Fast iterative local development and testing of Apache Airflow workflows.
The idea of whirl is pretty simple: use Docker containers to start up Apache Airflow and the other components used in your workflow. This gives you a copy of your production environment that runs on your local machine. You can run your DAG locally from start to finish - with the same code as in production. Seeing your pipeline succeed gives you more confidence about the logic you are creating/refactoring and how it integrates with other components. It also gives new developers an isolated environment for experimenting with your workflows.
whirl connects the code of your DAG and your (mock) data to the Apache Airflow container that it spins up. Using volume mounts you are able to make changes to your code in your favorite IDE and immediately see the effect in the running Apache Airflow UI on your machine. This also works with custom Python modules that you are developing and using in your DAGs.
NOTE: whirl is not intended to replace proper (unit) testing of the logic you are orchestrating with Apache Airflow.
whirl relies on Docker and Docker Compose. Make sure you have it installed. If using Docker for Mac or Windows ensure that you have configured it with sufficient RAM (8GB or more recommended) for running all your containers.
When you want to use whirl in your CI pipeline, you need to have jq installed. For example, with Homebrew:
brew install jqThe current implementation was developed on macOS but is intended to work with any platform supported by Docker. In our experience, Linux and macOS are fine. You can run it on native Windows 10 using WSL. Unfortunately, Docker on Windows 10 (version 1809) is hamstrung because it relies on Windows File Sharing (CIFS) to establish the volume mounts. Airflow hammers the volume a little harder than CIFS can handle, and you'll see intermittent FileNotFound errors in the volume mount. This may improve in the future. For now, running whirl inside a Linux VM in Hyper-V gives more reliable results.
As of January 2021, Whirl uses Airflow 2.x.x as the default version. A specific tag was made for Airflow 1.10.x, which can be found here
Clone this repository:
git clone https://github.com/godatadriven/whirl.git <target directory of whirl>
For ease of use you can add the base directory to your PATH environment variable, so the command whirl is available.
export PATH=<target directory of whirl>:${PATH}
Download the latest Whirl release artifact
Extract the file (for example into /usr/local/opt)
tar -xvzf whirl-release.tar.gz -C /usr/local/optMake sure the whirl script is available on your path
export PATH=/usr/local/opt/whirl:$PATHThe whirl script is used to perform all actions.
$ whirl -h
$ whirl --helpThe default action is to start the DAG in your current directory.
With the [-x example] commandline argument you can run whirl from anywhere and tell whirl which example dag to run. The example refers to a directory with the same name in the examples directory located near the whirl script.
Whirl expects an environment to be configured. You can pass this as a command line argument [-e environment] or you can configure it as environment variable WHIRL_ENVIRONMENT in a .whirl.env file. (See Configuring environment variables.) The environment refers to a directory with the same name in the envs directory located near the whirl script.
$ whirl [-x example] [-e <environment>] [start]Specifying the start command line argument is a more explicit way to start whirl.
$ whirl [-x example] [-e <environment>] stopStops the configured environment.
If you want to stop all containers from a specific environment you can add the -e or --environment commandline argument with the name of the environment. This name corresponds with a directory in the envs directory.
We run most of the examples from within our own CI (github actions, see for implementation details our github workflow.
You are able to run an example in ci mode on your local system by useing the whirl ci command. This will:
- run the Docker containers daemonized in the background;
- ensure the DAG(s) are unpaused; and
- wait for the pipeline to either succeed or fail.
Upon success the containers will be stopped and exit successfully.
In case of failure (or success if failure is expected)we print out the logs of the failed task and cleanup before indicating the pipeline has failed.
Instead of using the environment option each time you run whirl, you can also configure your environment in a .whirl.env file. This can be in three places. They are applied in order:
- A
.whirl.envfile in the root of this repository. This can also specify a default environment to be used when starting whirl. You do this by setting theWHIRL_ENVIRONMENTwhich references a directory in theenvsfolder. This repository contains an example you can modify. It specifies the defaultPYTHON_VERSIONto be used in any environment. - A
.whirl.envfile in yourenvs/{your-env}subdirectory. The environment directory to use can be set by any of the other.whirl.envfiles or specified on the commandline. This is helpful to set environment specific variables. Of course it doesn't make much sense to set theWHIRL_ENVIRONMENThere. - A
.whirl.envin your DAG directory to override any environment variables. This can be useful for example to overwrite the (default)WHIRL_ENVIRONMENT.
Inside the whirl script the following environment variables are set:
| Environment Variable | Value | Description |
|---|---|---|
DOCKER_CONTEXT_FOLDER |
${SCRIPT_DIR}/docker |
Base build context folder for Docker builds referenced in Docker Compose |
ENVIRONMENT_FOLDER |
${SCRIPT_DIR}/envs/<environment> |
Base folder for environment to start. Contains docker-compose.yml and environment specific preparation scripts. |
DAG_FOLDER |
$(pwd) |
Current working directory. Used as Airflow DAG folder. Can contain preparation scripts to prepare for this specific DAG. |
PROJECTNAME |
$(basename ${DAG_FOLDER}) |
This project is based on docker-compose and the notion of different environments where Airflow is a central part. The rest of the environment depends on the tools/setup of the production environment used in your situation.
The whirl script combines the DAG and the environment to make a fully functional setup.
To accommodate different examples:
- The environments are split up into separate environment-specific directories inside the
envs/directory. - The DAGS are split into sub-directories in the
examples/directory.
Environments use Docker Compose to start containers which together mimic your production environment. The basis of the environment is the docker-compose.yml file which as a minimum declares the Airflow container to run. Extra tools (e.g. s3, sftp) can be linked together in the docker-compose file to form your specific environment.
Each environment also contains some setup code needed for Airflow to understand the environment, for example Connections and Variables. Each environment has a whirl.setup.d/ directory which is mounted in the Airflow container. On startup all scripts in this directory are executed. This is a location for installing and configuring extra client libraries that are needed to make the environment function correctly; for example awscli if S3 access is required.
The DAGs in this project are inside the examples/ directory. In your own project you can have your code in its own location outside this repository.
Each example directory consists of at least one example DAG. Also project- specific code can be placed there. As with the environment the DAG directory can contain a whirl.setup.d/ directory which is also mounted inside the Airflow container. Upon startup all scripts in this directory are executed. The environment-specific whirl.setup.d/ is executed first, followed by the DAG one.
This is also a location for installing and configuring extra client libraries that are needed to make the DAG function correctly; for example a mock API endpoint.
This repository contains some example environments and workflows. The components used might serve as a starting point for your own environment. If you have a good example you'd like to add, please submit a merge request!
Each example contains it's own README file to explain the specifics of that example.
To run a example:
$ cd ./examples/<example-dag-directory>
# Note: here we pass the whirl environment as a command-line argument. It can also be configured with the WHIRL_ENVIRONMENT variable
$ whirl -e <environment to use>or
$
# Note: here we pass the whirl environment as a command-line argument. It can also be configured with the WHIRL_ENVIRONMENT variable
$ whirl -x <example to run> -e <environment to use>Open your browser to http://localhost:5000 to access the Airflow UI. Manually enable the DAG and watch the pipeline run to successful completion.
An early version of whirl was brought to life at ING. Bas Beelen gave a presentation describing how whirl was helpful in their infrastructure during the 2nd Apache Airflow Meetup, January 23 2019, hosted at Google London HQ.
