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#
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#
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from __future__ import annotations
import json
import subprocess
from datetime import datetime
import boto3
from airflow import DAG
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.providers.amazon.aws.hooks.eks import ClusterStates, NodegroupStates
from airflow.providers.amazon.aws.operators.eks import EksCreateClusterOperator, EksDeleteClusterOperator
from airflow.providers.amazon.aws.operators.emr import EmrContainerOperator, EmrEksCreateClusterOperator
from airflow.providers.amazon.aws.operators.s3 import (
S3CreateBucketOperator,
S3CreateObjectOperator,
S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.eks import EksClusterStateSensor, EksNodegroupStateSensor
from airflow.providers.amazon.aws.sensors.emr import EmrContainerSensor
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder
[docs]DAG_ID = "example_emr_eks"
# Externally fetched variables
[docs]ROLE_ARN_KEY = "ROLE_ARN"
[docs]JOB_ROLE_ARN_KEY = "JOB_ROLE_ARN"
[docs]sys_test_context_task = (
SystemTestContextBuilder()
.add_variable(ROLE_ARN_KEY)
.add_variable(JOB_ROLE_ARN_KEY)
.add_variable(SUBNETS_KEY, split_string=True)
.build()
)
[docs]S3_FILE_CONTENT = """
k = 1
s = 0
for i in range(1000000):
if i % 2 == 0:
s += 4/k
else:
s -= 4/k
k += 2
print(s)
"""
@task
[docs]def enable_access_emr_on_eks(cluster, ns):
# Install eksctl and enable access for EMR on EKS
# See https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/setting-up-cluster-access.html
file = "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz"
commands = f"""
curl --silent --location "{file}" | tar xz -C /tmp &&
sudo mv /tmp/eksctl /usr/local/bin &&
eksctl create iamidentitymapping --cluster {cluster} --namespace {ns} --service-name "emr-containers"
"""
build = subprocess.Popen(
commands,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
_, err = build.communicate()
if build.returncode != 0:
raise RuntimeError(err)
@task
[docs]def get_execution_role_name() -> str:
return boto3.client("sts").get_caller_identity()["Arn"].split("/")[-2]
@task
[docs]def update_trust_policy_execution_role(cluster_name, cluster_namespace, role_name):
# Remove any already existing trusted entities added with "update-role-trust-policy"
# Prevent getting an error "Cannot exceed quota for ACLSizePerRole"
client = boto3.client("iam")
role_trust_policy = client.get_role(RoleName=role_name)["Role"]["AssumeRolePolicyDocument"]
# We assume if the action is sts:AssumeRoleWithWebIdentity, the statement had been added with
# "update-role-trust-policy". Removing it to not exceed the quota
role_trust_policy["Statement"] = list(
filter(
lambda statement: statement["Action"] != "sts:AssumeRoleWithWebIdentity",
role_trust_policy["Statement"],
)
)
client.update_assume_role_policy(
RoleName=role_name,
PolicyDocument=json.dumps(role_trust_policy),
)
# See https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/setting-up-trust-policy.html
# The action "update-role-trust-policy" is not available in boto3, thus we need to do it using AWS CLI
commands = (
f"aws emr-containers update-role-trust-policy --cluster-name {cluster_name} "
f"--namespace {cluster_namespace} --role-name {role_name}"
)
build = subprocess.Popen(
commands,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
_, err = build.communicate()
if build.returncode != 0:
raise RuntimeError(err)
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_virtual_cluster(virtual_cluster_id):
boto3.client("emr-containers").delete_virtual_cluster(
id=virtual_cluster_id,
)
with DAG(
dag_id=DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
tags=["example"],
catchup=False,
) as dag:
[docs] test_context = sys_test_context_task()
env_id = test_context[ENV_ID_KEY]
role_arn = test_context[ROLE_ARN_KEY]
subnets = test_context[SUBNETS_KEY]
job_role_arn = test_context[JOB_ROLE_ARN_KEY]
s3_bucket_name = f"{env_id}-bucket"
eks_cluster_name = f"{env_id}-cluster"
virtual_cluster_name = f"{env_id}-virtual-cluster"
nodegroup_name = f"{env_id}-nodegroup"
eks_namespace = "default"
# [START howto_operator_emr_eks_config]
job_driver_arg = {
"sparkSubmitJobDriver": {
"entryPoint": f"s3://{s3_bucket_name}/{S3_FILE_NAME}",
"sparkSubmitParameters": "--conf spark.executors.instances=2 --conf spark.executors.memory=2G "
"--conf spark.executor.cores=2 --conf spark.driver.cores=1",
}
}
configuration_overrides_arg = {
"monitoringConfiguration": {
"cloudWatchMonitoringConfiguration": {
"logGroupName": "/emr-eks-jobs",
"logStreamNamePrefix": "airflow",
}
},
}
# [END howto_operator_emr_eks_config]
create_bucket = S3CreateBucketOperator(
task_id="create_bucket",
bucket_name=s3_bucket_name,
)
upload_s3_file = S3CreateObjectOperator(
task_id="upload_s3_file",
s3_bucket=s3_bucket_name,
s3_key=S3_FILE_NAME,
data=S3_FILE_CONTENT,
)
create_cluster_and_nodegroup = EksCreateClusterOperator(
task_id="create_cluster_and_nodegroup",
cluster_name=eks_cluster_name,
nodegroup_name=nodegroup_name,
cluster_role_arn=role_arn,
# Opting to use the same ARN for the cluster and the nodegroup here,
# but a different ARN could be configured and passed if desired.
nodegroup_role_arn=role_arn,
resources_vpc_config={"subnetIds": subnets},
)
await_create_nodegroup = EksNodegroupStateSensor(
task_id="await_create_nodegroup",
cluster_name=eks_cluster_name,
nodegroup_name=nodegroup_name,
target_state=NodegroupStates.ACTIVE,
)
emr_access_on_eks = enable_access_emr_on_eks(eks_cluster_name, eks_namespace)
# [START howto_operator_emr_eks_create_cluster]
create_emr_eks_cluster = EmrEksCreateClusterOperator(
task_id="create_emr_eks_cluster",
virtual_cluster_name=virtual_cluster_name,
eks_cluster_name=eks_cluster_name,
eks_namespace=eks_namespace,
)
# [END howto_operator_emr_eks_create_cluster]
trust_policy_update = update_trust_policy_execution_role(
eks_cluster_name, eks_namespace, get_execution_role_name()
)
# [START howto_operator_emr_container]
job_starter = EmrContainerOperator(
task_id="start_job",
virtual_cluster_id=str(create_emr_eks_cluster.output),
execution_role_arn=job_role_arn,
release_label="emr-6.3.0-latest",
job_driver=job_driver_arg,
configuration_overrides=configuration_overrides_arg,
name="pi.py",
)
# [END howto_operator_emr_container]
job_starter.wait_for_completion = False
# [START howto_sensor_emr_container]
job_waiter = EmrContainerSensor(
task_id="job_waiter",
virtual_cluster_id=str(create_emr_eks_cluster.output),
job_id=str(job_starter.output),
)
# [END howto_sensor_emr_container]
delete_eks_cluster = EksDeleteClusterOperator(
task_id="delete_eks_cluster",
cluster_name=eks_cluster_name,
force_delete_compute=True,
trigger_rule=TriggerRule.ALL_DONE,
)
await_delete_eks_cluster = EksClusterStateSensor(
task_id="await_delete_eks_cluster",
cluster_name=eks_cluster_name,
target_state=ClusterStates.NONEXISTENT,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_bucket = S3DeleteBucketOperator(
task_id="delete_bucket",
bucket_name=s3_bucket_name,
force_delete=True,
trigger_rule=TriggerRule.ALL_DONE,
)
chain(
# TEST SETUP
test_context,
create_bucket,
upload_s3_file,
create_cluster_and_nodegroup,
await_create_nodegroup,
emr_access_on_eks,
trust_policy_update,
# TEST BODY
create_emr_eks_cluster,
job_starter,
job_waiter,
# TEST TEARDOWN
delete_virtual_cluster(str(create_emr_eks_cluster.output)),
delete_eks_cluster,
await_delete_eks_cluster,
delete_bucket,
)
from tests.system.utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)