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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2021/11/17 21:48:46 UTC
[GitHub] [beam] chamikaramj commented on a change in pull request #16001: created quickstart guide for multi-language pipelines (Python)
chamikaramj commented on a change in pull request #16001:
URL: https://github.com/apache/beam/pull/16001#discussion_r751656251
##########
File path: website/www/site/content/en/documentation/sdks/python-multi-language-pipelines.md
##########
@@ -0,0 +1,213 @@
+---
+type: languages
+title: "Python multi-language pipelines"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Python multi-language pipelines
+
+This page provides a high-level overview of creating multi-language pipelines with the Apache Beam SDK for Python. To build and run a multi-language Python pipeline, you need a Python environment with the Beam SDK installed. If you don’t have an environment set up, first complete the [Apache Beam Python SDK Quickstart](/get-started/quickstart-py/).
+
+A *multi-language pipeline* is a pipeline that’s built in one Beam SDK language and uses one or more transforms from another Beam SDK language. These “other-language” transforms are called *cross-language transforms*. The idea is to make pipeline components easier to share across the Beam SDKs, and to grow the pool of available transforms for all the SDKs. In the examples below, the multi-language pipeline is built with the Beam Python SDK, and the cross-language transforms are built with the Beam Java SDK.
+
+## Create a cross-language transform
+
+Here's a simple Java transform that adds a prefix to an input string:
+
+```java
+public class JavaPrefix extends PTransform<PCollection<String>, PCollection<String>> {
+
+ final String prefix;
+
+ public JavaPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+
+ class AddPrefixDoFn extends DoFn<String, String> {
+
+ @ProcessElement
+ public void process(@Element String input, OutputReceiver<String> o) {
+ o.output(prefix + input);
+ }
+ }
+
+ @Override
+ public PCollection<String> expand(PCollection<String> input) {
+ return input
+ .apply(
+ "AddPrefix",
+ ParDo.of(new AddPrefixDoFn()));
+ }
+}
+```
+
+To make this available as a cross-language transform, you have to add a config object and a builder.
+
+> **Note:** Starting with Beam 2.34.0, Python SDK users can use some Java transforms without writing additional Java code. To learn more, see [Creating cross-language Java transforms](/documentation/programming-guide/#1311-creating-cross-language-java-transforms).
+
+The config object is a simple Java object (POJO) that has fields required by the transform.
+
+```java
+public class JavaPrefixConfiguration {
+
+ String prefix;
+
+ public void setPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+}
+```
+
+The builder class must implement [ExternalTransformBuilder](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/ExternalTransformBuilder.html) and override `buildExternal`, which uses the config object.
+
+```java
+public class JavaPrefixBuilder implements
+ ExternalTransformBuilder<JavaPrefixConfiguration, PCollection<String>, PCollection<String>> {
+
+ @Override
+ public PTransform<PCollection<String>, PCollection<String>> buildExternal(
+ JavaPrefixConfiguration configuration) {
+ return new JavaPrefix(configuration.prefix);
+ }
+}
+```
+
+You also need to add a registrar class to register your transform with the expansion service.
+
+```java
+@AutoService(ExternalTransformRegistrar.class)
+public class JavaPrefixRegistrar implements ExternalTransformRegistrar {
+
+ final String URN = "my.beam.transform.javaprefix";
+
+ @Override
+ public Map<String, ExternalTransformBuilder<?, ?, ?>> knownBuilderInstances() {
+ return ImmutableMap.of(URN,new JavaPrefixBuilder());
+ }
+}
+```
+
+The registrar must implement [ExternalTransformRegistrar](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/ExternalTransformRegistrar.html), which has one method, `knownBuilderInstances`. This returns a map that maps a unique URN to an instance of your builder. You can use the [AutoService](https://github.com/google/auto/tree/master/service) annotation to register this class with the expansion service.
+
+## Choose an expansion service
+
+When building a job for a multi-language pipeline, Beam uses an [expansion service](/documentation/glossary/#expansion-service) to expand [composite transforms](/documentation/glossary/#composite-transform). You must have at least one expansion service per remote SDK.
+
+In most cases, you can use the default Java [ExpansionService](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/service/ExpansionService.html). The service takes a single parameter, which specifies the port of the expansion service. The address is then provided by the Python pipeline.
+
+When you start the expansion service, you need to add dependencies to the classpath. You can use more than one JAR, but it’s often easier to create a single shaded JAR. Both Python and Java dependencies will be staged for the runner by the Python SDK.
+
+## Create a Python pipeline
+
+Your Python pipeline can now use the [ExternalTransform](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.ExternalTransform) API to configure your cross-language transform. Here’s an example:
+
+```py
+with beam.Pipeline(options=pipeline_options) as p:
+ input = p | 'Read' >> ReadFromText(input_path).with_output_types(str)
+
+ java_output = (
+ input
+ | 'JavaPrefix' >> beam.ExternalTransform(
+ 'my.beam.transform.javaprefix',
+ ImplicitSchemaPayloadBuilder({'prefix': 'java:'}),
+ "localhost:12345"))
+
+ def python_prefix(record):
+ return 'python:%s' % record
+
+ output = java_output | 'PythonPrefix' >> beam.Map(python_prefix)
+ output | 'Write' >> WriteToText(output_path)
+```
+
+`ExternalTransform` takes three parameters:
+
+* The URN for the cross-language transform
+* The payload, either as a byte string or a [PayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.PayloadBuilder)
+* An expansion service
+
+The URN is simply a unique Beam identifier for the transform, and the expansion service has already been discussed. The PayloadBuilder is a new concept, discussed next.
+
+> **NOTE**: To ensure that your URN doesn't run into confilcts with URNs from other transforms, follow the URN conventions described at [Selecting a URN for Cross-language Transforms](/documentation/programming-guide/#1314-selecting-a-urn-for-cross-language-transforms).
+
+## Provide a payload builder
+
+The Python pipeline example above provides an [ImplicitSchemaPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.ImplicitSchemaPayloadBuilder) as the second argument to `ExternalTransform`. The `ImplicitSchemaPayloadBuilder` builds a payload that generates a schema from the provided values. In this case, the provided values are contained in the following key-value pair: `{'prefix': 'java:'}`. The `JavaPrefix` transform expects a `prefix` argument, and the payload builder passes in the string `java:`, which will be prepended to each input element.
+
+In general, payload builders help build the payload for the transform in the expansion request. If you provide a Beam schema to the payload builder, the builder uses it to perform a type/field mapping. Instead of the `ImplicitSchemaPayloadBuilder`, you could use a [NamedTupleBasedPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.NamedTupleBasedPayloadBuilder), which builds a payload based on a named tuple schema, or an [AnnotationBasedPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.AnnotationBasedPayloadBuilder), which builds a schema based on type annotations. For a complete list of available payload builders, see the [transforms.external API reference](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html).
Review comment:
"If you provide a Beam schema to the payload builder, the builder uses it to perform a type/field mapping" I don't think we support directly specifying a schema. So probably let's drop that sentence (unless I misunderstood something).
##########
File path: website/www/site/content/en/documentation/sdks/python-multi-language-pipelines.md
##########
@@ -0,0 +1,213 @@
+---
+type: languages
+title: "Python multi-language pipelines"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Python multi-language pipelines
+
+This page provides a high-level overview of creating multi-language pipelines with the Apache Beam SDK for Python. To build and run a multi-language Python pipeline, you need a Python environment with the Beam SDK installed. If you don’t have an environment set up, first complete the [Apache Beam Python SDK Quickstart](/get-started/quickstart-py/).
+
+A *multi-language pipeline* is a pipeline that’s built in one Beam SDK language and uses one or more transforms from another Beam SDK language. These “other-language” transforms are called *cross-language transforms*. The idea is to make pipeline components easier to share across the Beam SDKs, and to grow the pool of available transforms for all the SDKs. In the examples below, the multi-language pipeline is built with the Beam Python SDK, and the cross-language transforms are built with the Beam Java SDK.
Review comment:
Should we add a link to the main documentation [1] here so that users who need a more comprehensive guide can go there ?
Also, probably good to add a link to the top of the main guide regarding the quick start.
WDYT ?
[1] https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines
##########
File path: website/www/site/content/en/documentation/sdks/python-multi-language-pipelines.md
##########
@@ -0,0 +1,213 @@
+---
+type: languages
+title: "Python multi-language pipelines"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Python multi-language pipelines
+
+This page provides a high-level overview of creating multi-language pipelines with the Apache Beam SDK for Python. To build and run a multi-language Python pipeline, you need a Python environment with the Beam SDK installed. If you don’t have an environment set up, first complete the [Apache Beam Python SDK Quickstart](/get-started/quickstart-py/).
+
+A *multi-language pipeline* is a pipeline that’s built in one Beam SDK language and uses one or more transforms from another Beam SDK language. These “other-language” transforms are called *cross-language transforms*. The idea is to make pipeline components easier to share across the Beam SDKs, and to grow the pool of available transforms for all the SDKs. In the examples below, the multi-language pipeline is built with the Beam Python SDK, and the cross-language transforms are built with the Beam Java SDK.
+
+## Create a cross-language transform
+
+Here's a simple Java transform that adds a prefix to an input string:
+
+```java
+public class JavaPrefix extends PTransform<PCollection<String>, PCollection<String>> {
+
+ final String prefix;
+
+ public JavaPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+
+ class AddPrefixDoFn extends DoFn<String, String> {
+
+ @ProcessElement
+ public void process(@Element String input, OutputReceiver<String> o) {
+ o.output(prefix + input);
+ }
+ }
+
+ @Override
+ public PCollection<String> expand(PCollection<String> input) {
+ return input
+ .apply(
+ "AddPrefix",
+ ParDo.of(new AddPrefixDoFn()));
+ }
+}
+```
+
+To make this available as a cross-language transform, you have to add a config object and a builder.
+
+> **Note:** Starting with Beam 2.34.0, Python SDK users can use some Java transforms without writing additional Java code. To learn more, see [Creating cross-language Java transforms](/documentation/programming-guide/#1311-creating-cross-language-java-transforms).
+
+The config object is a simple Java object (POJO) that has fields required by the transform.
+
+```java
+public class JavaPrefixConfiguration {
+
+ String prefix;
+
+ public void setPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+}
+```
+
+The builder class must implement [ExternalTransformBuilder](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/ExternalTransformBuilder.html) and override `buildExternal`, which uses the config object.
+
+```java
+public class JavaPrefixBuilder implements
+ ExternalTransformBuilder<JavaPrefixConfiguration, PCollection<String>, PCollection<String>> {
+
+ @Override
+ public PTransform<PCollection<String>, PCollection<String>> buildExternal(
+ JavaPrefixConfiguration configuration) {
+ return new JavaPrefix(configuration.prefix);
+ }
+}
+```
+
+You also need to add a registrar class to register your transform with the expansion service.
+
+```java
+@AutoService(ExternalTransformRegistrar.class)
+public class JavaPrefixRegistrar implements ExternalTransformRegistrar {
+
+ final String URN = "my.beam.transform.javaprefix";
Review comment:
Let's change this to "beam:transform:my.beam.test:javaprefix:v1" to resemble the new convention.
Also have to change the URN mentioned in the Python code below.
##########
File path: website/www/site/content/en/documentation/sdks/python-multi-language-pipelines.md
##########
@@ -0,0 +1,213 @@
+---
+type: languages
+title: "Python multi-language pipelines"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Python multi-language pipelines
+
+This page provides a high-level overview of creating multi-language pipelines with the Apache Beam SDK for Python. To build and run a multi-language Python pipeline, you need a Python environment with the Beam SDK installed. If you don’t have an environment set up, first complete the [Apache Beam Python SDK Quickstart](/get-started/quickstart-py/).
+
+A *multi-language pipeline* is a pipeline that’s built in one Beam SDK language and uses one or more transforms from another Beam SDK language. These “other-language” transforms are called *cross-language transforms*. The idea is to make pipeline components easier to share across the Beam SDKs, and to grow the pool of available transforms for all the SDKs. In the examples below, the multi-language pipeline is built with the Beam Python SDK, and the cross-language transforms are built with the Beam Java SDK.
+
+## Create a cross-language transform
+
+Here's a simple Java transform that adds a prefix to an input string:
+
+```java
+public class JavaPrefix extends PTransform<PCollection<String>, PCollection<String>> {
+
+ final String prefix;
+
+ public JavaPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+
+ class AddPrefixDoFn extends DoFn<String, String> {
+
+ @ProcessElement
+ public void process(@Element String input, OutputReceiver<String> o) {
+ o.output(prefix + input);
+ }
+ }
+
+ @Override
+ public PCollection<String> expand(PCollection<String> input) {
+ return input
+ .apply(
+ "AddPrefix",
+ ParDo.of(new AddPrefixDoFn()));
+ }
+}
+```
+
+To make this available as a cross-language transform, you have to add a config object and a builder.
+
+> **Note:** Starting with Beam 2.34.0, Python SDK users can use some Java transforms without writing additional Java code. To learn more, see [Creating cross-language Java transforms](/documentation/programming-guide/#1311-creating-cross-language-java-transforms).
+
+The config object is a simple Java object (POJO) that has fields required by the transform.
+
+```java
+public class JavaPrefixConfiguration {
+
+ String prefix;
+
+ public void setPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+}
+```
+
+The builder class must implement [ExternalTransformBuilder](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/ExternalTransformBuilder.html) and override `buildExternal`, which uses the config object.
+
+```java
+public class JavaPrefixBuilder implements
+ ExternalTransformBuilder<JavaPrefixConfiguration, PCollection<String>, PCollection<String>> {
+
+ @Override
+ public PTransform<PCollection<String>, PCollection<String>> buildExternal(
+ JavaPrefixConfiguration configuration) {
+ return new JavaPrefix(configuration.prefix);
+ }
+}
+```
+
+You also need to add a registrar class to register your transform with the expansion service.
+
+```java
+@AutoService(ExternalTransformRegistrar.class)
+public class JavaPrefixRegistrar implements ExternalTransformRegistrar {
+
+ final String URN = "my.beam.transform.javaprefix";
+
+ @Override
+ public Map<String, ExternalTransformBuilder<?, ?, ?>> knownBuilderInstances() {
+ return ImmutableMap.of(URN,new JavaPrefixBuilder());
+ }
+}
+```
+
+The registrar must implement [ExternalTransformRegistrar](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/ExternalTransformRegistrar.html), which has one method, `knownBuilderInstances`. This returns a map that maps a unique URN to an instance of your builder. You can use the [AutoService](https://github.com/google/auto/tree/master/service) annotation to register this class with the expansion service.
+
+## Choose an expansion service
+
+When building a job for a multi-language pipeline, Beam uses an [expansion service](/documentation/glossary/#expansion-service) to expand [composite transforms](/documentation/glossary/#composite-transform). You must have at least one expansion service per remote SDK.
+
+In most cases, you can use the default Java [ExpansionService](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/service/ExpansionService.html). The service takes a single parameter, which specifies the port of the expansion service. The address is then provided by the Python pipeline.
+
+When you start the expansion service, you need to add dependencies to the classpath. You can use more than one JAR, but it’s often easier to create a single shaded JAR. Both Python and Java dependencies will be staged for the runner by the Python SDK.
+
+## Create a Python pipeline
+
+Your Python pipeline can now use the [ExternalTransform](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.ExternalTransform) API to configure your cross-language transform. Here’s an example:
+
+```py
+with beam.Pipeline(options=pipeline_options) as p:
+ input = p | 'Read' >> ReadFromText(input_path).with_output_types(str)
+
+ java_output = (
+ input
+ | 'JavaPrefix' >> beam.ExternalTransform(
+ 'my.beam.transform.javaprefix',
Review comment:
Ditto regarding updating the URN.
##########
File path: website/www/site/content/en/documentation/sdks/python-multi-language-pipelines.md
##########
@@ -0,0 +1,213 @@
+---
+type: languages
+title: "Python multi-language pipelines"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Python multi-language pipelines
+
+This page provides a high-level overview of creating multi-language pipelines with the Apache Beam SDK for Python. To build and run a multi-language Python pipeline, you need a Python environment with the Beam SDK installed. If you don’t have an environment set up, first complete the [Apache Beam Python SDK Quickstart](/get-started/quickstart-py/).
+
+A *multi-language pipeline* is a pipeline that’s built in one Beam SDK language and uses one or more transforms from another Beam SDK language. These “other-language” transforms are called *cross-language transforms*. The idea is to make pipeline components easier to share across the Beam SDKs, and to grow the pool of available transforms for all the SDKs. In the examples below, the multi-language pipeline is built with the Beam Python SDK, and the cross-language transforms are built with the Beam Java SDK.
+
+## Create a cross-language transform
+
+Here's a simple Java transform that adds a prefix to an input string:
+
+```java
+public class JavaPrefix extends PTransform<PCollection<String>, PCollection<String>> {
+
+ final String prefix;
+
+ public JavaPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+
+ class AddPrefixDoFn extends DoFn<String, String> {
+
+ @ProcessElement
+ public void process(@Element String input, OutputReceiver<String> o) {
+ o.output(prefix + input);
+ }
+ }
+
+ @Override
+ public PCollection<String> expand(PCollection<String> input) {
+ return input
+ .apply(
+ "AddPrefix",
+ ParDo.of(new AddPrefixDoFn()));
+ }
+}
+```
+
+To make this available as a cross-language transform, you have to add a config object and a builder.
+
+> **Note:** Starting with Beam 2.34.0, Python SDK users can use some Java transforms without writing additional Java code. To learn more, see [Creating cross-language Java transforms](/documentation/programming-guide/#1311-creating-cross-language-java-transforms).
+
+The config object is a simple Java object (POJO) that has fields required by the transform.
+
+```java
+public class JavaPrefixConfiguration {
+
+ String prefix;
+
+ public void setPrefix(String prefix) {
+ this.prefix = prefix;
+ }
+}
+```
+
+The builder class must implement [ExternalTransformBuilder](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/ExternalTransformBuilder.html) and override `buildExternal`, which uses the config object.
+
+```java
+public class JavaPrefixBuilder implements
+ ExternalTransformBuilder<JavaPrefixConfiguration, PCollection<String>, PCollection<String>> {
+
+ @Override
+ public PTransform<PCollection<String>, PCollection<String>> buildExternal(
+ JavaPrefixConfiguration configuration) {
+ return new JavaPrefix(configuration.prefix);
+ }
+}
+```
+
+You also need to add a registrar class to register your transform with the expansion service.
+
+```java
+@AutoService(ExternalTransformRegistrar.class)
+public class JavaPrefixRegistrar implements ExternalTransformRegistrar {
+
+ final String URN = "my.beam.transform.javaprefix";
+
+ @Override
+ public Map<String, ExternalTransformBuilder<?, ?, ?>> knownBuilderInstances() {
+ return ImmutableMap.of(URN,new JavaPrefixBuilder());
+ }
+}
+```
+
+The registrar must implement [ExternalTransformRegistrar](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/ExternalTransformRegistrar.html), which has one method, `knownBuilderInstances`. This returns a map that maps a unique URN to an instance of your builder. You can use the [AutoService](https://github.com/google/auto/tree/master/service) annotation to register this class with the expansion service.
+
+## Choose an expansion service
+
+When building a job for a multi-language pipeline, Beam uses an [expansion service](/documentation/glossary/#expansion-service) to expand [composite transforms](/documentation/glossary/#composite-transform). You must have at least one expansion service per remote SDK.
+
+In most cases, you can use the default Java [ExpansionService](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/expansion/service/ExpansionService.html). The service takes a single parameter, which specifies the port of the expansion service. The address is then provided by the Python pipeline.
+
+When you start the expansion service, you need to add dependencies to the classpath. You can use more than one JAR, but it’s often easier to create a single shaded JAR. Both Python and Java dependencies will be staged for the runner by the Python SDK.
+
+## Create a Python pipeline
+
+Your Python pipeline can now use the [ExternalTransform](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.ExternalTransform) API to configure your cross-language transform. Here’s an example:
+
+```py
+with beam.Pipeline(options=pipeline_options) as p:
+ input = p | 'Read' >> ReadFromText(input_path).with_output_types(str)
+
+ java_output = (
+ input
+ | 'JavaPrefix' >> beam.ExternalTransform(
+ 'my.beam.transform.javaprefix',
+ ImplicitSchemaPayloadBuilder({'prefix': 'java:'}),
+ "localhost:12345"))
+
+ def python_prefix(record):
+ return 'python:%s' % record
+
+ output = java_output | 'PythonPrefix' >> beam.Map(python_prefix)
+ output | 'Write' >> WriteToText(output_path)
+```
+
+`ExternalTransform` takes three parameters:
+
+* The URN for the cross-language transform
+* The payload, either as a byte string or a [PayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.PayloadBuilder)
+* An expansion service
+
+The URN is simply a unique Beam identifier for the transform, and the expansion service has already been discussed. The PayloadBuilder is a new concept, discussed next.
+
+> **NOTE**: To ensure that your URN doesn't run into confilcts with URNs from other transforms, follow the URN conventions described at [Selecting a URN for Cross-language Transforms](/documentation/programming-guide/#1314-selecting-a-urn-for-cross-language-transforms).
+
+## Provide a payload builder
+
+The Python pipeline example above provides an [ImplicitSchemaPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.ImplicitSchemaPayloadBuilder) as the second argument to `ExternalTransform`. The `ImplicitSchemaPayloadBuilder` builds a payload that generates a schema from the provided values. In this case, the provided values are contained in the following key-value pair: `{'prefix': 'java:'}`. The `JavaPrefix` transform expects a `prefix` argument, and the payload builder passes in the string `java:`, which will be prepended to each input element.
+
+In general, payload builders help build the payload for the transform in the expansion request. If you provide a Beam schema to the payload builder, the builder uses it to perform a type/field mapping. Instead of the `ImplicitSchemaPayloadBuilder`, you could use a [NamedTupleBasedPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.NamedTupleBasedPayloadBuilder), which builds a payload based on a named tuple schema, or an [AnnotationBasedPayloadBuilder](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html#apache_beam.transforms.external.AnnotationBasedPayloadBuilder), which builds a schema based on type annotations. For a complete list of available payload builders, see the [transforms.external API reference](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.external.html).
+
+## Use standard element types
+
+At a multi-language boundary, you have to use element types that all the Beam SDKs understand. These are types represented by the [Beam standard coders](https://github.com/apache/beam/blob/42e1ae8f8d07fb9c6fde14bd688c5a4b763d9d6e/model/pipeline/src/main/proto/beam_runner_api.proto#L784):
+
+* `BYTES`
+* `STRING_UTF8`
+* `KV`
+* `BOOL`
+* `VARINT`
+* `DOUBLE`
+* `ITERABLE`
+* `TIMER`
+* `WINDOWED_VALUE`
+* `ROW`
+
+For arbitrary structured types (for example, an arbitrary Java object), use `ROW` (`PCollection<Row>`). You may have to develop a new Java composite transform that produces a `PCollection<Row>`.
+
Review comment:
Also add:
"You many use SDK specific coders within a composite cross-language transform, if such coders do not get used by PCollections that are consumed by the other SDKs."
##########
File path: website/www/site/layouts/partials/section-menu/en/sdks.html
##########
@@ -39,6 +39,7 @@
<li><a href="/documentation/sdks/python-streaming/">Python streaming pipelines</a></li>
<li><a href="/documentation/sdks/python-type-safety/">Ensuring Python type safety</a></li>
<li><a href="/documentation/sdks/python-pipeline-dependencies/">Managing pipeline dependencies</a></li>
+ <li><a href="/documentation/sdks/python-multi-language-pipelines/">Python multi-language pipelines</a></li>
Review comment:
Ditto
##########
File path: website/www/site/content/en/documentation/sdks/python.md
##########
@@ -49,3 +49,7 @@ language-specific implementation guidance.
## Using Beam Python SDK in your ML pipelines
[TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx) is an end-to-end platform for deploying production ML pipelines. TFX is integrated with Beam. For more information, see [TFX user guide](https://www.tensorflow.org/tfx/guide).
+
+## Python multi-language pipelines
Review comment:
"Python multi-language pipelines quickstart" ? (not to confuse with the main documentation) ?
##########
File path: website/www/site/content/en/documentation/sdks/python.md
##########
@@ -49,3 +49,7 @@ language-specific implementation guidance.
## Using Beam Python SDK in your ML pipelines
[TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx) is an end-to-end platform for deploying production ML pipelines. TFX is integrated with Beam. For more information, see [TFX user guide](https://www.tensorflow.org/tfx/guide).
+
+## Python multi-language pipelines
+
+Apache Beam lets you combine transforms written in any supported SDK language and use them in one multi-language pipeline. To learn how to create a multi-language pipeline using the Python SDK, see [Python multi-language pipelines](/documentation/sdks/python-multi-language-pipelines).
Review comment:
Ditto.
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