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Kubernetes Engine Configuration

How to configure DBND to use your Kubernetes Cluster.

Configuring Kubernetes Engine Guide

To direct DBND to interact with your cluster, you need to update the databand-system.cfg configuration file with the Kubernetes Cluster connection details.

Step 1. In the environments configuration, add a new environment for the Kubernetes cluster. For example, kubernetes_cluster_env - you can use an existing one, but in this example, let's assume its name is 'kubernetes_cluster_env':

environments = ['local', 'local_minikube', 'kubernetes_cluster_env']

Step 2. In the configuration file, add the environments' specification for your new or existing environment (kubernetes_cluster_env):

    _from = local
    remote_engine = your_kubernetes_engine
Parameter `_from` means "from where to draw previous definitions". In this example, the `[local]` section is used (see [Extending Configurations](doc:custom-environment)).
The `remote_engine` setting defines what engine is going to run your remote tasks (submitted tasks).

Step 3. In the configuration file, add the engine configuration.
The following example describes the engine configuration:

    _type = kubernetes

    container_repository = databand_examples
    container_tag =

    docker_build = True
    docker_build_push = True

    cluster_context = databand_context
    namespace = databand
    service_account_name = databand

    debug = False
    secrets = [
       { "type":"env", "target": "AIRFLOW__CORE__SQL_ALCHEMY_CONN", "secret" : "databand-secret", "key": "airflow_sql_alchemy_conn"},
       { "type":"env", "target": "AIRFLOW__CORE__FERNET_KEY", "secret" : "databand-secret", "key": "airflow_fernet_key"},
       { "type":"env", "target": "DBND__CORE__DATABAND_URL", "secret" : "databand-secret", "key": "databand_url"}

    pod_error_cfg_source_dict = {
                                "255": {"retry_count": 3, "retry_delay": "3m"},
                                "err_image_pull": {"retry_count": 0, "retry_delay": "3m"},
                                "err_pod_deleted": {"retry_count": 3, "retry_delay": "3s"},

Databand Kubernetes Config Reference

Docker Image configuration

  • container_repository - Where is the Docker image repository to pull the pod images from? If you are running user code, this is where you need to supply your repository and tag settings.
  • container_tag - If defined, Docker will not be built and the specified tag will be used.
  • image_pull_secrets - The secret with the connection information for the container_repository.
  • docker_build - Should the Kubernetes executor build the Docker image on the fly? Useful if you want a different image every time.
  • docker_build_push - Should the built Docker image be pushed to the repository? Useful for specific cases.

Cluster related variables

  • in_cluster - Defines what Kubernetes configuration is used for the kube client. Use false to enforce using local credentials, use true to enforce the in_cluster mode. Default: None (Databand will automatically decide what mode to use).
  • cluster_context - The Kubernetes context; you can check which context you are on by using kubectl config get-contexts.
  • namespace - The namespace in which Databand is installed inside the cluster (databand in this case).
  • service_account_name - You need permissions to create pods for tasks, namely - you need to have a service_account with the correct permissions.

Pod Scheduling Configuration

  • labels - Set a list of pods' labels (see Labels)
  • node_selectors and affinity - Assign nodeSelector or affinity to the pods (see Assigning Pods to Nodes)
  • annotations - Assign annotations to the pod (see Annotations)
  • tolerations - Assign tolerations to the pod (see Taints and Tolerations)
  • requests and limits - Setting the requests and limits for the pod can be achieved by setting those. You can provide a standard Kubernetes Dict, however, you can also use explicit keys like request_memory , request_cpu, limit_memory or limit_cpu
    For more information see Manage Container Resources and make sure you are aware of Quality of Service for Pods

Pod Runtime Configuration

  • secrets - Assing secrets to the pod.
  • env_vars - Assign environment variables to the pod.

Pod Error Handling

  • pod_error_cfg_source_dict (optional) - Allows flexibility of sending retry on pods that have failed with specific exit codes. You can provide "PROCESS EXIT CODE" as a key (for example, 137) or Kubernetes error string.
pod_error_cfg_source_dict = {
                                "255": {"retry_count": 3, "retry_delay": "3m"},
                                "err_image_pull": {"retry_count": 0, "retry_delay": "3m"},
                                "err_pod_deleted": {"retry_count": 3, "retry_delay": "3s"},

Supported Kubernetes errors:

  • "err_image_pull" happens on Image pull error.
  • "err_config_error" happens on Pod configuration error (you should not retry on this one).
  • "err_pod_deleted" happens on Pod deletion (very unique case of Kubernetes autoscaling).
  • "err_pod_evicted" happens on Pod relocation to a different Node.

Databand System

  • debug - When true, displays all pod requests sent to Kubernetes and more useful debugging data.
  • keep_finished_pods - do not delete finished pods (default=False)
  • keep_failed_pods - do not delete failed pods (default=False). You can use it if you need to debug the system.
  • _type - Implies that this is a Kubernetes Engine Config (see Extending Configurations). You can use it to create your own version of the Kubernetes Engine config.


Custom Configuration per Task

You can adjust configuration settings of a specific task:

from dbnd import task

@task(task_config=dict(kubernetes=dict( limits={"": 1})))
def prepare_data_gpu(data):

Using this configuration, you'll add an extra limit to the pod definition of this specific task.

You can adjust requested resources and set the limits of memory and CPU:

from dbnd import task
        "kubernetes": {"limit_memory": "128Mi",
                       "request_memory": "64Mi",
                       "limit_cpu": "500m",
                       "request_cpu": "250m"}
def prepare_data_gpu(data):

You can also change engine config via CLI and even extend some values like labelsand other properties with List type. See more information at Task Configuration and Extending Values.

Providing Access to AWS (using environment variables)

If you want to provide access to AWS Services explicitly, you can do it by using secrets:

secrets = [   { "type":"env", "target": "AWS_ACCESS_KEY_ID", "secret" : "aws-secrets" , "key" :"aws_access_key_id"},
          { "type":"env", "target": "AWS_SECRET_ACCESS_KEY", "secret" : "aws-secrets" , "key" :"aws_secret_access_key"}]

Providing Access to Google (using file)

If you want to provide access to GCP Services explicitly, you can do it by using secrets:

secrets = [ { "type":"volume", "target": "/var/secrets/google", "secret" : "gcp-secrets" }]]

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