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The following commit(s) were added to refs/heads/asf-site by this push: new 4f6f75d Publishing website 2021/11/13 06:01:42 at commit fd00fc9 4f6f75d is described below commit 4f6f75db23f7a49547d4680b39204c63f16126f1 Author: jenkins <bui...@apache.org> AuthorDate: Sat Nov 13 06:01:43 2021 +0000 Publishing website 2021/11/13 06:01:42 at commit fd00fc9 --- website/generated-content/documentation/index.xml | 158 +++++++++++++++++++++ .../patterns/cross-language/index.html | 4 +- .../documentation/programming-guide/index.html | 60 +++++++- website/generated-content/sitemap.xml | 2 +- 4 files changed, 217 insertions(+), 7 deletions(-) diff --git a/website/generated-content/documentation/index.xml b/website/generated-content/documentation/index.xml index 75db27a..bfc9450 100644 --- a/website/generated-content/documentation/index.xml +++ b/website/generated-content/documentation/index.xml @@ -11379,6 +11379,115 @@ use case.</p> <p>At runtime, the Beam runner will execute both Python and Java transforms to execute your pipeline.</p> <p>In this section, we will use <a href="https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/kafka/KafkaIO.Read.html">KafkaIO.Read</a> to illustrate how to create a cross-language transform for Java and a test example for Python.</p> <h4 id="1311-creating-cross-language-java-transforms">13.1.1. Creating cross-language Java transforms</h4> +<p>There are two ways to make Java transforms available to other SDKs.</p> +<ul> +<li>Option 1: In some cases, you can use existing Java transforms from other SDKs without writing any additional Java code.</li> +<li>Option 2: You can use arbitrary Java Transforms from other SDKs by adding a few Java classes.</li> +</ul> +<h5 id="13111-using-existing-java-transforms-from-other-sdks-without-writing-more-java-code">13.1.1.1 Using Existing Java Transforms from Other SDKs Without Writing more Java Code</h5> +<p>Starting with Beam 2.34.0, Python SDK users can use some Java transforms without writing additional Java code. This can be useful in many cases. For example,</p> +<ul> +<li>A developer not familiar with Java may need to use an existing Java transform from a Python pipeline</li> +<li>A developer may need to make an existing Java transform available to a Python pipeline without writing/releasing more Java code</li> +</ul> +<blockquote> +<p><strong>Note:</strong> This feature is currently only available when using Java transforms from a Python pipeline.</p> +</blockquote> +<p>To be eligible for direct usage, the API of the Java transform has to follow the following pattern.</p> +<ul> +<li>Requirement 1: The Java transform can be constructed using an available public constructor or a public static method (a constructor method) in the same Java class.</li> +<li>Requirement 2: The Java transform can be configured using one or more builder methods. Each builder method should be public and should return an instance of the Java transform.</li> +</ul> +<p>See below for the structure of an example Java class that can be directly used from the Python API.</p> +<div class="snippet"> +<div class="notebook-skip code-snippet without_switcher"> +<a class="copy" type="button" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Copy to clipboard"> +<img src="/images/copy-icon.svg"/> +</a> +<pre><code>public class JavaDataGenerator extends PTransform&lt;PBegin, PCollection&lt;String&gt;&gt; { +. . . +// Following method satisfies the Requirement 1. +// Note that you may also use a class constructor instead of a static method. +public static JavaDataGenerator create(Integer size) { +return new JavaDataGenerator(size); +} +static class JavaDataGeneratorConfig implements Serializable { +public String prefix; +public long length; +public String suffix; +. . . +} +// Following method conforms to the Requirement 2 +public JavaDataGenerator withJavaDataGeneratorConfig(JavaDataGeneratorConfig dataConfig) { +return new JavaDataGenerator(this.size, javaDataGeneratorConfig); +} +. . . +}</code></pre> +</div> +</div> +<p>To use a Java class that conforms to the above requirement from a Python SDK pipeline you may do the following.</p> +<ul> +<li>Step 1: create an allowlist file in the <em>yaml</em> format that describes the Java transform classes and methods that will be directly accessed from Python.</li> +<li>Step 2: start an Expansion Service with the <code>javaClassLookupAllowlistFile</code> option passing path to the allowlist defined in Step 1 as the value.</li> +<li>Step 3: Use the Python <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/external.py">JavaExternalTransform</a> API to directly +access Java transforms defined in the allowlist from the Python side.</li> +</ul> +<p>Starting with Beam 2.35.0, Step 1 and 2 may be skipped as described in corresponding sections below.</p> +<h5 id="step-1">Step 1</h5> +<p>To use this Java transform from Python, you may define an allowlist file in the <em>yaml</em> format. This allowlist lists the class names, +constructor methods, and builder methods that are directly available to be used from the Python side.</p> +<p>Starting with Beam 2.35.0, you have the option to specify <code>*</code> to the <code>javaClassLookupAllowlistFile</code> option instead of defining an actual allowlist which +denotes that all supported transforms in the classpath of the expansion service may be accessed through the API.</p> +<div class="snippet"> +<div class="notebook-skip code-snippet without_switcher"> +<a class="copy" type="button" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Copy to clipboard"> +<img src="/images/copy-icon.svg"/> +</a> +<pre><code>version: v1 +allowedClasses: +- className: my.beam.transforms.JavaDataGenerator +allowedConstructorMethods: +- create +allowedBuilderMethods: +- withJavaDataGeneratorConfig</code></pre> +</div> +</div> +<h5 id="step-2">Step 2</h5> +<p>The allowlist is provided as an argument when starting up the Java expansion service. For example, the expansion service can be started +as a local Java process using the following command.</p> +<div class="snippet"> +<div class="notebook-skip code-snippet without_switcher"> +<a class="copy" type="button" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Copy to clipboard"> +<img src="/images/copy-icon.svg"/> +</a> +<pre><code>java -jar &lt;jar file&gt; &lt;port&gt; --javaClassLookupAllowlistFile=&lt;path to the allowlist file&gt;</code></pre> +</div> +</div> +<p>Starting with Beam 2.35.0, Beam ``JavaExternalTransform<code>API will automatically startup an expansion service with a given set of</code>jar` file dependencies +if an expansion service address was not provided.</p> +<h5 id="step-3">Step 3</h5> +<p>You can directly use the Java class from your Python pipeline using a stub transform created using the <code>JavaExternalTransform</code> API. This API allows you to construct the transform +using the Java class name and allows you to invoke builder methods to configure the class.</p> +<p>Constructor and method parameter types are mapped between Python and Java using a Beam Schema. The Schema is auto-generated using the object types +provided on the Python side. If the Java class constructor method or builder method accepts any complex object types, make sure that the Beam Schema +for these objects is registered and available for the Java expansion service. If a schema has not been registered, the Java expansion service will +try to register a schema using <a href="https://beam.apache.org/documentation/programming-guide/#creating-schemas">JavaFieldSchema</a>. In Python arbitrary objects +can be represented using <code>NamedTuple</code>s which will be represented as Beam Rows in the Schema. See below for a Python stub transform that represents the above +mentioned Java transform.</p> +<div class="snippet"> +<div class="notebook-skip code-snippet without_switcher"> +<a class="copy" type="button" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Copy to clipboard"> +<img src="/images/copy-icon.svg"/> +</a> +<pre><code>JavaDataGeneratorConfig = typing.NamedTuple( +&#39;JavaDataGeneratorConfig&#39;, [(&#39;prefix&#39;, str), (&#39;length&#39;, int), (&#39;suffix&#39;, str)]) +data_config = JavaDataGeneratorConfig(prefix=&#39;start&#39;, length=20, suffix=&#39;end&#39;) +java_transform = JavaExternalTransform( +&#39;my.beam.transforms.JavaDataGenerator&#39;, expansion_service=&#39;localhost:&lt;port&gt;&#39;).create(numpy.int32(100)).withJavaDataGeneratorConfig(data_config)</code></pre> +</div> +</div> +<p>This transform can be used in a Python pipeline along with other Python transforms.</p> +<h5 id="13112-full-api-for-making-existing-java-transforms-available-to-other-sdks">13.1.1.2 Full API for Making Existing Java Transforms Available to Other SDKs</h5> <p>To make your Apache Beam Java SDK transform portable across SDK languages, you must implement two interfaces: <a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/ExternalTransformBuilder.java">ExternalTransformBuilder</a> and <a href="https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/expansion/ExternalTransformRegistrar.java">ExternalTransformRegistrar</a>. The <code [...] <p><strong>Implementing the interfaces</strong></p> <ol> @@ -11634,6 +11743,52 @@ return CombinePerKeyTransform() <h4 id="1313-creating-cross-language-go-transforms">13.1.3. Creating cross-language Go transforms</h4> <p>Go currently does not support creating cross-language transforms, only using cross-language transforms from other languages; see more at <a href="https://issues.apache.org/jira/browse/BEAM-9923">BEAM-9923</a>.</p> +<h4 id="1314-selecting-a-urn-for-cross-language-transforms">13.1.4. Selecting a URN for Cross-language Transforms</h4> +<p>Developing a cross-language transform involves defining a URN for registering the transform with an expansion service. In this section +we provide a convention for defining such URNs. Following this convention is optional but it will ensure that your transform +will not run into conflicts when registering in an expansion service along with transforms developed by other developers.</p> +<h5 id="schema">Schema</h5> +<p>A URN should consist of the following components:</p> +<ul> +<li>ns-id: A namespace identifier. Default recommendation is <code>beam:transform</code>.</li> +<li>org-identifier: Identifies the organization where the transform was defined. Transforms defined in Apache Beam use <code>org.apache.beam</code> for this.</li> +<li>functionality-identifier - Identifies the functionality of the cross-language transform.</li> +<li>version - a version number for the transform</li> +</ul> +<p>We provide the schema from the URN convention in <a href="https://en.wikipedia.org/wiki/Augmented_Backus%E2%80%93Naur_form">augmented Backus–Naur</a> form. +Keywords in upper case are from the <a href="https://datatracker.ietf.org/doc/html/rfc8141">URN spec</a>.</p> +<div class="snippet"> +<div class="notebook-skip code-snippet without_switcher"> +<a class="copy" type="button" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Copy to clipboard"> +<img src="/images/copy-icon.svg"/> +</a> +<pre><code>transform-urn = ns-id “:” org-identifier “:” functionality-identifier “:” version +ns-id = (“beam” / NID) “:” “transform” +id-char = ALPHA / DIGIT / &#34;-&#34; / &#34;.&#34; / &#34;_&#34; / &#34;~&#34; ; A subset of characters allowed in a URN +org-identifier = 1*id-char +functionality-identifier = 1*id-char +version = “v” 1*(DIGIT / “.”) ; For example, ‘v1.2’</code></pre> +</div> +</div> +<h5 id="examples">Examples</h5> +<p>Below we’ve given some example transform classes and corresponding URNs to be used.</p> +<ul> +<li>A transform offered with Apache Beam that writes Parquet files. +<ul> +<li><code>beam:transform:org.apache.beam:parquet_write:v1</code></li> +</ul> +</li> +<li>A transform offered with Apache Beam that reads from Kafka with metadata. +<ul> +<li><code>beam:transform:org.apache.beam:kafka_read_with_metadata:v1</code></li> +</ul> +</li> +<li>A transform developed by organization abc.org that reads from data store MyDatastore. +<ul> +<li><code>beam:transform:org.abc:mydatastore_read:v1</code></li> +</ul> +</li> +</ul> <h3 id="use-x-lang-transforms">13.2. Using cross-language transforms</h3> <p>Depending on the SDK language of the pipeline, you can use a high-level SDK-wrapper class, or a low-level transform class to access a cross-language transform.</p> <h4 id="1321-using-cross-language-transforms-in-a-java-pipeline">13.2.1. Using cross-language transforms in a Java pipeline</h4> @@ -14469,6 +14624,9 @@ limitations under the License. --> <h1 id="cross-language-transforms">Cross-language transforms</h1> <p>With the samples on this page we will demonstrate how to create and leverage cross-language pipelines.</p> +<blockquote> +<p><strong>Note:</strong> Please see the <a href="https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines">Beam Programming Guide</a> for full documentation on cross-language transforms.</p> +</blockquote> <p>The goal of a cross-language pipeline is to incorporate transforms from one SDK (e.g. the Python SDK) into a pipeline written using another SDK (e.g. the Java SDK). This enables having already developed transforms (e.g. ML transforms in Python) and libraries (e.g. the vast library of IOs in Java), and strengths of certain languages at your disposal in whichever language you are more comfortable authoring pipelines while vastly expanding your toolkit in given language.</p> <p>In this section we will cover a specific use-case: incorporating a Python transform that does inference on a model but is part of a larger Java pipeline. The section is broken down into 2 parts:</p> <ol> diff --git a/website/generated-content/documentation/patterns/cross-language/index.html b/website/generated-content/documentation/patterns/cross-language/index.html index 4224a39..7aa7354 100644 --- a/website/generated-content/documentation/patterns/cross-language/index.html +++ b/website/generated-content/documentation/patterns/cross-language/index.html @@ -18,7 +18,7 @@ function addPlaceholder(){$('input:text').attr('placeholder',"What are you looking for?");} function endSearch(){var search=document.querySelector(".searchBar");search.classList.add("disappear");var icons=document.querySelector("#iconsBar");icons.classList.remove("disappear");} function blockScroll(){$("body").toggleClass("fixedPosition");} -function openMenu(){addPlaceholder();blockScroll();}</script><div class="clearfix container-main-content"><div class="section-nav closed" data-offset-top=90 data-offset-bottom=500><span class="section-nav-back glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list data-section-nav><li><span class=section-nav-list-main-title>Documentation</span></li><li><a href=/documentation>Using the Documentation</a></li><li class=section-nav-item--collapsible><span class=section-nav-lis [...] +function openMenu(){addPlaceholder();blockScroll();}</script><div class="clearfix container-main-content"><div class="section-nav closed" data-offset-top=90 data-offset-bottom=500><span class="section-nav-back glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list data-section-nav><li><span class=section-nav-list-main-title>Documentation</span></li><li><a href=/documentation>Using the Documentation</a></li><li class=section-nav-item--collapsible><span class=section-nav-lis [...] <span class=kd>private</span> <span class=kd>static</span> <span class=kd>final</span> <span class=n>String</span> <span class=n>URN</span> <span class=o>=</span> <span class=s>"beam:transforms:xlang:pythontransform"</span><span class=o>;</span> <span class=kd>private</span> <span class=kd>static</span> <span class=n>String</span> <span class=n>expansionAddress</span><span class=o>;</span> @@ -96,7 +96,7 @@ function openMenu(){addPlaceholder();blockScroll();}</script><div class="clearfi <span class=n>logging</span><span class=o>.</span><span class=n>getLogger</span><span class=p>()</span><span class=o>.</span><span class=n>setLevel</span><span class=p>(</span><span class=n>logging</span><span class=o>.</span><span class=n>INFO</span><span class=p>)</span> <span class=n>main</span><span class=p>(</span><span class=n>sys</span><span class=o>.</span><span class=n>argv</span><span class=p>)</span></code></pre></div></div></div><h2 id=how-to-run-the-cross-language-pipeline>How to run the cross-language pipeline?</h2><p>In this section, the steps to run a cross-language pipeline are set out:</p><ol><li><p>Start the <strong>expansion service</strong> with your Python transforms: <code>python expansion_service.py -p 9097</code></p></li><li><p>S [...] <code>./gradlew :runners:spark:job-server:runShadow</code></li><li>Using the pre-build Docker container: -<code>docker run -net=host apache/beam_spark_job_server</code></li></ul></li><li><p><strong>Run pipeline</strong>: <code>mvn exec:java -Dexec.mainClass=CrossLanguagePipeline \ -Pportable-runner \ -Dexec.args=" \ --runner=PortableRunner \ --jobEndpoint=localhost:8099 \ --useExternal=true \ --expansionServiceURL=localhost:9097 \ --experiments=beam_fn_api"</code></p></li></ol><div class=feedback><p class=update>Last updated on 2021/10/04</p><h3>Have you found everything you were looking for [...] +<code>docker run -net=host apache/beam_spark_job_server</code></li></ul></li><li><p><strong>Run pipeline</strong>: <code>mvn exec:java -Dexec.mainClass=CrossLanguagePipeline \ -Pportable-runner \ -Dexec.args=" \ --runner=PortableRunner \ --jobEndpoint=localhost:8099 \ --useExternal=true \ --expansionServiceURL=localhost:9097 \ --experiments=beam_fn_api"</code></p></li></ol><div class=feedback><p class=update>Last updated on 2021/11/12</p><h3>Have you found everything you were looking for [...] <a href=http://www.apache.org>The Apache Software Foundation</a> | <a href=/privacy_policy>Privacy Policy</a> | <a href=/feed.xml>RSS Feed</a><br><br>Apache Beam, Apache, Beam, the Beam logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. All other products or name brands are trademarks of their respective holders, including The Apache Software Foundation.</div></div></div></div></footer></body></html> \ No newline at end of file diff --git a/website/generated-content/documentation/programming-guide/index.html b/website/generated-content/documentation/programming-guide/index.html index db188c3..44f84a5 100644 --- a/website/generated-content/documentation/programming-guide/index.html +++ b/website/generated-content/documentation/programming-guide/index.html @@ -18,7 +18,7 @@ function addPlaceholder(){$('input:text').attr('placeholder',"What are you looking for?");} function endSearch(){var search=document.querySelector(".searchBar");search.classList.add("disappear");var icons=document.querySelector("#iconsBar");icons.classList.remove("disappear");} function blockScroll(){$("body").toggleClass("fixedPosition");} -function openMenu(){addPlaceholder();blockScroll();}</script><div class="clearfix container-main-content"><div class="section-nav closed" data-offset-top=90 data-offset-bottom=500><span class="section-nav-back glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list data-section-nav><li><span class=section-nav-list-main-title>Documentation</span></li><li><a href=/documentation>Using the Documentation</a></li><li class=section-nav-item--collapsible><span class=section-nav-lis [...] +function openMenu(){addPlaceholder();blockScroll();}</script><div class="clearfix container-main-content"><div class="section-nav closed" data-offset-top=90 data-offset-bottom=500><span class="section-nav-back glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list data-section-nav><li><span class=section-nav-list-main-title>Documentation</span></li><li><a href=/documentation>Using the Documentation</a></li><li class=section-nav-item--collapsible><span class=section-nav-lis [...] 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 @@ -4127,7 +4127,51 @@ use case.</p><div class="language-java snippet"><div class="notebook-skip code-s <span class=c1># Register callback function for this bundle that performs the side</span> <span class=c1># effect.</span> - <span class=n>bundle_finalizer</span><span class=o>.</span><span class=n>register</span><span class=p>(</span><span class=n>my_callback_func</span><span class=p>)</span></code></pre></div></div></div><div class="language-go snippet"><div class="notebook-skip code-snippet"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><div class=highlight><pre class=chroma><code class=language-go data-lang=go><spa [...] + <span class=n>bundle_finalizer</span><span class=o>.</span><span class=n>register</span><span class=p>(</span><span class=n>my_callback_func</span><span class=p>)</span></code></pre></div></div></div><div class="language-go snippet"><div class="notebook-skip code-snippet"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><div class=highlight><pre class=chroma><code class=language-go data-lang=go><spa [...] + . . . + + // Following method satisfies the Requirement 1. + // Note that you may also use a class constructor instead of a static method. + public static JavaDataGenerator create(Integer size) { + return new JavaDataGenerator(size); + } + + static class JavaDataGeneratorConfig implements Serializable { + public String prefix; + public long length; + public String suffix; + . . . + } + + // Following method conforms to the Requirement 2 + public JavaDataGenerator withJavaDataGeneratorConfig(JavaDataGeneratorConfig dataConfig) { + return new JavaDataGenerator(this.size, javaDataGeneratorConfig); + } + + . . . +}</code></pre></div></div><p>To use a Java class that conforms to the above requirement from a Python SDK pipeline you may do the following.</p><ul><li>Step 1: create an allowlist file in the <em>yaml</em> format that describes the Java transform classes and methods that will be directly accessed from Python.</li><li>Step 2: start an Expansion Service with the <code>javaClassLookupAllowlistFile</code> option passing path to the allowlist defined in Step 1 as the value.</li><li>Step 3: Us [...] +access Java transforms defined in the allowlist from the Python side.</li></ul><p>Starting with Beam 2.35.0, Step 1 and 2 may be skipped as described in corresponding sections below.</p><h5 id=step-1>Step 1</h5><p>To use this Java transform from Python, you may define an allowlist file in the <em>yaml</em> format. This allowlist lists the class names, +constructor methods, and builder methods that are directly available to be used from the Python side.</p><p>Starting with Beam 2.35.0, you have the option to specify <code>*</code> to the <code>javaClassLookupAllowlistFile</code> option instead of defining an actual allowlist which +denotes that all supported transforms in the classpath of the expansion service may be accessed through the API.</p><div class=snippet><div class="notebook-skip code-snippet without_switcher"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><pre><code>version: v1 +allowedClasses: +- className: my.beam.transforms.JavaDataGenerator + allowedConstructorMethods: + - create + allowedBuilderMethods: + - withJavaDataGeneratorConfig</code></pre></div></div><h5 id=step-2>Step 2</h5><p>The allowlist is provided as an argument when starting up the Java expansion service. For example, the expansion service can be started +as a local Java process using the following command.</p><div class=snippet><div class="notebook-skip code-snippet without_switcher"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><pre><code>java -jar <jar file> <port> --javaClassLookupAllowlistFile=<path to the allowlist file></code></pre></div></div><p>Starting with Beam 2.35.0, Beam ``JavaExternalTransform<code>API will automatical [...] +if an expansion service address was not provided.</p><h5 id=step-3>Step 3</h5><p>You can directly use the Java class from your Python pipeline using a stub transform created using the <code>JavaExternalTransform</code> API. This API allows you to construct the transform +using the Java class name and allows you to invoke builder methods to configure the class.</p><p>Constructor and method parameter types are mapped between Python and Java using a Beam Schema. The Schema is auto-generated using the object types +provided on the Python side. If the Java class constructor method or builder method accepts any complex object types, make sure that the Beam Schema +for these objects is registered and available for the Java expansion service. If a schema has not been registered, the Java expansion service will +try to register a schema using <a href=https://beam.apache.org/documentation/programming-guide/#creating-schemas>JavaFieldSchema</a>. In Python arbitrary objects +can be represented using <code>NamedTuple</code>s which will be represented as Beam Rows in the Schema. See below for a Python stub transform that represents the above +mentioned Java transform.</p><div class=snippet><div class="notebook-skip code-snippet without_switcher"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><pre><code>JavaDataGeneratorConfig = typing.NamedTuple( +'JavaDataGeneratorConfig', [('prefix', str), ('length', int), ('suffix', str)]) +data_config = JavaDataGeneratorConfig(prefix='start', length=20, suffix='end') + +java_transform = JavaExternalTransform( +'my.beam.transforms.JavaDataGenerator', expansion_service='localhost:<port>').create(numpy.int32(100)).withJavaDataGeneratorConfig(data_config)</code></pre></div></div><p>This transform can be used in a Python pipeline along with other Python transforms.</p><h5 id=13112-full-api-for-making-existing-java-transforms-available-to-other-sdks>13.1.1.2 Full API for Making Existing Java Transforms Available to Other SDKs</h5><p>To make your Apache Beam Java SDK transform p [...] abstract static class Builder<K, V> implements ExternalTransformBuilder<External.Configuration, PBegin, PCollection<KV<K, V>>> { abstract Builder<K, V> setConsumerConfig(Map<String, Object> config); @@ -4201,7 +4245,15 @@ def from_runner_api_parameter( </code></pre></div></div></li></ol><p><strong>Starting the expansion service</strong></p><p>An expansion service can be used with multiple transforms in the same pipeline. Python has a default expansion service included and available in the Apache Beam Python SDK for you to use with your Python transforms. You are free to write your own expansion service, but that is generally not needed, so it is not covered in this section.</p><p>Perform the following steps to start up the default [...] </code></pre></div></div></li><li><p>Import any modules that contain transforms to be made available using the expansion service.</p><div class=snippet><div class="notebook-skip code-snippet without_switcher"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><pre><code>$ python -m apache_beam.runners.portability.expansion_service_test -p $PORT_FOR_EXPANSION_SERVICE </code></pre></div></div></li><li><p>This expansion service is not ready to serve up transforms on the address <code>localhost:$PORT_FOR_EXPANSION_SERVICE</code>.</p></li></ol><p><strong>Including dependencies</strong></p><p>Currently Python external transforms are limited to dependencies available in core Beam SDK Harness.</p><h4 id=1313-creating-cross-language-go-transforms>13.1.3. Creating cross-language Go transforms</h4><p>Go currently does not support creating cross-language tr [...] -transforms from other languages; see more at <a href=https://issues.apache.org/jira/browse/BEAM-9923>BEAM-9923</a>.</p><h3 id=use-x-lang-transforms>13.2. Using cross-language transforms</h3><p>Depending on the SDK language of the pipeline, you can use a high-level SDK-wrapper class, or a low-level transform class to access a cross-language transform.</p><h4 id=1321-using-cross-language-transforms-in-a-java-pipeline>13.2.1. Using cross-language transforms in a Java pipeline</h4><p>Current [...] +transforms from other languages; see more at <a href=https://issues.apache.org/jira/browse/BEAM-9923>BEAM-9923</a>.</p><h4 id=1314-selecting-a-urn-for-cross-language-transforms>13.1.4. Selecting a URN for Cross-language Transforms</h4><p>Developing a cross-language transform involves defining a URN for registering the transform with an expansion service. In this section +we provide a convention for defining such URNs. Following this convention is optional but it will ensure that your transform +will not run into conflicts when registering in an expansion service along with transforms developed by other developers.</p><h5 id=schema>Schema</h5><p>A URN should consist of the following components:</p><ul><li>ns-id: A namespace identifier. Default recommendation is <code>beam:transform</code>.</li><li>org-identifier: Identifies the organization where the transform was defined. Transforms defined in Apache Beam use <code>org.apache.beam</code> for this.</li><li>functionality-identifi [...] +Keywords in upper case are from the <a href=https://datatracker.ietf.org/doc/html/rfc8141>URN spec</a>.</p><div class=snippet><div class="notebook-skip code-snippet without_switcher"><a class=copy type=button data-bs-toggle=tooltip data-bs-placement=bottom title="Copy to clipboard"><img src=/images/copy-icon.svg></a><pre><code>transform-urn = ns-id “:” org-identifier “:” functionality-identifier “:” version +ns-id = (“beam” / NID) “:” “transform” +id-char = ALPHA / DIGIT / "-" / "." / "_" / "~" ; A subset of characters allowed in a URN +org-identifier = 1*id-char +functionality-identifier = 1*id-char +version = “v” 1*(DIGIT / “.”) ; For example, ‘v1.2’</code></pre></div></div><h5 id=examples>Examples</h5><p>Below we’ve given some example transform classes and corresponding URNs to be used.</p><ul><li>A transform offered with Apache Beam that writes Parquet files.<ul><li><code>beam:transform:org.apache.beam:parquet_write:v1</code></li></ul></li><li>A transform offered with Apache Beam that reads from Kafka with metadata.<ul><li><code>beam:transform:org.apache.beam:kafka_read_with_meta [...] kafka_records = ( pipeline @@ -4255,7 +4307,7 @@ expansionAddr := "localhost:8097" outT := beam.UnnamedOutput(typex.New(reflectx.String)) res := beam.CrossLanguage(s, urn, payload, expansionAddr, beam.UnnamedInput(inputPCol), outT) </code></pre></div></div></li><li><p>After the job has been submitted to the Beam runner, shutdown the expansion service by -terminating the expansion service process.</p></li></ol><h3 id=x-lang-transform-runner-support>13.3. Runner Support</h3><p>Currently, portable runners such as Flink, Spark, and the Direct runner can be used with multi-language pipelines.</p><p>Google Cloud Dataflow supports multi-language pipelines through the Dataflow Runner v2 backend architecture.</p><div class=feedback><p class=update>Last updated on 2021/11/10</p><h3>Have you found everything you were looking for?</h3><p class=descr [...] +terminating the expansion service process.</p></li></ol><h3 id=x-lang-transform-runner-support>13.3. Runner Support</h3><p>Currently, portable runners such as Flink, Spark, and the Direct runner can be used with multi-language pipelines.</p><p>Google Cloud Dataflow supports multi-language pipelines through the Dataflow Runner v2 backend architecture.</p><div class=feedback><p class=update>Last updated on 2021/11/12</p><h3>Have you found everything you were looking for?</h3><p class=descr [...] <a href=http://www.apache.org>The Apache Software Foundation</a> | <a href=/privacy_policy>Privacy Policy</a> | <a href=/feed.xml>RSS Feed</a><br><br>Apache Beam, Apache, Beam, the Beam logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. 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