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Apache Beam PortableRunner using Python SDK on Apache Spark

Particularly when running on Mac or Windows, it can be challenging to get Beam's PortableRunner to work. Communication with the SDK harness is expected to be done via localhost, requiring support for Docker's host network. Unfortunately, the host network isn't available on Docker for Mac or Windows.

This repo contains a docker-compose file to showcase how to use the Beam's portability framework with Apache Spark and a Python worker pool on Docker (on a Mac / Windows) for testing purposes.

Prerequisites

Until the Beam issue #21092 is released, you have to build the image for the Spark job-server yourself.

git clone git@github.com:apache/beam.git
cd beam
./gradlew :runners:spark:3:job-server:container:docker

Run job in docker

To start the entire stack including the sample wordcount job, run:

docker-compose up

Run job from CLI

Alternatively you can also start the Spark job-server including necessary dependencies:

docker-compose up -d beam-job-server

And then submit a job from your local shell:

python wordcount.py --runner=PortableRunner --job_endpoint=localhost:8099 --artifact_endpoint=localhost:8098 --environment_type=EXTERNAL --environment_config=pysdk:50000 --output=/tmp/result

In case you have to setup a Python environment for Beam first, you can use conda to do so:

# conda config --add channels conda-forge
conda create -n beamjob-py38 python=3.8 "apache-beam[gcp]=2.41.0"
conda activate beamjob-py38