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Version: 0.28

Run the KubernetesPodOperator on Astronomer Software

Overview

A widely-used and performant alternative to Airflow's older DockerOperator, the KubernetesPodOperator is able to natively launch a Kubernetes Pod to run an individual task - and terminate that pod when the task is completed. Similarly to the Kubernetes Executor, the operator uses the Kubernetes Python Client to generate a Kubernetes API request that dynamically launches those individual pods.

The KubernetesPodOperator enables task-level resource configuration and is optimal for those who have custom Python dependencies. Ultimately, it allows Airflow to act a job orchestrator - no matter the language those jobs are written in.

At its core, the KubernetesPodOperator is built to run any Docker image with Airflow regardless of the language it's written in. It's the next generation of the DockerOperator and is optimized to leverage Kubernetes functionality, allowing users to specify resource requests and pass Kubernetes specific parameters into the task.

If you're using the Kubernetes Executor, you can also configure task-level Kubernetes resources using a pod template. For more information, read Use a Pod Template in a Task.

Pre-Requisites

To run the KubernetesPodOperator on Astronomer, make sure you:

  • Have a running Airflow Deployment on Astronomer Software
  • Run Astronomer Airflow 1.10+

Note: If you haven't already, we'd encourage you to first test the KubernetesPodOperator in your local environment. Follow our Running KubernetesPodOperator Locally for guidelines.

The KubernetesPodOperator on Astronomer

Import the Operator

First ensure you have the apache-airflow-providers-cncf-kubernetes package installed:

pip install apache-airflow-providers-cncf-kubernetes

You can then import the KubernetesPodOperator:

from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator

Specify Parameters

From here, instantiate the operator based on your image and setup:

from airflow.configuration import conf
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator


namespace = conf.get("kubernetes", "NAMESPACE")

KubernetesPodOperator(
namespace=namespace,
image="ubuntu:16.04",
cmds=["bash", "-cx"],
arguments=["echo", "10", "echo pwd"],
labels={"foo": "bar"},
name="airflow-test-pod",
is_delete_operator_pod=True,
in_cluster=True,
task_id="task-two",
get_logs=True,
)

To successfully instantiate the operator, you'll need to make note of a few parameters.

  1. namespace
    • On Astronomer, each Airflow deployment sits on top of a corresponding Kubernetes Namespace
    • If you're running the KubernetesPodOperator, it needs to know which namespace to run in and where to look for the config file
    • On Astronomer Software, this would be a combination of your platform namespace and your deployment's release name in the following format: base-namespace-deployment-release-name (e.g. astronomer-frigid-vacuum-0996)
    • The namespace variable is injected into your deployment's airflow.cfg, which means you can programmatically import the namespace as an Environment Variable (shown above)
  2. in_cluster
    • Set the in_cluster parameter to True in your code
    • This will tell your task to look inside the cluster for the Kubernetes config. In this setup, your workers are tied to a role with the right privileges in the cluster
  3. is_delete_operator_pod
    • Set the is_delete_operator_pod parameter to True in your code
    • This will delete completed pods in the namespace as they finish, keeping Airflow below its resource quotas

Add Resources to your Deployment on Astronomer

The KubernetesPodOperator is entirely powered by the resources allocated to the Extra Capacity slider of your deployment's Configure page in the Software UI in lieu of needing a Celery Worker (or Scheduler resources for those running the Local Executor). Raising the slider will increase your namespace's resource quota such that Airflow has permissions to successfully launch pods within your deployment's namespace.

Note: Your Airflow Scheduler and Webserver will remain necessary fixed resources that ensure the rest of your tasks can execute and that your deployment stays up and running.

In terms of resource allocation, we recommend starting with 10AU in Extra Capacity and scaling up from there as needed. If it's set to 0, you'll get a permissions error:

ERROR - Exception when attempting to create Namespace Pod.
Reason: Forbidden
"Failure","message":"pods is forbidden: User \"system:serviceaccount:astronomer-cloud-solar-orbit-4143:solar-orbit-4143-airflow-worker\" cannot create pods in the namespace \"datarouter\"","reason":"Forbidden","details":{"kind":"pods"},"code":403}

On Astronomer Software, the largest node a single pod can occupy is dependent on the size of your underlying node pool.

Note: If you need to increase your limit range on Astronomer Software, contact your system admin. \

Define Resources per Task

A notable advantage of leveraging Airflow's KubernetesPodOperator is that you can control compute resources in the task definition.

Note: If you're using the KubernetesExecutor, note that this value is separate from the executor_config parameter. In this case, the executor_config would only define the Airflow worker that is launching your Kubernetes task.

Example Task Definition:

from datetime import datetime, timedelta

from airflow import DAG
from airflow.configuration import conf
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from kubernetes.client import models as k8s

default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2019, 1, 1),
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
}

namespace = conf.get('kubernetes', 'NAMESPACE')

# This will detect the default namespace locally and read the
# environment namespace when deployed to Astronomer.
if namespace =='default':
config_file = '/usr/local/airflow/include/.kube/config'
in_cluster = False
else:
in_cluster = True
config_file = None

dag = DAG("example_kubernetes_pod", schedule_interval="@once", default_args=default_args)

# This is where we define our desired resources.
compute_resources = k8s.V1ResourceRequirements(
limits={"cpu": "800m", "memory": "3Gi"},
requests={"cpu": "800m", "memory": "3Gi"}
)

with dag:
KubernetesPodOperator(
namespace=namespace,
image="hello-world",
labels={"foo": "bar"},
name="airflow-test-pod",
task_id="task-one",
in_cluster=in_cluster, # if set to true, will look in the cluster, if false, looks for file
cluster_context="docker-for-desktop", # is ignored when in_cluster is set to True
config_file=config_file,
resources=compute_resources,
is_delete_operator_pod=True,
get_logs=True,
)

In the example above, we define resources by building the following V1ResourceRequirements object:

from kubernetes.client import models as k8s

compute_resources = k8s.V1ResourceRequirements(
limits={"cpu": "800m", "memory": "3Gi"},
requests={"cpu": "800m", "memory": "3Gi"}
)

This object allows you to specify Memory and CPU requests and limits for any given task and its corresponding Kubernetes Pod. For more information, read Kubernetes Documentation on Requests and Limits.

Once you've created the object, apply it to the resources parameter of the task. When this DAG runs, it will launch a Pod that runs the hello-world image, which is pulled from Docker Hub, in your Airflow Deployment's namespace with the resource requests defined above. Once the task finishes, the Pod will be gracefully terminate.

info

On Astronomer, the equivalent of 1AU is: requests={"cpu": "100m", "memory": "384Mi"}, limits={"cpu": "100m", "memory": "384Mi"}.

Pulling Images from a Private Registry

By default, the KubernetesPodOperator will look for images hosted publicly on Docker Hub. If you want to pull images from a private registry, you may do so.

Note: The KubernetesPodOperator doesn't support passing in image_pull_secrets until Airflow 1.10.2.

To pull images from a private registry on Astronomer Software:

  1. Retrieve a config.json file that contains your Docker credentials by following the Docker documentation. The generated file should look something like this:

    {
    "auths": {
    "https://index.docker.io/v1/": {
    "auth": "c3R...zE2"
    }
    }
    }
  2. Follow the Kubernetes documentation to create a secret based on your credentials.

  3. In your DAG code, import models from kubernetes.client and specify image_pull_secrets with your Kubernetes secret. After configuring this value, you can pull an image as you would from a public registry like in the following example.

    from kubernetes.client import models as k8s

    KubernetesPodOperator(
    namespace=namespace,
    image_pull_secrets=[k8s.V1LocalObjectReference("<your-secret-name>")],
    image="<your-docker-image>",
    cmds=["<commands-for-image>"],
    arguments=["<arguments-for-image>"],
    labels={"<pod-label>": "<label-name>"},
    name="<pod-name>",
    is_delete_operator_pod=True,
    in_cluster=True,
    task_id="<task-name>",
    get_logs=True,
    )

Local Testing

We recommend testing your DAGs locally before pushing them to a Deployment on Astronomer. For more information, read How to Run the KubernetesPodOperator Locally. That guide provides information on how to use MicroK8s or Docker for Kubernetes to run tasks with the KubernetesPodOperator in a local environment.

Note: To pull images from a private registry locally, you'll have to create a secret in your local namespace and similarly call it in your operator following the guidelines above.