JozoVilcek commented on code in PR #24837: URL: https://github.com/apache/beam/pull/24837#discussion_r1092118237
########## runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java: ########## @@ -0,0 +1,371 @@ +/* + * 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.ArrayDeque; +import java.util.Iterator; +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.Preconditions; +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.util.concurrent.ThreadFactoryBuilder; +import org.checkerframework.checker.nullness.qual.MonotonicNonNull; +import org.checkerframework.checker.nullness.qual.Nullable; +import scala.Tuple2; + +/** + * Processes Spark's input data iterators 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> Iterator<OutputT> process( + Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx); + + /** + * Creates {@link SparkInputDataProcessor} which does processing in calling thread. It is doing so + * by processing input element completely and then iterating over the output retrieved from that + * processing. The result of processing one element must fit into memory. + */ + static <FnInputT, FnOutputT> + SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> + createUnbounded() { + return new UnboundedSparkInputDataProcessor<>(); + } + + /** + * Creates {@link SparkInputDataProcessor} which does process input elements in separate thread + * and observes produced outputs via bounded queue in other thread. This does not require results + * of processing one element to fit into the memory. + */ + static <FnInputT, FnOutputT> + SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> + createBounded() { + return new BoundedSparkInputDataProcessor<>(); + } +} + +class UnboundedSparkInputDataProcessor<FnInputT, FnOutputT> + implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager(); + + @Override + public OutputManager getOutputManager() { + return outputManager; + } + + @Override + public <K> Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> process( + Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) { + return new UnboundedInOutIterator<>(input, ctx); + } + + private static class UnboundedDoFnOutputManager + implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final ArrayDeque<Tuple2<TupleTag<?>, WindowedValue<?>>> outputs = new ArrayDeque<>(); + + public void clear() { + outputs.clear(); + } + + @Override + public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() { + return outputs.iterator(); + } + + @Override + public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) { + outputs.push(new Tuple2<>(tag, output)); + } + } + + private class UnboundedInOutIterator<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; + + UnboundedInOutIterator( + 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 BoundedSparkInputDataProcessor<FnInputT, FnOutputT> + implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final BoundedDoFnOutputManager outputManager = new BoundedDoFnOutputManager(); + + @Override + public OutputManager getOutputManager() { + return outputManager; + } + + @Override + public <K> Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> process( + Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) { + return new BoundedInOutIterator<>(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 static class BoundedDoFnOutputManager + 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<?>>>() { + + private @Nullable Tuple2<TupleTag<?>, WindowedValue<?>> value = null; + + @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) { + try { + value = queue.poll(1000, TimeUnit.MILLISECONDS); + if (value != null) { + return true; + } + if (stopped) { + return false; + } + } catch (InterruptedException e) { + Thread.currentThread().interrupt(); + throw new IllegalStateException(e); + } + } + } + + @Override + @SuppressWarnings({"nullness"}) + public Tuple2<TupleTag<?>, WindowedValue<?>> next() { + return value; + } + }; + } + + @Override + public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) { + try { + Preconditions.checkState(!stopped, "Output called on already stopped manager"); + queue.put(new Tuple2<>(tag, output)); + } catch (InterruptedException e) { + Thread.currentThread().interrupt(); + throw new IllegalStateException(e); + } + } + } + + private class BoundedInOutIterator<K, InputT, OutputT> + extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final SparkProcessContext<K, InputT, OutputT> ctx; + private final Iterator<WindowedValue<InputT>> inputIterator; + private final Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator; + private final ExecutorService executorService; + private @Nullable Future<?> outputProducerTask = null; + + private volatile @MonotonicNonNull RuntimeException inputConsumeFailure = null; + + BoundedInOutIterator( + Iterator<WindowedValue<InputT>> iterator, SparkProcessContext<K, InputT, OutputT> ctx) { + this.inputIterator = iterator; + this.ctx = ctx; + this.outputIterator = outputManager.iterator(); + this.executorService = + Executors.newSingleThreadScheduledExecutor( + new ThreadFactoryBuilder() + .setNameFormat("bounded-in/out-iterator-" + ctx.getStepName() + "-%d") + .setDaemon(true) + .build()); + } + + @Override + protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() { + + if (outputProducerTask == null) { + outputProducerTask = + executorService.submit( + () -> { + try { + ctx.getDoFnRunner().startBundle(); + while (true) { + if (inputIterator.hasNext()) { + // grab the next element and process it. + WindowedValue<InputT> next = inputIterator.next(); + ctx.getDoFnRunner().processElement(next); + + } else if (ctx.getTimerDataIterator().hasNext()) { + fireTimer(ctx.getTimerDataIterator().next()); + } else { + + // no more input to consume, but finishBundle can produce more output + ctx.getDoFnRunner().finishBundle(); + DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown(); + + outputManager.stop(); + break; + } + } + + } catch (RuntimeException ex) { + inputConsumeFailure = ex; + DoFnInvokers.invokerFor(ctx.getDoFn()).invokeTeardown(); + outputManager.stop(); + } + }); + } + + while (true) { Review Comment: Fixed ########## runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java: ########## @@ -0,0 +1,371 @@ +/* + * 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.ArrayDeque; +import java.util.Iterator; +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.Preconditions; +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.util.concurrent.ThreadFactoryBuilder; +import org.checkerframework.checker.nullness.qual.MonotonicNonNull; +import org.checkerframework.checker.nullness.qual.Nullable; +import scala.Tuple2; + +/** + * Processes Spark's input data iterators 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> Iterator<OutputT> process( + Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx); + + /** + * Creates {@link SparkInputDataProcessor} which does processing in calling thread. It is doing so + * by processing input element completely and then iterating over the output retrieved from that + * processing. The result of processing one element must fit into memory. + */ + static <FnInputT, FnOutputT> + SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> + createUnbounded() { + return new UnboundedSparkInputDataProcessor<>(); + } + + /** + * Creates {@link SparkInputDataProcessor} which does process input elements in separate thread + * and observes produced outputs via bounded queue in other thread. This does not require results + * of processing one element to fit into the memory. + */ + static <FnInputT, FnOutputT> + SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> + createBounded() { + return new BoundedSparkInputDataProcessor<>(); + } +} + +class UnboundedSparkInputDataProcessor<FnInputT, FnOutputT> + implements SparkInputDataProcessor<FnInputT, FnOutputT, Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final UnboundedDoFnOutputManager outputManager = new UnboundedDoFnOutputManager(); + + @Override + public OutputManager getOutputManager() { + return outputManager; + } + + @Override + public <K> Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> process( + Iterator<WindowedValue<FnInputT>> input, SparkProcessContext<K, FnInputT, FnOutputT> ctx) { + return new UnboundedInOutIterator<>(input, ctx); + } + + private static class UnboundedDoFnOutputManager + implements OutputManager, Iterable<Tuple2<TupleTag<?>, WindowedValue<?>>> { + + private final ArrayDeque<Tuple2<TupleTag<?>, WindowedValue<?>>> outputs = new ArrayDeque<>(); + + public void clear() { + outputs.clear(); + } + + @Override + public Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> iterator() { + return outputs.iterator(); + } + + @Override + public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) { + outputs.push(new Tuple2<>(tag, output)); Review Comment: Fixed -- This is an automated message from the Apache Git Service. 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