Regenerate website
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/12121557 Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/12121557 Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/12121557 Branch: refs/heads/asf-site Commit: 12121557b155ec7d0aea865afa5c6f2801217d56 Parents: 9fb214f Author: Davor Bonaci <da...@google.com> Authored: Fri May 12 16:17:49 2017 -0700 Committer: Davor Bonaci <da...@google.com> Committed: Fri May 12 16:17:49 2017 -0700 ---------------------------------------------------------------------- .../documentation/programming-guide/index.html | 2 +- .../sdks/python-custom-io/index.html | 2 +- .../get-started/wordcount-example/index.html | 80 +++++++++++--------- 3 files changed, 47 insertions(+), 37 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/content/documentation/programming-guide/index.html ---------------------------------------------------------------------- diff --git a/content/documentation/programming-guide/index.html b/content/documentation/programming-guide/index.html index 1e6e9a7..3b3cb14 100644 --- a/content/documentation/programming-guide/index.html +++ b/content/documentation/programming-guide/index.html @@ -1939,7 +1939,7 @@ Subsequent transforms, however, are applied to the result of the <code class="hi <span class="n">unix_timestamp</span> <span class="o">=</span> <span class="n">extract_timestamp_from_log_entry</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> <span class="c"># Wrap and emit the current entry and new timestamp in a</span> <span class="c"># TimestampedValue.</span> - <span class="k">yield</span> <span class="n">beam</span><span class="o">.</span><span class="n">TimestampedValue</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">unix_timestamp</span><span class="p">)</span> + <span class="k">yield</span> <span class="n">beam</span><span class="o">.</span><span class="n">window</span><span class="o">.</span><span class="n">TimestampedValue</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">unix_timestamp</span><span class="p">)</span> <span class="n">timestamped_items</span> <span class="o">=</span> <span class="n">items</span> <span class="o">|</span> <span class="s">'timestamp'</span> <span class="o">>></span> <span class="n">beam</span><span class="o">.</span><span class="n">ParDo</span><span class="p">(</span><span class="n">AddTimestampDoFn</span><span class="p">())</span> http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/content/documentation/sdks/python-custom-io/index.html ---------------------------------------------------------------------- diff --git a/content/documentation/sdks/python-custom-io/index.html b/content/documentation/sdks/python-custom-io/index.html index 629ef0f..fb2646f 100644 --- a/content/documentation/sdks/python-custom-io/index.html +++ b/content/documentation/sdks/python-custom-io/index.html @@ -464,7 +464,7 @@ numbers = p | 'ProduceNumbers' >> beam.io.Read(CountingSource(count)) <h4 id="filesink">FileSink</h4> -<p>If your data source uses files, you can derive your <code class="highlighter-rouge">Sink</code> and <code class="highlighter-rouge">Writer</code> classes from the <code class="highlighter-rouge">FileSink</code> and <code class="highlighter-rouge">FileSinkWriter</code> classes, which can be found in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/fileio.py";>fileio.py</a> module. These classes implement code common sinks that interact with files, including:</p> +<p>If your data source uses files, you can derive your <code class="highlighter-rouge">Sink</code> and <code class="highlighter-rouge">Writer</code> classes from the <code class="highlighter-rouge">FileBasedSink</code> and <code class="highlighter-rouge">FileBasedSinkWriter</code> classes, which can be found in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/filebasedsink.py";>filebasedsink.py</a> module. These classes implement code common sinks that interact with files, including:</p> <ul> <li>Setting file headers and footers</li> http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/content/get-started/wordcount-example/index.html ---------------------------------------------------------------------- diff --git a/content/get-started/wordcount-example/index.html b/content/get-started/wordcount-example/index.html index 5cc32f3..333cfb9 100644 --- a/content/get-started/wordcount-example/index.html +++ b/content/get-started/wordcount-example/index.html @@ -172,6 +172,7 @@ <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> + <li><a href="#apache-apex-runner" id="markdown-toc-apache-apex-runner">Apache Apex 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> @@ -228,7 +229,7 @@ <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>The first step in creating a Beam pipeline is to create a <code class="highlighter-rouge">PipelineOptions</code> object. 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 pipelineâs runner.</p> @@ -273,7 +274,7 @@ <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>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 unbounded data sets.</p> <p><img src="/images/wordcount-pipeline.png" alt="Word Count pipeline diagram" /> Figure 1: The pipeline data flow.</p> @@ -282,7 +283,7 @@ Figure 1: The pipeline data flow.</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 (âgs://â).</p> + <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 uses input data stored in a publicly accessible Google Cloud Storage bucket (âgs://â).</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> @@ -299,7 +300,9 @@ Figure 1: The pipeline data flow.</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="c1">// \p{L} denotes the category of Unicode letters,</span> + <span class="c1">// so this pattern will match on everything that is not a letter.</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">"[^\\p{L}]+"</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> @@ -330,7 +333,7 @@ Figure 1: The pipeline data flow.</p> <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>The map transform 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>, the map transform applies a function that produces exactly one output element.</p> + <p>The map transform 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>, the map transform applies a function that produces exactly one output element.</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> @@ -526,14 +529,6 @@ Figure 1: The pipeline data flow.</p> <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 jobâs 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 pipelineâs <code class="highlighter-rouge">DoFn</code> instances that will appear in Cloud Logging as your pipeline runs.</p> - <div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="c1">// This example uses .trace and .debug:</span> <span class="kd">public</span> <span class="kd">class</span> <span class="nc">DebuggingWordCount</span> <span class="o">{</span> @@ -592,7 +587,15 @@ Figure 1: The pipeline data flow.</p> </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 âINFOâ 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 âDEBUGâ or higher level logs for the package in addition to the default âINFOâ or higher level logs.</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 Stackdriver Logging. Stackdriver Logging aggregates the logs from all of your Dataflow jobâs workers to a single location in the Google Cloud Platform Console. You can use Stackdriver Logging to search and access the logs from all of the workers that Dataflow has spun up to complete your Dataflow job. Logging statements in your pipelineâs <code class="highlighter-rouge">DoFn</code> instances will appear in Stackdriver Logging as your pipeline runs.</p> + +<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 Stackdriver Logging by default at âINFOâ 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, Stackdriver Logging would contain only âDEBUGâ or higher level logs for the package in addition to the default âINFOâ 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 âDEBUGâ 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> @@ -608,11 +611,17 @@ Figure 1: The pipeline data flow.</p> <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> +<h4 id="apache-apex-runner">Apache Apex 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-2285";>BEAM-2285</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 Hamcrestâs 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><code class="highlighter-rouge">PAssert</code> is a set of convenient PTransforms in the style of Hamcrestâs 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/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java";>DebuggingWordCountTest</a> for an example unit test.</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 produce any output, and pipeline will only succeed if all of the expectations are met. See <a href="https://github.com/apache/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> @@ -646,7 +655,7 @@ Figure 1: The pipeline data flow.</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, itâs considered a âboundedâ data set. If your input is continuously updating, then itâs considered âunboundedâ.</p> +<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 PCollections of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, itâs considered a âboundedâ data set. If your input is continuously updating, then itâs considered âunboundedâ.</p> <p>Recall that the input for this example is a a set of Shakespeareâs texts, finite data. Therefore, this example reads bounded data from a text file:</p> @@ -679,20 +688,24 @@ Figure 1: The pipeline data flow.</p> <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 elementâs timestamp. There are no timestamps inherent in the works of Shakespeare, so in this case weâve 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="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="kd">private</span> <span class="kd">final</span> <span class="n">Instant</span> <span class="n">maxTimestamp</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="n">AddTimestampFn</span><span class="o">(</span><span class="n">Instant</span> <span class="n">minTimestamp</span><span class="o">,</span> <span class="n">Instant</span> <span class="n">maxTimestamp</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="n">minTimestamp</span><span class="o">;</span> + <span class="k">this</span><span class="o">.</span><span class="na">maxTimestamp</span> <span class="o">=</span> <span class="n">maxTimestamp</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="c1">// Set the data element with that timestamp.</span> + <span class="n">Instant</span> <span class="n">randomTimestamp</span> <span class="o">=</span> + <span class="k">new</span> <span class="nf">Instant</span><span class="o">(</span> + <span class="n">ThreadLocalRandom</span><span class="o">.</span><span class="na">current</span><span class="o">()</span> + <span class="o">.</span><span class="na">nextLong</span><span class="o">(</span><span class="n">minTimestamp</span><span class="o">.</span><span class="na">getMillis</span><span class="o">(),</span> <span class="n">maxTimestamp</span><span class="o">.</span><span class="na">getMillis</span><span class="o">()));</span> + + <span class="cm">/** + * Concept #2: 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> @@ -705,9 +718,9 @@ Figure 1: The pipeline data flow.</p> <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>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. PTransforms 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> +<p>The <code class="highlighter-rouge">WindowedWordCount</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="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> @@ -721,7 +734,7 @@ Figure 1: The pipeline data flow.</p> <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> +<p>You can reuse existing PTransforms that were created for manipulating simple PCollections over windowed PCollections as well.</p> <div class="language-java highlighter-rouge"><pre class="highlight"><code><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">windowedWords</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="k">new</span> <span class="n">WordCount</span><span class="o">.</span><span class="na">CountWords</span><span class="o">());</span> </code></pre> @@ -733,16 +746,13 @@ Figure 1: The pipeline data flow.</p> <h3 id="write-results-to-an-unbounded-sink">Write Results to an Unbounded Sink</h3> -<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>When 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, while others support both bounded and unbounded outputs. By using a <code class="highlighter-rouge">FilenamePolicy</code>, we can use <code class="highlighter-rouge">TextIO</code> to files that are partitioned by windows. We use a composite <code class="highlighter-rouge">PTransform</code> that uses such a policy internally to write a single sharded file per window.</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="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> +<div class="language-java highlighter-rouge"><pre class="highlight"><code> <span class="n">wordCounts</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="k">new</span> <span class="n">WordCount</span><span class="o">.</span><span class="na">FormatAsTextFn</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">WriteOneFilePerWindow</span><span class="o">(</span><span class="n">output</span><span class="o">,</span> <span class="n">options</span><span class="o">.</span><span class="na">getNumShards</span><span class="o">()));</span> </code></pre> </div>
