examples/multi-language/README.md - beam - Git at Google


            
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            # Example multi-language pipelines


            This project provides examples of Apache Beam

            
            [multi-language pipelines](https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines):


            ## Using Java transforms from Python


            * **python/wordcount_external** - A Python pipeline that runs the Word Count workflow using three external Java

            
                            SchemaTransforms. This example demonstrates the updated `ExternalTransformProvider` API.

            
                #### _Outdated examples:_

            
            * **python/addprefix** - A Python pipeline that reads a text file and attaches a prefix on the Java side to each input.

            
            * **python/javacount** - A Python pipeline that counts words using the Java `Count.perElement()` transform.

            
            * **python/javadatagenerator** - A Python pipeline that produces a set of strings generated from Java.

            
                                              This example demonstrates the `JavaExternalTransform` API.


            ### Instructions for running the pipelines


            #### 1) Start the expansion service


            1. Download the latest 'beam-examples-multi-language' JAR. Starting with Apache Beam 2.36.0,

            
               you can find it in [the Maven Central Repository](https://search.maven.org/search?q=g:org.apache.beam).

            
            2. Run the following command, replacing `<version>` and `<port>` with valid values:

            
              `java -jar beam-examples-multi-language-<version>.jar <port> --javaClassLookupAllowlistFile='*'`


            #### 2) Set up a Python virtual environment for Beam


            1. See [the Python quickstart](https://beam.apache.org/get-started/quickstart-py/)

            
               for more information.


            #### 3) Execute the Python pipeline


            1. In a new shell, run a pipeline in the **python** directory using a Beam runner that supports

            
               multi-language pipelines.


               The Python files contain details about the actual commands to run.


            ## Using Python transforms from Java


            ### Sklearn Mnist Classification


            Performs image classification on handwritten digits from the [MNIST](https://en.wikipedia.org/wiki/MNIST_database)

            
            database.


            Please see [here](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) for

            
            context and information regarding the corresponding Python pipeline.


            Please note that the Java pipeline is

            
            [available in the Beam Java examples module](https://github.com/apache/beam/tree/master/examples/java/src/main/java/org/apache/beam/examples/multilanguage/SklearnMnistClassification.java).


            #### Setup


            * Obtain/generate a csv input file that contains labels and pixels to feed into the model and store it in

            
            GCS. An example input is available

            
            [here](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_input.csv).


            * Create a model file that contains the pickled file of a scikit-learn model

            
            trained on MNIST data and store it in GCS. An example model file is available

            
            [here](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_model).

            
            This model was generated by by running the program given

            
            [here](https://python-course.eu/machine-learning/training-and-testing-with-mnist.php)

            
            on the

            
            [example input dataset](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_input.csv).


            * Perform Beam runner specific setup according to instructions

            
            [here](https://beam.apache.org/get-started/quickstart-java/#run-a-pipeline).


            Following instructions are for running the pipeline with the Dataflow runner. For other portable runners,

            
            please modify the instructions according to the guidelines

            
            [here](https://beam.apache.org/documentation/sdks/java-multi-language-pipelines/#run-with-directrunner)


            #### Instructions for running the Java pipeline on released Beam (Beam 2.43.0 and later).


            * Checkout the Beam examples Maven archetype for the relevant Beam version.


            ```

            
            export BEAM_VERSION=<Beam version>


            mvn archetype:generate \

            
                -DarchetypeGroupId=org.apache.beam \

            
                -DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \

            
                -DarchetypeVersion=$BEAM_VERSION \

            
                -DgroupId=org.example \

            
                -DartifactId=multi-language-beam \

            
                -Dversion="0.1" \

            
                -Dpackage=org.apache.beam.examples \

            
                -DinteractiveMode=false

            
            ```


            * Run the pipeline.


            ```

            
            export GCP_PROJECT=<GCP project>

            
            export GCP_BUCKET=<GCP bucket>

            
            export GCP_REGION=<GCP region>


            mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.multilanguage.SklearnMnistClassification \

            
                -Dexec.args="--runner=DataflowRunner --project=$GCP_PROJECT \

            
                             --region=$GCP_REGION \

            
                             --gcpTempLocation=gs://$GCP_BUCKET/multi-language-beam/tmp \

            
                             --output=gs://$GCP_BUCKET/multi-language-beam/output" \

            
                -Pdataflow-runner

            
            ```


            * Inspect the output. Each line has data separated by a comma ",". The first item is the actual label of

            
            the digit. The second item is the predicted label of the digit.


            ```

            
            gcloud storage cat gs://$GCP_BUCKET/multi-language-beam/output*

            
            ```


            #### Instructions for running the Java pipeline at HEAD (Beam 2.41.0 and 2.42.0).


            * Activate a new virtual environment following

            
            [these instructions](https://beam.apache.org/get-started/quickstart-py/#create-and-activate-a-virtual-environment).


            * 2. Install Apache Beam package with gcp support and the `sklearn` package.


            ```

            
            pip install apache-beam[gcp]

            
            pip install sklearn

            
            ```


            * Startup the expansion service


            ```

            
            python -m apache_beam.runners.portability.expansion_service_main -p <PORT> --fully_qualified_name_glob "*"

            
            ```


            * Make sure that Docker is installed and available on your system.


            * In a different shell, build and push Python and Java Docker containers.


            ```

            
            export DOCKER_ROOT=<Docker root>


            ./gradlew :sdks:python:container:py39:docker -Pdocker-repository-root=$DOCKER_ROOT -Pdocker-tag=latest


            docker push $DOCKER_ROOT/beam_python3.9_sdk:latest


            ./gradlew :sdks:java:container:java11:docker -Pdocker-repository-root=$DOCKER_ROOT -Pdocker-tag=latest -Pjava11Home=$JAVA_HOME


            docker push $DOCKER_ROOT/beam_java11_sdk:latest

            
            ```


            * Run the pipeline using the following Gradle command (this guide assumes Dataflow runner).

            
            Note that we override both the Java and Python SDK harness containers here.


            ```

            
            export GCP_PROJECT=<GCP project>

            
            export GCP_BUCKET=<GCP bucket>

            
            export GCP_REGION=<GCP region>

            
            export EXPANSION_SERVICE_PORT=<PORT>


            # This removes any existing output.

            
            gcloud storage rm gs://$GCP_BUCKET/multi-language-beam/output*


            ./gradlew :examples:multi-language:sklearnMinstClassification --args=" \

            
            --runner=DataflowRunner \

            
            --project=$GCP_PROJECT \

            
            --gcpTempLocation=gs://$GCP_BUCKET/multi-language-beam/tmp \

            
            --output=gs://$GCP_BUCKET/multi-language-beam/output \

            
            --sdkContainerImage=$DOCKER_ROOT/beam_java11_sdk:latest \

            
            --sdkHarnessContainerImageOverrides=.*python.*,$DOCKER_ROOT/beam_python3.9_sdk:latest \

            
            --expansionService=localhost:$EXPANSION_SERVICE_PORT \

            
            --region=${GCP_REGION}"

            
            ```


            * Inspect the output. Each line has data separated by a comma ",". The first item is the actual label

            
            of the digit. The second item is the predicted label of the digit.


            ```

            
            gcloud storage cat gs://$GCP_BUCKET/multi-language-beam/output*

            
            ```


            ### Python Dataframe Wordcount


            This example is covered in the [Java multi-language pipelines quickstart](https://beam.apache.org/documentation/sdks/java-multi-language-pipelines/).

            
            The pipeline source code is available at

            
            [PythonDataframeWordCount.java](https://github.com/apache/beam/tree/master/examples/java/src/main/java/org/apache/beam/examples/multilanguage/PythonDataframeWordCount.java).
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	with the License. You may obtain a copy of the License at

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	KIND, either express or implied. See the License for the
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	# Example multi-language pipelines

	This project provides examples of Apache Beam
	[multi-language pipelines](https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines):

	## Using Java transforms from Python

	* python/wordcount_external - A Python pipeline that runs the Word Count workflow using three external Java
	SchemaTransforms. This example demonstrates the updated `ExternalTransformProvider` API.
	#### _Outdated examples:_
	* python/addprefix - A Python pipeline that reads a text file and attaches a prefix on the Java side to each input.
	* python/javacount - A Python pipeline that counts words using the Java `Count.perElement()` transform.
	* python/javadatagenerator - A Python pipeline that produces a set of strings generated from Java.
	This example demonstrates the `JavaExternalTransform` API.

	### Instructions for running the pipelines

	#### 1) Start the expansion service

	1. Download the latest 'beam-examples-multi-language' JAR. Starting with Apache Beam 2.36.0,
	you can find it in [the Maven Central Repository](https://search.maven.org/search?q=g:org.apache.beam).
	2. Run the following command, replacing `<version>` and `<port>` with valid values:
	`java -jar beam-examples-multi-language-<version>.jar <port> --javaClassLookupAllowlistFile='*'`

	#### 2) Set up a Python virtual environment for Beam

	1. See [the Python quickstart](https://beam.apache.org/get-started/quickstart-py/)
	for more information.

	#### 3) Execute the Python pipeline

	1. In a new shell, run a pipeline in the python directory using a Beam runner that supports
	multi-language pipelines.

	The Python files contain details about the actual commands to run.

	## Using Python transforms from Java

	### Sklearn Mnist Classification

	Performs image classification on handwritten digits from the [MNIST](https://en.wikipedia.org/wiki/MNIST_database)
	database.

	Please see [here](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) for
	context and information regarding the corresponding Python pipeline.

	Please note that the Java pipeline is
	[available in the Beam Java examples module](https://github.com/apache/beam/tree/master/examples/java/src/main/java/org/apache/beam/examples/multilanguage/SklearnMnistClassification.java).

	#### Setup

	* Obtain/generate a csv input file that contains labels and pixels to feed into the model and store it in
	GCS. An example input is available
	[here](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_input.csv).

	* Create a model file that contains the pickled file of a scikit-learn model
	trained on MNIST data and store it in GCS. An example model file is available
	[here](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_model).
	This model was generated by by running the program given
	[here](https://python-course.eu/machine-learning/training-and-testing-with-mnist.php)
	on the
	[example input dataset](https://storage.googleapis.com/apache-beam-samples/multi-language/mnist/example_input.csv).

	* Perform Beam runner specific setup according to instructions
	[here](https://beam.apache.org/get-started/quickstart-java/#run-a-pipeline).

	Following instructions are for running the pipeline with the Dataflow runner. For other portable runners,
	please modify the instructions according to the guidelines
	[here](https://beam.apache.org/documentation/sdks/java-multi-language-pipelines/#run-with-directrunner)

	#### Instructions for running the Java pipeline on released Beam (Beam 2.43.0 and later).

	* Checkout the Beam examples Maven archetype for the relevant Beam version.

	```
	export BEAM_VERSION=<Beam version>

	mvn archetype:generate \
	-DarchetypeGroupId=org.apache.beam \
	-DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
	-DarchetypeVersion=$BEAM_VERSION \
	-DgroupId=org.example \
	-DartifactId=multi-language-beam \
	-Dversion="0.1" \
	-Dpackage=org.apache.beam.examples \
	-DinteractiveMode=false
	```

	* Run the pipeline.

	```
	export GCP_PROJECT=<GCP project>
	export GCP_BUCKET=<GCP bucket>
	export GCP_REGION=<GCP region>

	mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.multilanguage.SklearnMnistClassification \
	-Dexec.args="--runner=DataflowRunner --project=$GCP_PROJECT \
	--region=$GCP_REGION \
	--gcpTempLocation=gs://$GCP_BUCKET/multi-language-beam/tmp \
	--output=gs://$GCP_BUCKET/multi-language-beam/output" \
	-Pdataflow-runner
	```

	* Inspect the output. Each line has data separated by a comma ",". The first item is the actual label of
	the digit. The second item is the predicted label of the digit.

	```
	gcloud storage cat gs://$GCP_BUCKET/multi-language-beam/output*
	```

	#### Instructions for running the Java pipeline at HEAD (Beam 2.41.0 and 2.42.0).

	* Activate a new virtual environment following
	[these instructions](https://beam.apache.org/get-started/quickstart-py/#create-and-activate-a-virtual-environment).

	* 2. Install Apache Beam package with gcp support and the `sklearn` package.

	```
	pip install apache-beam[gcp]
	pip install sklearn
	```

	* Startup the expansion service

	```
	python -m apache_beam.runners.portability.expansion_service_main -p <PORT> --fully_qualified_name_glob "*"
	```

	* Make sure that Docker is installed and available on your system.

	* In a different shell, build and push Python and Java Docker containers.

	```
	export DOCKER_ROOT=<Docker root>

	./gradlew :sdks:python:container:py39:docker -Pdocker-repository-root=$DOCKER_ROOT -Pdocker-tag=latest

	docker push $DOCKER_ROOT/beam_python3.9_sdk:latest

	./gradlew :sdks:java:container:java11:docker -Pdocker-repository-root=$DOCKER_ROOT -Pdocker-tag=latest -Pjava11Home=$JAVA_HOME

	docker push $DOCKER_ROOT/beam_java11_sdk:latest
	```

	* Run the pipeline using the following Gradle command (this guide assumes Dataflow runner).
	Note that we override both the Java and Python SDK harness containers here.

	```
	export GCP_PROJECT=<GCP project>
	export GCP_BUCKET=<GCP bucket>
	export GCP_REGION=<GCP region>
	export EXPANSION_SERVICE_PORT=<PORT>

	# This removes any existing output.
	gcloud storage rm gs://$GCP_BUCKET/multi-language-beam/output*

	./gradlew :examples:multi-language:sklearnMinstClassification --args=" \
	--runner=DataflowRunner \
	--project=$GCP_PROJECT \
	--gcpTempLocation=gs://$GCP_BUCKET/multi-language-beam/tmp \
	--output=gs://$GCP_BUCKET/multi-language-beam/output \
	--sdkContainerImage=$DOCKER_ROOT/beam_java11_sdk:latest \
	--sdkHarnessContainerImageOverrides=.python.,$DOCKER_ROOT/beam_python3.9_sdk:latest \
	--expansionService=localhost:$EXPANSION_SERVICE_PORT \
	--region=${GCP_REGION}"
	```

	* Inspect the output. Each line has data separated by a comma ",". The first item is the actual label
	of the digit. The second item is the predicted label of the digit.

	```
	gcloud storage cat gs://$GCP_BUCKET/multi-language-beam/output*
	```

	### Python Dataframe Wordcount

	This example is covered in the [Java multi-language pipelines quickstart](https://beam.apache.org/documentation/sdks/java-multi-language-pipelines/).
	The pipeline source code is available at
	[PythonDataframeWordCount.java](https://github.com/apache/beam/tree/master/examples/java/src/main/java/org/apache/beam/examples/multilanguage/PythonDataframeWordCount.java).