Source code for airflow.providers.amazon.aws.example_dags.example_eks_with_fargate_in_one_step

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from datetime import datetime
from os import environ

from airflow.models.dag import DAG
from airflow.providers.amazon.aws.hooks.eks import ClusterStates, FargateProfileStates
from airflow.providers.amazon.aws.operators.eks import (
    EKSCreateClusterOperator,
    EKSDeleteClusterOperator,
    EKSPodOperator,
)
from airflow.providers.amazon.aws.sensors.eks import EKSClusterStateSensor, EKSFargateProfileStateSensor

CLUSTER_NAME = 'fargate-all-in-one'
FARGATE_PROFILE_NAME = f'{CLUSTER_NAME}-profile'

ROLE_ARN = environ.get('EKS_DEMO_ROLE_ARN', 'arn:aws:iam::123456789012:role/role_name')
SUBNETS = environ.get('EKS_DEMO_SUBNETS', 'subnet-12345ab subnet-67890cd').split(' ')
VPC_CONFIG = {
    'subnetIds': SUBNETS,
    'endpointPublicAccess': True,
    'endpointPrivateAccess': False,
}


with DAG(
    dag_id='example-create-cluster-and-fargate-all-in-one',
    default_args={'cluster_name': CLUSTER_NAME},
    schedule_interval=None,
    start_date=datetime(2021, 1, 1),
    max_active_runs=1,
    tags=['example'],
) as dag:

    # [START howto_operator_eks_create_cluster_with_fargate_profile]
    # Create an Amazon EKS cluster control plane and an AWS Fargate compute platform in one step.
    create_cluster_and_fargate_profile = EKSCreateClusterOperator(
        task_id='create_eks_cluster_and_fargate_profile',
        cluster_role_arn=ROLE_ARN,
        resources_vpc_config=VPC_CONFIG,
        compute='fargate',
        fargate_profile_name=FARGATE_PROFILE_NAME,
        # Opting to use the same ARN for the cluster and the pod here,
        # but a different ARN could be configured and passed if desired.
        fargate_pod_execution_role_arn=ROLE_ARN,
    )
    # [END howto_operator_eks_create_cluster_with_fargate_profile]

    await_create_fargate_profile = EKSFargateProfileStateSensor(
        task_id='wait_for_create_fargate_profile',
        fargate_profile_name=FARGATE_PROFILE_NAME,
        target_state=FargateProfileStates.ACTIVE,
    )

    start_pod = EKSPodOperator(
        task_id="run_pod",
        pod_name="run_pod",
        image="amazon/aws-cli:latest",
        cmds=["sh", "-c", "echo Test Airflow; date"],
        labels={"demo": "hello_world"},
        get_logs=True,
        # Delete the pod when it reaches its final state, or the execution is interrupted.
        is_delete_operator_pod=True,
    )

    # An Amazon EKS cluster can not be deleted with attached resources such as nodegroups or Fargate profiles.
    # Setting the `force` to `True` will delete any attached resources before deleting the cluster.
    delete_all = EKSDeleteClusterOperator(
        task_id='delete_fargate_profile_and_cluster', force_delete_compute=True
    )

    await_delete_cluster = EKSClusterStateSensor(
        task_id='wait_for_delete_cluster',
        target_state=ClusterStates.NONEXISTENT,
    )

    (
        create_cluster_and_fargate_profile
        >> await_create_fargate_profile
        >> start_pod
        >> delete_all
        >> await_delete_cluster
    )

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