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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
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from datetime import datetime
from os import getenv
from airflow import DAG
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.operators.glue import GlueJobOperator
from airflow.providers.amazon.aws.operators.glue_crawler import GlueCrawlerOperator
from airflow.providers.amazon.aws.sensors.glue import GlueJobSensor
from airflow.providers.amazon.aws.sensors.glue_crawler import GlueCrawlerSensor
[docs]GLUE_DATABASE_NAME = getenv('GLUE_DATABASE_NAME', 'glue_database_name')
[docs]GLUE_EXAMPLE_S3_BUCKET = getenv('GLUE_EXAMPLE_S3_BUCKET', 'glue_example_s3_bucket')
# Role needs putobject/getobject access to the above bucket as well as the glue
# service role, see docs here: https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role.html
[docs]GLUE_CRAWLER_ROLE = getenv('GLUE_CRAWLER_ROLE', 'glue_crawler_role')
[docs]GLUE_CRAWLER_NAME = 'example_crawler'
[docs]GLUE_CRAWLER_CONFIG = {
'Name': GLUE_CRAWLER_NAME,
'Role': GLUE_CRAWLER_ROLE,
'DatabaseName': GLUE_DATABASE_NAME,
'Targets': {
'S3Targets': [
{
'Path': f'{GLUE_EXAMPLE_S3_BUCKET}/input',
}
]
},
}
# Example csv data used as input to the example AWS Glue Job.
[docs]EXAMPLE_CSV = '''
apple,0.5
milk,2.5
bread,4.0
'''
# Example Spark script to operate on the above sample csv data.
[docs]EXAMPLE_SCRIPT = f'''
from pyspark.context import SparkContext
from awsglue.context import GlueContext
glueContext = GlueContext(SparkContext.getOrCreate())
datasource = glueContext.create_dynamic_frame.from_catalog(
database='{GLUE_DATABASE_NAME}', table_name='input')
print('There are %s items in the table' % datasource.count())
datasource.toDF().write.format('csv').mode("append").save('s3://{GLUE_EXAMPLE_S3_BUCKET}/output')
'''
@task(task_id='setup__upload_artifacts_to_s3')
[docs]def upload_artifacts_to_s3():
'''Upload example CSV input data and an example Spark script to be used by the Glue Job'''
s3_hook = S3Hook()
s3_load_kwargs = {"replace": True, "bucket_name": GLUE_EXAMPLE_S3_BUCKET}
s3_hook.load_string(string_data=EXAMPLE_CSV, key='input/input.csv', **s3_load_kwargs)
s3_hook.load_string(string_data=EXAMPLE_SCRIPT, key='etl_script.py', **s3_load_kwargs)
with DAG(
dag_id='example_glue',
schedule_interval=None,
start_date=datetime(2021, 1, 1),
tags=['example'],
catchup=False,
) as glue_dag:
[docs] setup_upload_artifacts_to_s3 = upload_artifacts_to_s3()
# [START howto_operator_glue_crawler]
crawl_s3 = GlueCrawlerOperator(
task_id='crawl_s3',
config=GLUE_CRAWLER_CONFIG,
wait_for_completion=False,
)
# [END howto_operator_glue_crawler]
# [START howto_sensor_glue_crawler]
wait_for_crawl = GlueCrawlerSensor(task_id='wait_for_crawl', crawler_name=GLUE_CRAWLER_NAME)
# [END howto_sensor_glue_crawler]
# [START howto_operator_glue]
job_name = 'example_glue_job'
submit_glue_job = GlueJobOperator(
task_id='submit_glue_job',
job_name=job_name,
wait_for_completion=False,
script_location=f's3://{GLUE_EXAMPLE_S3_BUCKET}/etl_script.py',
s3_bucket=GLUE_EXAMPLE_S3_BUCKET,
iam_role_name=GLUE_CRAWLER_ROLE.split('/')[-1],
create_job_kwargs={'GlueVersion': '3.0', 'NumberOfWorkers': 2, 'WorkerType': 'G.1X'},
)
# [END howto_operator_glue]
# [START howto_sensor_glue]
wait_for_job = GlueJobSensor(
task_id='wait_for_job',
job_name=job_name,
# Job ID extracted from previous Glue Job Operator task
run_id=submit_glue_job.output,
)
# [END howto_sensor_glue]
chain(setup_upload_artifacts_to_s3, crawl_s3, wait_for_crawl, submit_glue_job, wait_for_job)
