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import os
from datetime import timedelta
from airflow import DAG
from airflow.contrib.operators.emr_add_steps_operator import EmrAddStepsOperator
from airflow.contrib.operators.emr_create_job_flow_operator import EmrCreateJobFlowOperator
from airflow.contrib.sensors.emr_step_sensor import EmrStepSensor
from airflow.models import Variable
from airflow.utils.dates import days_ago
# ************** AIRFLOW VARIABLES **************
bootstrap_bucket = Variable.get('bootstrap_bucket')
emr_ec2_key_pair = Variable.get('emr_ec2_key_pair')
job_flow_role = Variable.get('job_flow_role')
logs_bucket = Variable.get('logs_bucket')
release_label = Variable.get('release_label')
service_role = Variable.get('service_role')
work_bucket = Variable.get('work_bucket')
# ***********************************************
DAG_ID = os.path.basename(__file__).replace('.py', '')
DEFAULT_ARGS = {
'owner': 'airflow',
'depends_on_past': False,
'email': ["{{ dag_run.conf['airflow_email'] }}"],
'email_on_failure': ["{{ dag_run.conf['email_on_failure'] }}"],
'email_on_retry': ["{{ dag_run.conf['email_on_retry'] }}"],
}
SPARK_STEPS = [
{
'Name': 'Bakery Sales',
'ActionOnFailure': 'CONTINUE',
'HadoopJarStep': {
'Jar': 'command-runner.jar',
'Args': [
'spark-submit',
'--deploy-mode',
'cluster',
'--master',
'yarn',
'--conf',
'spark.yarn.submit.waitAppCompletion=true',
's3a://{{ var.value.work_bucket }}/analyze/bakery_sales_ssm.py'
]
}
}
]
JOB_FLOW_OVERRIDES = {
'Name': 'demo-cluster-airflow',
'ReleaseLabel': '{{ var.value.release_label }}',
'LogUri': 's3n://{{ var.value.logs_bucket }}',
'Applications': [
{
'Name': 'Spark'
},
],
'Instances': {
'InstanceFleets': [
{
'Name': 'MASTER',
'InstanceFleetType': 'MASTER',
'TargetSpotCapacity': 1,
'InstanceTypeConfigs': [
{
'InstanceType': 'm5.xlarge',
},
]
},
{
'Name': 'CORE',
'InstanceFleetType': 'CORE',
'TargetSpotCapacity': 2,
'InstanceTypeConfigs': [
{
'InstanceType': 'r5.xlarge',
},
],
},
],
'KeepJobFlowAliveWhenNoSteps': False,
'TerminationProtected': False,
'Ec2KeyName': '{{ var.value.emr_ec2_key_pair }}',
},
'BootstrapActions': [
{
'Name': 'string',
'ScriptBootstrapAction': {
'Path': 's3://{{ var.value.bootstrap_bucket }}/bootstrap_actions.sh',
}
},
],
'Configurations': [
{
'Classification': 'spark-hive-site',
'Properties': {
'hive.metastore.client.factory.class': 'com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory'
}
}
],
'VisibleToAllUsers': True,
'JobFlowRole': '{{ var.value.job_flow_role }}',
'ServiceRole': '{{ var.value.service_role }}',
'EbsRootVolumeSize': 32,
'StepConcurrencyLevel': 1,
'Tags': [
{
'Key': 'Environment',
'Value': 'Development'
},
{
'Key': 'Name',
'Value': 'Airflow EMR Demo Project'
},
{
'Key': 'Owner',
'Value': 'Data Analytics Team'
}
]
}
with DAG(
dag_id=DAG_ID,
description='Analyze Bakery Sales with Amazon EMR',
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
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