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Posted to commits@beam.apache.org by dh...@apache.org on 2016/08/02 17:46:42 UTC
[11/15] incubator-beam-site git commit: Regenerated site after merge.
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/8797d044/content/docs/learn/programming-guide.html
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- The Apache Beam project is in the process of bootstrapping. This includes the creation of project resources, the refactoring of the initial code submission, and the formulation of project documentation, planning, and design documents. For more information about Beam see the <a href="/getting_started/">getting started page</a>.
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-<h1 id="beam-programming-guide">Beam Programming Guide</h1>
-
-<p>The <strong>Beam Programming Guide</strong> is intended for Beam users who want to use the Beam SDKs to create data processing pipelines. It provides guidance for using the Beam SDK classes to build and test your pipeline. It is not intended as an exhaustive reference, but as a language-agnostic, high-level guide to programmatically building your Beam pipeline. As the programming guide is filled out, the text will include code samples in multiple languages to help illustrate how to implement Beam concepts in your programs.</p>
-
-<h2 id="contents">Contents</h2>
-
-<ul>
- <li><a href="#overview">Overview</a></li>
- <li><a href="#pipeline">Creating the Pipeline</a></li>
- <li><a href="#pcollection">Working with PCollections</a>
- <ul>
- <li><a href="#pccreate">Creating a PCollection</a></li>
- <li><a href="#pccharacteristics">PCollection Characteristics</a>
- <ul>
- <li><a href="#pcelementtype">Element Type</a></li>
- <li><a href="#pcimmutability">Immutability</a></li>
- <li><a href="#pcrandomaccess">Random Access</a></li>
- <li><a href="#pcsizebound">Size and Boundedness</a></li>
- <li><a href="#pctimestamps">Element Timestamps</a></li>
- </ul>
- </li>
- </ul>
- </li>
- <li><a href="#transform">Applying Transforms</a>
- <ul>
- <li><a href="#transforms-pardo">Using ParDo</a></li>
- <li><a href="#transforms-gbk">Using GroupByKey</a></li>
- <li><a href="#transforms-combine">Using Combine</a></li>
- <li><a href="#transforms-usercodereqs">General Requirements for Writing User Code for Beam Transforms</a></li>
- <li><a href="#transforms-sideio">Side Inputs and Side Outputs</a></li>
- </ul>
- </li>
- <li><a href="#io">I/O</a></li>
- <li><a href="#running">Running the Pipeline</a></li>
- <li><a href="#coders">Data Encoding and Type Safety</a></li>
- <li><a href="#windowing">Working with Windowing</a></li>
- <li><a href="#triggers">Working with Triggers</a></li>
-</ul>
-
-<h2 id="a-nameoverviewaoverview"><a name="#overview"></a>Overview</h2>
-
-<p>To use Beam, you need to first create a driver program using the classes in one of the Beam SDKs. Your driver program <em>defines</em> your pipeline, including all of the inputs, transforms, and outputs; it also sets execution options for your pipeline (typically passed in using command-line options). These include the Pipeline Runner, which, in turn, determines what back-end your pipeline will run on.</p>
-
-<p>The Beam SDKs provide a number of abstractions that simplify the mechanics of large-scale distributed data processing. The same Beam abstractions work with both batch and streaming data sources. When you create your Beam pipeline, you can think about your data processing task in terms of these abstractions. They include:</p>
-
-<ul>
- <li>
- <p><code class="highlighter-rouge">Pipeline</code>: A <code class="highlighter-rouge">Pipeline</code> encapsulates your entire data processing task, from start to finish. This includes reading input data, transforming that data, and writing output data. All Beam driver programs must create a <code class="highlighter-rouge">Pipeline</code>. When you create the <code class="highlighter-rouge">Pipeline</code>, you must also specify the execution options that tell the <code class="highlighter-rouge">Pipeline</code> where and how to run.</p>
- </li>
- <li>
- <p><code class="highlighter-rouge">PCollection</code>: A <code class="highlighter-rouge">PCollection</code> represents a distributed data set that your Beam pipeline operates on. The data set can be <em>bounded</em>, meaning it comes from a fixed source like a file, or <em>unbounded</em>, meaning it comes from a continuously updating source via a subscription or other mechanism. Your pipeline typically creates an initial <code class="highlighter-rouge">PCollection</code> by reading data from an external data source, but you can also create a <code class="highlighter-rouge">PCollection</code> from in-memory data within your driver program. From there, <code class="highlighter-rouge">PCollection</code>s are the inputs and outputs for each step in your pipeline.</p>
- </li>
- <li>
- <p><code class="highlighter-rouge">Transform</code>: A <code class="highlighter-rouge">Transform</code> represents a data processing operation, or a step, in your pipeline. Every <code class="highlighter-rouge">Transform</code> takes one or more <code class="highlighter-rouge">PCollection</code> objects as input, perfroms a processing function that you provide on the elements of that <code class="highlighter-rouge">PCollection</code>, and produces one or more output <code class="highlighter-rouge">PCollection</code> objects.</p>
- </li>
- <li>
- <p>I/O <code class="highlighter-rouge">Source</code> and <code class="highlighter-rouge">Sink</code>: Beam provides <code class="highlighter-rouge">Source</code> and <code class="highlighter-rouge">Sink</code> APIs to represent reading and writing data, respectively. <code class="highlighter-rouge">Source</code> encapsulates the code necessary to read data into your Beam pipeline from some external source, such as cloud file storage or a subscription to a streaming data source. <code class="highlighter-rouge">Sink</code> likewise encapsulates the code necessary to write the elements of a <code class="highlighter-rouge">PCollection</code> to an external data sink.</p>
- </li>
-</ul>
-
-<p>A typical Beam driver program works as follows:</p>
-
-<ul>
- <li>Create a <code class="highlighter-rouge">Pipeline</code> object and set the pipeline execution options, including the Pipeline Runner.</li>
- <li>Create an initial <code class="highlighter-rouge">PCollection</code> for pipeline data, either using the <code class="highlighter-rouge">Source</code> API to read data from an external source, or using a <code class="highlighter-rouge">Create</code> transform to build a <code class="highlighter-rouge">PCollection</code> from in-memory data.</li>
- <li>Apply <strong>Transforms</strong> to each <code class="highlighter-rouge">PCollection</code>. Transforms can change, filter, group, analyze, or otherwise process the elements in a <code class="highlighter-rouge">PCollection</code>. A transform creates a new output <code class="highlighter-rouge">PCollection</code> <em>without consuming the input collection</em>. A typical pipeline applies subsequent transforms to the each new output <code class="highlighter-rouge">PCollection</code> in turn until processing is complete.</li>
- <li>Output the final, transformed <code class="highlighter-rouge">PCollection</code>(s), typically using the <code class="highlighter-rouge">Sink</code> API to write data to an external source.</li>
- <li><strong>Run</strong> the pipeline using the designated Pipeline Runner.</li>
-</ul>
-
-<p>When you run your Beam driver program, the Pipeline Runner that you designate constructs a <strong>workflow graph</strong> of your pipeline based on the <code class="highlighter-rouge">PCollection</code> objects you\u2019ve created and transforms that you\u2019ve applied. That graph is then executed using the appropriate distributed processing back-end, becoming an asynchronous \u201cjob\u201d (or equivalent) on that back-end.</p>
-
-<h2 id="a-namepipelineacreating-the-pipeline"><a name="#pipeline"></a>Creating the Pipeline</h2>
-
-<p>The <code class="highlighter-rouge">Pipeline</code> abstraction encapsulates all the data and steps in your data processing task. Your Beam driver program typically starts by constructing a <a href="https://github.com/apache/incubator-beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/Pipeline.java">Pipeline</a> object, and then using that object as the basis for creating the pipeline\u2019s data sets as <code class="highlighter-rouge">PCollection</code>s and its operations as <code class="highlighter-rouge">Transform</code>s.</p>
-
-<p>To use Beam, your driver program must first create an instance of the Beam SDK class <code class="highlighter-rouge">Pipeline</code> (typically in the <code class="highlighter-rouge">main()</code> function). When you create your <code class="highlighter-rouge">Pipeline</code>, you\u2019ll also need to set some <strong>configuration options</strong>. You can set your pipeline\u2019s configuration options programatically, but it\u2019s often easier to set the options ahead of time (or read them from the command line) and pass them to the <code class="highlighter-rouge">Pipeline</code> object when you create the object.</p>
-
-<p>The pipeline configuration options determine, among other things, the <code class="highlighter-rouge">PipelineRunner</code> that determines where the pipeline gets executed: locally, or using a distributed back-end of your choice. Depending on where your pipeline gets executed and what your specifed Runner requires, the options can also help you specify other aspects of execution.</p>
-
-<p>To set your pipeline\u2019s configuration options and create the pipeline, create an object of type <a href="https://github.com/apache/incubator-beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PipelineOptions.java">PipelineOptions</a> and pass it to <code class="highlighter-rouge">Pipeline.Create()</code>. The most common way to do this is by parsing arguments from the command-line:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>
- <span class="c1">// Will parse the arguments passed into the application and construct a PipelineOptions</span>
- <span class="c1">// Note that --help will print registered options, and --help=PipelineOptionsClassName</span>
- <span class="c1">// will print out usage for the specific class.</span>
- <span class="n">PipelineOptions</span> <span class="n">options</span> <span class="o">=</span>
- <span class="n">PipelineOptionsFactory</span><span class="o">.</span><span class="na">fromArgs</span><span class="o">(</span><span class="n">args</span><span class="o">).</span><span class="na">create</span><span class="o">();</span>
-
- <span class="n">Pipeline</span> <span class="n">p</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">options</span><span class="o">);</span>
-</code></pre>
-</div>
-
-<p>The Beam SDKs contain various subclasses of <code class="highlighter-rouge">PipelineOptions</code> that correspond to different Runners. For example, <code class="highlighter-rouge">DirectPipelineOptions</code> contains options for the Direct (local) pipeline runner, while <code class="highlighter-rouge">DataflowPipelineOptions</code> contains options for using the runner for Google Cloud Dataflow. You can also define your own custom <code class="highlighter-rouge">PipelineOptions</code> by creating an interface that extends the Beam SDKs\u2019 <code class="highlighter-rouge">PipelineOptions</code> class.</p>
-
-<h2 id="a-namepcollectionaworking-with-pcollections"><a name="#pcollection"></a>Working with PCollections</h2>
-
-<p>The <a href="https://github.com/apache/incubator-beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/values/PCollection.java">PCollection</a> abstraction represents a potentially distributed, multi-element data set. You can think of a <code class="highlighter-rouge">PCollection</code> as \u201cpipeline\u201d data; Beam transforms use <code class="highlighter-rouge">PCollection</code> objects as inputs and outputs. As such, if you want to work with data in your pipeline, it must be in the form of a <code class="highlighter-rouge">PCollection</code>.</p>
-
-<p>After you\u2019ve created your <code class="highlighter-rouge">Pipeline</code>, you\u2019ll need to begin by creating at least one <code class="highlighter-rouge">PCollection</code> in some form. The <code class="highlighter-rouge">PCollection</code> you create serves as the input for the first operation in your pipeline.</p>
-
-<h3 id="a-namepccreateacreating-a-pcollection"><a name="#pccreate"></a>Creating a PCollection</h3>
-
-<p>You create a <code class="highlighter-rouge">PCollection</code> by either reading data from an external source using Beam\u2019s <a href="#io">Source API</a>, or you can create a <code class="highlighter-rouge">PCollection</code> of data stored in an in-memory collection class in your driver program. The former is typically how a production pipeline would ingest data; Beam\u2019s Source APIs contain adapters to help you read from external sources like large cloud-based files, databases, or subscription services. The latter is primarily useful for testing and debugging purposes.</p>
-
-<h4 id="reading-from-an-external-source">Reading from an External Source</h4>
-
-<p>To read from an external source, you use one of the <a href="#io">Beam-provided I/O adapters</a>. The adapters vary in their exact usage, but all of them from some external data source and return a <code class="highlighter-rouge">PCollection</code> whose elements represent the data records in that source.</p>
-
-<p>Each data source adapter has a <code class="highlighter-rouge">Read</code> transform; to read, you must apply that transform to the <code class="highlighter-rouge">Pipeline</code> object itself. <code class="highlighter-rouge">TextIO.Read</code>, for example, reads from an external text file and returns a <code class="highlighter-rouge">PCollection</code> whose elements are of type <code class="highlighter-rouge">String</code>; each <code class="highlighter-rouge">String</code> represents one line from the text file. Here\u2019s how you would apply <code class="highlighter-rouge">TextIO.Read</code> to your <code class="highlighter-rouge">Pipeline</code> to create a <code class="highlighter-rouge">PCollection</code>:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>
- <span class="c1">// Create the pipeline.</span>
- <span class="n">PipelineOptions</span> <span class="n">options</span> <span class="o">=</span>
- <span class="n">PipelineOptionsFactory</span><span class="o">.</span><span class="na">fromArgs</span><span class="o">(</span><span class="n">args</span><span class="o">).</span><span class="na">create</span><span class="o">();</span>
- <span class="n">Pipeline</span> <span class="n">p</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">options</span><span class="o">);</span>
-
- <span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">lines</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span>
- <span class="n">TextIO</span><span class="o">.</span><span class="na">Read</span><span class="o">.</span><span class="na">named</span><span class="o">(</span><span class="s">"ReadMyFile"</span><span class="o">).</span><span class="na">from</span><span class="o">(</span><span class="s">"gs://some/inputData.txt"</span><span class="o">));</span>
-<span class="o">}</span>
-</code></pre>
-</div>
-
-<p>See the <a href="#io">section on I/O</a> to learn more about how to read from the various data sources supported by the Beam SDK.</p>
-
-<h4 id="creating-a-pcollection-from-in-memory-data">Creating a PCollection from In-Memory Data</h4>
-
-<p>To create a <code class="highlighter-rouge">PCollection</code> from an in-memory Java <code class="highlighter-rouge">Collection</code>, you use the Beam-provided <code class="highlighter-rouge">Create</code> transform. Much like a data adapter\u2019s <code class="highlighter-rouge">Read</code>, you apply <code class="highlighter-rouge">Create</code> sirectly to your <code class="highlighter-rouge">Pipeline</code> object itself.</p>
-
-<p>As parameters, <code class="highlighter-rouge">Create</code> accepts the Java <code class="highlighter-rouge">Collection</code> and a <code class="highlighter-rouge">Coder</code> object. The <code class="highlighter-rouge">Coder</code> specifies how the elements in the <code class="highlighter-rouge">Collection</code> should be <a href="#pcelementtype">encoded</a>.</p>
-
-<p>The following example code shows how to create a <code class="highlighter-rouge">PCollection</code> from an in-memory Java <code class="highlighter-rouge">List</code>:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>
- <span class="c1">// Create a Java Collection, in this case a List of Strings.</span>
- <span class="kd">static</span> <span class="kd">final</span> <span class="n">List</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">LINES</span> <span class="o">=</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span>
- <span class="s">"To be, or not to be: that is the question: "</span><span class="o">,</span>
- <span class="s">"Whether 'tis nobler in the mind to suffer "</span><span class="o">,</span>
- <span class="s">"The slings and arrows of outrageous fortune, "</span><span class="o">,</span>
- <span class="s">"Or to take arms against a sea of troubles, "</span><span class="o">);</span>
-
- <span class="c1">// Create the pipeline.</span>
- <span class="n">PipelineOptions</span> <span class="n">options</span> <span class="o">=</span>
- <span class="n">PipelineOptionsFactory</span><span class="o">.</span><span class="na">fromArgs</span><span class="o">(</span><span class="n">args</span><span class="o">).</span><span class="na">create</span><span class="o">();</span>
- <span class="n">Pipeline</span> <span class="n">p</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="o">.</span><span class="na">create</span><span class="o">(</span><span class="n">options</span><span class="o">);</span>
-
- <span class="c1">// Apply Create, passing the list and the coder, to create the PCollection.</span>
- <span class="n">p</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">Create</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="n">LINES</span><span class="o">)).</span><span class="na">setCoder</span><span class="o">(</span><span class="n">StringUtf8Coder</span><span class="o">.</span><span class="na">of</span><span class="o">())</span>
-<span class="o">}</span>
-</code></pre>
-</div>
-<p>### <a name="#pccharacteristics">PCollection Characteristics</a></p>
-
-<p>A <code class="highlighter-rouge">PCollection</code> is owned by the specific <code class="highlighter-rouge">Pipeline</code> object for which it is created; multiple pipelines cannot share a <code class="highlighter-rouge">PCollection</code>. In some respects, a <code class="highlighter-rouge">PCollection</code> functions like a collection class. However, a <code class="highlighter-rouge">PCollection</code> can differ in a few key ways:</p>
-
-<h4 id="a-namepcelementtypeaelement-type"><a name="#pcelementtype"></a>Element Type</h4>
-
-<p>The elements of a <code class="highlighter-rouge">PCollection</code> may be of any type, but must all be of the same type. However, to support distributed processing, Beam needs to be able to encode each individual element as a byte string (so elements can be passed around to distributed workers). The Beam SDKs provide a data encoding mechanism that includes built-in encoding for commonly-used types as well as support for specifying custom encodings as needed.</p>
-
-<h4 id="a-namepcimmutabilityaimmutability"><a name="#pcimmutability"></a>Immutability</h4>
-
-<p>A <code class="highlighter-rouge">PCollection</code> is immutable. Once created, you cannot add, remove, or change individual elements. A Beam Transform might process each element of a <code class="highlighter-rouge">PCollection</code> and generate new pipeline data (as a new <code class="highlighter-rouge">PCollection</code>), <em>but it does not consume or modify the original input collection</em>.</p>
-
-<h4 id="a-namepcrandomaccessarandom-access"><a name="#pcrandomaccess"></a>Random Access</h4>
-
-<p>A <code class="highlighter-rouge">PCollection</code> does not support random access to individual elements. Instead, Beam Transforms consider every element in a <code class="highlighter-rouge">PCollection</code> individually.</p>
-
-<h4 id="a-namepcsizeboundasize-and-boundedness"><a name="#pcsizebound"></a>Size and Boundedness</h4>
-
-<p>A <code class="highlighter-rouge">PCollection</code> is a large, immutable \u201cbag\u201d of elements. There is no upper limit on how many elements a <code class="highlighter-rouge">PCollection</code> can contain; any given <code class="highlighter-rouge">PCollection</code> might fit in memory on a single machine, or it might represent a very large distributed data set backed by a persistent data store.</p>
-
-<p>A <code class="highlighter-rouge">PCollection</code> can be either <strong>bounded</strong> or <strong>unbounded</strong> in size. A <strong>bounded</strong> <code class="highlighter-rouge">PCollection</code> represents a data set of a known, fixed size, while an <strong>unbounded</strong> <code class="highlighter-rouge">PCollection</code> represents a data set of unlimited size. Whether a <code class="highlighter-rouge">PCollection</code> is bounded or unbounded depends on the source of the data set that it represents. Reading from a batch data source, such as a file or a database, creates a bounded <code class="highlighter-rouge">PCollection</code>. Reading from a streaming or continously-updating data source, such as Pub/Sub or Kafka, creates an unbounded <code class="highlighter-rouge">PCollection</code> (unless you explicitly tell it not to).</p>
-
-<p>The bounded (or unbounded) nature The bounded (or unbounded) nature of your <code class="highlighter-rouge">PCollection</code> affects how Beam processes your data. A bounded <code class="highlighter-rouge">PCollection</code> can be processed using a batch job, which might read the entire data set once, and perform processing in a job of finite length. An unbounded <code class="highlighter-rouge">PCollection</code> must be processed using a streaming job that runs continuously, as the entire collection can never be available for processing at any one time.</p>
-
-<p>When performing an operation that groups elements in an unbounded <code class="highlighter-rouge">PCollection</code>, Beam requires a concept called <strong>Windowing</strong> to divide a continuously updating data set into logical windows of finite size. Beam processes each window as a bundle, and processing continues as the data set is generated. These logical windows are determined by some characteristic associated with a data element, such as a <strong>timestamp</strong>.</p>
-
-<h4 id="a-namepctimestampsaelement-timestamps"><a name="#pctimestamps"></a>Element Timestamps</h4>
-
-<p>Each element in a <code class="highlighter-rouge">PCollection</code> has an associated intrinsic <strong>timestamp</strong>. The timestamp for each element is initially assigned by the <a href="#io">Source</a> that creates the <code class="highlighter-rouge">PCollection</code>. Sources that create an unbounded <code class="highlighter-rouge">PCollection</code> often assign each new element a timestamp that corresponds to when the element was read or added.</p>
-
-<blockquote>
- <p><strong>Note</strong>: Sources that create a bounded <code class="highlighter-rouge">PCollection</code> for a fixed data set also automatically assign timestamps, but the most common behavior is to assign every element the same timestamp (<code class="highlighter-rouge">Long.MIN_VALUE</code>).</p>
-</blockquote>
-
-<p>Timestamps are useful for a <code class="highlighter-rouge">PCollection</code> that contains elements with an inherent notion of time. If your pipeline is reading a stream of events, like Tweets or other social media messages, each element might use the time the event was posted as the element timestamp.</p>
-
-<p>You can manually assign timestamps to the elements of a <code class="highlighter-rouge">PCollection</code> if the source doesn\u2019t do it for you. You\u2019ll want to do this if the elements have an inherent timestamp, but the timestamp is somewhere in the structure of the element itself (such as a \u201ctime\u201d field in a server log entry). Beam has <a href="#transforms">Transforms</a> that take a <code class="highlighter-rouge">PCollection</code> as input and output an identical <code class="highlighter-rouge">PCollection</code> with timestamps attached; see <a href="#windowing">Assigning Timestamps</a> for more information on how to do so.</p>
-
-<h2 id="a-nametransformaapplying-transforms"><a name="#transform"></a>Applying Transforms</h2>
-
-<p>In the Beam SDKs, <strong>transforms</strong> are the operations in your pipeline. A transform takes a <code class="highlighter-rouge">PCollection</code> (or more than one <code class="highlighter-rouge">PCollection</code>) as input, performs an operation that you specify on each element in that collection, and produces a new output <code class="highlighter-rouge">PCollection</code>. To invoke a transform, you must <strong>apply</strong> it to the input <code class="highlighter-rouge">PCollection</code>.</p>
-
-<p>In Beam SDK for Java, each transform has a generic <code class="highlighter-rouge">apply</code> method. In the Beam SDK for Python, you use the pipe operator (<code class="highlighter-rouge">|</code>) to apply a transform. Invoking multiple Beam transforms is similar to <em>method chaining</em>, but with one slight difference: You apply the transform to the input <code class="highlighter-rouge">PCollection</code>, passing the transform itself as an argument, and the operation returns the output <code class="highlighter-rouge">PCollection</code>. This takes the general form:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="o">[</span><span class="n">Output</span> <span class="n">PCollection</span><span class="o">]</span> <span class="o">=</span> <span class="o">[</span><span class="n">Input</span> <span class="n">PCollection</span><span class="o">].</span><span class="na">apply</span><span class="o">([</span><span class="n">Transform</span><span class="o">])</span>
-</code></pre>
-</div>
-
-<p>Because Beam uses a generic <code class="highlighter-rouge">apply</code> method for <code class="highlighter-rouge">PCollection</code>, you can both chain transforms sequentially and also apply transforms that contain other transforms nested within (called <strong>composite transforms</strong> in the Beam SDKs).</p>
-
-<p>How you apply your pipeline\u2019s transforms determines the structure of your pipeline. The best way to think of your pipeline is as a directed acyclic graph, where the nodes are <code class="highlighter-rouge">PCollection</code>s and the edges are transforms. For example, you can chain transforms to create a sequential pipeline, like this one:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="o">[</span><span class="n">Final</span> <span class="n">Output</span> <span class="n">PCollection</span><span class="o">]</span> <span class="o">=</span> <span class="o">[</span><span class="n">Initial</span> <span class="n">Input</span> <span class="n">PCollection</span><span class="o">].</span><span class="na">apply</span><span class="o">([</span><span class="n">First</span> <span class="n">Transform</span><span class="o">])</span>
- <span class="o">.</span><span class="na">apply</span><span class="o">([</span><span class="n">Second</span> <span class="n">Transform</span><span class="o">])</span>
- <span class="o">.</span><span class="na">apply</span><span class="o">([</span><span class="n">Third</span> <span class="n">Transform</span><span class="o">])</span>
-</code></pre>
-</div>
-
-<p>The resulting workflow graph of the above pipeline looks like this:</p>
-
-<p>[Sequential Graph Graphic]</p>
-
-<p>However, note that a transform <em>does not consume or otherwise alter</em> the input collection\u2013remember that a <code class="highlighter-rouge">PCollection</code> is immutable by definition. This means that you can apply multiple transforms to the same input <code class="highlighter-rouge">PCollection</code> to create a branching pipeline, like so:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="o">[</span><span class="n">Output</span> <span class="n">PCollection</span> <span class="mi">1</span><span class="o">]</span> <span class="o">=</span> <span class="o">[</span><span class="n">Input</span> <span class="n">PCollection</span><span class="o">].</span><span class="na">apply</span><span class="o">([</span><span class="n">Transform</span> <span class="mi">1</span><span class="o">])</span>
-<span class="o">[</span><span class="n">Output</span> <span class="n">PCollection</span> <span class="mi">2</span><span class="o">]</span> <span class="o">=</span> <span class="o">[</span><span class="n">Input</span> <span class="n">PCollection</span><span class="o">].</span><span class="na">apply</span><span class="o">([</span><span class="n">Transform</span> <span class="mi">2</span><span class="o">])</span>
-</code></pre>
-</div>
-
-<p>The resulting workflow graph from the branching pipeline abouve looks like this:</p>
-
-<p>[Branching Graph Graphic]</p>
-
-<p>You can also build your own <a href="#transform-composite">composite transforms</a> that nest multiple sub-steps inside a single, larger transform. Composite transforms are particularly useful for building a reusable sequence of simple steps that get used in a lot of different places.</p>
-
-<h3 id="transforms-in-the-beam-sdk">Transforms in the Beam SDK</h3>
-
-<p>The transforms in the Beam SDKs provide a generic <strong>processing framework</strong>, where you provide processing logic in the form of a function object (colloquially referred to as \u201cuser code\u201d). The user code gets applied to the elements of the input <code class="highlighter-rouge">PCollection</code>. Instances of your user code might then be executed in parallel by many different workers across a cluster, depending on the pipeline runner and back-end that you choose to execute your Beam pipeline. The user code running on each worker generates the output elements that are ultimately added to the final output <code class="highlighter-rouge">PCollection</code> that the transform produces.</p>
-
-<h3 id="core-beam-transforms">Core Beam Transforms</h3>
-
-<p>Beam provides the following transforms, each of which represents a different processing paradigm:</p>
-
-<ul>
- <li><code class="highlighter-rouge">ParDo</code></li>
- <li><code class="highlighter-rouge">GroupByKey</code></li>
- <li><code class="highlighter-rouge">Combine</code></li>
- <li><code class="highlighter-rouge">Flatten</code></li>
-</ul>
-
-<h4 id="a-nametransforms-pardoapardo"><a name="#transforms-pardo"></a>ParDo</h4>
-
-<p><code class="highlighter-rouge">ParDo</code> is a Beam transform for generic parallel processing. The <code class="highlighter-rouge">ParDo</code> processing paradigm is similar to the \u201cMap\u201d phase of a Map/Shuffle/Reduce-style algorithm: a <code class="highlighter-rouge">ParDo</code> transform considers each element in the input <code class="highlighter-rouge">PCollection</code>, performs some processing function (your user code) on that element, and emits zero, one, or multiple elements to an output <code class="highlighter-rouge">PCollection</code>.</p>
-
-<p><code class="highlighter-rouge">ParDo</code> is useful for a variety of common data processing operations, including:</p>
-
-<ul>
- <li><strong>Filtering a data set.</strong> You can use <code class="highlighter-rouge">ParDo</code> to consider each element in a <code class="highlighter-rouge">PCollection</code> and either output that element to a new collection, or discard it.</li>
- <li><strong>Formatting or type-converting each element in a data set.</strong> If your input <code class="highlighter-rouge">PCollection</code> contains elements that are of a different type or format than you want, you can use <code class="highlighter-rouge">ParDo</code> to perform a conversion on each element and output the result to a new <code class="highlighter-rouge">PCollection</code>.</li>
- <li><strong>Extracting parts of each element in a data set.</strong> If you have a <code class="highlighter-rouge">PCollection</code> of records with multiple fields, for example, you can use a <code class="highlighter-rouge">ParDo</code> to parse out just the fields you want to consider into a new <code class="highlighter-rouge">PCollection</code>.</li>
- <li><strong>Performing computations on each element in a data set.</strong> You can use <code class="highlighter-rouge">ParDo</code> to perform simple or complex computations on every element, or certain elements, of a <code class="highlighter-rouge">PCollection</code> and output the results as a new <code class="highlighter-rouge">PCollection</code>.</li>
-</ul>
-
-<p>In such roles, <code class="highlighter-rouge">ParDo</code> is a common intermediate step in a pipeline. You might use it to extract certain fields from a set of raw input records, or convert raw input into a different format; you might also use <code class="highlighter-rouge">ParDo</code> to convert processed data into a format suitable for output, like database table rows or printable strings.</p>
-
-<p>When you apply a <code class="highlighter-rouge">ParDo</code> transform, you\u2019ll need to provide user code in the form of a <code class="highlighter-rouge">DoFn</code> object. <code class="highlighter-rouge">DoFn</code> is a Beam SDK class that defines a distribured processing function.</p>
-
-<blockquote>
- <p>When you create a subclass of <code class="highlighter-rouge">DoFn</code>, note that your subclass should adhere to the <a href="#transforms-usercodereqs">General Requirements for Writing User Code for Beam Transforms</a>.</p>
-</blockquote>
-
-<h5 id="applying-pardo">Applying ParDo</h5>
-
-<p>Like all Beam transforms, you apply <code class="highlighter-rouge">ParDo</code> by calling the <code class="highlighter-rouge">apply</code> method on the input <code class="highlighter-rouge">PCollection</code> and passing <code class="highlighter-rouge">ParDo</code> as an argument, as shown in the following example code:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="c1">// The input PCollection of Strings.</span>
-<span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">words</span> <span class="o">=</span> <span class="o">...;</span>
-
-<span class="c1">// The DoFn to perform on each element in the input PCollection.</span>
-<span class="kd">static</span> <span class="kd">class</span> <span class="nc">ComputeWordLengthFn</span> <span class="kd">extends</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="o">{</span> <span class="o">...</span> <span class="o">}</span>
-
-<span class="c1">// Apply a ParDo to the PCollection "words" to compute lengths for each word.</span>
-<span class="n">PCollection</span><span class="o"><</span><span class="n">Integer</span><span class="o">></span> <span class="n">wordLengths</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span>
- <span class="n">ParDo</span>
- <span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="k">new</span> <span class="n">ComputeWordLengthFn</span><span class="o">()));</span> <span class="c1">// The DoFn to perform on each element, which</span>
- <span class="c1">// we define above.</span>
-</code></pre>
-</div>
-
-<p>In the example, our input <code class="highlighter-rouge">PCollection</code> contains <code class="highlighter-rouge">String</code> values. We apply a <code class="highlighter-rouge">ParDo</code> transform that specifies a function (<code class="highlighter-rouge">ComputeWordLengthFn</code>) to compute the length of each string, and outputs the result to a new <code class="highlighter-rouge">PCollection</code> of <code class="highlighter-rouge">Integer</code> values that stores the length of each word.</p>
-
-<h5 id="creating-a-dofn">Creating a DoFn</h5>
-
-<p>The <code class="highlighter-rouge">DoFn</code> object that you pass to <code class="highlighter-rouge">ParDo</code> contains the processing logic that gets applied to the elements in the input collection. When you use Beam, often the most important pieces of code you\u2019ll write are these <code class="highlighter-rouge">DoFn</code>s\u2013they\u2019re what define your pipeline\u2019s exact data processing tasks.</p>
-
-<blockquote>
- <p><strong>Note:</strong> When you create your <code class="highlighter-rouge">DoFn</code>, be mindful of the <a href="#transforms-usercodereqs">General Requirements for Writing User Code for Beam Transforms</a> and ensure that your code follows them.</p>
-</blockquote>
-
-<p>A <code class="highlighter-rouge">DoFn</code> processes one element at a time from the input <code class="highlighter-rouge">PCollection</code>. When you create a subclass of <code class="highlighter-rouge">DoFn</code>, you\u2019ll need to provide type paraemters that match the types of the input and output elements. If your <code class="highlighter-rouge">DoFn</code> processes incoming <code class="highlighter-rouge">String</code> elements and produces <code class="highlighter-rouge">Integer</code> elements for the output collection (like our previous example, <code class="highlighter-rouge">ComputeWordLengthFn</code>), your class declaration would look like this:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="kd">static</span> <span class="kd">class</span> <span class="nc">ComputeWordLengthFn</span> <span class="kd">extends</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="o">{</span> <span class="o">...</span> <span class="o">}</span>
-</code></pre>
-</div>
-
-<p>Inside your <code class="highlighter-rouge">DoFn</code> subclass, you\u2019ll need to override the method <code class="highlighter-rouge">processElement</code>, where you provide the actual processing logic. You don\u2019t need to manually extract the elements from the input collection; the Beam SDKs handle that for you. Your override of <code class="highlighter-rouge">processElement</code> should accept an object of type <code class="highlighter-rouge">ProcessContext</code>. The <code class="highlighter-rouge">ProcessContext</code> object gives you access to an input element and a method for emitting an output element:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="kd">static</span> <span class="kd">class</span> <span class="nc">ComputeWordLengthFn</span> <span class="kd">extends</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">></span> <span class="o">{</span>
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">processElement</span><span class="o">(</span><span class="n">ProcessContext</span> <span class="n">c</span><span class="o">)</span> <span class="o">{</span>
- <span class="c1">// Get the input element from ProcessContext.</span>
- <span class="n">String</span> <span class="n">word</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="na">element</span><span class="o">();</span>
- <span class="c1">// Use ProcessContext.output to emit the output element.</span>
- <span class="n">c</span><span class="o">.</span><span class="na">output</span><span class="o">(</span><span class="n">word</span><span class="o">.</span><span class="na">length</span><span class="o">());</span>
- <span class="o">}</span>
-<span class="o">}</span>
-</code></pre>
-</div>
-
-<blockquote>
- <p><strong>Note:</strong> If the elements in your input <code class="highlighter-rouge">PCollection</code> are key/value pairs, you can access the key or value by using <code class="highlighter-rouge">ProcessContext.element().getKey()</code> or <code class="highlighter-rouge">ProcessContext.element().getValue()</code>, respectively.</p>
-</blockquote>
-
-<p>A given <code class="highlighter-rouge">DoFn</code> instance generally gets invoked one or more times to process some arbitrary bundle of elements. However, Beam doesn\u2019t guarantee an exact number of invocations; it may be invoked multiple times on a given worker node to account for failures and retries. As such, you can cache information across multiple calls to <code class="highlighter-rouge">processElement</code>, but if you do so, make sure the implementation <strong>does not depend on the number of invocations</strong>.</p>
-
-<p>When you override <code class="highlighter-rouge">processElement</code>, you\u2019ll need to meet some immutability requirements to ensure that Beam and the processing back-end can safely serialize and cache the values in your pipeline. Your method should meet the following requirements:</p>
-
-<ul>
- <li>You should not in any way modify an element returned by <code class="highlighter-rouge">ProcessContext.element()</code> or <code class="highlighter-rouge">ProcessContext.sideInput()</code> (the incoming elements from the input collection).</li>
- <li>Once you output a value using <code class="highlighter-rouge">ProcessContext.output()</code> or <code class="highlighter-rouge">ProcessContext.sideOutput()</code>, you should not modify that value in any way.</li>
-</ul>
-
-<h5 id="lightweight-dofns-and-other-abstractions">Lightweight DoFns and Other Abstractions</h5>
-
-<p>If your function is relatively straightforward, you can simply your use of <code class="highlighter-rouge">ParDo</code> by providing a lightweight <code class="highlighter-rouge">DoFn</code> in-line. In Java, you can specify your <code class="highlighter-rouge">DoFn</code> as an anonymous inner class instance, and in Python you can use a <code class="highlighter-rouge">Callable</code>.</p>
-
-<p>Here\u2019s the previous example, <code class="highlighter-rouge">ParDo</code> with <code class="highlighter-rouge">ComputeLengthWordsFn</code>, with the <code class="highlighter-rouge">DoFn</code> specified as an anonymous inner class instance:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="c1">// The input PCollection.</span>
-<span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">words</span> <span class="o">=</span> <span class="o">...;</span>
-
-<span class="c1">// Apply a ParDo with an anonymous DoFn to the PCollection words.</span>
-<span class="c1">// Save the result as the PCollection wordLengths.</span>
-<span class="n">PCollection</span><span class="o"><</span><span class="n">Integer</span><span class="o">></span> <span class="n">wordLengths</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span>
- <span class="n">ParDo</span>
- <span class="o">.</span><span class="na">named</span><span class="o">(</span><span class="s">"ComputeWordLengths"</span><span class="o">)</span> <span class="c1">// the transform name</span>
- <span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="k">new</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>()</span> <span class="o">{</span> <span class="c1">// a DoFn as an anonymous inner class instance</span>
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">processElement</span><span class="o">(</span><span class="n">ProcessContext</span> <span class="n">c</span><span class="o">)</span> <span class="o">{</span>
- <span class="n">c</span><span class="o">.</span><span class="na">output</span><span class="o">(</span><span class="n">c</span><span class="o">.</span><span class="na">element</span><span class="o">().</span><span class="na">length</span><span class="o">());</span>
- <span class="o">}</span>
- <span class="o">}));</span>
-</code></pre>
-</div>
-
-<p>If your <code class="highlighter-rouge">ParDo</code> performs a one-to-one mapping of input elements to output elements\u2013that is, for each input element, it applies a function that produces <em>exactly one</em> output element, you can use the higher-level <code class="highlighter-rouge">MapElements</code> transform. <code class="highlighter-rouge">MapElements</code> can accept an anonymous Java 8 lambda function for additional brevity.</p>
-
-<p>Here\u2019s the previous example using <code class="highlighter-rouge">MapElements</code>:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code><span class="c1">// The input PCollection.</span>
-<span class="n">PCollection</span><span class="o">&</span><span class="n">lt</span><span class="o">;</span><span class="n">String</span><span class="o">&</span><span class="n">gt</span><span class="o">;</span> <span class="n">words</span> <span class="o">=</span> <span class="o">...;</span>
-
-<span class="c1">// Apply a MapElements with an anonymous lambda function to the PCollection words.</span>
-<span class="c1">// Save the result as the PCollection wordLengths.</span>
-<span class="n">PCollection</span><span class="o">&</span><span class="n">lt</span><span class="o">;</span><span class="n">Integer</span><span class="o">&</span><span class="n">gt</span><span class="o">;</span> <span class="n">wordLengths</span> <span class="o">=</span> <span class="n">words</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span>
- <span class="n">MapElements</span><span class="o">.</span><span class="na">via</span><span class="o">((</span><span class="n">String</span> <span class="n">word</span><span class="o">)</span> <span class="o">-&</span><span class="n">gt</span><span class="o">;</span> <span class="n">word</span><span class="o">.</span><span class="na">length</span><span class="o">())</span>
- <span class="o">.</span><span class="na">withOutputType</span><span class="o">(</span><span class="k">new</span> <span class="n">TypeDescriptor</span><span class="o">&</span><span class="n">lt</span><span class="o">;</span><span class="n">Integer</span><span class="o">&</span><span class="n">gt</span><span class="o">;()</span> <span class="o">{});</span>
-</code></pre>
-</div>
-
-<blockquote>
- <p><strong>Note:</strong> You can use Java 8 lambda functions with several other Beam transforms, including <code class="highlighter-rouge">Filter</code>, <code class="highlighter-rouge">FlatMapElements</code>, and <code class="highlighter-rouge">Partition</code>.</p>
-</blockquote>
-
-<h4 id="a-nametransforms-gbkausing-groupbykey"><a name="#transforms-gbk"></a>Using GroupByKey</h4>
-
-<p><code class="highlighter-rouge">GroupByKey</code> is a Beam transform for processing collections of key/value pairs. It\u2019s a parallel reduction operation, analagous to the Shuffle phase of a Map/Shuffle/Reduce-style algorithm. The input to <code class="highlighter-rouge">GroupByKey</code> is a collection of key/value pairs that represents a <em>multimap</em>, where the collection contains multiple pairs that have the same key, but different values. Given such a collection, you use <code class="highlighter-rouge">GroupByKey</code> to collect all of the values associated with each unique key.</p>
-
-<p><code class="highlighter-rouge">GroupByKey</code> is a good way to aggregate data that has something in common. For example, if you have a collection that stores records of customer orders, you might want to group together all the orders from the same postal code (wherein the \u201ckey\u201d of the key/value pair is the postal code field, and the \u201cvalue\u201d is the remainder of the record).</p>
-
-<p>Let\u2019s examine the mechanics of <code class="highlighter-rouge">GroupByKey</code> with a simple xample case, where our data set consists of words from a text file and the line number on which they appear. We want to group together all the line numbers (values) that share the same word (key), letting us see all the places in the text where a particular word appears.</p>
-
-<p>Our input is a <code class="highlighter-rouge">PCollection</code> of key/value pairs where each word is a key, and the value is a line number in the file where the word appears. Here\u2019s a list of the key/value pairs in the input collection:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code>cat, 1
-dog, 5
-and, 1
-jump, 3
-tree, 2
-cat, 5
-dog, 2
-and, 2
-cat, 9
-and, 6
-...
-</code></pre>
-</div>
-
-<p><code class="highlighter-rouge">GroupByKey</code> gathers up all the values with the same key and outputs a new pair consisting of the unique key and a collection of all of the values that were associated with that key in the input collection. If we apply <code class="highlighter-rouge">GroupByKey</code> to our input collection above, the output collection would look like this:</p>
-
-<div class="highlighter-rouge"><pre class="highlight"><code>cat, [1,5,9]
-dog, [5,2]
-and, [1,2,6]
-jump, [3]
-tree, [2]
-...
-</code></pre>
-</div>
-
-<p>Thus, <code class="highlighter-rouge">GroupByKey</code> represents a transform from a multimap (multiple keys to individual values) to a uni-map (unique keys to collections of values).</p>
-
-<blockquote>
- <p><strong>A Note on Key/Value Pairs:</strong> Beam represents key/value pairs slightly differently depending on the language and SDK you\u2019re using. In the Beam SDK for Java, you represent a key/value pair with an object of type <code class="highlighter-rouge">KV<K, V></code>. In Python, you represent key/value pairs with 2-tuples.</p>
-</blockquote>
-
-<h4 id="a-nametransforms-combineausing-combine"><a name="#transforms-combine"></a>Using Combine</h4>
-
-<h4 id="a-nametransforms-usercodereqsageneral-requirements-for-writing-user-code-for-beam-transforms"><a name="#transforms-usercodereqs"></a>General Requirements for Writing User Code for Beam Transforms</h4>
-
-<p>When you build user code for a Beam transform, you should keep in mind the distributed nature of execution. For example, there might be many copies of your function running on a lot of different machines in parallel, and those copies function independently, without communicating or sharing state with any of the other copies. Depending on the Pipeline Runner and processing back-end you choose for your pipeline, each copy of your user code function may be retried or run multiple times. As such, you should be cautious about including things like state dependency in your user code.</p>
-
-<p>In general, your user code must fulfill at least these requirements:</p>
-
-<ul>
- <li>Your function object must be <strong>serializable</strong>.</li>
- <li>Your function object must be <strong>thread-compatible</strong>, and be aware that <em>the Beam SDKs are not thread-safe</em>.</li>
-</ul>
-
-<p>In addition, it\u2019s recommended that you make your function object <strong>idempotent</strong>.</p>
-
-<blockquote>
- <p><strong>Note:</strong> These requirements apply to subclasses of <code class="highlighter-rouge">DoFn</code> (a function object used with the <a href="#transforms-pardo">ParDo</a> transform), <code class="highlighter-rouge">CombineFn</code> (a function object used with the <a href="#transforms-combine">Combine</a> transform), and <code class="highlighter-rouge">WindowFn</code> (a function object used with the <a href="#windowing">Window</a> transform).</p>
-</blockquote>
-
-<h5 id="serializability">Serializability</h5>
-
-<p>Any function object you provide to a transform must be <strong>fully serializable</strong>. This is because a copy of the function needs to be serialized and transmitted to a remote worker in your processing cluster. The base classes for user code, such as <code class="highlighter-rouge">DoFn</code>, <code class="highlighter-rouge">CombineFn</code>, and <code class="highlighter-rouge">WindowFn</code>, already implement <code class="highlighter-rouge">Serializable</code>; however, your subclass must not add any non-serializable members.</p>
-
-<p>Some other serializability factors you should keep in mind are:</p>
-
-<ul>
- <li>Transient fields in your function object are <em>not</em> transmitted to worker instances, because they are not automatically serialized.</li>
- <li>Avoid loading a field with a large amount of data before serialization.</li>
- <li>Individual instances of your function object cannot share data.</li>
- <li>Mutating a function object after it gets applied will have no effect.</li>
- <li>Take care when declaring your function object inline by using an anonymous inner class instance. In a non-static context, your inner class instance will implicitly contain a pointer to the enclosing class and that class\u2019 state. That enclosing class will also be serialized, and thus the same considerations that apply to the function object itself also apply to this outer class.</li>
-</ul>
-
-<h5 id="thread-compatibility">Thread-Compatibility</h5>
-
-<p>Your function object should be thread-compatible. Each instance of your function object is accessed by a single thread on a worker instance, unless you explicitly create your own threads. Note, however, that <strong>the Beam SDKs are not thread-safe</strong>. If you create your own threads in your user code, you must provide your own synchronization. Note that static members in your function object are not passed to worker instances and that multiple instances of your function may be accessed from different threads.</p>
-
-<h5 id="idempotence">Idempotence</h5>
-
-<p>It\u2019s recommended that you make your function object idempotent\u2013that is, that it can be repeated or retried as often as necessary without causing unintended side effects. The Beam model provides no guarantees as to the number of times your user code might be invoked or retried; as such, keeping your function object idempotent keeps your pipeline\u2019s output deterministic, and your transforms\u2019 behavior more predictable and easier to debug.</p>
-
-<blockquote>
- <p><strong>Note:</strong> This guide is still in progress. There is an open issue to finish the guide (<a href="https://issues.apache.org/jira/browse/BEAM-193">BEAM-193</a>)</p>
-</blockquote>
-
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- <h1 id="apache-beam-overview">Apache Beam Overview</h1>
-
-<p>Apache Beam is an open source, unified programming model that you can use to create a data processing <strong>pipeline</strong>. You start by building a program that defines the pipeline using one of the open source Beam SDKs. The pipeline is then executed by one of Beam\u2019s supported <strong>distributed processing back-ends</strong>, which include <a href="http://flink.apache.org">Apache Flink</a>, <a href="http://spark.apache.org">Apache Spark</a>, and <a href="https://cloud.google.com/dataflow">Google Cloud Dataflow</a>.</p>
-
-<p>Beam is particularly useful for <a href="http://en.wikipedia.org/wiki/Embarassingly_parallel">Embarrassingly Parallel</a> data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.</p>
-
-<h2 id="apache-beam-sdks">Apache Beam SDKs</h2>
-
-<p>The Beam SDKs provide a unified programming model that can represent and transform data sets of any size, whether the input is a finite data set from a batch data source, or an infinite data set from a streaming data source. The Beam SDKs use the same classes to represent both bounded and unbounded data, and the same transforms to operate on that data. You use the Beam SDK of your choice to build a program that defines your data processing pipeline.</p>
-
-<p>Beam currently supports the following language-specific SDKs:</p>
-
-<table class="table table-condensed">
-<tr>
- <th>Language</th>
- <th>SDK Status</th>
-</tr>
-<tr>
- <td>Java</td>
- <td>Active Development</td>
-</tr>
-<tr>
- <td>Python</td>
- <td>Coming Soon</td>
-</tr>
-<tr>
- <td>Other</td>
- <td>TBD</td>
-</tr>
-</table>
-
-<h2 id="apache-beam-pipeline-runners">Apache Beam Pipeline Runners</h2>
-
-<p>The Beam Pipeline Runners translate the data processing pipeline you define with your Beam program into the API compatible with the distributed processing back-end of your choice. When you run your Beam program, you\u2019ll need to specify the appropriate runner for the back-end where you want to execute your pipeline.</p>
-
-<p>Beam currently supports Runners that work with the following distributed processing back-ends:</p>
-
-<table class="table table-condensed">
-<tr>
- <th>Runner</th>
- <th>Status</th>
-</tr>
-<tr>
- <td>Google Cloud Dataflow</td>
- <td>In Development</td>
-</tr>
-<tr>
- <td>Apache Flink</td>
- <td>In Development</td>
-</tr>
-<tr>
- <td>Apache Spark</td>
- <td>In Development</td>
-</tr>
-</table>
-
-<p><strong>Note:</strong> You can always execute your pipeline locally for testing and debugging purposes.</p>
-
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+<h1>Redirecting\u2026</h1>
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+<script>location="http://beam.incubator.apache.org/use/beam-overview/"</script>
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