Production Guide¶

The following are things to consider when using this Helm chart in a production environment.

Database¶

It is advised to set up an external database for the Airflow metastore. The default Helm chart deploys a Postgres database running in a container. For production usage, a database running on a dedicated machine or leveraging a cloud provider’s database service such as AWS RDS should be used because the embedded Postgres lacks stability, monitoring and persistence features that you need for a production database. It is only there to make it easier to test the Helm Chart in a “standalone” version but you might experience data loss when you are using it. Supported databases and versions can be found at Set up a Database Backend.

Note

When using the helm chart, you do not need to initialize the db with airflow db migrate as outlined in Set up a Database Backend.

First disable Postgres so the chart won’t deploy its own Postgres container:

postgresql:
  enabled: false

To provide the database credentials to Airflow, you have 2 options - in your values file or in a Kubernetes Secret.

Values file¶

This is the simpler options, as the chart will create a Kubernetes Secret for you. However, keep in mind your credentials will be in your values file.

data:
  metadataConnection:
    user: <username>
    pass: <password>
    protocol: postgresql
    host: <hostname>
    port: 5432
    db: <database name>

Kubernetes Secret¶

You can also store the credentials in a Kubernetes Secret you create. Note that special characters in the username/password must be URL encoded.

kubectl create secret generic mydatabase --from-literal=connection=postgresql://user:pass@host:5432/db

Finally, configure the chart to use the secret you created:

data:
  metadataSecretName: mydatabase

Warning

If you use CeleryExecutor and Airflow version < 2.4, keep in mind that resultBackendSecretName expects a url that starts with db+postgresql://, while metadataSecretName expects postgresql:// and won’t work with db+postgresql://. You’ll need to create separate secrets with the correct scheme. For Airflow version >= 2.4 it is possible to omit the result backend secret, as Airflow will use sql_alchemy_conn (specified in metadataSecret) with a db+ scheme prefix by default.

PgBouncer¶

If you are using PostgreSQL as your database, you will likely want to enable PgBouncer as well. Airflow can open a lot of database connections due to its distributed nature and using a connection pooler can significantly reduce the number of open connections on the database.

pgbouncer:
  enabled: true

Depending on the size of your Airflow instance, you may want to adjust the following as well (defaults are shown):

pgbouncer:
  # The maximum number of connections to PgBouncer
  maxClientConn: 100
  # The maximum number of server connections to the metadata database from PgBouncer
  metadataPoolSize: 10
  # The maximum number of server connections to the result backend database from PgBouncer
  resultBackendPoolSize: 5

Webserver Secret Key¶

You should set a static webserver secret key when deploying with this chart as it will help ensure your Airflow components only restart when necessary.

Warning

You should use a different secret key for every instance you run, as this key is used to sign session cookies and perform other security related functions!

First, generate a strong secret key:

python3 -c 'import secrets; print(secrets.token_hex(16))'

Now add the secret to your values file:

webserverSecretKey: <secret_key>

Alternatively, create a Kubernetes Secret and use webserverSecretKeySecretName:

webserverSecretKeySecretName: my-webserver-secret
# where the random key is under `webserver-secret-key` in the k8s Secret

Example to create a Kubernetes Secret from kubectl:

kubectl create secret generic my-webserver-secret --from-literal="webserver-secret-key=$(python3 -c 'import secrets; print(secrets.token_hex(16))')"

The webserver key is also used to authorize requests to Celery workers when logs are retrieved. The token generated using the secret key has a short expiry time though - make sure that time on ALL the machines that you run airflow components on is synchronized (for example using ntpd) otherwise you might get “forbidden” errors when the logs are accessed.

Extending and customizing Airflow Image¶

The Apache Airflow community, releases Docker Images which are reference images for Apache Airflow. However, Airflow has more than 60 community managed providers (installable via extras) and some of the default extras/providers installed are not used by everyone, sometimes others extras/providers are needed, sometimes (very often actually) you need to add your own custom dependencies, packages or even custom providers, or add custom tools and binaries that are needed in your deployment.

In Kubernetes and Docker terms this means that you need another image with your specific requirements. This is why you should learn how to build your own Docker (or more properly Container) image.

Typical scenarios where you would like to use your custom image:

Adding apt packages
Adding PyPI packages
Adding binary resources necessary for your deployment
Adding custom tools needed in your deployment

See Building the image for more details on how you can extend and customize the Airflow image.

Managing DAG Files¶

See Manage DAGs files.

knownHosts¶

If you are using dags.gitSync.sshKeySecret, you should also set dags.gitSync.knownHosts. Here we will show the process for GitHub, but the same can be done for any provider:

Grab GitHub’s public key:

ssh-keyscan -t rsa github.com > github_public_key

Next, print the fingerprint for the public key:

ssh-keygen -lf github_public_key

Compare that output with GitHub’s SSH key fingerprints.

They match, right? Good. Now, add the public key to your values. It’ll look something like this:

dags:
  gitSync:
    knownHosts: |
      github.com ssh-rsa AAAA...1/wsjk=

External Scheduler¶

To use an external Scheduler instance:

scheduler:
  enabled: false

Ensure that your external webserver/scheduler is connected to the same redis host. This will ensure the scheduler is aware of the workers deployed in the helm-chart.

Accessing the Airflow UI¶

How you access the Airflow UI will depend on your environment; however, the chart does support various options:

External Webserver¶

To use an external Webserver:

webserver:
  enabled: false

Ensure that your external webserver/scheduler is connected to the same redis host. This will ensure the scheduler is aware of the workers deployed in the helm-chart.

Ingress¶

You can create and configure Ingress objects. See the Ingress chart parameters. For more information on Ingress, see the Kubernetes Ingress documentation.

LoadBalancer Service¶

You can change the Service type for the webserver to be LoadBalancer, and set any necessary annotations:

webserver:
  service:
    type: LoadBalancer

For more information on LoadBalancer Services, see the Kubernetes LoadBalancer Service Documentation.

Logging¶

Depending on your choice of executor, task logs may not work out of the box. All logging choices can be found at Manage logs.

Metrics¶

The chart can support sending metrics to an existing StatsD instance or provide a Prometheus endpoint.

Prometheus¶

The metrics endpoint is available at svc/{{ .Release.Name }}-statsd:9102/metrics.

External StatsD¶

To use an external StatsD instance:

statsd:
  enabled: false
config:
  metrics:  # or 'scheduler' for Airflow 1
    statsd_on: true
    statsd_host: ...
    statsd_port: ...

Datadog¶

If you are using a Datadog agent in your environment, this will enable Airflow to export metrics to the Datadog agent.

statsd:
  enabled: false
config:
  metrics: # or 'scheduler' for Airflow 1
    statsd_on: true
    statsd_port: 8125
extraEnv: |-
  - name: AIRFLOW__METRICS__STATSD_HOST
    valueFrom:
      fieldRef:
        fieldPath: status.hostIP

Celery Backend¶

If you are using CeleryExecutor or CeleryKubernetesExecutor, you can bring your own Celery backend.

By default, the chart will deploy Redis. However, you can use any supported Celery backend instead:

redis:
  enabled: false
data:
  brokerUrl: redis://redis-user:password@redis-host:6379/0

For more information about setting up a Celery broker, refer to the exhaustive Celery documentation on the topic.

Security Context Constraints¶

A Security Context Constraint (SCC) is a OpenShift construct that works as a RBAC rule; however, it targets Pods instead of users. When defining a SCC, one can control actions and resources a POD can perform or access during startup and runtime.

The SCCs are split into different levels or categories with the restricted SCC being the default one assigned to Pods. When deploying Airflow to OpenShift, one can leverage the SCCs and allow the Pods to start containers utilizing the anyuid SCC.

In order to enable the usage of SCCs, one must set the parameter rbac.createSCCRoleBinding to true as shown below:

rbac:
  create: true
  createSCCRoleBinding: true

In this chart, SCCs are bound to the Pods via RoleBindings meaning that the option rbac.create must also be set to true in order to fully enable the SCC usage.

For more information about SCCs and what can be achieved with this construct, please refer to Managing security context constraints.

Security Context¶

In Kubernetes a securityContext can be used to define user ids, group ids and capabilities such as running a container in privileged mode.

When deploying an application to Kubernetes, it is recommended to give the least privilege to containers so as to reduce access and protect the host where the container is running.

In the Airflow Helm chart, the securityContext can be configured in several ways:

uid (configures the global uid or RunAsUser)

gid (configures the global gid or fsGroup)

securityContexts (same as uid but allows for setting all Pod securityContext options and Container securityContext options)

The same way one can configure the global securityContexts, it is also possible to configure different values for specific workloads by setting their local securityContexts as follows:

workers:
  securityContexts:
    pod:
      runAsUser: 5000
      fsGroup: 0
    containers:
      allowPrivilegeEscalation: false

In the example above, the workers Pod securityContexts will be set to runAsUser: 5000 and fsGroup: 0. The containers pod will be set to allowPrivilegeEscalation: false.

As one can see, the local setting will take precedence over the global setting when defined. The following explains the precedence rule for securityContexts options in this chart:

uid: 40000
gid: 0

securityContexts:
  pod:
    runAsUser: 50000
    fsGroup: 0

workers:
  securityContexts:
    pod:
      runAsUser: 1001
      fsGroup: 0

This will generate the following worker deployment:

kind: StatefulSet
apiVersion: apps/v1
metadata:
  name: airflow-worker
spec:
  serviceName: airflow-worker
  template:
    spec:
      securityContext:    # As the securityContexts was defined in ``workers``, its value will take priority
        runAsUser: 1001
        fsGroup: 0

If we remove both the securityContexts and workers.securityContexts from the example above, the output will be the following:

uid: 40000
gid: 0

securityContexts: {}

workers:
  securityContexts: {}

This will generate the following worker deployment:

kind: StatefulSet
apiVersion: apps/v1
metadata:
  name: airflow-worker
spec:
  serviceName: airflow-worker
  template:
    spec:
      securityContext:
        runAsUser: 40000   # As the securityContext was not defined in ``workers`` or ``podSecurity``, the value from uid will be used
        fsGroup: 0         # As the securityContext was not defined in ``workers`` or ``podSecurity``, the value from gid will be used
      initContainers:
        - name: wait-for-airflow-migrations
      ...
      containers:
        - name: worker
      ...

And finally if we set securityContexts but not workers.securityContexts:

uid: 40000
gid: 0

securityContexts:
  pod:
    runAsUser: 50000
    fsGroup: 0

workers:
  securityContexts: {}

This will generate the following worker deployment:

kind: StatefulSet
apiVersion: apps/v1
metadata:
  name: airflow-worker
spec:
  serviceName: airflow-worker
  template:
    spec:
      securityContext:     # As the securityContexts was not defined in ``workers``, the values from securityContexts will take priority
        runAsUser: 50000
        fsGroup: 0
      initContainers:
        - name: wait-for-airflow-migrations
      ...
      containers:
        - name: worker
      ...

Built-in secrets and environment variables¶

The Helm Chart by default uses Kubernetes Secrets to store secrets that are needed by Airflow. The contents of those secrets are by default turned into environment variables that are read by Airflow (some of the environment variables have several variants to support older versions of Airflow).

By default, the secret names are determined from the Release Name used when the Helm Chart is deployed, but you can also use a different secret to set the variables or disable using secrets entirely and rely on environment variables (specifically if you want to use _CMD or __SECRET variant of the environment variable.

However, Airflow supports other variants of setting secret configuration - you can specify a system command to retrieve and automatically rotate the secret (by defining variable with _CMD suffix) or to retrieve a variable from secret backed (by defining the variable with _SECRET suffix).

If the <VARIABLE_NAME>> is set, it takes precedence over the _CMD and _SECRET variant, so if you want to set one of the _CMD or _SECRET variants, you MUST disable the built in variables retrieved from Kubernetes secrets, by setting .Values.enableBuiltInSecretEnvVars.<VARIABLE_NAME> to false.

For example in order to use a command to retrieve the DB connection you should (in your values.yaml file) specify:

extraEnv:
  AIRFLOW_CONN_AIRFLOW_DB_CMD: "/usr/local/bin/retrieve_connection_url"
enableBuiltInSecretEnvVars:
  AIRFLOW_CONN_AIRFLOW_DB: false

Here is the full list of secrets that can be disabled and replaced by _CMD and _SECRET variants:

Default secret name if secret name not specified	Use a different Kubernetes Secret	Airflow Environment Variable
`<RELEASE_NAME>-airflow-metadata`	`.Values.data.metadataSecretName`	`AIRFLOW_CONN_AIRFLOW_DB` `AIRFLOW__DATABASE__SQL_ALCHEMY_CONN`
`<RELEASE_NAME>-fernet-key`	`.Values.fernetKeySecretName`	`AIRFLOW__CORE__FERNET_KEY`
`<RELEASE_NAME>-webserver-secret-key`	`.Values.webserverSecretKeySecretName`	`AIRFLOW__WEBSERVER__SECRET_KEY`
`<RELEASE_NAME>-airflow-result-backend`	`.Values.data.resultBackendSecretName`	`AIRFLOW__CELERY__CELERY_RESULT_BACKEND` `AIRFLOW__CELERY__RESULT_BACKEND`
`<RELEASE_NAME>-airflow-brokerUrl`	`.Values.data.brokerUrlSecretName`	`AIRFLOW__CELERY__BROKER_URL`
`<RELEASE_NAME>-elasticsearch`	`.Values.elasticsearch.secretName`	`AIRFLOW__ELASTICSEARCH__HOST` `AIRFLOW__ELASTICSEARCH__ELASTICSEARCH_HOST`

There are also a number of secrets, which names are also determined from the release name, that do not need to be disabled. This is because either they do not follow the _CMD or _SECRET pattern, are variables which do not start with AIRFLOW__, or they do not have a corresponding variable.

There is also one _AIRFLOW__* variable, AIRFLOW__CELERY__FLOWER_BASIC_AUTH, that does not need to be disabled, even if you want set the _CMD and _SECRET variant. This variable is not set by default. It is only set when .Values.flower.secretName is set or when .Values.flower.user and .Values.flower.password are set. So if you do not set any of the .Values.flower.* variables, you can freely configure flower Basic Auth using the _CMD or _SECRET variant without disabling the basic variant.

Default secret name if secret name not specified	Use a different Kubernetes Secret	Airflow Environment Variable
`<RELEASE_NAME>-redis-password`	`.Values.redis.passwordSecretName`	`REDIS_PASSWORD`
`<RELEASE_NAME>-pgbouncer-config`	`.Values.pgbouncer.configSecretName`
`<RELEASE_NAME>-pgbouncer-certificates`
`<RELEASE_NAME>-registry`	`.Values.registry.secretName`
`<RELEASE_NAME>-kerberos-keytab`
`<RELEASE_NAME>-flower`	`.Values.flower.secretName`	`AIRFLOW__CELERY__FLOWER_BASIC_AUTH`

You can read more about advanced ways of setting configuration variables in the Setting Configuration Options.