Astronomer's Helm Chart for Apache Airflow

This chart will bootstrap an Airflow deployment on a Kubernetes cluster using the Helm package manager.

Quickstart

To install this helm chart remotely (using helm 3)

kubectl create namespace airflow

helm repo add astronomer https://helm.astronomer.io
helm install airflow --namespace airflow astronomer/airflow

To install airflow with the KEDA autoscaler

helm repo add kedacore https://kedacore.github.io/charts
helm repo add astronomer https://helm.astronomer.io

helm repo update

kubectl create namespace keda
helm install keda \
    --namespace keda kedacore/keda

kubectl create namespace airflow

helm install airflow \
    --set executor=CeleryExecutor \
    --set workers.keda.enabled=true \
    --set workers.persistence.enabled=false \
    --namespace airflow \
    astronomer/airflow

To install this repository from source

kubectl create namespace airflow
helm install --namespace airflow .

Prerequisites

• Kubernetes 1.12+
• Helm 2.11+ or Helm 3.0+
• PV provisioner support in the underlying infrastructure

Installing the Chart

To install the chart with the release name my-release:

helm install --name my-release .

The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation.

Tip: List all releases using helm list

Upgrading the Chart

To upgrade the chart with the release name my-release:

helm upgrade --name my-release .

Uninstalling the Chart

To uninstall/delete the my-release deployment:

helm delete my-release

The command removes all the Kubernetes components associated with the chart and deletes the release.

Updating DAGs

The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (docker build -t my-company/airflow:8a0da78 .), push it to an accessible registry (docker push my-company/airflow:8a0da78), then update the Airflow pods with that image:

helm upgrade my-release . \
  --set images.airflow.repository=my-company/airflow \
  --set images.airflow.tag=8a0da78

Docker Images

• The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow.
• Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor.

Parameters

The following tables lists the configurable parameters of the Airflow chart and their default values.

Parameter	Description	Default
uid	UID to run airflow pods under	nil
gid	GID to run airflow pods under	nil
nodeSelector	Node labels for pod assignment	{}
affinity	Affinity labels for pod assignment	{}
tolerations	Toleration labels for pod assignment	[]
labels	Common labels to add to all objects defined in this chart	{}
privateRegistry.enabled	Enable usage of a private registry for Airflow base image	false
privateRegistry.repository	Repository where base image lives (eg: quay.io)	~
ingress.enabled	Enable Kubernetes Ingress support	false
ingress.acme	Add acme annotations to Ingress object	false
ingress.tlsSecretName	Name of secret that contains a TLS secret	~
ingress.baseDomain	Base domain for VHOSTs	~
ingress.class	Ingress class to associate with	nginx
ingress.auth.enabled	Enable auth with Astronomer Platform	true
networkPolicies.enabled	Enable Network Policies to restrict traffic	true
airflowHome	Location of airflow home directory	/usr/local/airflow
rbacEnabled	Deploy pods with Kubernetes RBAC enabled	true
airflowVersion	Default Airflow image version	1.10.5
executor	Airflow executor (eg SequentialExecutor, LocalExecutor, CeleryExecutor, KubernetesExecutor)	KubernetesExecutor
allowPodLaunching	Allow airflow pods to talk to Kubernetes API to launch more pods	true
defaultAirflowRepository	Fallback docker repository to pull airflow image from	quay.io/astronomer/ap-airflow
defaultAirflowTag	Fallback docker image tag to deploy. This image is also used to Run Database Migrations for Airflow.	1.10.7-alpine3.10
images.airflow.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-airflow
images.airflow.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.airflow.pullPolicy	PullPolicy for airflow image	IfNotPresent
images.flower.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-airflow
images.flower.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.flower.pullPolicy	PullPolicy for flower image	IfNotPresent
images.statsd.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-statsd-exporter
images.statsd.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.statsd.pullPolicy	PullPolicy for statsd-exporter image	IfNotPresent
images.redis.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-redis
images.redis.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.redis.pullPolicy	PullPolicy for redis image	IfNotPresent
images.pgbouncer.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-pgbouncer
images.pgbouncer.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.pgbouncer.pullPolicy	PullPolicy for pgbouncer image	IfNotPresent
images.pgbouncerExporter.repository	Docker repository to pull image from. Update this to deploy a custom image	quay.io/astronomer/ap-pgbouncer-exporter
images.pgbouncerExporter.tag	Docker image tag to pull image from. Update this to deploy a new custom image tag	~
images.pgbouncerExporter.pullPolicy	PullPolicy for pgbouncer-exporter image	IfNotPresent
env	Environment variables key/values to mount into Airflow pods	[]
secret	Secret name/key pairs to mount into Airflow pods	[]
data.metadataSecretName	Secret name to mount Airflow connection string from	~
data.resultBackendSecretName	Secret name to mount Celery result backend connection string from	~
data.metadataConection	Field separated connection data (alternative to secret name)	{}
data.resultBackendConnection	Field separated connection data (alternative to secret name)	{}
fernetKey	String representing an Airflow fernet key	~
fernetKeySecretName	Secret name for Airflow fernet key	~
workers.replicas	Replica count for Celery workers (if applicable)	1
workers.keda.enabled	Enable KEDA autoscaling features	false
workers.keda.pollingInverval	How often KEDA should poll the backend database for metrics in seconds	5
workers.keda.cooldownPeriod	How often KEDA should wait before scaling down in seconds	30
workers.keda.maxReplicaCount	Maximum number of Celery workers KEDA can scale to	10
workers.persistence.enabled	Enable log persistence in workers via StatefulSet	false
workers.persistence.size	Size of worker volumes if enabled	100Gi
workers.persistence.storageClassName	StorageClass worker volumes should use if enabled	default
workers.resources.limits.cpu	CPU Limit of workers	~
workers.resources.limits.memory	Memory Limit of workers	~
workers.resources.requests.cpu	CPU Request of workers	~
workers.resources.requests.memory	Memory Request of workers	~
workers.terminationGracePeriodSeconds	How long Kubernetes should wait for Celery workers to gracefully drain before force killing	600
workers.autoscaling.enabled	Traditional HorizontalPodAutoscaler	false
workers.autoscaling.minReplicas	Minimum amount of workers	1
workers.autoscaling.maxReplicas	Maximum amount of workers	10
workers.targetCPUUtilization	Target CPU Utilization of workers	80
workers.targetMemoryUtilization	Target Memory Utilization of workers	80
workers.safeToEvict	Allow Kubernetes to evict worker pods if needed (node downscaling)	true
scheduler.podDisruptionBudget.enabled	Enable PDB on Airflow scheduler	false
scheduler.podDisruptionBudget.config.maxUnavailable	MaxUnavailable pods for scheduler	1
scheduler.resources.limits.cpu	CPU Limit of scheduler	~
scheduler.resources.limits.memory	Memory Limit of scheduler	~
scheduler.resources.requests.cpu	CPU Request of scheduler	~
scheduler.resources.requests.memory	Memory Request of scheduler	~
scheduler.airflowLocalSettings	Custom Airflow local settings python file	~
scheduler.safeToEvict	Allow Kubernetes to evict scheduler pods if needed (node downscaling)	true
webserver.livenessProbe.initialDelaySeconds	Webserver LivenessProbe initial delay	15
webserver.livenessProbe.timeoutSeconds	Webserver LivenessProbe timeout seconds	30
webserver.livenessProbe.failureThreshold	Webserver LivenessProbe failure threshold	20
webserver.livenessProbe.periodSeconds	Webserver LivenessProbe period seconds	5
webserver.readinessProbe.initialDelaySeconds	Webserver ReadinessProbe initial delay	15
webserver.readinessProbe.timeoutSeconds	Webserver ReadinessProbe timeout seconds	30
webserver.readinessProbe.failureThreshold	Webserver ReadinessProbe failure threshold	20
webserver.readinessProbe.periodSeconds	Webserver ReadinessProbe period seconds	5
webserver.replicas	How many Airflow webserver replicas should run	1
webserver.resources.limits.cpu	CPU Limit of webserver	~
webserver.resources.limits.memory	Memory Limit of webserver	~
webserver.resources.requests.cpu	CPU Request of webserver	~
webserver.resources.requests.memory	Memory Request of webserver	~
webserver.jwtSigningCertificateSecretName	Name of secret to mount Airflow Webserver JWT singing certificate from	~
webserver.defaultUser	Optional default airflow user information	{}
webserver.useDefaultAirflowImage	Prevent Airflow webserver reboot only for Airflow 2.0.0	false

Specify each parameter using the --set key=value[,key=value] argument to helm install. For example,

helm install --name my-release \
  --set executor=CeleryExecutor \
  --set enablePodLaunching=false .

Autoscaling with KEDA

KEDA stands for Kubernetes Event Driven Autoscaling. KEDA is a custom controller that allows users to create custom bindings to the Kubernetes Horizontal Pod Autoscaler. We've built an experimental scaler that allows users to create scalers based on postgreSQL queries. For the moment this exists on a separate branch, but will be merged upstream soon. To install our custom version of KEDA on your cluster, please run

helm repo add kedacore https://kedacore.github.io/charts

helm repo update

helm install \
    --set image.keda=docker.io/kedacore/keda:1.2.0 \
    --set image.metricsAdapter=docker.io/kedacore/keda-metrics-adapter:1.2.0 \
    --namespace keda --name keda kedacore/keda

Once KEDA is installed (which should be pretty quick since there is only one pod). You can try out KEDA autoscaling on this chart by setting workers.keda.enabled=true your helm command or in the values.yaml. (Note: KEDA does not support StatefulSets so you need to set worker.persistence.enabled to false)

helm repo add astronomer https://helm.astronomer.io
helm repo update

kubectl create namespace airflow

helm install airflow \
    --set executor=CeleryExecutor \
    --set workers.keda.enabled=true \
    --set workers.persistence.enabled=false \
    --namespace airflow \
    astronomer/airflow

Walkthrough using kind

Install kind, and create a cluster:

We recommend testing with Kubernetes 1.15, as this image doesn't support Kubernetes 1.16+ for CeleryExecutor presently.

kind create cluster \
  --image kindest/node:v1.15.7@sha256:e2df133f80ef633c53c0200114fce2ed5e1f6947477dbc83261a6a921169488d

Confirm it's up:

kubectl cluster-info --context kind-kind

Add Astronomer's Helm repo:

helm repo add astronomer https://helm.astronomer.io
helm repo update

Create namespace + install the chart:

kubectl create namespace airflow
helm install airflow -n airflow astronomer/airflow

It may take a few minutes. Confirm the pods are up:

kubectl get pods --all-namespaces
helm list -n airflow

Run kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working.

Build a Docker image from your DAGs:

1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with astro airflow start.

    mkdir my-airflow-project && cd my-airflow-project
    astro dev init
   

2. Then build the image:

    docker build -t my-dags:0.0.1 .
   

3. Load the image into kind:

    kind load docker-image my-dags:0.0.1
   

4. Upgrade Helm deployment:

    helm upgrade airflow -n airflow \
        --set images.airflow.repository=my-dags \
        --set images.airflow.tag=0.0.1 \
        astronomer/airflow
   

Contributing

Check out our contributing guide!

License

Apache 2.0 with Commons Clause