paulosdchaves / airflow-pipeline

An Airflow docker image preconfigured to work well with Spark and Hadoop/EMR

Airflow Pipeline Docker Image Set-up

This repo is a GitHub Actions build matrix set-up to generate Docker images of Airflow, and other major applications as below:

• Airflow
• Spark
• Hadoop integration with Spark
• Python
• SQL Alchemy

Additionally, poetry is used to perform all Python related installations at a predefined global project directory, so that it is easy to add on new packages without conflicting dependency package versions, which raw pip cannot achieve. See https://github.com/dsaidgovsg/spark-k8s-addons#how-to-properly-manage-pip-packages for more information.

For builds involving Airflow v2 onwards, note that poetry is not officially supported as an installation tool, but it is used anyway to make sure dependencies are compatible and tested to work across multiple builds with different versions.

See apache/airflow#13149 for a related discussion and how to resolve possible conflicts when installing packages on top of this base image.

Entrypoint

Also, for convenience, the current version runs both the webserver and scheduler together in the same instance by the default entrypoint, with the webserver being at the background and scheduler at the foreground. All the convenient environment variables only works on the basis that the entrypoint is used without any extra command.

If there is a preference to run the various Airflow CLI services separately, you can simply pass the full command into the Docker command, but it will no longer trigger any of the convenient environment variables / functionalities.

The above convenience functionalities include:

1. Discovering if database (sqlite and postgres) is ready
2. Automatically running airflow db init and airflow db upgrade
3. Easy creation of Airflow Web UI admin user by simple env vars.

See entrypoint.sh for more details and the list of convenient environment variables.

Also note that the command that will be run will also be run as airflow user/group, unless the host overrides the user/group to run the Docker container.

Running locally

You will need docker-compose and docker command installed.

Default Combined Airflow Webserver and Scheduler

docker-compose up --build

Navigate to http://localhost:8080/, and log in using the following RBAC credentials to try out the DAGs:

• Username: admin
• Password: Password123

Note that the webserver logs are suppressed by default.

CTRL-C to gracefully terminate the services.

Separate Airflow Webserver and Scheduler

docker-compose -f docker-compose.split.yml up --build

Navigate to http://localhost:8080/ to try out the DAGs.

Both webserver and scheduler logs are shown separately.

CTRL-C to gracefully terminate the services.

Versioning

Starting from Docker tags that give self-version v1, any Docker image usage related breaking change will generate a new self-version so that this will minimize any impact on the user-facing side trying to use the most updated image.

These are considered breaking changes:

• Change of Linux distro, e.g. Alpine <-> Debian. This will automatically lead to a difference in the package management tool used such as apk vs apt. Note that however this does not include upgrading of Linux distro that may affect the package management, e.g. alpine:3.9 vs alpine:3.10.
• Removal of advertized installed CLI tools that is not listed within the Docker tag. E.g. Spark and Hadoop are part of the Docker tag, so they are not part of the advertized CLI tools.
• Removal of advertized environment variables
• Change of any environment variable value

In the case where a CLI tool is known to perform a major version upgrade, this set-up will try to also release a new self-version number. But note that this is at a best effort scale only because most of the tools are inherited upstream, or simply unable / undesirable to specify the version to install.

Airflow provider packages

Airflow provider packages have been removed from the image from version v8 onwards and users will have to manually install them instead. Note that provider packages follow their own versioning independent of Airflow's.

See https://airflow.apache.org/docs/apache-airflow/2.1.0/backport-providers.html#backport-providers for more details.

# Airflow V2
poetry add apache-airflow-provider-apache-spark==1.0.3

# Airflow V1
poetry add apache-airflow[spark]==1.10.z

Changelogs

All self-versioned change logs are listed in CHANGELOG.md.

The advertized CLI tools and env vars are also listed in the detailed change logs.

How to Manually Build Docker Image

Example build command:

AIRFLOW_VERSION=2.3
SPARK_VERSION=3.3.0
HADOOP_VERSION=3.3.2
SCALA_VERSION=2.12
JAVA_VERSION=11
PYTHON_VERSION=3.9
SQLALCHEMY_VERSION=1.4
docker build -t airflow-pipeline \
  --build-arg "AIRFLOW_VERSION=${AIRFLOW_VERSION}" \
  --build-arg "SPARK_VERSION=${SPARK_VERSION}" \
  --build-arg "HADOOP_VERSION=${HADOOP_VERSION}" \
  --build-arg "SCALA_VERSION=${SCALA_VERSION}" \
  --build-arg "PYTHON_VERSION=${PYTHON_VERSION}" \
  --build-arg "JAVA_VERSION=${JAVA_VERSION}" \
  --build-arg "SQLALCHEMY_VERSION=${SQLALCHEMY_VERSION}" \
  .

You may refer to the vars.yml to have a sensing of all the possible build arguments to combine.

Caveat

Because this image is based on Spark with Kubernetes compatible image, which always generates Debian based Docker images, the images generated from this repository are likely to stay Debian based as well. But note that there is no guarantee that this is always true, but such changes are always marked with Docker image release tag.

Also, currently the default entrypoint without command logic assumes that a Postgres server will always be used (the default sqlite can work as an alternative). As such, when using in this mode, an external Postgres server has to be made available for Airflow services to access.