Apache Beam Python SDK Quickstart

This guide shows you how to set up your Python development environment, get the Apache Beam SDK for Python, and run an example pipeline.

Set up your environment

Install pip

Install pip, Python’s package manager. Check that you have version 7.0.0 or newer, by running:

pip --version

Install Python virtual environment

It is recommended that you install a Python virtual environment for initial experiments. If you do not have virtualenv version 13.1.0 or newer, install it by running:

pip install --upgrade virtualenv

If you do not want to use a Python virtual environment (not recommended), ensure setuptools is installed on your machine. If you do not have setuptools version 17.1 or newer, install it by running:

pip install --upgrade setuptools

Get Apache Beam

Create and activate a virtual environment

A virtual environment is a directory tree containing its own Python distribution. To create a virtual environment, create a directory and run:

virtualenv /path/to/directory

A virtual environment needs to be activated for each shell that is to use it. Activating it sets some environment variables that point to the virtual environment’s directories.

To activate a virtual environment in Bash, run:

. /path/to/directory/bin/activate

That is, source the script bin/activate under the virtual environment directory you created.

For instructions using other shells, see the virtualenv documentation.

Download and install

Install the latest Python SDK from PyPI:

pip install apache-beam

Execute a pipeline locally

The Apache Beam examples directory has many examples. All examples can be run locally by passing the required arguments described in the example script.

For example, to run wordcount.py, run:

python -m apache_beam.examples.wordcount --input README.md --output counts
# As part of the initial setup, install Google Cloud Platform specific extra components.
pip install apache-beam[gcp]
python -m apache_beam.examples.wordcount --input gs://dataflow-samples/shakespeare/kinglear.txt \
                                         --output gs://<your-gcs-bucket>/counts \
                                         --runner DataflowRunner \
                                         --project your-gcp-project \
                                         --temp_location gs://<your-gcs-bucket>/tmp/

Next Steps

Please don’t hesitate to reach out if you encounter any issues!