Version: 0.28

Customize Your Image on Astronomer Software

Overview

The Astronomer CLI was built to be the easiest way to develop with Apache Airflow, whether you're developing on your local machine or deploying code to Astronomer. The guidelines below will cover a few ways you can customize the Docker Image that gets pushed up to Airflow every time you rebuild your image locally via $ astro dev start or deploy to Astronomer via $ astro deploy.

More specifically, this doc includes instructions for how to:

Add Python and OS-level Packages
Add dependencies
Run commands on build
Access the Airflow CLI
Add Environment Variables Locally
Build from a Private Repository

Note: The guidelines below assume that you've initialized a project on Astronomer via $ astro dev init. If you haven't done so already, refer to our "CLI Quickstart" doc.

Add Python and OS-level Packages

To build Python and OS-level packages into your Airflow Deployment, add them to your requirements.txt and packages.txt files on Astronomer. Both files were automatically generated when you initialized an Airflow project locally via $ astro dev init. Steps below.

Add your Python or OS-Level Package

Add all Python packages to your requirements.txt and any OS-level packages you'd like to include to your packages.txt file.

To pin a version of that package, use the following syntax:

<package-name>==<version>

If you'd like to exclusively use Pymongo 3.7.2, for example, you'd add the following in your requirements.txt:

pymongo==3.7.2

If you do not pin a package to a version, the latest version of the package that's publicly available will be installed by default.

Rebuild your Image

Once you've saved those packages in your text editor or version control tool, rebuild your image by running:

astro dev stop

followed by

astro dev start

This process stops your running Docker containers and restarts them with your updated image.

Confirm your Package was Installed (Optional)

If you added pymongo to your requirements.txt file, for example, you can confirm that it was properly installed by running a $ docker exec command into your Scheduler.

Run $ docker ps to identify the 3 running docker containers on your machine
Grab the container ID of your Scheduler container
Run the following:

docker exec -it <scheduler-container-id> pip freeze | grep pymongo

pymongo==3.7.2

Note: Astronomer Certified, Astronomer's distribution of Apache Airflow, is available both as a Debian and Alpine base. We strongly recommend using Debian, as it's much easier to install dependencies and often presents less incompatibility issues than an Alpine Linux image. For details on both, refer to our Airflow Versioning Doc.

Add Other Dependencies

In the same way you can build Python and OS-level Packages into your image, you're free to build additional dependencies and files for your DAGs to use.

In the example below, we'll add a folder of helper_functions with a file (or set of files) that our Airflow DAGs can then use.

Add the folder into your project directory

.
├── airflow_settings.yaml
├── dags
│   └── example-dag.py
├── Dockerfile
├── helper_functions
│   └── helper.py
├── include
├── packages.txt
├── plugins
│   └── example-plugin.py
└── requirements.txt

Rebuild your Image

Follow the instructions in the "Rebuild your Image" section above.

Confirm your files were added (Optional)

Similar to the pymongo example above, you can confirm that helper.py was properly built into your image by running a $ docker exec command into your Scheduler.

Run $ docker ps to identify the 3 running docker containers on your machine
Grab the container ID of your Scheduler container
Run the following:

docker exec -it <scheduler-container-id> /bin/bash
bash-4.4$ ls
Dockerfile  airflow_settings.yaml  helper_functions  logs  plugins  unittests.cfg
airflow.cfg  dags  include  packages.txt  requirements.txt

Notice that helper_functions folder has been built into your image.

Configure airflow_settings.yaml

When you first initialize a new Airflow project on Astronomer, a file titled airflow_settings.yaml will be automatically generated. With this file you can configure and programmatically generate Airflow Connections, Pools, and Variables when you're developing locally.

For security reasons, the airflow_settings.yaml file is currently only for local development and should not be used for pushing up code to Astronomer via $ astro deploy. For the same reason, we'd recommend adding this file to your .gitignore.

tip

If you're interested in programmatically managing Airflow Connections, Variables or Environment Variables on Astronomer Software, we recommend integrating a Secret Backend.

Add Airflow Connections, Pools, Variables

By default, the airflow_settings.yaml file includes the following template:

airflow:
  connections: ## conn_id and conn_type are required
    - conn_id: my_new_connection
      conn_type: postgres
      conn_host: 123.0.0.4
      conn_schema: airflow
      conn_login: user
      conn_password: pw
      conn_port: 5432
      conn_extra:
  pools: ## pool_name, pool_slot, and pool_description are required
    - pool_name: my_new_pool
      pool_slot: 5
      pool_description:
  variables: ## variable_name and variable_value are required
    - variable_name: my_variable
      variable_value: my_value

Make sure to specify all required fields that correspond to the objects you create. If you don't specify them, you will see a build error on $ astro dev start.

Additional Entries

If you want to add a second Connection/Pool/Variable, copy the existing fields and make a new entry like so:

variables:
  - variable_name: my_first_variable
    variable_value: value123
  - variable_name: my_second_variable
    variable_value: value987

Run Commands on Build

If you're interested in running any extra commands when your Airflow Image builds, it can be added to your Dockerfile as a RUN command. These will run as the last step in the image build process.

For example, if you wanted to run ls when your image builds, your Dockerfile would look like this:

FROM quay.io/astronomer/ap-airflow:1.10.12-buster-onbuild
RUN ls

Docker Compose Override

The Astronomer CLI is built on top of Docker Compose, a tool for defining and running multi-container Docker applications. If you're interested in overriding any of our CLI's default configurations (found here), you're free to do so by adding a docker-compose.override.yml file to your Astronomer project directory. Any values in this file will override default settings run upon every $ astro dev start.

To add another volume mount for a directory named custom_dependencies, for example, add the following to your docker-compose.override.yml:

version: "3.1"
services:
  scheduler:
    volumes:
      - /home/astronomer_project/custom_dependencies:/usr/local/airflow/custom_dependencies:ro

Make sure to specify version: "3.1" and mimic the format of the source code file linked above.

When your image builds on $ astro dev start, any changes made within the custom_dependencies directory will be picked up automatically the same way they are with files in your dags directory:

$ docker exec -it astronomer_project239673_scheduler_1 ls -al
total 76
drwxr-xr-x    1 astro    astro         4096 Dec 30 17:21 .
drwxr-xr-x    1 root     root          4096 Dec 14  2018 ..
-rw-rw-r--    1 root     root            38 Oct  8 00:07 .dockerignore
-rw-rw-r--    1 root     root            31 Oct  8 00:07 .gitignore
-rw-rw-r--    1 root     root            50 Oct  8 00:10 Dockerfile
-rw-r--r--    1 astro    astro        20770 Dec 30 17:21 airflow.cfg
drwxrwxr-x    2 1000     1000          4096 Oct  8 00:07 dags
-rw-r--r--    1 root     root           153 Dec 30 17:21 docker-compose.override.yml
drwxrwxr-x    2 1000     1000          4096 Oct  8 00:07 include
drwxr-xr-x    4 astro    astro         4096 Oct  8 00:11 logs
drwxr-xr-x    2 1000     1000          4096 Dec 30 17:15 custom_dependencies
-rw-rw-r--    1 root     root             0 Oct  8 00:07 packages.txt
drwxrwxr-x    2 1000     1000          4096 Oct  8 00:07 plugins
-rw-rw-r--    1 root     root             0 Oct  8 00:07 requirements.txt
-rw-r--r--    1 astro    astro         2338 Dec 30 17:21 unittests.cfg

Note: The Astronomer CLI does not currently support overrides to Environment Variables. For more information on how to set, configure and customize those values, refer to our "Environment Variables" doc.

Access to the Airflow CLI

You're free to use native Airflow CLI commands on Astronomer when developing locally by wrapping them around docker commands.

To add a connection, for example, you can run:

docker exec -it SCHEDULER_CONTAINER bash -c "airflow connections -a --conn_id test_three  --conn_type ' ' --conn_login etl --conn_password pw --conn_extra {"account":"blah"}"

Refer to the native Airflow CLI for a list of all commands.

Add Environment Variables Locally

The Astronomer CLI comes with the ability to bring in Environment Variables from a specified file by running $ astro dev start with an --env flag as seen below:

astro dev start --env .env

Note: This feature is limited to local development only. Whatever .env you use locally will not be bundled up when you deploy to Astronomer.
For more detail on how to add Environment Variables both locally and on Astronomer, refer to our Environment Variables doc.

Install Python Packages from Private Sources

Python packages can be installed from public and private locations into your image. To install public packages listed on PyPI, follow the steps in Add Python and OS-level Packages. To install packages listed on private PyPI indices or a private git-based repository, you need to complete additional configuration in your project.

Depending on where your private packages are stored, use one of the following setups to install your packages to an Astro project by customizing your Runtime image.

Install Python Packages from a Private GitHub Repository

This topic provides instructions for building your Astro project with Python packages from a private GitHub repository. At a high level, this setup entails specifying your private packages in requirements.txt, creating a custom Docker image that mounts a GitHub SSH key for your private GitHub repositories, and building your project with this Docker image.

Although this setup is based on GitHub, the general steps can be completed with any hosted Git repository.

info

The following setup has been validated only with a single SSH key. Due to the nature of ssh-agent, you might need to modify this setup when using more than one SSH key per Docker image.

Prerequisites

To install Python packages from a private GitHub repository on Astronomer Software, you need:

The Astronomer CLI.
A Software project.
Custom Python packages that are installable via pip.
A private GitHub repository for each of your custom Python packages.
A GitHub SSH Private Key authorized to access your private GitHub repositories.

warning

If your organization enforces SAML single sign-on (SSO), you must first authorize your key to be used with that authentication method. For instructions, see GitHub documentation.

This setup assumes that each custom Python package is hosted within its own private GitHub repository. Installing multiple packages from a single private GitHub repository is not supported.

Step 1: Specify the Private Repository in Your Project

To add a Python package from a private repository to your Software project, specify the repository's SSH URL in your project's requirements.txt file. This URL should be formatted as git+ssh://git@github.com/<your-github-organization-name>/<your-private-repository>.git.

For example, to install the mypackage1 & mypackage2 from myorganization, as well as numpy v 1.22.1, you would add the following to requirements.txt:

git+ssh://git@github.com/myorganization/mypackage1.git
git+ssh://git@github.com/myorganization/mypackage2.git
numpy==1.22.1

This example assumes that the name of each of your Python packages is identical to the name of its corresponding GitHub repository. In other words,mypackage1 is both the name of the package and the name of the repository.

Step 2. Create Dockerfile.build

In your Astro project, create a duplicate of your Dockerfile and name it Dockerfile.build.
In Dockerfile.build, add AS stage to the FROM line which specifies your Astronomer Certified image. For example, if you use Certified 2.2.5, your FROM line would be:
```
FROM quay.io/astronomer/ap-airflow:2.2.5 AS stage1
```
caution
If you use the default distribution of Astronomer Certified, make sure to remove the -onbuild part of the image. The Astronomer Certified distribution that does not specify -onbuild is built to be customizable and does not include default build logic. For more information, see Distributions
In Dockerfile.build after the FROM line specifying your Certified image, add the following configuration:
```
LABEL maintainer="Astronomer <humans@astronomer.io>"
ARG BUILD_NUMBER=-1
LABEL io.astronomer.docker=true
LABEL io.astronomer.docker.build.number=$BUILD_NUMBER
LABEL io.astronomer.docker.airflow.onbuild=true
# Install Python and OS-Level Packages
COPY packages.txt .
RUN apt-get update && cat packages.txt | xargs apt-get install -y

FROM stage1 AS stage2
USER root
RUN apt-get -y install git python3 openssh-client \
  && mkdir -p -m 0600 ~/.ssh && ssh-keyscan github.com >> ~/.ssh/known_hosts
# Install Python Packages
COPY requirements.txt .
RUN --mount=type=ssh,id=github pip install --no-cache-dir -q -r requirements.txt

FROM stage1 AS stage3
# Copy requirements directory
COPY --from=stage2 /usr/local/lib/python3.9/site-packages /usr/local/lib/python3.9/site-packages
COPY . .
```
In order, these commands:
- Install any OS-level packages specified in packages.txt.
- Securely mount your GitHub SSH key at build time. This ensures that the key itself is not stored in the resulting Docker image filesystem or metadata.
- Install all Python-level packages in requirements.txt file, including those from a private GitHub repository.
tip
This example Dockerfile.build assumes Python 3.9, but some versions of Astronomer Certified may be based on a different version of Python. If your image is based on a version of Python that is not 3.9, replace python 3.9 in the COPY commands listed under the ## Copy requirements directory section of your Dockerfile.build with the correct Python version.
To identify the Python version in your AC image, run:
docker run quay.io/astronomer/ap-airflow:<astronomer-certified-version> python --version
Make sure to replace <astronomer-certified-version> with your own.
info
If your repository is hosted somewhere other than GitHub, replace the domain in the ssh-keyscan command with the domain where the package is hosted.

Step 3. Build a Custom Docker Image

Run the following command to create a new Docker image from your Dockerfile.build file, making sure to replace <ssh-key> with your SSH private key file name and <certified-image> with your Certified image:
```
DOCKER_BUILDKIT=1 docker build -f Dockerfile.build --progress=plain --ssh=github="$HOME/.ssh/<ssh-key>" -t custom-<certified-image> .
```
For example, if you have quay.io/astronomer/ap-airflow:2.2.5 in your Dockerfile.build, this command would be:
```
DOCKER_BUILDKIT=1 docker build -f Dockerfile.build --progress=plain --ssh=github="$HOME/.ssh/<authorized-key>" -t custom-ap-airflow:2.2.5 .
```
info
If your repository is hosted somewhere other than GitHub, replace the location of your SSH key in the --ssh flag.
Replace the contents of your Software project's Dockerfile with the following:
```
FROM custom-<certified-image>
```
For example, if your base Certified image was quay.io/astronomer/ap-airflow:2.2.5, this line would be:
```
FROM custom-ap-airflow:2.2.5
```

Your Software project can now utilize Python packages from your private GitHub repository.

Install Python Packages from a Private PyPI Index

This topic provides instructions for building your Astro project using Python packages from a private PyPI index. In some organizations, python packages are prebuilt and pushed to a hosted private pip server (such as pypiserver or Nexus Repository) or managed service (such as PackageCloud or Gitlab). At a high level, this setup requires specifying your private packages in requirements.txt, creating a custom Docker image that changes where pip looks for packages, and building your project with this Docker image.

Prerequisites

To build from a private repository, you need:

A Software project.
A private PyPI index with username and password authentication.

Step 1: Add privately hosted packages to requirements.txt

Privately hosted packages should already be built and pushed to the private repository. Depending on the repository used, it should be possible to browse and find the necessary package and version required. The package name and (optional) version can be added to requirements.txt in the same syntax as for publicly listed packages on PyPI. The requirements.txt can contain a mixture of both publicly accessible and private packages.

caution

Ensure that the name of the package on the private repository does not clash with any existing python packages. The order that pip will search indices might produce unexpected results.

Step 2: Create Dockerfile.build

In your Astro project, create a duplicate of your Dockerfile named Dockerfile.build.
In Dockerfile.build, add AS stage to the FROM line which specifies your Runtime image. For example, if you use Runtime 5.0.0, your FROM line would be:
```
quay.io/astronomer/astro-runtime:5.0.0-base AS stage1
```
caution
If you use the default distribution of Astronomer Certified, make sure to remove the -onbuild part of the image. The Astronomer Certified distribution that does not specify -onbuild is built to be customizable and does not include default build logic. For more information, see Distributions

In Dockerfile.build after the FROM line specifying your Runtime image, add the following configuration. Make sure to replace <url-to-packages> with the URL leading to the directory with your Python packages:

LABEL maintainer="Astronomer <humans@astronomer.io>"
ARG BUILD_NUMBER=-1
LABEL io.astronomer.docker=true
LABEL io.astronomer.docker.build.number=$BUILD_NUMBER
LABEL io.astronomer.docker.airflow.onbuild=true
# Install Python and OS-Level Packages
COPY packages.txt .
RUN apt-get update && cat packages.txt | xargs apt-get install -y

FROM stage1 AS stage2
# Install Python Packages
ARG PIP_EXTRA_INDEX_URL
ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
COPY requirements.txt .
RUN pip install --no-cache-dir -q -r requirements.txt

FROM stage1 AS stage3
# Copy requirements directory
COPY --from=stage2 /usr/local/lib/python3.9/site-packages /usr/local/lib/python3.9/site-packages
COPY . .

In order, these commands:

Complete the standard installation of OS-level packages in packages.txt.
Add the environment variable PIP_EXTRA_INDEX_URL to instruct pip on where to look for non-public packages.
Install public and private Python-level packages from your requirements.txt file.

Step 3: Build a Custom Docker Image

Run the following command to create a new Docker image from your Dockerfile.build file, making sure to substitute in the pip repository and associated credentials:

DOCKER_BUILDKIT=1 docker build -f Dockerfile.build --progress=plain --build-arg PIP_EXTRA_INDEX_URL=https://${<repo-username>}:${<repo-password>}@<private-pypi-repo-domain-name> -t custom-<airflow-image> .

For example, if you have quay.io/astronomer/ap-airflow:2.2.5 in your Dockerfile.build, this command would be:

DOCKER_BUILDKIT=1 docker build -f Dockerfile.build --progress=plain --build-arg PIP_EXTRA_INDEX_URL=https://${<repo-username>}:${<repo-password>}@<private-pypi-repo-domain-name> -t custom-ap-airflow:2.2.5 .

Replace the contents of your Software project's Dockerfile with the following:
```
FROM custom-<certified-image>
```
For example, if your base Certified image was quay.io/astronomer/ap-airflow:2.2.5, this line would be:
```
FROM custom-ap-airflow:2.2.5
```
Your Software project can now utilize Python packages from your private PyPi index.

Build with a Different Python Version

While the Astronomer Certified (AC) Python Wheel supports Python versions 3.6, 3.7, and 3.8, AC Docker images have been tested and built only for Python 3.7.

To run Astronomer Certified on Docker with Python versions 3.6 or 3.8, you need to create a custom version of the image, specify the PYTHON_MAJOR_MINOR_VERSION build argument, and push the custom image to an existing Docker registry. To do so:

Using docker build, build a custom Astronomer Certified Docker image and specify PYTHON_MAJOR_MINOR_VERSION for the version of Python you'd like to support. For example, the command for building a custom Astronomer Certified image for Airflow 2.0.0 with Python 3.8 would look something like this:
```
docker build --build-arg PYTHON_MAJOR_MINOR_VERSION=3.8 -t <your-registry>/ap-airflow:<image-tag> https://github.com/astronomer/ap-airflow.git#master:2.0.0/buster
```
We recommend using an image tag that indicates the image is using a different Python version, such as 2.0.0-buster-python3.8.
Note: To use a different version of Airflow, update the URL to point towards the desired Airflow version. For instance, if you're running Airflow 1.10.14, the GitHub URL here would be: https://github.com/astronomer/ap-airflow.git#master:1.10.14/buster
Push the custom image to your Docker registry. Based on the example in the previous step, the command to do so would look something like this:
```
docker push <your-registry>/ap-airflow:<image-tag>
```
Update the FROM line of your Dockerfile to reference the custom image. Based on the previous example, the line would read:
```
FROM <your-registry>/ap-airflow:<image-tag>
```

Note: Astronomer Certified Docker images for Apache Airflow 1.10.14+ are Debian-based only. To run Docker images based on Alpine-Linux for Airflow versions 1.10.7, 1.10.10, or 1.10.12, specify alpine3.10 instead of buster in the GitHub URL.

Overview​

Add Python and OS-level Packages​

Add your Python or OS-Level Package​

Rebuild your Image​

Confirm your Package was Installed (Optional)​

Add Other Dependencies​

Add the folder into your project directory​

Rebuild your Image​

Confirm your files were added (Optional)​

Configure airflow_settings.yaml​

tip

Add Airflow Connections, Pools, Variables​

Additional Entries​

Run Commands on Build​

Docker Compose Override​

Access to the Airflow CLI​

Add Environment Variables Locally​

Install Python Packages from Private Sources​

Install Python Packages from a Private GitHub Repository​

info

Prerequisites​

warning

Step 1: Specify the Private Repository in Your Project​

Step 2. Create Dockerfile.build​

caution

tip

info

Step 3. Build a Custom Docker Image​

info

Install Python Packages from a Private PyPI Index​

Prerequisites​

Step 1: Add privately hosted packages to requirements.txt​

caution

Step 2: Create Dockerfile.build​

caution

Step 3: Build a Custom Docker Image​

Build with a Different Python Version​

Overview

Add Python and OS-level Packages

Add your Python or OS-Level Package

Rebuild your Image

Confirm your Package was Installed (Optional)

Add Other Dependencies

Add the folder into your project directory

Rebuild your Image

Confirm your files were added (Optional)

Configure airflow_settings.yaml

Add Airflow Connections, Pools, Variables

Additional Entries

Run Commands on Build

Docker Compose Override

Access to the Airflow CLI

Add Environment Variables Locally

Install Python Packages from Private Sources

Install Python Packages from a Private GitHub Repository

Prerequisites

Step 1: Specify the Private Repository in Your Project

Step 2. Create Dockerfile.build

Step 3. Build a Custom Docker Image

Install Python Packages from a Private PyPI Index

Prerequisites

Step 1: Add privately hosted packages to requirements.txt

Step 2: Create Dockerfile.build

Step 3: Build a Custom Docker Image

Build with a Different Python Version