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commit d747c76fdccd138a9c6e7af3c2374c1177d4bd54
Author: melissa <meliss...@google.com>
AuthorDate: Fri Sep 29 10:26:40 2017 -0700
Manually add missed files from content dir
---
content/documentation/execution-model/index.html | 402 +++++++++++++++++++++
content/documentation/runners/jstorm/index.html | 299 +++++++++++++++
content/documentation/runners/mapreduce/index.html | 275 ++++++++++++++
content/images/execution_model_bundling.svg | 357 ++++++++++++++++++
.../execution_model_bundling_coupled_failure.svg | 59 +++
content/images/execution_model_bundling_gantt.svg | 50 +++
.../images/execution_model_bundling_gantt_max.svg | 67 ++++
content/images/execution_model_bundling_multi.svg | 336 +++++++++++++++++
.../execution_model_bundling_multi_gantt.svg | 52 +++
content/images/execution_model_failure_retry.svg | 56 +++
content/images/logos/runners/jstorm.png | Bin 0 -> 6375 bytes
src/documentation/runners/mapreduce.md | 1 +
12 files changed, 1954 insertions(+)
diff --git a/content/documentation/execution-model/index.html
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+++ b/content/documentation/execution-model/index.html
@@ -0,0 +1,402 @@
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+ <h1 id="apache-beam-execution-model">Apache Beam Execution Model</h1>
+
+<p>The Beam model allows runners to execute your pipeline in different ways.
You
+may observe various effects as a result of the runner’s choices. This page
+describes these effects so you can better understand how Beam pipelines
execute.</p>
+
+<ul id="markdown-toc">
+ <li><a href="#processing-of-elements"
id="markdown-toc-processing-of-elements">Processing of elements</a> <ul>
+ <li><a href="#serialization-and-communication"
id="markdown-toc-serialization-and-communication">Serialization and
communication</a></li>
+ <li><a href="#bundling-and-persistence"
id="markdown-toc-bundling-and-persistence">Bundling and persistence</a></li>
+ </ul>
+ </li>
+ <li><a href="#parallelism" id="markdown-toc-parallelism">Failures and
parallelism within and between transforms</a> <ul>
+ <li><a href="#data-parallelism"
id="markdown-toc-data-parallelism">Data-parallelism within one
transform</a></li>
+ <li><a href="#dependent-parallellism"
id="markdown-toc-dependent-parallellism">Dependent-parallelism between
transforms</a></li>
+ <li><a href="#failures-within-one-transform"
id="markdown-toc-failures-within-one-transform">Failures within one
transform</a></li>
+ <li><a href="#coupled-failure" id="markdown-toc-coupled-failure">Coupled
failure: Failures between transforms</a></li>
+ </ul>
+ </li>
+</ul>
+
+<h2 id="processing-of-elements">Processing of elements</h2>
+
+<p>The serialization and communication of elements between machines is one of
the
+most expensive operations in a distributed execution of your pipeline. Avoiding
+this serialization may require re-processing elements after failures or may
+limit the distribution of output to other machines.</p>
+
+<h3 id="serialization-and-communication">Serialization and communication</h3>
+
+<p>The runner might serialize elements between machines for communication
purposes
+and for other reasons such as persistence.</p>
+
+<p>A runner may decide to transfer elements between transforms in a variety of
+ways, such as:</p>
+
+<ol>
+ <li>Routing elements to a worker for processing as part of a grouping
operation.
+This may involve serializing elements and grouping or sorting them by their
+key.</li>
+ <li>Redistributing elements between workers to adjust parallelism. This may
+involve serializing elements and communicating them to other workers.</li>
+ <li>Using the elements in a side input to a <code
class="highlighter-rouge">ParDo</code>. This may require
+serializing the elements and broadcasting them to all the workers executing
+the <code class="highlighter-rouge">ParDo</code>.</li>
+ <li>Passing elements between transforms that are running on the same worker.
+This may allow the runner to avoid serializing elements; instead, the runner
+can just pass the elements in memory.</li>
+</ol>
+
+<p>Some situations where the runner may serialize and persist elements are:</p>
+
+<ol>
+ <li>When used as part of a stateful <code
class="highlighter-rouge">DoFn</code>, the runner may persist values to some
+state mechanism.</li>
+ <li>When committing the results of processing, the runner may persist the
outputs
+as a checkpoint.</li>
+</ol>
+
+<h3 id="bundling-and-persistence">Bundling and persistence</h3>
+
+<p>Beam pipelines often focus on “<a
href="https://en.wikipedia.org/wiki/embarrassingly_parallel";>embarassingly
parallel</a>”
+problems. Because of this, the APIs emphasize processing elements in parallel,
+which makes it difficult to express actions like “assign a sequence number to
+each element in a PCollection”. This is intentional as such algorithms are much
+more likely to suffer from scalability problems.</p>
+
+<p>Processing all elements in parallel also has some drawbacks. Specifically,
it
+makes it impossible to batch any operations, such as writing elements to a sink
+or checkpointing progress during processing.</p>
+
+<p>Instead of processing all elements simultaneously, the elements in a
+<code class="highlighter-rouge">PCollection</code> are processed in
<em>bundles</em>. The division of the collection into
+bundles is arbitrary and selected by the runner. This allows the runner to
+choose an appropriate middle-ground between persisting results after every
+element, and having to retry everything if there is a failure. For example, a
+streaming runner may prefer to process and commit small bundles, and a batch
+runner may prefer to process larger bundles.</p>
+
+<h2 id="parallelism">Failures and parallelism within and between
transforms</h2>
+
+<p>In this section, we discuss how elements in the input collection are
processed
+in parallel, and how transforms are retried when failures occur.</p>
+
+<h3 id="data-parallelism">Data-parallelism within one transform</h3>
+
+<p>When executing a single <code class="highlighter-rouge">ParDo</code>, a
runner might divide an example input
+collection of nine elements into two bundles as shown in figure 1.</p>
+
+<p><img src="/images/execution_model_bundling.svg" alt="bundling" /></p>
+
+<p><strong>Figure 1:</strong> A runner divides an input collection with nine
elements
+into two bundles.</p>
+
+<p>When the <code class="highlighter-rouge">ParDo</code> executes, workers may
process the two bundles in parallel as
+shown in figure 2.</p>
+
+<p><img src="/images/execution_model_bundling_gantt.svg" alt="bundling_gantt"
/></p>
+
+<p><strong>Figure 2:</strong> Two workers process the two bundles in parallel.
The elements in
+each bundle are processed in sequence.</p>
+
+<p>Since elements cannot be split, the maximum parallelism for a transform
depends
+on the number of elements in the collection. In our example, the input
+collection has nine elements, so the maximum parallelism is nine.</p>
+
+<p><img src="/images/execution_model_bundling_gantt_max.svg"
alt="bundling_gantt_max" /></p>
+
+<p><strong>Figure 3:</strong> The maximum parallelism is nine, as there are
nine elements in the
+input collection.</p>
+
+<p>Note: Splittable ParDo allows splitting the processing of a single input
across
+multiple bundles. This feature is a work in progress.</p>
+
+<h3 id="dependent-parallellism">Dependent-parallelism between transforms</h3>
+
+<p><code class="highlighter-rouge">ParDo</code> transforms that are in
sequence may be <em>dependently parallel</em> if the
+runner chooses to execute the consuming transform on the producing transform’s
+output elements without altering the bundling. In figure 4, <code
class="highlighter-rouge">ParDo1</code> and
+<code class="highlighter-rouge">ParDo2</code> are <em>dependently
parallel</em> if the output of <code class="highlighter-rouge">ParDo1</code>
for a given
+element must be processed on the same worker.</p>
+
+<p><img src="/images/execution_model_bundling_multi.svg" alt="bundling_multi"
/></p>
+
+<p><strong>Figure 4:</strong> Two transforms in sequence and their
corresponding input collections.</p>
+
+<p>Figure 5 shows how these dependently parallel transforms might execute. The
+first worker executes <code class="highlighter-rouge">ParDo1</code> on the
elements in bundle A (which results in
+bundle C), and then executes <code class="highlighter-rouge">ParDo2</code> on
the elements in bundle C. Similarly,
+the second worker executes <code class="highlighter-rouge">ParDo1</code> on
the elements in bundle B (which results
+in bundle D), and then executes <code class="highlighter-rouge">ParDo2</code>
on the elements in bundle D.</p>
+
+<p><img src="/images/execution_model_bundling_multi_gantt.svg"
alt="bundling_multi_gantt.svg" /></p>
+
+<p><strong>Figure 5:</strong> Two workers execute dependently parallel ParDo
transforms.</p>
+
+<p>Executing transforms this way allows a runner to avoid redistributing
elements
+between workers, which saves on communication costs. However, the maximum
parallelism
+now depends on the maximum parallelism of the first of the dependently parallel
+steps.</p>
+
+<h3 id="failures-within-one-transform">Failures within one transform</h3>
+
+<p>If processing of an element within a bundle fails, the entire bundle fails.
The
+elements in the bundle must be retried (otherwise the entire pipeline fails),
+although they do not need to be retried with the same bundling.</p>
+
+<p>For this example, we will use the <code
class="highlighter-rouge">ParDo</code> from figure 1 that has an input
+collection with nine elements and is divided into two bundles.</p>
+
+<p>In figure 6, the first worker successfully processes all five elements in
bundle
+A. The second worker processes the four elements in bundle B: the first two
+elements were successfully processed, the third element’s processing failed,
and
+there is one element still awaiting processing.</p>
+
+<p>We see that the runner retries all elements in bundle B and the processing
+completes successfully the second time. Note that the retry does not
necessarily
+happen on the same worker as the original processing attempt, as shown in the
+diagram.</p>
+
+<p><img src="/images/execution_model_failure_retry.svg" alt="failure_retry"
/></p>
+
+<p><strong>Figure 6:</strong> The processing of an element within bundle B
fails, and another worker
+retries the entire bundle.</p>
+
+<p>Because we encountered a failure while processing an element in the input
+bundle, we had to reprocess <em>all</em> of the elements in the input bundle.
This means
+the runner must throw away the entire output bundle since all of the results it
+contains will be recomputed.</p>
+
+<p>Note that if the failed transform is a <code
class="highlighter-rouge">ParDo</code>, then the <code
class="highlighter-rouge">DoFn</code> instance is torn
+down and abandoned.</p>
+
+<h3 id="coupled-failure">Coupled failure: Failures between transforms</h3>
+
+<p>If a failure to process an element in <code
class="highlighter-rouge">ParDo2</code> causes <code
class="highlighter-rouge">ParDo1</code> to re-execute,
+these two steps are said to be <em>co-failing</em>.</p>
+
+<p>For this example, we will use the two <code
class="highlighter-rouge">ParDo</code>s from figure 4.</p>
+
+<p>In figure 7, worker two successfully executes <code
class="highlighter-rouge">ParDo1</code> on all elements in bundle
+B. However, the worker fails to process an element in bundle D, so <code
class="highlighter-rouge">ParDo2</code>
+fails (shown as the red X). As a result, the runner must discard and recompute
+the output of <code class="highlighter-rouge">ParDo2</code>. Because the
runner was executing <code class="highlighter-rouge">ParDo1</code> and <code
class="highlighter-rouge">ParDo2</code>
+together, the output bundle from <code class="highlighter-rouge">ParDo1</code>
must also be thrown away, and all
+elements in the input bundle must be retried. These two <code
class="highlighter-rouge">ParDo</code>s are co-failing.</p>
+
+<p><img src="/images/execution_model_bundling_coupled_failure.svg"
alt="bundling_coupled failure" /></p>
+
+<p><strong>Figure 7:</strong> Processing of an element within bundle D fails,
so all elements in
+the input bundle are retried.</p>
+
+<p>Note that the retry does not necessarily have the same processing time as
the
+original attempt, as shown in the diagram.</p>
+
+<p>All <code class="highlighter-rouge">DoFns</code> that experience coupled
failures are terminated and must be torn
+down since they aren’t following the normal <code
class="highlighter-rouge">DoFn</code> lifecycle .</p>
+
+<p>Executing transforms this way allows a runner to avoid persisting elements
+between transforms, saving on persistence costs.</p>
+
+
+ </div>
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diff --git a/content/documentation/runners/jstorm/index.html
b/content/documentation/runners/jstorm/index.html
new file mode 100644
index 0000000..e511c8c
--- /dev/null
+++ b/content/documentation/runners/jstorm/index.html
@@ -0,0 +1,299 @@
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model and set of language-specific SDKs for defining and executing data
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Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs).
Dataflow pipelines simplify the mechanics of large-scale batch and streaming
data processing and can run on a number of runtimes like Apache Flink, Apache
Spark, and Google Cloud Dataflow [...]
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+
+ <div class="body__contained">
+ <h1 id="using-the-jstorm-runner">Using the JStorm Runner</h1>
+
+<p>The JStorm Runner can be used to execute Beam pipelines using <a
href="http://jstorm.io/";>JStorm</a>, while providing:</p>
+
+<ul>
+ <li>High throughput and low latency.</li>
+ <li>At-least-once and exactly-once fault tolerance.</li>
+</ul>
+
+<p>Like a native JStorm topology, users can execute Beam topology with local
mode, standalone cluster or jstorm-on-yarn cluster.</p>
+
+<p>The <a href="/documentation/runners/capability-matrix/">Beam Capability
Matrix</a> documents the currently supported capabilities of the JStorm
Runner.</p>
+
+<h2 id="jstorm-runner-prerequisites-and-setup">JStorm Runner prerequisites and
setup</h2>
+
+<p>The JStorm runner currently supports JStorm version 2.5.0-SNAPSHOT.</p>
+
+<p>You can add a dependency on the latest version of the JStorm runner by
adding the following to your pom.xml:</p>
+<div class="language-java highlighter-rouge"><pre
class="highlight"><code><span class="o"><</span><span
class="n">dependency</span><span class="o">></span>
+ <span class="o"><</span><span class="n">groupId</span><span
class="o">></span><span class="n">org</span><span class="o">.</span><span
class="na">apache</span><span class="o">.</span><span
class="na">beam</span><span class="o"></</span><span
class="n">groupId</span><span class="o">></span>
+ <span class="o"><</span><span class="n">artifactId</span><span
class="o">></span><span class="n">beam</span><span class="o">-</span><span
class="n">runners</span><span class="o">-</span><span
class="n">jstorm</span><span class="o"></</span><span
class="n">artifactId</span><span class="o">></span>
+ <span class="o"><</span><span class="n">version</span><span
class="o">></span><span class="mf">2.1</span><span class="o">.</span><span
class="mi">0</span><span class="o"></</span><span
class="n">version</span><span class="o">></span>
+<span class="o"></</span><span class="n">dependency</span><span
class="o">></span>
+</code></pre>
+</div>
+
+<h3 id="deploying-jstorm-with-your-application">Deploying JStorm with your
application</h3>
+
+<p>To run against a Standalone cluster, you can package your program with all
Beam dependencies into a fat jar, and then submit the topology with the
following command.</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>jstorm jar
WordCount.jar org.apache.beam.examples.WordCount
--runner=org.apache.beam.runners.jstorm.JStormRunner
+</code></pre>
+</div>
+
+<p>If you don’t want to package a fat jar, you can upload the Beam
dependencies onto all cluster nodes(<code
class="highlighter-rouge">$JSTORM_HOME/lib/ext/beam</code>) first.
+When you submit a topology with argument <code
class="highlighter-rouge">"--external-libs beam"</code>, JStorm will load the
Beam dependencies automatically.</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>jstorm jar
WordCount.jar org.apache.beam.examples.WordCount --external-libs beam
--runner=org.apache.beam.runners.jstorm.JStormRunner
+</code></pre>
+</div>
+
+<p>To learn about deploying a JStorm cluster, please refer to <a
href="http://jstorm.io/QuickStart/Deploy/index.html";>JStorm cluster
deploy</a></p>
+
+<h2 id="pipeline-options-for-the-jstorm-runner">Pipeline options for the
JStorm Runner</h2>
+
+<p>When executing your pipeline with the JStorm Runner, you should consider
the following pipeline options.</p>
+
+<table class="table table-bordered">
+<tr>
+ <th>Field</th>
+ <th>Description</th>
+ <th>Default Value</th>
+</tr>
+<tr>
+ <td><code>runner</code></td>
+ <td>The pipeline runner to use. This option allows you to determine the
pipeline runner at runtime.</td>
+ <td>Set to <code>JStormRunner</code> to run using JStorm.</td>
+</tr>
+<tr>
+ <td><code>topologyConfig</code></td>
+ <td>System topology config of JStorm</td>
+ <td>DefaultMapValueFactory.class</td>
+</tr>
+<tr>
+ <td><code>workerNumber</code></td>
+ <td>Worker number of topology</td>
+ <td>1</td>
+</tr>
+<tr>
+ <td><code>parallelism</code></td>
+ <td>Global parallelism number of a component</td>
+ <td>1</td>
+</tr>
+<tr>
+ <td><code>parallelismMap</code></td>
+ <td>Parallelism number of a specified composite PTransform</td>
+ <td>DefaultMapValueFactory.class</td>
+</tr>
+<tr>
+ <td><code>exactlyOnceTopology</code></td>
+ <td>Indicate if it is an exactly once topology</td>
+ <td>false</td>
+</tr>
+<tr>
+ <td><code>localMode</code></td>
+ <td>Indicate if the topology is running on local machine or distributed
cluster</td>
+ <td>false</td>
+</tr>
+<tr>
+ <td><code>localModeExecuteTimeSec</code></td>
+ <td>Executing time(sec) of topology on local mode.</td>
+ <td>60</td>
+</tr>
+</table>
+
+<h2 id="additional-notes">Additional notes</h2>
+
+<h3 id="monitoring-your-job">Monitoring your job</h3>
+<p>You can monitor your job with the JStorm UI, which displays all JStorm
system metrics and Beam metrics.
+For testing on local mode, you can retreive the Beam metrics with the metrics
method of PipelineResult.</p>
+
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diff --git a/content/documentation/runners/mapreduce/index.html
b/content/documentation/runners/mapreduce/index.html
new file mode 100644
index 0000000..17c422e
--- /dev/null
+++ b/content/documentation/runners/mapreduce/index.html
@@ -0,0 +1,275 @@
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Dataflow pipelines simplify the mechanics of large-scale batch and streaming
data processing and can run on a number of runtimes like Apache Flink, Apache
Spark, and Google Cloud Dataflow [...]
+">
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+</nav>
+
+ <div class="body__contained">
+ <h1 id="using-the-apache-hadoop-mapreduce-runner">Using the Apache
Hadoop MapReduce Runner</h1>
+
+<p>The Apache Hadoop MapReduce Runner can be used to execute Beam pipelines
using <a href="http://hadoop.apache.org/";>Apache Hadoop</a>.</p>
+
+<p>The <a href="/documentation/runners/capability-matrix/">Beam Capability
Matrix</a> documents the currently supported capabilities of the Apache Hadoop
MapReduce Runner.</p>
+
+<h2 id="apache-hadoop-mapreduce-runner-prerequisites-and-setup">Apache Hadoop
MapReduce Runner prerequisites and setup</h2>
+<p>You need to have an Apache Hadoop environment with either <a
href="https://hadoop.apache.org/docs/r1.2.1/single_node_setup.html";>Single Node
Setup</a> or <a
href="https://hadoop.apache.org/docs/r1.2.1/cluster_setup.html";>Cluster
Setup</a></p>
+
+<p>The Apache Hadoop MapReduce runner currently supports Apache Hadoop version
2.8.1.</p>
+
+<p>You can add a dependency on the latest version of the Apache Hadoop
MapReduce runner by adding the following to your pom.xml:</p>
+<div class="highlighter-rouge"><pre class="highlight"><code><dependency>
+ <groupId>org.apache.beam</groupId>
+ <artifactId>beam-runners-mapreduce</artifactId>
+ <version>2.1.0</version>
+</dependency>
+</code></pre>
+</div>
+
+<h2 id="deploying-apache-hadoop-mapreduce-with-your-application">Deploying
Apache Hadoop MapReduce with your application</h2>
+<p>To execute in a local Hadoop environment, use this command:</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>$ mvn exec:java
-Dexec.mainClass=org.apache.beam.examples.WordCount \
+ -Pmapreduce-runner \
+ -Dexec.args="--runner=MapReduceRunner \
+ --inputFile=/path/to/pom.xml \
+ --output=/path/to/counts \
+ --fileOutputDir=<directory for intermediate outputs>"
+</code></pre>
+</div>
+
+<p>To execute in a Hadoop cluster, package your program along with all
dependencies in a fat jar.</p>
+
+<p>If you are following through the <a
href="/get-started/quickstart-java/">Beam Java SDK Quickstart</a>, you can run
this command:</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>$ mvn package
-Pflink-runner
+</code></pre>
+</div>
+
+<p>For actually running the pipeline you would use this command</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>$ yarn jar
word-count-beam-bundled-0.1.jar \
+ org.apache.beam.examples.WordCount \
+ --runner=MapReduceRunner \
+ --inputFile=/path/to/pom.xml \
+ --output=/path/to/counts \
+ --fileOutputDir=<directory for intermediate outputs>"
+</code></pre>
+</div>
+
+<h2 id="pipeline-options-for-the-apache-hadoop-mapreduce-runner">Pipeline
options for the Apache Hadoop MapReduce Runner</h2>
+
+<p>When executing your pipeline with the Apache Hadoop MapReduce Runner, you
should consider the following pipeline options.</p>
+
+<table class="table table-bordered">
+<tr>
+ <th>Field</th>
+ <th>Description</th>
+ <th>Default Value</th>
+</tr>
+<tr>
+ <td><code>runner</code></td>
+ <td>The pipeline runner to use. This option allows you to determine the
pipeline runner at runtime.</td>
+ <td>Set to <code>MapReduceRunner</code> to run using Apache Hadoop
MapReduce.</td>
+</tr>
+<tr>
+ <td><code>jarClass</code></td>
+ <td>The jar class of the user Beam program.</td>
+ <td>JarClassInstanceFactory.class</td>
+</tr>
+<tr>
+ <td><code>fileOutputDir</code></td>
+ <td>The directory for output files.</td>
+ <td>"/tmp/mapreduce/"</td>
+</tr>
+</table>
+
+ </div>
+ <footer class="footer">
+ <div class="footer__contained">
+ <div class="footer__cols">
+ <div class="footer__cols__col">
+ <div class="footer__cols__col__logo">
+ <img src="/images/beam_logo_circle.svg" class="footer__logo"
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+ <img src="/images/apache_logo_circle.svg" class="footer__logo"
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+ <div class="footer__cols__col footer__cols__col--md">
+ <div class="footer__cols__col__title">Start</div>
+ <div class="footer__cols__col__link"><a
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+ <div class="footer__cols__col__link"><a
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+ </div>
+ </div>
+ </div>
+ <div class="footer__bottom">
+ ©
+ <a href="http://www.apache.org";>The Apache Software Foundation</a>
+ | <a href="/privacy_policy">Privacy Policy</a>
+ | <a href="/feed.xml">RSS Feed</a>
+ <br><br>
+ Apache Beam, Apache, Beam, the Beam logo, and the Apache feather logo are
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diff --git a/src/documentation/runners/mapreduce.md
b/src/documentation/runners/mapreduce.md
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title: "Apache Hadoop MapReduce Runner"
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# Using the Apache Hadoop MapReduce Runner
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To stop receiving notification emails like this one, please contact
"commits@beam.apache.org" <commits@beam.apache.org>.
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