How to Set up Airflow on Kubernetes?

The blog walks you through the steps on how to deploy Airflow on Kubernetes.

This blog walks you through the steps on how to deploy Airflow on Kubernetes. If you to jump on the code directly here's the GitHub repo.

What is Airflow

Airflow is a platform created by the community to programmatically author, schedule, and monitor workflows.

Airflow lets you define workflow as a directed acyclic graph(DAG) defined in a Python file. The most famous usecase of airflow is data/machine learning engineers constructing data pipelines that performs transformations.

pageAn Introduction to Apache AirflowpageAirflow Celery vs Kubernetes Executor

Airflow with Kubernetes

There are a bunch of advantages of running Airflow over Kubernetes.


Airflow runs one worker pod per airflow task, enabling Kubernetes to spin up and destroy pods depending on the load.

Resource Optimization

Kubernetes spins up worker pods only when there is a new job. Whereas the alternatives such as celery always have worker pods running to pick up tasks as they arrive.


  1. Kubectl

  2. Docker

  3. A Docker image registry to push your Docker images

  4. Kubernetes cluster on GCP/AWS.

Airflow Architecture

Airflow has 3 major components.

  1. Webserver - Which serves you the fancy UI with a list of DAGs, logs, and tasks.

  2. Scheduler - Which runs on the background and schedules tasks and manages them

  3. Workers/Executors - These are the processes that execute the tasks. Worker processes are spun up by Schedulers and tracked on their completion

Apart from these, there are

  1. Dag folders

  2. Log folders

  3. Database

There are different kinds of Executors one can use with Airflow.

  1. LocalExecutor - Used mostly for playing around in the local machine.

  2. CeleryExecutor - Uses celery workers to run the tasks

  3. KubernetesExecutor - Uses Kubernetes pods to run the worker tasks

aAirflow with Kubernetes

On scheduling a task with airflow Kubernetes executor, the scheduler spins up a pod and runs the tasks. On completion of the task, the pod gets killed. It ensures maximum utilization of resources, unlike celery, which at any point must have a minimum number of workers running.

Building the Docker Image

The core part of building a docker image is doing a pip install.

RUN pip install --upgrade pip

RUN pip install apache-airflow==1.10.10
RUN pip install 'apache-airflow[kubernetes]'

We also need a script that would run the webserver or scheduler based on the Kubernetes pod or container. We have a file called to do the same.

if [ "$1" = "webserver" ]
    exec airflow webserver

if [ "$1" = "scheduler" ]
    exec airflow scheduler

Let's add them to the docker file too.

RUN chmod +x /

Let's build and push the image

docker build -t <image-repo-url:tag> .
docker push <image-repo-url:tag>

Kubernetes configuration

This section explains various parts of build/airflow.yaml.

  1. A Kubernetes deployment running a pod running both webserver and scheduler containers

apiVersion: extensions/v1beta1
kind: Deployment
 name: airflow
 namespace: airflow-example
 replicas: 1
 name: airflow
 serviceAccountName: airflow
 - name: webserver
 - name: scheduler
  1. A service whose external IP is mapped to Airflow's webserver

apiVersion: v1
kind: Service
  name: Airflow
  type: LoadBalancer
    - port: 8080
  name: airflow
  1. A serviceaccount which with Role to spin up and delete new pods. These provide permissions to the Airflow scheduler to spin up the worker pods.

apiVersion: v1
kind: ServiceAccount
 name: airflow
 namespace: airflow-example
kind: Role
 namespace: airflow-example
 name: airflow
- apiGroups: [""] # "" indicates the core API group
 resources: ["pods"]
 verbs: ["get", "list", "watch", "create", "update", "delete"]
- apiGroups: ["batch", "extensions"]
 resources: ["jobs"]
 verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
  1. Two persistent volumes for storing dags and logs

kind: PersistentVolume
apiVersion: v1
  name: airflow-dags
    - ReadOnlyMany
  storage: 2Gi
  path: /airflow-dags/
kind: PersistentVolumeClaim
apiVersion: v1
  name: airflow-dags
    - ReadOnlyMany
  storage: 2Gi
  1. An airflow config file is created as a kubernetes config map and attached to the pod. Checkout build/configmaps.yaml

  2. The Postgres configuration is handled via a separate deployment

  3. The secrets like Postgres password are created using Kubernetes secrets

  4. If you want to additional env variables, use Kubernetes configmap.


You can deploy the airflow pods in 2 modes.

  1. Use persistent volume to store DAGS

  2. Get use git to pull dags from

To set up the pods, we need to run a script that does the following

  1. Convert the templatized config under templates to Kube config files under build.

  2. Deletes existing pods, deployments. if any in the namespace

  3. Create new pods, deployments, and other Kube resources

export TAG=<IMAGE_TAG>
cd airflow-kube-setup/scripts/kube
./ -d persistent_mode

Testing the Setup

By default, this setup copies all the examples into the dags; we can just run one of them and see if everything is working fine.

  1. Get the airflow URL by running kubectl get services

  2. Log into the Airflow by using airflow and airflow. You can change this value in

  3. Pick one of the DAG files listed

  4. On your terminal run kubectl get pods --watch to notice when worker pods are created

  5. Click on TriggerDag to trigger one of the jobs

  6. On the graph view, you can see the tasks running, and on your terminal new pods are created and shut down completing the tasks.

Maintainance and modification

Once it is deployed, you don't have to run this script every time. You can use basic kubectl commands to delete or restart pods.

kubectl get pods --watch
kubectl logs <POD_NAME> <Container_name>
kubectl exec -it $pod_name --container webserver -- /bin/bash

Got a Question?

Raise them as issues on the git repo

Next Up...

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