Apache Airflow¶

Apache Airflow is a popular open-source workflow management platform. It is a suitable engine to orchestrate and execute a pipeline authored with Kedro because workflows in Airflow are modelled and organised as DAGs.

How to run a Kedro pipeline on Apache Airflow using a Kubernetes cluster¶

The kedro-airflow-k8s plugin from GetInData | Part of Xebia enables you to run a Kedro pipeline on Airflow with a Kubernetes cluster. The plugin can be used together with kedro-docker to prepare a docker image for pipeline execution. At present, the plugin is available for versions of Kedro < 0.18 only.

Consult the GitHub repository for kedro-airflow-k8s for further details, or take a look at the documentation.

How to run a Kedro pipeline on Apache Airflow with Astronomer¶

The following tutorial uses a different approach and shows how to deploy a Kedro project on Apache Airflow with Astronomer.

Astronomer is a managed Airflow platform which allows users to spin up and run an Airflow cluster easily in production. Additionally, it also provides a set of tools to help users get started with Airflow locally in the easiest way possible.

The tutorial discusses how to run the example Iris classification pipeline on a local Airflow cluster with Astronomer. You may also consider using our astro-airflow-iris starter which provides a template containing the boilerplate code that the tutorial describes:

kedro new --starter=astro-airflow-iris

Strategy¶

The general strategy to deploy a Kedro pipeline on Apache Airflow is to run every Kedro node as an Airflow task while the whole pipeline is converted into a DAG for orchestration purpose. This approach mirrors the principles of running Kedro in a distributed environment.

Prerequisites¶

To follow this tutorial, ensure you have the following:

An Airflow cluster: you can follow Astronomer’s quickstart guide to set one up.
The Astro CLI installed
kedro>=0.17 installed

Tutorial project setup¶

Initialise an Airflow project with Astro. Let’s call it kedro-airflow-iris
```
mkdir kedro-airflow-iris
cd kedro-airflow-iris
astro dev init
```
Create a new Kedro project using the pandas-iris starter. You can use the default value in the project creation process:
```
kedro new --starter=pandas-iris
```

Copy all files and directories under new-kedro-project, which was the default project name created in step 2, to the root directory so Kedro and Astro CLI share the same project root:

cp new-kedro-project/* .
rm -r new-kedro-project

After this step, your project should have the following structure:

.
├── Dockerfile
├── README.md
├── airflow_settings.yaml
├── conf
├── dags
├── data
├── docs
├── include
├── notebooks
├── packages.txt
├── plugins
├── pyproject.toml
├── requirements.txt
├── setup.cfg
└── src

Install kedro-airflow~=0.4. We will use this plugin to convert the Kedro pipeline into an Airflow DAG.
```
pip install kedro-airflow~=0.4
```
Run pip install -r src/requirements.txt to install all dependencies.

Deployment process¶

Step 1. Create new configuration environment to prepare a compatible `DataCatalog`¶

Create a conf/airflow directory in your Kedro project
Create a catalog.yml file in this directory with the following content

example_iris_data:
  type: pandas.CSVDataSet
  filepath: data/01_raw/iris.csv
example_train_x:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_train_x.pkl
example_train_y:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_train_y.pkl
example_test_x:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_test_x.pkl
example_test_y:
  type: pickle.PickleDataSet
  filepath: data/05_model_input/example_test_y.pkl
example_model:
  type: pickle.PickleDataSet
  filepath: data/06_models/example_model.pkl
example_predictions:
  type: pickle.PickleDataSet
  filepath: data/07_model_output/example_predictions.pkl

This ensures that all datasets are persisted so all Airflow tasks can read them without the need to share memory. In the example here we assume that all Airflow tasks share one disk, but for distributed environment you would need to use non-local filepaths.

Step 2. Package the Kedro pipeline as an Astronomer-compliant Docker image¶

Step 2.1: Package the Kedro pipeline as a Python package so you can install it into the container later on:

kedro package

This step should produce a wheel file called new_kedro_project-0.1-py3-none-any.whl located at dist/.

Step 2.2: Add the src/ directory to .dockerignore, as it’s not necessary to bundle the entire code base with the container once we have the packaged wheel file.

echo "src/" >> .dockerignore

Step 2.3: Modify the Dockerfile to have the following content:

FROM quay.io/astronomer/ap-airflow:2.0.0-buster-onbuild

RUN pip install --user dist/new_kedro_project-0.1-py3-none-any.whl

Step 3. Convert the Kedro pipeline into an Airflow DAG with `kedro airflow`¶

kedro airflow create --target-dir=dags/ --env=airflow

Step 4. Launch the local Airflow cluster with Astronomer¶

astro dev start

If you visit the Airflow UI, you should now see the Kedro pipeline as an Airflow DAG: