[
https://issues.apache.org/jira/browse/BEAM-2590?focusedWorklogId=221994&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-221994
]
ASF GitHub Bot logged work on BEAM-2590:
----------------------------------------
Author: ASF GitHub Bot
Created on: 02/Apr/19 21:08
Start Date: 02/Apr/19 21:08
Worklog Time Spent: 10m
Work Description: ibzib commented on pull request #8115: [BEAM-2590]
Implement basic Spark portable runner
URL: https://github.com/apache/beam/pull/8115#discussion_r271496729
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkBatchPortablePipelineTranslator.java
##########
@@ -0,0 +1,210 @@
+/*
+ * 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.runners.fnexecution.translation.PipelineTranslatorUtils.createOutputMap;
+import static
org.apache.beam.runners.fnexecution.translation.PipelineTranslatorUtils.getWindowingStrategy;
+
+import java.io.IOException;
+import java.util.Collections;
+import java.util.Map;
+import java.util.Set;
+import javax.annotation.Nullable;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.model.pipeline.v1.RunnerApi.PCollection;
+import org.apache.beam.runners.core.SystemReduceFn;
+import org.apache.beam.runners.core.construction.PTransformTranslation;
+import org.apache.beam.runners.core.construction.graph.ExecutableStage;
+import org.apache.beam.runners.core.construction.graph.PipelineNode;
+import
org.apache.beam.runners.core.construction.graph.PipelineNode.PCollectionNode;
+import
org.apache.beam.runners.core.construction.graph.PipelineNode.PTransformNode;
+import org.apache.beam.runners.core.construction.graph.QueryablePipeline;
+import org.apache.beam.runners.fnexecution.wire.WireCoders;
+import org.apache.beam.runners.spark.SparkPipelineOptions;
+import org.apache.beam.runners.spark.aggregators.AggregatorsAccumulator;
+import org.apache.beam.sdk.coders.ByteArrayCoder;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.transforms.join.RawUnionValue;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.TimestampCombiner;
+import org.apache.beam.sdk.transforms.windowing.WindowFn;
+import org.apache.beam.sdk.util.WindowedValue;
+import org.apache.beam.sdk.util.WindowedValue.WindowedValueCoder;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.WindowingStrategy;
+import org.apache.beam.vendor.guava.v20_0.com.google.common.collect.BiMap;
+import
org.apache.beam.vendor.guava.v20_0.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v20_0.com.google.common.collect.Iterables;
+import org.apache.spark.HashPartitioner;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.JavaRDD;
+
+/** Translates a bounded portable pipeline into a Spark job. */
+public class SparkBatchPortablePipelineTranslator {
+
+ private ImmutableMap<String, PTransformTranslator> urnToTransformTranslator;
+
+ interface PTransformTranslator {
+
+ /** Translates transformNode from Beam into the Spark context. */
+ void translate(
+ PTransformNode transformNode, RunnerApi.Pipeline pipeline,
SparkTranslationContext context);
+ }
+
+ public Set<String> knownUrns() {
+ return urnToTransformTranslator.keySet();
+ }
+
+ public SparkBatchPortablePipelineTranslator() {
+ ImmutableMap.Builder<String, PTransformTranslator> translatorMap =
ImmutableMap.builder();
+ translatorMap.put(
+ PTransformTranslation.IMPULSE_TRANSFORM_URN,
+ SparkBatchPortablePipelineTranslator::translateImpulse);
+ translatorMap.put(
+ PTransformTranslation.GROUP_BY_KEY_TRANSFORM_URN,
+ SparkBatchPortablePipelineTranslator::translateGroupByKey);
+ translatorMap.put(
+ ExecutableStage.URN,
SparkBatchPortablePipelineTranslator::translateExecutableStage);
+ this.urnToTransformTranslator = translatorMap.build();
+ }
+
+ /** Translates pipeline from Beam into the Spark context. */
+ public void translate(final RunnerApi.Pipeline pipeline,
SparkTranslationContext context) {
+ QueryablePipeline p =
+ QueryablePipeline.forTransforms(
+ pipeline.getRootTransformIdsList(), pipeline.getComponents());
+ for (PipelineNode.PTransformNode transformNode :
p.getTopologicallyOrderedTransforms()) {
+ urnToTransformTranslator
+ .getOrDefault(
+ transformNode.getTransform().getSpec().getUrn(),
+ SparkBatchPortablePipelineTranslator::urnNotFound)
+ .translate(transformNode, pipeline, context);
+ }
+ }
+
+ private static void urnNotFound(
+ PTransformNode transformNode, RunnerApi.Pipeline pipeline,
SparkTranslationContext context) {
+ throw new IllegalArgumentException(
+ String.format(
+ "Transform %s has unknown URN %s",
+ transformNode.getId(),
transformNode.getTransform().getSpec().getUrn()));
+ }
+
+ private static void translateImpulse(
+ PTransformNode transformNode, RunnerApi.Pipeline pipeline,
SparkTranslationContext context) {
+ BoundedDataset<byte[]> output =
+ new BoundedDataset<>(
+ Collections.singletonList(new byte[0]), context.getSparkContext(),
ByteArrayCoder.of());
+ context.pushDataset(getOutputId(transformNode), output);
+ }
+
+ private static <K, V> void translateGroupByKey(
+ PTransformNode transformNode, RunnerApi.Pipeline pipeline,
SparkTranslationContext context) {
+
+ RunnerApi.Components components = pipeline.getComponents();
+ String inputId = getInputId(transformNode);
+ PCollection inputPCollection = components.getPcollectionsOrThrow(inputId);
+ Dataset inputDataset = context.popDataset(inputId);
+ JavaRDD<WindowedValue<KV<K, V>>> inputRdd = ((BoundedDataset<KV<K, V>>)
inputDataset).getRDD();
+ PCollectionNode inputPCollectionNode = PipelineNode.pCollection(inputId,
inputPCollection);
+ WindowedValueCoder<KV<K, V>> inputCoder;
+ try {
+ inputCoder =
+ (WindowedValueCoder)
+ WireCoders.instantiateRunnerWireCoder(inputPCollectionNode,
components);
+ } catch (IOException e) {
+ throw new RuntimeException(e);
+ }
+ KvCoder<K, V> inputKvCoder = (KvCoder<K, V>) inputCoder.getValueCoder();
+ Coder<K> inputKeyCoder = inputKvCoder.getKeyCoder();
+ Coder<V> inputValueCoder = inputKvCoder.getValueCoder();
+ WindowingStrategy windowingStrategy = getWindowingStrategy(inputId,
components);
+ WindowFn<Object, BoundedWindow> windowFn = windowingStrategy.getWindowFn();
+ WindowedValue.WindowedValueCoder<V> wvCoder =
+ WindowedValue.FullWindowedValueCoder.of(inputValueCoder,
windowFn.windowCoder());
+
+ JavaRDD<WindowedValue<KV<K, Iterable<V>>>> groupedByKeyAndWindow;
+ if (windowingStrategy.getWindowFn().isNonMerging()
+ && windowingStrategy.getTimestampCombiner() ==
TimestampCombiner.END_OF_WINDOW) {
+ // we can have a memory sensitive translation for non-merging windows
+ groupedByKeyAndWindow =
+ GroupNonMergingWindowsFunctions.groupByKeyAndWindow(
+ inputRdd, inputKeyCoder, inputValueCoder, windowingStrategy);
+ } else {
+ Partitioner partitioner = getPartitioner(context);
+ JavaRDD<WindowedValue<KV<K, Iterable<WindowedValue<V>>>>>
groupedByKeyOnly =
+ GroupCombineFunctions.groupByKeyOnly(inputRdd, inputKeyCoder,
wvCoder, partitioner);
+ // for batch, GroupAlsoByWindow uses an in-memory StateInternals.
+ groupedByKeyAndWindow =
+ groupedByKeyOnly.flatMap(
+ new SparkGroupAlsoByWindowViaOutputBufferFn<>(
+ windowingStrategy,
+ new TranslationUtils.InMemoryStateInternalsFactory<>(),
+ SystemReduceFn.buffering(inputValueCoder),
+ context.serializablePipelineOptions,
+ AggregatorsAccumulator.getInstance()));
+ }
+ context.pushDataset(getOutputId(transformNode), new
BoundedDataset<>(groupedByKeyAndWindow));
+ }
+
+ private static <InputT, OutputT> void translateExecutableStage(
+ PTransformNode transformNode, RunnerApi.Pipeline pipeline,
SparkTranslationContext context) {
+
+ RunnerApi.ExecutableStagePayload stagePayload;
+ try {
+ stagePayload =
+ RunnerApi.ExecutableStagePayload.parseFrom(
+ transformNode.getTransform().getSpec().getPayload());
+ } catch (IOException e) {
+ throw new RuntimeException(e);
+ }
+ String inputPCollectionId = stagePayload.getInput();
+ Dataset inputDataset = context.popDataset(inputPCollectionId);
+ JavaRDD<WindowedValue<InputT>> inputRdd = ((BoundedDataset<InputT>)
inputDataset).getRDD();
+ Map<String, String> outputs = transformNode.getTransform().getOutputsMap();
+ BiMap<String, Integer> outputMap = createOutputMap(outputs.values());
+
+ SparkExecutableStageFunction<InputT> function =
+ new SparkExecutableStageFunction<>(stagePayload, context.jobInfo,
outputMap);
+ JavaRDD<RawUnionValue> staged = inputRdd.mapPartitions(function);
+
+ for (String outputId : outputs.values()) {
Review comment:
I found there is an issue here--sometimes (e.g. for PAsserts) there will be
no outputs, in which case we need to create a sink (like
[this](https://github.com/ibzib/beam/blob/1f1660f0c97e65bb91e2c5aa7694b6d7b9215bbd/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkBatchPortablePipelineTranslator.java#L398-L403)).
I will add a fix in a future PR.
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Issue Time Tracking
-------------------
Worklog Id: (was: 221994)
Time Spent: 1h 40m (was: 1.5h)
> SparkRunner shim for Job API
> ----------------------------
>
> Key: BEAM-2590
> URL: https://issues.apache.org/jira/browse/BEAM-2590
> Project: Beam
> Issue Type: Sub-task
> Components: runner-spark
> Reporter: Kenneth Knowles
> Assignee: Kyle Weaver
> Priority: Major
> Labels: portability, triaged
> Time Spent: 1h 40m
> Remaining Estimate: 0h
>
> Whatever the result of https://s.apache.org/beam-job-api we will need a way
> for the JVM-based SparkRunner to receive and run pipelines authors in Python.
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