This guide shows you how to set up your Python development environment, get the Apache Beam SDK for Python, and run an example pipeline.
If you're interested in contributing to the Apache Beam Python codebase, see the Contribution Guide.
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The Python SDK supports Python 2.7, 3.5, 3.6, and 3.7. New Python SDK releases will stop supporting Python 2.7 in 2020 (BEAM-8371). For best results, use Beam with Python 3.
The Beam SDK requires Python 2 users to use Python 2.7 and Python 3 users to use Python 3.5 or higher. Check your version by running:
{{< highlight >}} python --version {{< /highlight >}}
Install pip, Python's package manager. Check that you have version 7.0.0 or newer by running:
{{< highlight >}} pip --version {{< /highlight >}}
If you do not have pip version 7.0.0 or newer, run the following command to install it. This command might require administrative privileges.
{{< highlight class=“shell-unix” >}} pip install --upgrade pip {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> python -m pip install --upgrade pip {{< /highlight >}}
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, run the following command to install it. This command might require administrative privileges.
{{< highlight class=“shell-unix” >}} pip install --upgrade virtualenv {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> python -m pip install --upgrade virtualenv {{< /highlight >}}
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, run the following command to install it.
{{< highlight class=“shell-unix” >}} pip install --upgrade setuptools {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> python -m pip install --upgrade setuptools {{< /highlight >}}
A virtual environment is a directory tree containing its own Python distribution. To create a virtual environment, create a directory and run:
{{< highlight class=“shell-unix” >}} virtualenv /path/to/directory {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> virtualenv C:\path\to\directory {{< /highlight >}}
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:
{{< highlight class=“shell-unix” >}} . /path/to/directory/bin/activate {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> C:\path\to\directory\Scripts\activate.ps1 {{< /highlight >}}
That is, execute the activate script under the virtual environment directory you created.
For instructions using other shells, see the virtualenv documentation.
Install the latest Python SDK from PyPI:
{{< highlight class=“shell-unix” >}} pip install apache-beam {{< /highlight >}}
{{< highlight class=“shell-PowerShell” >}} PS> python -m pip install apache-beam {{< /highlight >}}
The above installation will not install all the extra dependencies for using features like the Google Cloud Dataflow runner. Information on what extra packages are required for different features are highlighted below. It is possible to install multiple extra requirements using something like pip install apache-beam[feature1,feature2].
pip install apache-beam[gcp]pip install apache-beam[aws]pip install apache-beam[test]pip install apache-beam[docs]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, run wordcount.py with the following command:
{{< highlight class=“runner-direct” >}} python -m apache_beam.examples.wordcount --input /path/to/inputfile --output /path/to/write/counts {{< /highlight >}}
{{< highlight class=“runner-flink-local” >}} Currently, running wordcount.py on Flink requires a full download of the Beam source code. See https://beam.apache.org/roadmap/portability/#python-on-flink for more information. {{< /highlight >}}
{{< highlight class=“runner-flink-cluster” >}} Currently, running wordcount.py on Flink requires a full download of the Beam source code. See https://beam.apache.org/documentation/runners/flink/ for more information. {{< /highlight >}}
{{< highlight class=“runner-spark” >}} Currently, running wordcount.py on Spark requires a full download of the Beam source code. See https://beam.apache.org/roadmap/portability/#python-on-spark for more information. {{< /highlight >}}
{{< highlight class=“runner-dataflow” >}}
pip install apache-beam[gcp] python -m apache_beam.examples.wordcount --input gs://dataflow-samples/shakespeare/kinglear.txt
--output gs:///counts
--runner DataflowRunner
--project your-gcp-project
--region your-gcp-region
--temp_location gs:///tmp/ {{< /highlight >}}
{{< highlight class=“runner-nemo” >}} This runner is not yet available for the Python SDK. {{< /highlight >}}
After the pipeline completes, you can view the output files at your specified output path. For example, if you specify /dir1/counts for the --output parameter, the pipeline writes the files to /dir1/ and names the files sequentially in the format counts-0000-of-0001.
Please don't hesitate to reach out if you encounter any issues!