Apache Beam Python SDK quickstart
This quickstart shows you how to run an example pipeline written with the Apache Beam Python SDK, using the Direct Runner. The Direct Runner executes pipelines locally on your machine.
If you’re interested in contributing to the Apache Beam Python codebase, see the Contribution Guide.
On this page:
Set up your development environment
Apache Beam aims to work on released Python versions that have not yet reached end of life, but it may take a few releases until Apache Beam fully supports the most recently released Python minor version.
The minimum required Python version is listed in the Meta section of the apache-beam project page under Requires. The list of all supported Python versions is listed in the Classifiers section at the bottom of the page, under Programming Language.
Check your Python version by running:
If you don’t have a Python interpreter, you can download and install it from the Python downloads page.
If you need to install a different version of Python in addition to the version that you already have, you can find some recommendations in our Developer Wiki.
Clone the GitHub repository
Clone or download the
GitHub repository and change into the
Create and activate a virtual environment
A virtual environment is a directory tree containing its own Python distribution. We recommend using a virtual environment so that all dependencies of your project are installed in an isolated and self-contained environment. To set up a virtual environment, run the following commands:
If these commands do not work on your platform, see the
Install the project dependences
Run the following command to install the project’s dependencies from the
Run the quickstart
Run the following command:
The output is similar to the following:
The lines might appear in a different order.
Run the following command to deactivate the virtual environment:
Explore the code
The main code file for this quickstart is app.py (GitHub). The code performs the following steps:
- Create a Beam pipeline.
- Create an initial
- Apply a transform to the
- Run the pipeline, using the Direct Runner.
Create a pipeline
The code first creates a
Pipeline object. The
Pipeline object builds up the
graph of transformations to be executed.
with beam.Pipeline(options=beam_options) as pipeline:
beam_option variable shown here is a
PipelineOptions object, which
is used to set options for the pipeline. For more information, see
Configuring pipeline options.
Create an initial PCollection
PCollection abstraction represents a potentially distributed,
multi-element data set. A Beam pipeline needs a source of data to populate an
PCollection. The source can be bounded (with a known, fixed size) or
unbounded (with unlimited size).
This example uses the
method to create a
PCollection from an in-memory array of strings. The
PCollection contains the strings “Hello”, “World!", and a
user-provided input string.
pipeline | "Create elements" >> beam.Create(["Hello", "World!", input_text])
Note: The pipe operator
| is used to
Apply a transform to the PCollection
Transforms can change, filter, group, analyze, or otherwise process the
elements in a
PCollection. This example uses the
transform, which maps the elements of a collection into a new collection:
| "Print elements" >> beam.Map(print)
Run the pipeline
To run the pipeline, you can call the
However, by enclosing the
Pipeline object inside a
with statement, the
run method is automatically invoked.
with beam.Pipeline(options=beam_options) as pipeline: # ... # run() is called automatically
A Beam runner runs a Beam pipeline on a specific platform. If you don’t specify a runner, the Direct Runner is the default. The Direct Runner runs the pipeline locally on your machine. It is meant for testing and development, rather than being optimized for efficiency. For more information, see Using the Direct Runner.
For production workloads, you typically use a distributed runner that runs the pipeline on a big data processing system such as Apache Flink, Apache Spark, or Google Cloud Dataflow. These systems support massively parallel processing.
- Learn more about the Beam SDK for Python and look through the Python SDK API reference.
- Take a self-paced tour through our Learning Resources.
- Dive in to some of our favorite Videos and Podcasts.
- Join the Beam users@ mailing list.
Please don’t hesitate to reach out if you encounter any issues!
Last updated on 2023/03/30
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