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https://issues.apache.org/jira/browse/BEAM-5309?focusedWorklogId=156919&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-156919
]
ASF GitHub Bot logged work on BEAM-5309:
----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Oct/18 18:42
Start Date: 22/Oct/18 18:42
Worklog Time Spent: 10m
Work Description: b923 commented on a change in pull request #6691:
WIP:[BEAM-5309] Add streaming support for HadoopFormatIO
URL: https://github.com/apache/beam/pull/6691#discussion_r226975441
##########
File path:
sdks/java/io/hadoop-format/src/main/java/org/apache/beam/sdk/io/hadoop/format/HadoopFormatIO.java
##########
@@ -0,0 +1,1247 @@
+/*
+ * 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.sdk.io.hadoop.format;
+
+import static com.google.common.base.Preconditions.checkArgument;
+import static java.util.Objects.requireNonNull;
+
+import com.google.auto.value.AutoValue;
+import com.google.common.collect.Iterables;
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Objects;
+import java.util.Random;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.annotations.Experimental;
+import org.apache.beam.sdk.coders.AtomicCoder;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.transforms.Combine;
+import org.apache.beam.sdk.transforms.CombineFnBase;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.GroupByKey;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.View;
+import org.apache.beam.sdk.transforms.display.DisplayData;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.DefaultTrigger;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionView;
+import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.TypeDescriptor;
+import org.apache.beam.sdk.values.TypeDescriptors;
+import org.apache.beam.sdk.values.WindowingStrategy;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.mapreduce.JobID;
+import org.apache.hadoop.mapreduce.MRJobConfig;
+import org.apache.hadoop.mapreduce.OutputCommitter;
+import org.apache.hadoop.mapreduce.OutputFormat;
+import org.apache.hadoop.mapreduce.Partitioner;
+import org.apache.hadoop.mapreduce.RecordWriter;
+import org.apache.hadoop.mapreduce.TaskAttemptContext;
+import org.apache.hadoop.mapreduce.TaskID;
+import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
+import org.apache.hadoop.mapreduce.task.JobContextImpl;
+import org.joda.time.Duration;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * A {@link HadoopFormatIO} is a Transform for writing data to any sink which
implements Hadoop
+ * {@link OutputFormat}. For example - Cassandra, Elasticsearch, HBase, Redis,
Postgres etc. {@link
+ * HadoopFormatIO} has to make several performance trade-offs in connecting to
{@link OutputFormat},
+ * so if there is another Beam IO Transform specifically for connecting to
your data sink of choice,
+ * we would recommend using that one, but this IO Transform allows you to
connect to many data sinks
+ * that do not yet have a Beam IO Transform.
+ *
+ * <p>You will need to pass a Hadoop {@link Configuration} with parameters
specifying how the write
+ * will occur. Many properties of the Configuration are optional, and some are
required for certain
+ * {@link OutputFormat} classes, but the following properties must be set for
all OutputFormats:
+ *
+ * <ul>
+ * <li>{@code mapreduce.job.outputformat.class}: The {@link OutputFormat}
class used to connect to
+ * your data sink of choice.
+ * <li>{@code mapreduce.job.output.key.class}: The key class passed to the
{@link OutputFormat} in
+ * {@code mapreduce.job.outputformat.class}.
+ * <li>{@code mapreduce.job.output.value.class}: The value class passed to
the {@link
+ * OutputFormat} in {@code mapreduce.job.outputformat.class}.
+ * <li>{@code mapreduce.job.reduces}: Number of reduce tasks. Value is equal
to number of write
+ * tasks which will be genarated. This property is not required for
{@link
+ * Write.Builder#withConfigurationWithoutPartitioning(Configuration)}
write.
+ * <li>{@code mapreduce.job.partitioner.class}: Hadoop partitioner class
which will be used for
+ * distributing of records among partitions. This property is not
required for {@link
+ * Write.Builder#withConfigurationWithoutPartitioning(Configuration)}
write.
+ * </ul>
+ *
+ * <b>Note:</b> All mentioned values have appropriate constants. E.g.: {@link
+ * #OUTPUT_FORMAT_CLASS_ATTR}.
+ *
+ * <p>For example:
+ *
+ * <pre>{@code
+ * Configuration myHadoopConfiguration = new Configuration(false);
+ * // Set Hadoop OutputFormat, key and value class in configuration
+ * myHadoopConfiguration.setClass("mapreduce.job.outputformat.class",
+ * MyDbOutputFormatClass, OutputFormat.class);
+ * myHadoopConfiguration.setClass("mapreduce.job.output.key.class",
+ * MyDbOutputFormatKeyClass, Object.class);
+ * myHadoopConfiguration.setClass("mapreduce.job.output.value.class",
+ * MyDbOutputFormatValueClass, Object.class);
+ * myHadoopConfiguration.setClass("mapreduce.job.output.value.class",
+ * MyPartitionerClass, Object.class);
+ * myHadoopConfiguration.setInt("mapreduce.job.reduces", 2);
+ * }</pre>
+ *
+ * <p>You will need to set OutputFormat key and value class (i.e.
"mapreduce.job.output.key.class"
+ * and "mapreduce.job.output.value.class") in Hadoop {@link Configuration}
which are equal to {@code
+ * KeyT} and {@code ValueT}. If you set different OutputFormat key or value
class than
+ * OutputFormat's actual key or value class then, it will throw {@link
IllegalArgumentException}
+ *
+ * <h3>Writing using {@link HadoopFormatIO}</h3>
+ *
+ * <pre>{@code
+ * Pipeline p = ...; // Create pipeline.
+ * // Read data only with Hadoop configuration.
+ * p.apply("read",
+ * HadoopFormatIO.<OutputFormatKeyClass, OutputFormatKeyClass>write()
+ * .withConfiguration(myHadoopConfiguration);
+ * }</pre>
+ */
+@Experimental(Experimental.Kind.SOURCE_SINK)
+public class HadoopFormatIO {
+ private static final Logger LOGGER =
LoggerFactory.getLogger(HadoopFormatIO.class);
+
+ public static final String OUTPUT_FORMAT_CLASS_ATTR =
MRJobConfig.OUTPUT_FORMAT_CLASS_ATTR;
+ public static final String OUTPUT_KEY_CLASS = MRJobConfig.OUTPUT_KEY_CLASS;
+ public static final String OUTPUT_VALUE_CLASS =
MRJobConfig.OUTPUT_VALUE_CLASS;
+ public static final String NUM_REDUCES = MRJobConfig.NUM_REDUCES;
+ public static final String PARTITIONER_CLASS_ATTR =
MRJobConfig.PARTITIONER_CLASS_ATTR;
+
+ /**
+ * Creates an uninitialized {@link HadoopFormatIO.Write.Builder}. Before
use, the {@code Write}
+ * must be initialized with a {@link
Write.Builder#withConfiguration(Configuration)} or {@link
+ * Write.Builder#withConfigurationTransform(ConfigurationTransform)} or
{@link
+ * Write.Builder#withConfigurationWithoutPartitioning(Configuration)} that
specifies the sink.
+ *
+ * @param <KeyT> Type of keys to be written.
+ * @param <ValueT> Type of values to be written.
+ * @return Write builder
+ */
+ public static <KeyT, ValueT> Write.Builder<KeyT, ValueT> write() {
+ return new AutoValue_HadoopFormatIO_Write.Builder<>();
+ }
+
+ /**
+ * Interface for client definition of so called {@link Configuration}
"Map-Reduce" operation
+ * defined by methods {@link #getConfigTransform()} and {@link
#getConfigCombineFn()}
+ *
+ * <p>Client can define operations which will produce one particular
configuration from the input
+ * data by this interface. Generated configuration will be then used during
writing of data into
+ * one of the hadoop output formats.
+ *
+ * <p>This interface enables defining of special {@link Configuration} for
every particular
+ * window.
+ *
+ * @param <KeyT> Key type of writing data
+ * @param <ValueT> Value type of writing data
+ */
+ @FunctionalInterface
+ interface ConfigurationTransform<KeyT, ValueT> {
+
+ /** Default "reduce" function for extraction of one Configuration. */
+ Combine.IterableCombineFn<Configuration> DEFAULT_CONFIG_COMBINE_FN =
+ Combine.IterableCombineFn.of(
+ (configurations) -> {
+ Iterable<Configuration> filtered =
Iterables.filter(configurations, Objects::nonNull);
+ return Iterables.getFirst(filtered, null);
+ });
+
+ /**
+ * "Map" function which should transform one {@link KV} pair into hadoop
{@link Configuration}.
+ *
+ * <p><b>Note:</b> Default implementation of {@link #getConfigCombineFn()}
requires that from
+ * {@link KV} pair will be produced at least one {@link Configuration}
+ *
+ * @return transform function
+ */
+ PTransform<PCollection<? extends KV<KeyT, ValueT>>,
PCollection<Configuration>>
+ getConfigTransform();
+
+ /**
+ * "Reduce" function which collects all {@link Configuration}s created via
{@link
+ * #getConfigTransform()} and returns only one particular configuration
that will be used for
+ * storing of all {@link KV} pairs.
+ *
+ * @see #DEFAULT_CONFIG_COMBINE_FN
+ * @return Combine function
+ */
+ default CombineFnBase.GlobalCombineFn<Configuration, ?, Configuration>
getConfigCombineFn() {
+ return DEFAULT_CONFIG_COMBINE_FN;
+ }
+ }
+
+ /**
+ * Default implementation of Configuration transform. It requires only
particular {@link
+ * PTransform} to be specified.
+ *
+ * @param <KeyT> Key type which should be written
+ * @param <ValueT> Value type which should be written
+ */
+ private static class DefaultConfigurationTransform<KeyT, ValueT>
+ implements ConfigurationTransform<KeyT, ValueT> {
+
+ private PTransform<PCollection<? extends KV<KeyT, ValueT>>,
PCollection<Configuration>>
+ configTransform;
+
+ public DefaultConfigurationTransform(
+ PTransform<PCollection<? extends KV<KeyT, ValueT>>,
PCollection<Configuration>>
+ configTransform) {
+ this.configTransform = configTransform;
+ }
+
+ @Override
+ public PTransform<PCollection<? extends KV<KeyT, ValueT>>,
PCollection<Configuration>>
+ getConfigTransform() {
+ return configTransform;
+ }
+ }
+
+ /**
+ * Generates tasks for output pairs and groups them by this key.
+ *
+ * <p>This transformation is used when is configured write with partitioning.
+ *
+ * @param <KeyT> type of key
+ * @param <ValueT> type of value
+ */
+ private static class GroupDataByPartition<KeyT, ValueT>
+ extends PTransform<
+ PCollection<KV<KeyT, ValueT>>, PCollection<KV<Integer, KV<KeyT,
ValueT>>>> {
+
+ private PCollectionView<Configuration> configView;
+
+ private GroupDataByPartition(PCollectionView<Configuration> configView) {
+ this.configView = configView;
+ }
+
+ @Override
+ public PCollection<KV<Integer, KV<KeyT, ValueT>>>
expand(PCollection<KV<KeyT, ValueT>> input) {
+ return input
+ .apply(
+ "AssignTask",
+ ParDo.of(new AssignTaskFn<KeyT,
ValueT>(configView)).withSideInputs(configView))
+ .setTypeDescriptor(
+ TypeDescriptors.kvs(TypeDescriptors.integers(),
input.getTypeDescriptor()))
+ .apply("GroupByTaskId", GroupByKey.create())
+ .apply("FlattenGroupedTasks", ParDo.of(new FlattenGroupedTasks<>()));
+ }
+ }
+
+ /**
+ * Flattens grouped iterable {@link KV} pairs into triplets of
TaskID/Key/Value.
+ *
+ * @param <KeyT> Type of keys to be written.
+ * @param <ValueT> Type of values to be written.
+ */
+ private static class FlattenGroupedTasks<KeyT, ValueT>
+ extends DoFn<KV<Integer, Iterable<KV<KeyT, ValueT>>>, KV<Integer,
KV<KeyT, ValueT>>> {
+
+ @ProcessElement
+ public void processElement(
+ @Element KV<Integer, Iterable<KV<KeyT, ValueT>>> input,
+ OutputReceiver<KV<Integer, KV<KeyT, ValueT>>> outputReceiver) {
+ final Integer key = input.getKey();
+ for (KV<KeyT, ValueT> element :
+ requireNonNull(input.getValue(), "Iterable can not be null.")) {
+ outputReceiver.output(KV.of(key, element));
+ }
+ }
+ }
+
+ /**
+ * A {@link PTransform} that writes to any data sink which implements Hadoop
OutputFormat. For
+ * e.g. Cassandra, Elasticsearch, HBase, Redis, Postgres, etc. See the
class-level Javadoc on
+ * {@link HadoopFormatIO} for more information.
+ *
+ * @param <KeyT> Type of keys to be written.
+ * @param <ValueT> Type of values to be written.
+ * @see HadoopFormatIO
+ */
+ @AutoValue
+ public abstract static class Write<KeyT, ValueT>
+ extends PTransform<PCollection<KV<KeyT, ValueT>>, PDone> {
+
+ @Nullable
+ public abstract Configuration getConfiguration();
+
+ @Nullable
+ public abstract ConfigurationTransform<KeyT, ValueT> getConfigTransform();
+
+ public abstract boolean isWithPartitioning();
+
+ /**
+ * Builder for Write transformation.
+ *
+ * @param <KeyT> Key write type
+ * @param <ValueT> Value write type
+ */
+ @AutoValue.Builder
+ public abstract static class Builder<KeyT, ValueT> {
+
+ public abstract Builder<KeyT, ValueT> setConfiguration(Configuration
newConfiguration);
+
+ public abstract Builder<KeyT, ValueT> setConfigTransform(
+ ConfigurationTransform<KeyT, ValueT> newConfigTransform);
+
+ public abstract Builder<KeyT, ValueT> setWithPartitioning(boolean
newWithPartitioning);
+
+ abstract Write<KeyT, ValueT> build();
+
+ /**
+ * Writes to the sink using the options provided by the given hadoop
configuration.
+ *
+ * <p><b>Note:</b> Works only for {@link
+ * org.apache.beam.sdk.values.PCollection.IsBounded#BOUNDED} {@link
PCollection} with global
+ * {@link WindowingStrategy}.
+ *
+ * @param configuration hadoop configuration.
+ * @return Created write function
+ * @throws IllegalArgumentException when the configuration is null
+ */
+ @SuppressWarnings("unchecked")
+ public Write<KeyT, ValueT> withConfiguration(Configuration configuration)
+ throws IllegalArgumentException {
+ checkArgument(Objects.nonNull(configuration), "Configuration can not
be null");
+
+ return setConfiguration(new
Configuration(configuration)).setWithPartitioning(true).build();
+ }
+
+ /**
+ * Writes to the sink without need to partition output into specified
number of partitions.
+ *
+ * <p>This write operation doesn't do shuffle by the partition so it
saves transfer time
+ * before write operation itself. As a consequence it generates random
number of partitions.
+ *
+ * <p><b>Note:</b> Works only for {@link
+ * org.apache.beam.sdk.values.PCollection.IsBounded#BOUNDED} {@link
PCollection} with global
+ * {@link WindowingStrategy}.
+ *
+ * @param configuration hadoop configuration
+ * @return Created write function
+ * @throws IllegalArgumentException when the configuration is null
+ */
+ public Write<KeyT, ValueT>
withConfigurationWithoutPartitioning(Configuration configuration)
+ throws IllegalArgumentException {
+ checkArgument(Objects.nonNull(configuration), "Configuration can not
be null");
+
+ return setConfiguration(new Configuration(configuration))
+ .setWithPartitioning(false)
+ .build();
+ }
+
+ /**
+ * Writes to the sink using configuration created by provided {@code
+ * configurationTransformation}.
+ *
+ * <p>Parameter {@code configurationTransformation} should provide way
how to transform input
+ * data into {@link PCollection} of hadoop configurations.
+ *
+ * <p>PCollection of configuration is then reduced to one by {@link
+ * ConfigurationTransform#DEFAULT_CONFIG_COMBINE_FN}
+ *
+ * <p>This type is useful especially for processing unbounded windowed
data but can be used *
Review comment:
Deleted, thank you
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Issue Time Tracking
-------------------
Worklog Id: (was: 156919)
Time Spent: 4h 10m (was: 4h)
> Add streaming support for HadoopOutputFormatIO
> ----------------------------------------------
>
> Key: BEAM-5309
> URL: https://issues.apache.org/jira/browse/BEAM-5309
> Project: Beam
> Issue Type: Sub-task
> Components: io-java-hadoop
> Reporter: Alexey Romanenko
> Assignee: David Hrbacek
> Priority: Minor
> Time Spent: 4h 10m
> Remaining Estimate: 0h
>
> design doc: https://s.apache.org/beam-streaming-hofio
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