator = outputManager.iterator();
+ continue; // try to consume outputIterator from start of loop
+ }
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ return endOfData();
+ }
+ }
+ } catch (final RuntimeException re) {
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ throw re;
+ }
+ }
+
+ private void fireTimer(TimerInternals.TimerData timer) {
+ StateNamespace namespace = timer.getNamespace();
+ checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+ BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+ ctx.getDoFnRunner()
+ .onTimer(
+ timer.getTimerId(),
+ timer.getTimerFamilyId(),
+ ctx.getKey(),
+ window,
+ timer.getTimestamp(),
+ timer.getOutputTimestamp(),
+ timer.getDomain());
+ }
+ }
+}
+
+class AsyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final BlockingDoFnOutputManager outputManager = new BlockingDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new AsyncInOutIterator<>(input, ctx);
+ }
+
+ /**
+ * Output manager which can hold limited number of output elements. If capacity is reached, then
+ * attempt to output more elements will block until some elements are consumed.
+ */
+ private class BlockingDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final LinkedBlockingQueue<Tuple2<TupleTag<?>, WindowedValue<?>>> queue =
+ new LinkedBlockingQueue<>(500);
+ private volatile boolean stopped = false;
+
+ public void stop() {
+ stopped = true;
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ return new Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>>() {
+
+ @Override
+ public boolean hasNext() {
+ // expect elements appearing in queue until stop() is invoked.
+ // after that, no more inputs can arrive, so just drain the queue
+ while (true) {
+ if (queue.isEmpty()) {
+ try {
+ // Wait for a bit before checking again if more data is available
+ Thread.sleep(20);
+ if (stopped) {
+ return !queue.isEmpty();
+ }
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
Review Comment:
This should break the loop. Might be safer to rethrow the exception, because terminating the iteration looks dangerous.
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
+ isBundleStarted = true;
+ // call startBundle() before beginning to process the partition.
+ ctx.getDoFnRunner().startBundle();
+ }
+
+ while (true) {
+ if (outputIterator.hasNext()) {
+ return outputIterator.next();
+ }
+
+ outputManager.clear();
+ if (inputIterator.hasNext()) {
+ // grab the next element and process it.
+ ctx.getDoFnRunner().processElement(inputIterator.next());
+ outputIterator = outputManager.iterator();
+ } else if (ctx.getTimerDataIterator().hasNext()) {
+ fireTimer(ctx.getTimerDataIterator().next());
+ outputIterator = outputManager.iterator();
+ } else {
+ // no more input to consume, but finishBundle can produce more output
+ if (!isBundleFinished) {
+ isBundleFinished = true;
+ ctx.getDoFnRunner().finishBundle();
+ outputIterator = outputManager.iterator();
+ continue; // try to consume outputIterator from start of loop
+ }
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ return endOfData();
+ }
+ }
+ } catch (final RuntimeException re) {
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ throw re;
+ }
+ }
+
+ private void fireTimer(TimerInternals.TimerData timer) {
+ StateNamespace namespace = timer.getNamespace();
+ checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+ BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+ ctx.getDoFnRunner()
+ .onTimer(
+ timer.getTimerId(),
+ timer.getTimerFamilyId(),
+ ctx.getKey(),
+ window,
+ timer.getTimestamp(),
+ timer.getOutputTimestamp(),
+ timer.getDomain());
+ }
+ }
+}
+
+class AsyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final BlockingDoFnOutputManager outputManager = new BlockingDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new AsyncInOutIterator<>(input, ctx);
+ }
+
+ /**
+ * Output manager which can hold limited number of output elements. If capacity is reached, then
+ * attempt to output more elements will block until some elements are consumed.
+ */
+ private class BlockingDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final LinkedBlockingQueue<Tuple2<TupleTag<?>, WindowedValue<?>>> queue =
+ new LinkedBlockingQueue<>(500);
+ private volatile boolean stopped = false;
+
+ public void stop() {
+ stopped = true;
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ return new Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>>() {
+
+ @Override
+ public boolean hasNext() {
+ // expect elements appearing in queue until stop() is invoked.
+ // after that, no more inputs can arrive, so just drain the queue
+ while (true) {
+ if (queue.isEmpty()) {
+ try {
+ // Wait for a bit before checking again if more data is available
+ Thread.sleep(20);
+ if (stopped) {
+ return !queue.isEmpty();
+ }
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
+ }
+ } else {
+ return true;
+ }
+ }
+ }
+
+ @Override
+ public Tuple2<TupleTag<?>, WindowedValue<?>> next() {
Review Comment:
Do we lack bundle management here? I.e. starting bundle, finishing bundle, teardown?
##########
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java:
##########
@@ -0,0 +1,395 @@
+/*
+ * 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 static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument;
+
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.runners.core.DoFnRunners.OutputManager;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Function;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.AbstractIterator;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Iterators;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.LinkedListMultimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.collect.Multimap;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import scala.Tuple2;
+
+/** Processes Spark partitions using Beam's {@link org.apache.beam.runners.core.DoFnRunner}. */
+interface SparkInputDataProcessor<FnInputT, FnOutputT, OutputT> {
+
+ /**
+ * @return {@link OutputManager} to be used by {@link org.apache.beam.runners.core.DoFnRunner} for
+ * emitting processing results
+ */
+ OutputManager getOutputManager();
+
+ /**
+ * Processes input partition data and return results as {@link Iterable}.
+ *
+ * @param input input partition iterator
+ * @param ctx current processing context
+ */
+ <K> Iterable<OutputT> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx);
+
+ /**
+ * Creates a synchronous {@link SparkInputDataProcessor} which does processing in calling thread.
+ * It is doing so by processing fully input element and then iterating over the output retrieved
+ * from that processing. The result of processing one element fit into memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createSync() {
+ return new SyncSparkInputDataProcessor<>();
+ }
+
+ /**
+ * Create and asynchronous {@link SparkInputDataProcessor} which does process input elements in
+ * separate thread and observes produced outputs asynchronously. This does not require results of
+ * processing one element to fit into the memory.
+ */
+ static <FnInputT, FnOutputT>
+ SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>>
+ createAsync() {
+ return new AsyncSparkInputDataProcessor<>();
+ }
+}
+
+class SyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new SyncInOutIterator<>(input, ctx);
+ }
+
+ private static class UnboundedDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Multimap<TupleTag<?>, WindowedValue<?>> outputs = LinkedListMultimap.create();
+
+ public void clear() {
+ outputs.clear();
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ Iterator<Map.Entry<TupleTag<?>, WindowedValue<?>>> entryIter = outputs.entries().iterator();
+ return Iterators.transform(entryIter, this.entryToTupleFn());
+ }
+
+ private <K, V> Function<Map.Entry<K, V>, Tuple2<K, V>> entryToTupleFn() {
+ return en -> {
+ if (en == null) {
+ return null;
+ } else {
+ return new Tuple2<>(en.getKey(), en.getValue());
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ outputs.put(tag, output);
+ }
+ }
+
+ private class SyncInOutIterator<K>
+ extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final Iterator<WindowedValue<FnInputT>> inputIterator;
+ private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
+ private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+ private boolean isBundleStarted;
+ private boolean isBundleFinished;
+
+ SyncInOutIterator(
+ Iterator<WindowedValue<FnInputT>> iterator,
+ SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ this.inputIterator = iterator;
+ this.ctx = ctx;
+ this.outputIterator = outputManager.iterator();
+ ;
+ }
+
+ @Override
+ protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+ try {
+ // Process each element from the (input) iterator, which produces, zero, one or more
+ // output elements (of type V) in the output iterator. Note that the output
+ // collection (and iterator) is reset between each call to processElement, so the
+ // collection only holds the output values for each call to processElement, rather
+ // than for the whole partition (which would use too much memory).
+ if (!isBundleStarted) {
+ isBundleStarted = true;
+ // call startBundle() before beginning to process the partition.
+ ctx.getDoFnRunner().startBundle();
+ }
+
+ while (true) {
+ if (outputIterator.hasNext()) {
+ return outputIterator.next();
+ }
+
+ outputManager.clear();
+ if (inputIterator.hasNext()) {
+ // grab the next element and process it.
+ ctx.getDoFnRunner().processElement(inputIterator.next());
+ outputIterator = outputManager.iterator();
+ } else if (ctx.getTimerDataIterator().hasNext()) {
+ fireTimer(ctx.getTimerDataIterator().next());
+ outputIterator = outputManager.iterator();
+ } else {
+ // no more input to consume, but finishBundle can produce more output
+ if (!isBundleFinished) {
+ isBundleFinished = true;
+ ctx.getDoFnRunner().finishBundle();
+ outputIterator = outputManager.iterator();
+ continue; // try to consume outputIterator from start of loop
+ }
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ return endOfData();
+ }
+ }
+ } catch (final RuntimeException re) {
+ DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown();
+ throw re;
+ }
+ }
+
+ private void fireTimer(TimerInternals.TimerData timer) {
+ StateNamespace namespace = timer.getNamespace();
+ checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+ BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+ ctx.getDoFnRunner()
+ .onTimer(
+ timer.getTimerId(),
+ timer.getTimerFamilyId(),
+ ctx.getKey(),
+ window,
+ timer.getTimestamp(),
+ timer.getOutputTimestamp(),
+ timer.getDomain());
+ }
+ }
+}
+
+class AsyncSparkInputDataProcessor<FnInputT, FnOutputT>
+ implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final BlockingDoFnOutputManager outputManager = new BlockingDoFnOutputManager();
+
+ @Override
+ public OutputManager getOutputManager() {
+ return outputManager;
+ }
+
+ @Override
+ public <K> Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> process(
+ Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+ if (!input.hasNext()) {
+ return Collections.emptyList();
+ }
+ return () -> new AsyncInOutIterator<>(input, ctx);
+ }
+
+ /**
+ * Output manager which can hold limited number of output elements. If capacity is reached, then
+ * attempt to output more elements will block until some elements are consumed.
+ */
+ private class BlockingDoFnOutputManager
+ implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+
+ private final LinkedBlockingQueue<Tuple2<TupleTag<?>, WindowedValue<?>>> queue =
+ new LinkedBlockingQueue<>(500);
+ private volatile boolean stopped = false;
+
+ public void stop() {
+ stopped = true;
+ }
+
+ @Override
+ public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() {
+ return new Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>>() {
+
+ @Override
+ public boolean hasNext() {
+ // expect elements appearing in queue until stop() is invoked.
+ // after that, no more inputs can arrive, so just drain the queue
+ while (true) {
+ if (queue.isEmpty()) {
+ try {
+ // Wait for a bit before checking again if more data is available
+ Thread.sleep(20);
+ if (stopped) {
+ return !queue.isEmpty();
+ }
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
+ }
+ } else {
+ return true;
+ }
+ }
+ }
+
+ @Override
+ public Tuple2<TupleTag<?>, WindowedValue<?>> next() {
+ Tuple2<TupleTag<?>, WindowedValue<?>> poll;
+ try {
+ poll = queue.poll(Long.MAX_VALUE, TimeUnit.MILLISECONDS);
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
+ throw new RuntimeException(e);
+ }
+ return poll;
+ }
+ };
+ }
+
+ @Override
+ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
+ try {
+ if (stopped) {
+ throw new IllegalStateException("Output called on already stopped manager");
+ }
+ queue.put(new Tuple2<>(tag, output));
+ } catch (InterruptedException e) {
+ Thread.currentThread().interrupt();
Review Comment:
I think we should rethrow here as well.
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