# beam-starter-python

**Repository Path**: mirrors_apache/beam-starter-python

## Basic Information

- **Project Name**: beam-starter-python
- **Description**: Apache Beam starter repo for Python
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-09
- **Last Updated**: 2025-10-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Apache Beam starter for Python
The intent of this repo is to provide a very simple project structure so user can have a jump start. If you want to clone this repository to start your own project,
you can choose the license you prefer and feel free to delete anything related to the license you are dropping.
[Check here](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples) in case you are looking for example code and check [DataFlow cookbook](https://github.com/GoogleCloudPlatform/dataflow-cookbook) for more examples.

## Before you begin

Make sure you have a [Python 3](https://www.python.org/) development environment ready.
If you don't, you can download and install it from the
[Python downloads page](https://www.python.org/downloads/).

We recommend using a virtual environment to isolate your project's dependencies.

```sh
# Create a new Python virtual environment.
python -m venv env

# Activate the virtual environment.
source env/bin/activate
```

While activated, your `python` and `pip` commands will point to the virtual environment,
so any changes or install dependencies are self-contained.

As a one time setup, let's install the project's dependencies from the [`requirements.txt`](requirements.txt) file.

```py
# It's always a good idea to update pip before installing dependencies.
pip install -U pip

# Install the project as a local package, this installs all the dependencies as well.
pip install -e .
```

> ℹ️ Once you are done, you can run the `deactivate` command to go back to your global Python installation.

### Running the pipeline

Running your pipeline in Python is as easy as running the script file directly.

```sh
# You can run the script file directly.
python main.py

# To run passing command line arguments.
python main.py --input-text="🎉"

# To run the tests.
python -m unittest -v
```

## GitHub Actions automated testing

This project already comes with automated testing via [GitHub Actions](https://github.com/features/actions).

To configure it, look at the [`.github/workflows/test.yaml`](.github/workflows/test.yaml) file.

## Using other runners

To keep this template small, it only includes the [Direct Runner](https://beam.apache.org/documentation/runners/direct/).

For a comparison of what each runner currently supports, look at the [Beam Capability Matrix](https://beam.apache.org/documentation/runners/capability-matrix/).

To add a new runner, visit the runner's page for instructions on how to include it.

## Contributing

Thank you for your interest in contributing!
All contributions are welcome! 🎉🎊

Please refer to the [`CONTRIBUTING.md`](CONTRIBUTING.md) file for more information.

# License

This software is distributed under the terms of both the MIT license and the
Apache License (Version 2.0).

See [LICENSE](LICENSE) for details.