26 KiB
Helm Chart for Apache Airflow
Apache Airflow is a platform to programmatically author, schedule and monitor workflows.
Introduction
This chart will bootstrap an Airflow deployment on a Kubernetes cluster using the Helm package manager.
Prerequisites
- Kubernetes 1.12+ cluster
- Helm 2.11+ or Helm 3.0+
- PV provisioner support in the underlying infrastructure
Installing the Chart
To install this repository from source (using helm 3)
kubectl create namespace airflow
helm repo add stable https://kubernetes-charts.storage.googleapis.com
helm dep update
helm install airflow . --namespace airflow
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 airflow:
helm upgrade airflow . --namespace airflow
Uninstalling the Chart
To uninstall/delete the airflow deployment:
helm delete airflow --namespace airflow
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 DAG 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 airflow . \
--set images.airflow.repository=my-company/airflow \
--set images.airflow.tag=8a0da78
For local development purpose you can also build the image locally and use it via deployment method described by Breeze.
Mounting DAGS using Git-Sync side car with Persistence enabled
This option will use a Persistent Volume Claim with an accessMode of ReadWriteMany. The scheduler pod will sync DAGs from a git repository onto the PVC every configured number of seconds. The other pods will read the synced DAGs. Not all volume plugins have support for ReadWriteMany accessMode. Refer Persistent Volume Access Modes for details
helm upgrade airflow . \
--set dags.persistence.enabled=true \
--set dags.gitSync.enabled=true
# you can also override the other persistence or gitSync values
# by setting the dags.persistence.* and dags.gitSync.* values
# Please refer to values.yaml for details
Mounting DAGS using Git-Sync side car without Persistence
This option will use an always running Git-Sync side car on every scheduler,webserver and worker pods. The Git-Sync side car containers will sync DAGs from a git repository every configured number of seconds. If you are using the KubernetesExecutor, Git-sync will run as an initContainer on your worker pods.
helm upgrade airflow . \
--set dags.persistence.enabled=false \
--set dags.gitSync.enabled=true
# you can also override the other gitSync values
# by setting the dags.gitSync.* values
# Refer values.yaml for details
Mounting DAGS from an externally populated PVC
In this approach, Airflow will read the DAGs from a PVC which has ReadOnlyMany or ReadWriteMany accessMode. You will have to ensure that the PVC is populated/updated with the required DAGs(this won't be handled by the chart). You can pass in the name of the volume claim to the chart
helm upgrade airflow . \
--set dags.persistence.enabled=true \
--set dags.persistence.existingClaim=my-volume-claim
--set dags.gitSync.enabled=false
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 | 50000 |
gid |
GID to run airflow pods under | 50000 |
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.web.* |
Configs for the Ingress of the web Service | Please refer to values.yaml |
ingress.flower.* |
Configs for the Ingress of the flower Service | Please refer to values.yaml |
networkPolicies.enabled |
Enable Network Policies to restrict traffic | true |
airflowHome |
Location of airflow home directory | /opt/airflow |
rbacEnabled |
Deploy pods with Kubernets RBAC enabled | true |
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 | apache/airflow |
defaultAirflowTag |
Fallback docker image tag to deploy | 1.10.10.1-alpha2-python3.6 |
images.airflow.repository |
Docker repository to pull image from. Update this to deploy a custom image | ~ |
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 | ~ |
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 | astronomerinc/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 | redis |
images.redis.tag |
Docker image tag to pull image from. Update this to deploy a new custom image tag | 6-buster |
images.redis.pullPolicy |
PullPolicy for redis image | IfNotPresent |
images.pgbouncer.repository |
Docker repository to pull image from. Update this to deploy a custom image | astronomerinc/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 | astronomerinc/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 Airlow 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.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.service.annotations |
Annotations to be added to the webserver service | {} |
webserver.defaultUser |
Optional default airflow user information | {} |
dags.persistence.* |
Dag persistence configutation | Please refer to values.yaml |
dags.gitSync.* |
Git sync configuration | Please refer to values.yaml |
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)
kubectl create namespace airflow
helm install airflow . \
--namespace airflow \
--set executor=CeleryExecutor \
--set workers.keda.enabled=true \
--set workers.persistence.enabled=false
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 cofirm Airflow is working.
Build a Docker image from your DAGs:
-
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 -
Then build the image:
docker build -t my-dags:0.0.1 . -
Load the image into kind:
kind load docker-image my-dags:0.0.1 -
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!