本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。
以下代码示例演示了如何使用 Amazon EMR 和 Amazon MWAA 启用集成。
版本
-
本页上的示例代码可与 Python 3.7
中的 Apache Airflow v1 一起使用。
代码示例
"""
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
from airflow import DAG
from airflow.providers.amazon.aws.operators.emr import EmrAddStepsOperator
from airflow.providers.amazon.aws.sensors.emr import EmrStepSensor
from airflow.providers.amazon.aws.operators.emr import EmrCreateJobFlowOperator
from airflow.utils.dates import days_ago
from datetime import timedelta
import os
DAG_ID = os.path.basename(__file__).replace(".py", "")
DEFAULT_ARGS = {
'owner': 'airflow',
'depends_on_past': False,
'email': ['airflow@example.com'],
'email_on_failure': False,
'email_on_retry': False,
}
SPARK_STEPS = [
{
'Name': 'calculate_pi',
'ActionOnFailure': 'CONTINUE',
'HadoopJarStep': {
'Jar': 'command-runner.jar',
'Args': ['/usr/lib/spark/bin/run-example', 'SparkPi', '10'],
},
}
]
JOB_FLOW_OVERRIDES = {
'Name': 'my-demo-cluster',
'ReleaseLabel': 'emr-5.30.1',
'Applications': [
{
'Name': 'Spark'
},
],
'Instances': {
'InstanceGroups': [
{
'Name': "Master nodes",
'Market': 'ON_DEMAND',
'InstanceRole': 'MASTER',
'InstanceType': 'm5.xlarge',
'InstanceCount': 1,
},
{
'Name': "Slave nodes",
'Market': 'ON_DEMAND',
'InstanceRole': 'CORE',
'InstanceType': 'm5.xlarge',
'InstanceCount': 2,
}
],
'KeepJobFlowAliveWhenNoSteps': False,
'TerminationProtected': False,
'Ec2KeyName': 'mykeypair',
},
'VisibleToAllUsers': True,
'JobFlowRole': 'EMR_EC2_DefaultRole',
'ServiceRole': 'EMR_DefaultRole'
}
with DAG(
dag_id=DAG_ID,
default_args=DEFAULT_ARGS,
dagrun_timeout=timedelta(hours=2),
start_date=days_ago(1),
schedule_interval='@once',
tags=['emr'],
) as dag:
cluster_creator = EmrCreateJobFlowOperator(
task_id='create_job_flow',
job_flow_overrides=JOB_FLOW_OVERRIDES
)
step_adder = EmrAddStepsOperator(
task_id='add_steps',
job_flow_id="{{ task_instance.xcom_pull(task_ids='create_job_flow', key='return_value') }}",
aws_conn_id='aws_default',
steps=SPARK_STEPS,
)
step_checker = EmrStepSensor(
task_id='watch_step',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
step_id="{{ task_instance.xcom_pull(task_ids='add_steps', key='return_value')[0] }}",
aws_conn_id='aws_default',
)
cluster_creator >> step_adder >> step_checker 