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Posted to commits@beam.apache.org by fr...@apache.org on 2016/10/28 05:02:59 UTC
[3/5] incubator-beam-site git commit: Regenerate html for #40
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/27b5e76b/content/use/wordcount-example/index.html
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+ <h1 id="apache-beam-wordcount-example">Apache Beam WordCount Example</h1>
+
+<ul id="markdown-toc">
+ <li><a href="#minimalwordcount" id="markdown-toc-minimalwordcount">MinimalWordCount</a> <ul>
+ <li><a href="#creating-the-pipeline" id="markdown-toc-creating-the-pipeline">Creating the Pipeline</a></li>
+ <li><a href="#applying-pipeline-transforms" id="markdown-toc-applying-pipeline-transforms">Applying Pipeline Transforms</a></li>
+ <li><a href="#running-the-pipeline" id="markdown-toc-running-the-pipeline">Running the Pipeline</a></li>
+ </ul>
+ </li>
+ <li><a href="#wordcount-example" id="markdown-toc-wordcount-example">WordCount Example</a> <ul>
+ <li><a href="#specifying-explicit-dofns" id="markdown-toc-specifying-explicit-dofns">Specifying Explicit DoFns</a></li>
+ <li><a href="#creating-composite-transforms" id="markdown-toc-creating-composite-transforms">Creating Composite Transforms</a></li>
+ <li><a href="#using-parameterizable-pipelineoptions" id="markdown-toc-using-parameterizable-pipelineoptions">Using Parameterizable PipelineOptions</a></li>
+ </ul>
+ </li>
+ <li><a href="#debugging-wordcount-example" id="markdown-toc-debugging-wordcount-example">Debugging WordCount Example</a> <ul>
+ <li><a href="#logging" id="markdown-toc-logging">Logging</a> <ul>
+ <li><a href="#direct-runner" id="markdown-toc-direct-runner">Direct Runner</a></li>
+ <li><a href="#dataflow-runner" id="markdown-toc-dataflow-runner">Dataflow Runner</a></li>
+ <li><a href="#apache-spark-runner" id="markdown-toc-apache-spark-runner">Apache Spark Runner</a></li>
+ <li><a href="#apache-flink-runner" id="markdown-toc-apache-flink-runner">Apache Flink Runner</a></li>
+ </ul>
+ </li>
+ <li><a href="#testing-your-pipeline-via-passert" id="markdown-toc-testing-your-pipeline-via-passert">Testing your Pipeline via PAssert</a></li>
+ </ul>
+ </li>
+ <li><a href="#windowedwordcount" id="markdown-toc-windowedwordcount">WindowedWordCount</a> <ul>
+ <li><a href="#unbounded-and-bounded-pipeline-input-modes" id="markdown-toc-unbounded-and-bounded-pipeline-input-modes">Unbounded and bounded pipeline input modes</a></li>
+ <li><a href="#adding-timestamps-to-data" id="markdown-toc-adding-timestamps-to-data">Adding Timestamps to Data</a></li>
+ <li><a href="#windowing" id="markdown-toc-windowing">Windowing</a></li>
+ <li><a href="#reusing-ptransforms-over-windowed-pcollections" id="markdown-toc-reusing-ptransforms-over-windowed-pcollections">Reusing PTransforms over windowed PCollections</a></li>
+ </ul>
+ </li>
+ <li><a href="#write-results-to-an-unbounded-sink" id="markdown-toc-write-results-to-an-unbounded-sink">Write Results to an Unbounded Sink</a></li>
+</ul>
+
+<blockquote>
+ <p><strong>Note:</strong> This walkthrough is still in progress. Detailed instructions for running the example pipelines across multiple runners are yet to be added. There is an open issue to finish the walkthrough (<a href="https://issues.apache.org/jira/browse/BEAM-664">BEAM-664</a>).</p>
+</blockquote>
+
+<p>The WordCount examples demonstrate how to set up a processing pipeline that can read text, tokenize the text lines into individual words, and perform a frequency count on each of those words. The Beam SDKs contain a series of these four successively more detailed WordCount examples that build on each other. The input text for all the examples is a set of Shakespeare\u2019s texts.</p>
+
+<p>Each WordCount example introduces different concepts in the Beam programming model. Begin by understanding Minimal WordCount, the simplest of the examples. Once you feel comfortable with the basic principles in building a pipeline, continue on to learn more concepts in the other examples.</p>
+
+<ul>
+ <li><strong>Minimal WordCount</strong> demonstrates the basic principles involved in building a pipeline.</li>
+ <li><strong>WordCount</strong> introduces some of the more common best practices in creating re-usable and maintainable pipelines.</li>
+ <li><strong>Debugging WordCount</strong> introduces logging and debugging practices.</li>
+ <li><strong>Windowed WordCount</strong> demonstrates how you can use Beam\u2019s programming model to handle both bounded and unbounded datasets.</li>
+</ul>
+
+<h2 id="minimalwordcount">MinimalWordCount</h2>
+
+<p>Minimal WordCount demonstrates a simple pipeline that can read from a text file, apply transforms to tokenize and count the words, and write the data to an output text file. This example hard-codes the locations for its input and output files and doesn\u2019t perform any error checking; it is intended to only show you the \u201cbare bones\u201d of creating a Beam pipeline. This lack of parameterization makes this particular pipeline less portable across different runners than standard Beam pipelines. In later examples, we will parameterize the pipeline\u2019s input and output sources and show other best practices.</p>
+
+<p>To run this example, follow the instructions in the <a href="https://github.com/apache/incubator-beam/blob/master/examples/java/README.md#building-and-running">Beam Examples README</a>. To view the full code, see <strong><a href="https://github.com/apache/incubator-beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/MinimalWordCount.java">MinimalWordCount</a>.</strong></p>
+
+<p><strong>Key Concepts:</strong></p>
+
+<ul>
+ <li>Creating the Pipeline</li>
+ <li>Applying transforms to the Pipeline</li>
+ <li>Reading input (in this example: reading text files)</li>
+ <li>Applying ParDo transforms</li>
+ <li>Applying SDK-provided transforms (in this example: Count)</li>
+ <li>Writing output (in this example: writing to a text file)</li>
+ <li>Running the Pipeline</li>
+</ul>
+
+<p>The following sections explain these concepts in detail along with excerpts of the relevant code from the Minimal WordCount pipeline.</p>
+
+<h3 id="creating-the-pipeline">Creating the Pipeline</h3>
+
+<p>The first step in creating a Beam pipeline is to create a <code class="highlighter-rouge">PipelineOptions object</code>. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set <code class="highlighter-rouge">PipelineOptions</code>.</p>
+
+<p>You can specify a runner for executing your pipeline, such as the <code class="highlighter-rouge">DataflowRunner</code> or <code class="highlighter-rouge">SparkRunner</code>. If you omit specifying a runner, as in this example, your pipeline will be executed locally using the <code class="highlighter-rouge">DirectRunner</code>. In the next sections, we will specify the pipeline\u2019s runner.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code> <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">create</span><span class="o">();</span>
+
+ <span class="c1">// In order to run your pipeline, you need to make following runner specific changes:</span>
+ <span class="c1">//</span>
+ <span class="c1">// CHANGE 1/3: Select a Beam runner, such as DataflowRunner or FlinkRunner.</span>
+ <span class="c1">// CHANGE 2/3: Specify runner-required options.</span>
+ <span class="c1">// For DataflowRunner, set project and temp location as follows:</span>
+ <span class="c1">// DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class);</span>
+ <span class="c1">// dataflowOptions.setRunner(DataflowRunner.class);</span>
+ <span class="c1">// dataflowOptions.setProject("SET_YOUR_PROJECT_ID_HERE");</span>
+ <span class="c1">// dataflowOptions.setTempLocation("gs://SET_YOUR_BUCKET_NAME_HERE/AND_TEMP_DIRECTORY");</span>
+ <span class="c1">// For FlinkRunner, set the runner as follows. See {@code FlinkPipelineOptions}</span>
+ <span class="c1">// for more details.</span>
+ <span class="c1">// options.setRunner(FlinkRunner.class);</span>
+</code></pre>
+</div>
+
+<p>The next step is to create a Pipeline object with the options we\u2019ve just constructed. The Pipeline object builds up the graph of transformations to be executed, associated with that particular pipeline.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><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>
+
+<h3 id="applying-pipeline-transforms">Applying Pipeline Transforms</h3>
+
+<p>The Minimal WordCount pipeline contains several transforms to read data into the pipeline, manipulate or otherwise transform the data, and write out the results. Each transform represents an operation in the pipeline.</p>
+
+<p>Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class <code class="highlighter-rouge">PCollection</code>. <code class="highlighter-rouge">PCollection</code> is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including infinite data sets.</p>
+
+<p><img src=\u201d/images/wordcount-pipeline.png alt=\u201dWord Count pipeline diagram\u201d\u201d>
+Figure 1: The pipeline data flow.</p>
+
+<p>The Minimal WordCount pipeline contains five transforms:</p>
+
+<ol>
+ <li>
+ <p>A text file <code class="highlighter-rouge">Read</code> transform is applied to the Pipeline object itself, and produces a <code class="highlighter-rouge">PCollection</code> as output. Each element in the output PCollection represents one line of text from the input file. This example happens to use input data stored in a publicly accessible Google Cloud Storage bucket (\u201cgs://\u201d).</p>
+
+ <div class="language-java highlighter-rouge"><pre class="highlight"><code> <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">from</span><span class="o">(</span><span class="s">"gs://apache-beam-samples/shakespeare/*"</span><span class="o">))</span>
+</code></pre>
+ </div>
+ </li>
+ <li>
+ <p>A <a href="/learn/programming-guide/#transforms-pardo">ParDo</a> transform that invokes a <code class="highlighter-rouge">DoFn</code> (defined in-line as an anonymous class) on each element that tokenizes the text lines into individual words. The input for this transform is the <code class="highlighter-rouge">PCollection</code> of text lines generated by the previous <code class="highlighter-rouge">TextIO.Read</code> transform. The <code class="highlighter-rouge">ParDo</code> transform outputs a new <code class="highlighter-rouge">PCollection</code>, where each element represents an individual word in the text.</p>
+
+ <div class="language-java highlighter-rouge"><pre class="highlight"><code> <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="s">"ExtractWords"</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">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
+ <span class="nd">@ProcessElement</span>
+ <span class="kd">public</span> <span class="kt">void</span> <span class="nf">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="k">for</span> <span class="o">(</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="na">split</span><span class="o">(</span><span class="s">"[^a-zA-Z']+"</span><span class="o">))</span> <span class="o">{</span>
+ <span class="k">if</span> <span class="o">(!</span><span class="n">word</span><span class="o">.</span><span class="na">isEmpty</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">word</span><span class="o">);</span>
+ <span class="o">}</span>
+ <span class="o">}</span>
+ <span class="o">}</span>
+ <span class="o">}))</span>
+</code></pre>
+ </div>
+ </li>
+ <li>
+ <p>The SDK-provided <code class="highlighter-rouge">Count</code> transform is a generic transform that takes a <code class="highlighter-rouge">PCollection</code> of any type, and returns a <code class="highlighter-rouge">PCollection</code> of key/value pairs. Each key represents a unique element from the input collection, and each value represents the number of times that key appeared in the input collection.</p>
+
+ <p>In this pipeline, the input for <code class="highlighter-rouge">Count</code> is the <code class="highlighter-rouge">PCollection</code> of individual words generated by the previous <code class="highlighter-rouge">ParDo</code>, and the output is a <code class="highlighter-rouge">PCollection</code> of key/value pairs where each key represents a unique word in the text and the associated value is the occurrence count for each.</p>
+
+ <div class="language-java highlighter-rouge"><pre class="highlight"><code> <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">Count</span><span class="o">.<</span><span class="n">String</span><span class="o">></span><span class="n">perElement</span><span class="o">())</span>
+</code></pre>
+ </div>
+ </li>
+ <li>
+ <p>The next transform formats each of the key/value pairs of unique words and occurrence counts into a printable string suitable for writing to an output file.</p>
+
+ <p><code class="highlighter-rouge">MapElements</code> is a higher-level composite transform that encapsulates a simple <code class="highlighter-rouge">ParDo</code>; for each element in the input <code class="highlighter-rouge">PCollection</code>, <code class="highlighter-rouge">MapElements</code> applies a function that produces exactly one output element. In this example, <code class="highlighter-rouge">MapElements</code> invokes a <code class="highlighter-rouge">SimpleFunction</code> (defined in-line as an anonymous class) that does the formatting. As input, <code class="highlighter-rouge">MapElements</code> takes a <code class="highlighter-rouge">PCollection</code> of key/value pairs generated by <code class="highlighter-rouge">Count</code>, and produces a new <code class="highlighter-rouge">PCollection</code> of printable strings.</p>
+
+ <div class="language-java highlighter-rouge"><pre class="highlight"><code> <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="s">"FormatResults"</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="k">new</span> <span class="n">SimpleFunction</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
+ <span class="nd">@Override</span>
+ <span class="kd">public</span> <span class="n">String</span> <span class="nf">apply</span><span class="o">(</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">></span> <span class="n">input</span><span class="o">)</span> <span class="o">{</span>
+ <span class="k">return</span> <span class="n">input</span><span class="o">.</span><span class="na">getKey</span><span class="o">()</span> <span class="o">+</span> <span class="s">": "</span> <span class="o">+</span> <span class="n">input</span><span class="o">.</span><span class="na">getValue</span><span class="o">();</span>
+ <span class="o">}</span>
+ <span class="o">}))</span>
+</code></pre>
+ </div>
+ </li>
+ <li>
+ <p>A text file <code class="highlighter-rouge">Write</code>. This transform takes the final <code class="highlighter-rouge">PCollection</code> of formatted Strings as input and writes each element to an output text file. Each element in the input <code class="highlighter-rouge">PCollection</code> represents one line of text in the resulting output file.</p>
+
+ <div class="language-java highlighter-rouge"><pre class="highlight"><code> <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">Write</span><span class="o">.</span><span class="na">to</span><span class="o">(</span><span class="s">"gs://YOUR_OUTPUT_BUCKET/AND_OUTPUT_PREFIX"</span><span class="o">));</span>
+</code></pre>
+ </div>
+ </li>
+</ol>
+
+<p>Note that the <code class="highlighter-rouge">Write</code> transform produces a trivial result value of type <code class="highlighter-rouge">PDone</code>, which in this case is ignored.</p>
+
+<h3 id="running-the-pipeline">Running the Pipeline</h3>
+
+<p>Run the pipeline by calling the <code class="highlighter-rouge">run</code> method, which sends your pipeline to be executed by the pipeline runner that you specified when you created your pipeline.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="n">p</span><span class="o">.</span><span class="na">run</span><span class="o">();</span>
+</code></pre>
+</div>
+
+<h2 id="wordcount-example">WordCount Example</h2>
+
+<p>This WordCount example introduces a few recommended programming practices that can make your pipeline easier to read, write, and maintain. While not explicitly required, they can make your pipeline\u2019s execution more flexible, aid in testing your pipeline, and help make your pipeline\u2019s code reusable.</p>
+
+<p>This section assumes that you have a good understanding of the basic concepts in building a pipeline. If you feel that you aren\u2019t at that point yet, read the above section, <a href="#minimalwordcount">Minimal WordCount</a>.</p>
+
+<p>To run this example, follow the instructions in the <a href="https://github.com/apache/incubator-beam/blob/master/examples/java/README.md#building-and-running">Beam Examples README</a>. To view the full code, see <strong><a href="https://github.com/apache/incubator-beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/WordCount.java">WordCount</a>.</strong></p>
+
+<p><strong>New Concepts:</strong></p>
+
+<ul>
+ <li>Applying <code class="highlighter-rouge">ParDo</code> with an explicit <code class="highlighter-rouge">DoFn</code></li>
+ <li>Creating Composite Transforms</li>
+ <li>Using Parameterizable <code class="highlighter-rouge">PipelineOptions</code></li>
+</ul>
+
+<p>The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.</p>
+
+<h3 id="specifying-explicit-dofns">Specifying Explicit DoFns</h3>
+
+<p>When using <code class="highlighter-rouge">ParDo</code> transforms, you need to specify the processing operation that gets applied to each element in the input <code class="highlighter-rouge">PCollection</code>. This processing operation is a subclass of the SDK class <code class="highlighter-rouge">DoFn</code>. You can create the <code class="highlighter-rouge">DoFn</code> subclasses for each <code class="highlighter-rouge">ParDo</code> inline, as an anonymous inner class instance, as is done in the previous example (Minimal WordCount). However, it\u2019s often a good idea to define the <code class="highlighter-rouge">DoFn</code> at the global level, which makes it easier to unit test and can make the <code class="highlighter-rouge">ParDo</code> code more readable.</p>
+
+<p>In this example, <code class="highlighter-rouge">ExtractWordsFn</code> is a <code class="highlighter-rouge">DoFn</code> that is defined as a static class:</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">static</span> <span class="kd">class</span> <span class="nc">ExtractWordsFn</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">String</span><span class="o">></span> <span class="o">{</span>
+ <span class="o">...</span>
+
+ <span class="nd">@ProcessElement</span>
+ <span class="kd">public</span> <span class="kt">void</span> <span class="nf">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="o">...</span>
+ <span class="o">}</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<p>This <code class="highlighter-rouge">DoFn</code> (<code class="highlighter-rouge">ExtractWordsFn</code>) is the processing operation that <code class="highlighter-rouge">ParDo</code> applies to the <code class="highlighter-rouge">PCollection</code> of words:</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><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="n">lines</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">ExtractWordsFn</span><span class="o">()));</span>
+</code></pre>
+</div>
+
+<h3 id="creating-composite-transforms">Creating Composite Transforms</h3>
+
+<p>If you have a processing operation that consists of multiple transforms or <code class="highlighter-rouge">ParDo</code> steps, you can create it as a subclass of <code class="highlighter-rouge">PTransform</code>. Creating a <code class="highlighter-rouge">PTransform</code> subclass allows you to create complex reusable transforms, can make your pipeline\u2019s structure more clear and modular, and makes unit testing easier.</p>
+
+<p>In this example, two transforms are encapsulated as the <code class="highlighter-rouge">PTransform</code> subclass <code class="highlighter-rouge">CountWords</code>. <code class="highlighter-rouge">CountWords</code> contains the <code class="highlighter-rouge">ParDo</code> that runs <code class="highlighter-rouge">ExtractWordsFn</code> and the SDK-provided <code class="highlighter-rouge">Count</code> transform.</p>
+
+<p>When <code class="highlighter-rouge">CountWords</code> is defined, we specify its ultimate input and output; the input is the <code class="highlighter-rouge">PCollection<String></code> for the extraction operation, and the output is the <code class="highlighter-rouge">PCollection<KV<String, Long>></code> produced by the count operation.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">CountWords</span> <span class="kd">extends</span> <span class="n">PTransform</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">PCollection</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>>></span> <span class="o">{</span>
+ <span class="nd">@Override</span>
+ <span class="kd">public</span> <span class="n">PCollection</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="nf">apply</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="o">{</span>
+
+ <span class="c1">// Convert lines of text into individual words.</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="n">lines</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">ExtractWordsFn</span><span class="o">()));</span>
+
+ <span class="c1">// Count the number of times each word occurs.</span>
+ <span class="n">PCollection</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="n">wordCounts</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">Count</span><span class="o">.<</span><span class="n">String</span><span class="o">></span><span class="n">perElement</span><span class="o">());</span>
+
+ <span class="k">return</span> <span class="n">wordCounts</span><span class="o">;</span>
+ <span class="o">}</span>
+<span class="o">}</span>
+
+<span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">IOException</span> <span class="o">{</span>
+ <span class="n">Pipeline</span> <span class="n">p</span> <span class="o">=</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="o">.</span><span class="na">apply</span><span class="o">(</span><span class="k">new</span> <span class="n">CountWords</span><span class="o">())</span>
+ <span class="o">...</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h3 id="using-parameterizable-pipelineoptions">Using Parameterizable PipelineOptions</h3>
+
+<p>You can hard-code various execution options when you run your pipeline. However, the more common way is to define your own configuration options via command-line argument parsing. Defining your configuration options via the command-line makes the code more easily portable across different runners.</p>
+
+<p>Add arguments to be processed by the command-line parser, and specify default values for them. You can then access the options values in your pipeline code.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kd">interface</span> <span class="nc">WordCountOptions</span> <span class="kd">extends</span> <span class="n">PipelineOptions</span> <span class="o">{</span>
+ <span class="nd">@Description</span><span class="o">(</span><span class="s">"Path of the file to read from"</span><span class="o">)</span>
+ <span class="nd">@Default</span><span class="o">.</span><span class="na">String</span><span class="o">(</span><span class="s">"gs://dataflow-samples/shakespeare/kinglear.txt"</span><span class="o">)</span>
+ <span class="n">String</span> <span class="nf">getInputFile</span><span class="o">();</span>
+ <span class="kt">void</span> <span class="nf">setInputFile</span><span class="o">(</span><span class="n">String</span> <span class="n">value</span><span class="o">);</span>
+ <span class="o">...</span>
+<span class="o">}</span>
+
+<span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</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="n">WordCountOptions</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">withValidation</span><span class="o">()</span>
+ <span class="o">.</span><span class="na">as</span><span class="o">(</span><span class="n">WordCountOptions</span><span class="o">.</span><span class="na">class</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="o">...</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h2 id="debugging-wordcount-example">Debugging WordCount Example</h2>
+
+<p>The Debugging WordCount example demonstrates some best practices for instrumenting your pipeline code.</p>
+
+<p>To run this example, follow the instructions in the <a href="https://github.com/apache/incubator-beam/blob/master/examples/java/README.md#building-and-running">Beam Examples README</a>. To view the full code, see <strong><a href="https://github.com/apache/incubator-beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/DebuggingWordCount.java">DebuggingWordCount</a>.</strong></p>
+
+<p><strong>New Concepts:</strong></p>
+
+<ul>
+ <li>Logging</li>
+ <li>Testing your Pipeline via <code class="highlighter-rouge">PAssert</code></li>
+</ul>
+
+<p>The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.</p>
+
+<h3 id="logging">Logging</h3>
+
+<p>Each runner may choose to handle logs in its own way.</p>
+
+<h4 id="direct-runner">Direct Runner</h4>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DirectRunner</code>, it will print the log messages directly to your local console.</p>
+
+<h4 id="dataflow-runner">Dataflow Runner</h4>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can use Google Cloud Logging. Google Cloud Logging (currently in beta) aggregates the logs from all of your Dataflow job\u2019s workers to a single location in the Google Cloud Platform Console. You can use Cloud Logging to search and access the logs from all of the Compute Engine instances that Dataflow has spun up to complete your Dataflow job. You can add logging statements into your pipeline\u2019s <code class="highlighter-rouge">DoFn</code> instances that will appear in Cloud Logging as your pipeline runs.</p>
+
+<p>In this example, we use <code class="highlighter-rouge">.trace</code> and <code class="highlighter-rouge">.debug</code>:</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">class</span> <span class="nc">DebuggingWordCount</span> <span class="o">{</span>
+
+ <span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">FilterTextFn</span> <span class="kd">extends</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>,</span> <span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="o">{</span>
+ <span class="o">...</span>
+
+ <span class="kd">public</span> <span class="kt">void</span> <span class="nf">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="k">if</span> <span class="o">(...)</span> <span class="o">{</span>
+ <span class="o">...</span>
+ <span class="n">LOG</span><span class="o">.</span><span class="na">debug</span><span class="o">(</span><span class="s">"Matched: "</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">getKey</span><span class="o">());</span>
+ <span class="o">}</span> <span class="k">else</span> <span class="o">{</span>
+ <span class="o">...</span>
+ <span class="n">LOG</span><span class="o">.</span><span class="na">trace</span><span class="o">(</span><span class="s">"Did not match: "</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">getKey</span><span class="o">());</span>
+ <span class="o">}</span>
+ <span class="o">}</span>
+ <span class="o">}</span>
+<span class="o">}</span>
+
+</code></pre>
+</div>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Cloud Logging by default at \u201cINFO\u201d log level and higher. You can override log levels for specific logging namespaces by specifying: <code class="highlighter-rouge">--workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}</code>. For example, by specifying <code class="highlighter-rouge">--workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}</code> when executing this pipeline using the Dataflow service, Cloud Logging would contain only \u201cDEBUG\u201d or higher level logs for the package in addition to the default \u201cINFO\u201d or higher level logs.</p>
+
+<p>The default Dataflow worker logging configuration can be overridden by specifying <code class="highlighter-rouge">--defaultWorkerLogLevel=<one of TRACE, DEBUG, INFO, WARN, ERROR></code>. For example, by specifying <code class="highlighter-rouge">--defaultWorkerLogLevel=DEBUG</code> when executing this pipeline with the Dataflow service, Cloud Logging would contain all \u201cDEBUG\u201d or higher level logs. Note that changing the default worker log level to TRACE or DEBUG will significantly increase the amount of logs output.</p>
+
+<h4 id="apache-spark-runner">Apache Spark Runner</h4>
+
+<blockquote>
+ <p><strong>Note:</strong> This section is yet to be added. There is an open issue for this (<a href="https://issues.apache.org/jira/browse/BEAM-792">BEAM-792</a>).</p>
+</blockquote>
+
+<h4 id="apache-flink-runner">Apache Flink Runner</h4>
+
+<blockquote>
+ <p><strong>Note:</strong> This section is yet to be added. There is an open issue for this (<a href="https://issues.apache.org/jira/browse/BEAM-791">BEAM-791</a>).</p>
+</blockquote>
+
+<h3 id="testing-your-pipeline-via-passert">Testing your Pipeline via PAssert</h3>
+
+<p><code class="highlighter-rouge">PAssert</code> is a set of convenient <code class="highlighter-rouge">PTransform</code>s in the style of Hamcrest\u2019s collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. <code class="highlighter-rouge">PAssert</code> is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.</p>
+
+<p>Below, we verify that the set of filtered words matches our expected counts. Note that <code class="highlighter-rouge">PAssert</code> does not provide any output, and that successful completion of the pipeline implies that the expectations were met. See <a href="https://github.com/apache/incubator-beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java">DebuggingWordCountTest</a> for an example unit test.</p>
+
+<div class="language-java 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="o">(</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="o">...</span>
+ <span class="n">List</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="n">expectedResults</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="n">KV</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"Flourish"</span><span class="o">,</span> <span class="mi">3L</span><span class="o">),</span>
+ <span class="n">KV</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"stomach"</span><span class="o">,</span> <span class="mi">1L</span><span class="o">));</span>
+ <span class="n">PAssert</span><span class="o">.</span><span class="na">that</span><span class="o">(</span><span class="n">filteredWords</span><span class="o">).</span><span class="na">containsInAnyOrder</span><span class="o">(</span><span class="n">expectedResults</span><span class="o">);</span>
+ <span class="o">...</span>
+<span class="o">}</span>
+</code></pre>
+</div>
+
+<h2 id="windowedwordcount">WindowedWordCount</h2>
+
+<p>This example, <code class="highlighter-rouge">WindowedWordCount</code>, counts words in text just as the previous examples did, but introduces several advanced concepts.</p>
+
+<p><strong>New Concepts:</strong></p>
+
+<ul>
+ <li>Unbounded and bounded pipeline input modes</li>
+ <li>Adding timestamps to data</li>
+ <li>Windowing</li>
+ <li>Reusing PTransforms over windowed PCollections</li>
+</ul>
+
+<p>The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.</p>
+
+<h3 id="unbounded-and-bounded-pipeline-input-modes">Unbounded and bounded pipeline input modes</h3>
+
+<p>Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all <code class="highlighter-rouge">PCollections</code> of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it\u2019s considered a \u2018bounded\u2019 data set. If your input is continuously updating, then it\u2019s considered \u2018unbounded\u2019.</p>
+
+<p>Recall that the input for this example is a a set of Shakespeare\u2019s texts, finite data. Therefore, this example reads bounded data from a text file:</p>
+
+<div class="language-java 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="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">IOException</span> <span class="o">{</span>
+ <span class="n">Options</span> <span class="n">options</span> <span class="o">=</span> <span class="o">...</span>
+ <span class="n">Pipeline</span> <span class="n">pipeline</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="cm">/**
+ * Concept #1: The Beam SDK allows running the same pipeline with a bounded or unbounded input source.
+ */</span>
+ <span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">input</span> <span class="o">=</span> <span class="n">pipeline</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">from</span><span class="o">(</span><span class="n">options</span><span class="o">.</span><span class="na">getInputFile</span><span class="o">()))</span>
+
+</code></pre>
+</div>
+
+<h3 id="adding-timestamps-to-data">Adding Timestamps to Data</h3>
+
+<p>Each element in a <code class="highlighter-rouge">PCollection</code> has an associated <strong>timestamp</strong>. The timestamp for each element is initially assigned by the source that creates the <code class="highlighter-rouge">PCollection</code> and can be adjusted by a <code class="highlighter-rouge">DoFn</code>. In this example the input is bounded. For the purpose of the example, the <code class="highlighter-rouge">DoFn</code> method named <code class="highlighter-rouge">AddTimestampsFn</code> (invoked by <code class="highlighter-rouge">ParDo</code>) will set a timestamp for each element in the <code class="highlighter-rouge">PCollection</code>.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="c1">// Concept #2: Add an element timestamp, using an artificial time just to show windowing.</span>
+<span class="c1">// See AddTimestampFn for more details on this.</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">AddTimestampFn</span><span class="o">()));</span>
+</code></pre>
+</div>
+
+<p>Below is the code for <code class="highlighter-rouge">AddTimestampFn</code>, a <code class="highlighter-rouge">DoFn</code> invoked by <code class="highlighter-rouge">ParDo</code>, that sets the data element of the timestamp given the element itself. For example, if the elements were log lines, this <code class="highlighter-rouge">ParDo</code> could parse the time out of the log string and set it as the element\u2019s timestamp. There are no timestamps inherent in the works of Shakespeare, so in this case we\u2019ve made up random timestamps just to illustrate the concept. Each line of the input text will get a random associated timestamp sometime in a 2-hour period.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="cm">/**
+ * Concept #2: A DoFn that sets the data element timestamp. This is a silly method, just for
+ * this example, for the bounded data case. Imagine that many ghosts of Shakespeare are all
+ * typing madly at the same time to recreate his masterworks. Each line of the corpus will
+ * get a random associated timestamp somewhere in a 2-hour period.
+ */</span>
+ <span class="kd">static</span> <span class="kd">class</span> <span class="nc">AddTimestampFn</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">String</span><span class="o">></span> <span class="o">{</span>
+ <span class="kd">private</span> <span class="kd">static</span> <span class="kd">final</span> <span class="n">Duration</span> <span class="n">RAND_RANGE</span> <span class="o">=</span> <span class="n">Duration</span><span class="o">.</span><span class="na">standardHours</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span>
+ <span class="kd">private</span> <span class="kd">final</span> <span class="n">Instant</span> <span class="n">minTimestamp</span><span class="o">;</span>
+
+ <span class="n">AddTimestampFn</span><span class="o">()</span> <span class="o">{</span>
+ <span class="k">this</span><span class="o">.</span><span class="na">minTimestamp</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Instant</span><span class="o">(</span><span class="n">System</span><span class="o">.</span><span class="na">currentTimeMillis</span><span class="o">());</span>
+ <span class="o">}</span>
+
+ <span class="nd">@ProcessElement</span>
+ <span class="kd">public</span> <span class="kt">void</span> <span class="nf">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">// Generate a timestamp that falls somewhere in the past two hours.</span>
+ <span class="kt">long</span> <span class="n">randMillis</span> <span class="o">=</span> <span class="o">(</span><span class="kt">long</span><span class="o">)</span> <span class="o">(</span><span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">()</span> <span class="o">*</span> <span class="n">RAND_RANGE</span><span class="o">.</span><span class="na">getMillis</span><span class="o">());</span>
+ <span class="n">Instant</span> <span class="n">randomTimestamp</span> <span class="o">=</span> <span class="n">minTimestamp</span><span class="o">.</span><span class="na">plus</span><span class="o">(</span><span class="n">randMillis</span><span class="o">);</span>
+ <span class="cm">/**
+ * Set the data element with that timestamp.
+ */</span>
+ <span class="n">c</span><span class="o">.</span><span class="na">outputWithTimestamp</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="k">new</span> <span class="n">Instant</span><span class="o">(</span><span class="n">randomTimestamp</span><span class="o">));</span>
+ <span class="o">}</span>
+ <span class="o">}</span>
+</code></pre>
+</div>
+
+<h3 id="windowing">Windowing</h3>
+
+<p>Beam uses a concept called <strong>Windowing</strong> to subdivide a <code class="highlighter-rouge">PCollection</code> according to the timestamps of its individual elements. <code class="highlighter-rouge">PTransforms</code> that aggregate multiple elements, process each <code class="highlighter-rouge">PCollection</code> as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).</p>
+
+<p>The <code class="highlighter-rouge">WindowingWordCount</code> example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="cm">/**
+ * Concept #3: Window into fixed windows. The fixed window size for this example defaults to 1
+ * minute (you can change this with a command-line option).
+ */</span>
+<span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">windowedWords</span> <span class="o">=</span> <span class="n">input</span>
+ <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">Window</span><span class="o">.<</span><span class="n">String</span><span class="o">></span><span class="n">into</span><span class="o">(</span>
+ <span class="n">FixedWindows</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="n">Duration</span><span class="o">.</span><span class="na">standardMinutes</span><span class="o">(</span><span class="n">options</span><span class="o">.</span><span class="na">getWindowSize</span><span class="o">()))));</span>
+</code></pre>
+</div>
+
+<h3 id="reusing-ptransforms-over-windowed-pcollections">Reusing PTransforms over windowed PCollections</h3>
+
+<p>You can reuse existing <code class="highlighter-rouge">PTransform</code>s, that were created for manipulating simple <code class="highlighter-rouge">PCollection</code>s, over windowed <code class="highlighter-rouge">PCollection</code>s as well.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>/**
+ * Concept #4: Re-use our existing CountWords transform that does not have knowledge of
+ * windows over a PCollection containing windowed values.
+ */
+PCollection<KV<String, Long>> wordCounts = windowedWords.apply(new WordCount.CountWords());
+</code></pre>
+</div>
+
+<h2 id="write-results-to-an-unbounded-sink">Write Results to an Unbounded Sink</h2>
+
+<p>Since our input is unbounded, the same is true of our output <code class="highlighter-rouge">PCollection</code>. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, such as a text file. Google Cloud BigQuery is an output source that supports both bounded and unbounded input.</p>
+
+<p>In this example, we stream the results to a BigQuery table. The results are then formatted for a BigQuery table, and then written to BigQuery using BigQueryIO.Write.</p>
+
+<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="cm">/**
+ * Concept #5: Format the results for a BigQuery table, then write to BigQuery.
+ * The BigQuery output source supports both bounded and unbounded data.
+ */</span>
+<span class="n">wordCounts</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">FormatAsTableRowFn</span><span class="o">()))</span>
+ <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span>
+ <span class="o">.</span><span class="na">to</span><span class="o">(</span><span class="n">getTableReference</span><span class="o">(</span><span class="n">options</span><span class="o">))</span>
+ <span class="o">.</span><span class="na">withSchema</span><span class="o">(</span><span class="n">getSchema</span><span class="o">())</span>
+ <span class="o">.</span><span class="na">withCreateDisposition</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span><span class="o">.</span><span class="na">CreateDisposition</span><span class="o">.</span><span class="na">CREATE_IF_NEEDED</span><span class="o">)</span>
+ <span class="o">.</span><span class="na">withWriteDisposition</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span><span class="o">.</span><span class="na">WriteDisposition</span><span class="o">.</span><span class="na">WRITE_APPEND</span><span class="o">));</span>
+</code></pre>
+</div>
+
+
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