Aexus Answers

Airflow API Server Config Not Mounting: Troubleshooting

Alex Johnson
-
Airflow API Server Config Not Mounting: Troubleshooting

Airflow API Server Configuration Not Mounting: A Deep Dive

Hey guys! Let's tackle a common headache when working with Apache Airflow: the apiServerConfig not mounting correctly. This can be super frustrating, especially when you're trying to customize your Airflow setup, like integrating with Okta for authentication, as the user is trying to do. Based on the provided information, it seems like the user is having trouble getting their custom configuration, defined within the apiServerConfig section of their Helm chart values, to actually be applied to the Airflow API server pod. Let's break down the issue, explore potential causes, and outline steps to get this working. This article will go into detail so that you can be a Airflow expert!

Understanding the Problem: Configuration Disconnect

First things first, what's going on? The core issue is that the custom configuration provided in the apiServerConfig isn't being picked up by the Airflow API server. This means that any customizations you've made, such as setting up OAuth with Okta, configuring database connections, or adjusting security settings, aren't being applied. This configuration is critical for the proper function of Airflow, because without a proper configurations the user will not be able to log in to the Airflow dashboard.

In the user's case, this includes setting up the authentication with the OIDC and connecting with okta. Based on the values provided, the configuration includes OIDC setup, which involves specifying details like client_id, client_secret, api_base_url, and jwks_uri. The user is attempting to customize Airflow’s security manager to use an Okta team authorizer that maps Okta groups to Airflow roles. The problem, the user states, is that none of these configurations are applied to the API server. This is most likely because the configuration is not being mounted correctly in the pod.

Possible Causes and Troubleshooting Steps

Let's get our hands dirty and figure out why the apiServerConfig isn't playing nice. Here's a breakdown of potential causes and how to troubleshoot them:

  1. Helm Chart Configuration: The first place to look is your Helm chart configuration. Double-check that the apiServerConfig section in your values.yaml file is correctly formatted and that it's placed within the appropriate structure for the Airflow Helm chart you're using (version 1.18.0 in this case).

    • YAML Syntax: YAML can be very particular. Even a small syntax error (missing a space, incorrect indentation, etc.) can cause the entire configuration to fail. Use a YAML validator to ensure your values.yaml is valid. The user is having a hard time, because the configurations that he is trying to set, are not being saved in the pod.
    • Correct Key: Verify that you're using the correct key names for your configurations. Airflow Helm charts have specific ways of configuring things. Check the official documentation or the chart's values.yaml for the correct keys and structure.

  2. ConfigMap Creation: The apiServerConfig usually gets translated into a ConfigMap, which is then mounted into the Airflow API server pod. If the ConfigMap isn't being created, your configuration won't be applied. Here's how to check:

    • Check ConfigMap Existence: Use kubectl get configmaps -n <your-namespace> to see if the ConfigMap associated with the API server is present. The name usually follows a pattern related to your release name (e.g., <release-name>-airflow-api-server-config). The user states that this is the problem, the configmap is not even created.
    • Helm Release Inspection: Use helm get values <your-release-name> -n <your-namespace> to confirm that the apiServerConfig is actually included in the rendered values that Helm is using. If it's not there, the issue lies in your values.yaml or how you're deploying the chart.

  3. Pod Deployment and Mounting: Even if the ConfigMap exists, it might not be mounted correctly in the API server pod. The user state that it is not being mounted into the pod, so let's check these points. Here’s how to check the deployment:

    • Pod Inspection: Use kubectl describe pod <api-server-pod-name> -n <your-namespace> to examine the pod's details. Look at the 'Volumes' and 'Volume Mounts' sections. Verify that a volume is defined for the ConfigMap and that it's mounted to the correct path within the API server container (usually /opt/airflow/config or a similar location).
    • Container Logs: Check the API server pod's logs (kubectl logs <api-server-pod-name> -n <your-namespace>) for any errors or warnings related to configuration loading. Airflow often logs details about the configuration it's using, so this can provide valuable clues.

  4. Dockerfile Customizations: The user included some Dockerfile customizations, with pip install commands. Check your docker images to make sure that the correct packages are installed. If the packages were not installed correctly, you may run into some issues.

  5. Permissions: Incorrect permissions on the ConfigMap or the mount point can also prevent the configuration from being applied. Ensure the API server pod has the necessary permissions to read the ConfigMap and access the files within it. Verify your Kubernetes ServiceAccount and RoleBinding configurations.

Code Review and Recommendations

Let's take a look at the configuration that the user provided:

apiServerConfig:
  apiServerConfig: |
    from airflow.www.fab_security.manager import AUTH_OAUTH
    from airflow import configuration as conf
    SQLALCHEMY_DATABASE_URI = conf.get('core', 'SQL_ALCHEMY_CONN')
    basedir = os.path.abspath(os.path.dirname(__file__))
    # Flask-WTF flag for CSRF
    WTF_CSRF_ENABLED = True

    OIDC_SCOPES = ['openid', 'email', 'profile', 'groups']
    AUTH_TYPE = AUTH_OAUTH
    AUTH_USER_REGISTRATION = True
    AUTH_ROLE_ADMIN = "Admin"
    PERMANENT_SESSION_LIFETIME = 43200 # in seconds(12 hours)

    # use your Airflow’s Host URI
    OVERWRITE_REDIRECT_URI = "https://dash-airflow.cashmind.mx/oidc_callback"

    # Here, set your Okta creds -
    # the names of the credentials are self-explanatory. Also, we took advantage of
    # Airflow’s 'secret' section in the helm chart to retrieve those secrets from K8s-secrets.
    # You will probably need your IT Team to get those first and then store them as K8s secrets.

    OAUTH_PROVIDERS = [
    {
      "name": "okta",
      "icon": "fa-circle-o",
      "token_key": "access_token",
      "remote_app": {
        "client_id": "0oavzi34qoreu2hsm697",
        "client_secret": "PjcVV73N-yfznLT2LfOIpV3INUnplO-t0_k-w-6dA5y01o29Pr3n9fak1PBzDx-x",
        "api_base_url": os.getenv("kamet.okta.com/oauth2/v1/"),
        "client_kwargs": {
          "scope": "openid profile email groups"
        },
        "access_token_url": "https://kamet.okta.com/oauth2/v1/token",
        "authorize_url": "https://kamet.okta.com/oauth2/v1/authorize",
        "jwks_uri": "https://kamet.okta.com/oauth2/v1/keys"
      }
    }]

    FAB_ADMIN_ROLE = "Admin"
    FAB_VIEWER_ROLE = "Viewer"

    # your custom roles mapping, in the format - {Okta-Group: [Airflow-Role1,...]}
    # here is our map of Okta Group(team) to its Airflow Role which will be used and explained later on
    AUTH_ROLES_MAPPING = {
      "aws#cashmind-prod#platform-team-role#939276888979":["Admin"],
      "aws#cashmind-staging#stream-aligned-team-role#269031123106":["stream-aligned-team-role"],
    }

    from airflow.www.security import AirflowSecurityManager
    import logging
    from typing import Dict, Any, List, Union
    import os

    log = logging.getLogger(__name__)

    # AirflowSecurityManager Class Implementation - our custom Okta Authorizer Manager class
    # which allows us to relate a new user into his Airflow role and save it to Airflow’s DB
    class OktaTeamAuthorizer(AirflowSecurityManager):

        def get_airflow_role(self, user_groups: List[str]) -> str:
          for okta_group, airflow_role in AUTH_ROLES_MAPPING.items():
            if okta_group in user_groups:
              return okta_group

          return 'Viewer'

        def get_oauth_user_info(
            self, provider: str, resp: Any
        ) -> Dict[str, Union[str, List[str]]]:

            remote_app = self.appbuilder.sm.oauth_remotes[provider]
            me = remote_app.get("userinfo")
            user_data = me.json()
            user_groups = user_data.get("groups")

            role = self.get_airflow_role(user_groups)

            # How a new user record will be saved in the DB
            return {
                "username": "okta_" + user_data.get("preferred_username", ""),
                "first_name": user_data.get("given_name", ""),
                "last_name": user_data.get("family_name", ""),
                "email": user_data.get("email", ""),
                "role_keys": [role]
            }

    # set your custom class as the security_manager_class of airflow
    SECURITY_MANAGER_CLASS = OktaTeamAuthorizer

Here are some recommendations:

  • YAML Validation: Always validate your values.yaml file to avoid syntax errors, which is the most common mistake. There are several online YAML validators available. Make sure your indentation is correct.
  • Helm Chart Specifics: Be mindful of the specific requirements of the Airflow Helm chart version you are using. Check the official documentation for the correct keys and structure for the apiServerConfig. The way that configuration is set can depend on the Helm Chart version.
  • Secret Management: The user is retrieving the secrets from K8s-secrets, which is great, but double-check that the secret names and the way you are referencing them in your configuration are correct. Also, ensure the Airflow API server pod has permissions to access those secrets.
  • Testing: After making changes, thoroughly test your configuration. Verify that the API server is behaving as expected, and that the Okta integration works correctly.

Conclusion

Troubleshooting configuration mounting issues in Airflow can be a bit of a detective game, but by systematically checking these steps, you should be able to identify and fix the problem. Remember to pay close attention to YAML syntax, Helm chart specifics, and the logs. Good luck, and happy Airflow-ing!

If you're still stuck, check out these resources:

  • Apache Airflow Documentation: For in-depth information on Airflow configuration and security: https://airflow.apache.org/
  • Helm Chart Documentation: Consult the documentation for the specific Airflow Helm chart you are using.

By following these steps, you should be well on your way to resolving the issue and getting your Airflow API server configured correctly. Remember to be patient and methodical, and don't be afraid to ask for help from the Airflow community!

You may also like