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from __future__ import annotations
from datetime import datetime
import boto3
from botocore.client import BaseClient
from airflow import DAG
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.providers.amazon.aws.operators.glue import GlueJobOperator
from airflow.providers.amazon.aws.operators.glue_crawler import GlueCrawlerOperator
from airflow.providers.amazon.aws.operators.s3 import (
S3CreateBucketOperator,
S3CreateObjectOperator,
S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.glue import GlueJobSensor
from airflow.providers.amazon.aws.sensors.glue_crawler import GlueCrawlerSensor
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder, purge_logs
# Externally fetched variables:
# Role needs S3 putobject/getobject access as well as the glue service role,
# see docs here: https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role.html
[docs]ROLE_ARN_KEY = "ROLE_ARN"
[docs]sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()
# 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 = """
from pyspark.context import SparkContext
from awsglue.context import GlueContext
glueContext = GlueContext(SparkContext.getOrCreate())
datasource = glueContext.create_dynamic_frame.from_catalog(
database='{db_name}', table_name='input')
print('There are %s items in the table' % datasource.count())
datasource.toDF().write.format('csv').mode("append").save('s3://{bucket_name}/output')
"""
@task
[docs]def get_role_name(arn: str) -> str:
return arn.split("/")[-1]
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_logs(job_id: str, crawler_name: str) -> None:
"""
Glue generates four Cloudwatch log groups and multiple log streams and leaves them.
"""
generated_log_groups: list[tuple[str, str | None]] = [
# Format: ('log group name', 'log stream prefix')
("/aws-glue/crawlers", crawler_name),
("/aws-glue/jobs/logs-v2", job_id),
("/aws-glue/jobs/error", job_id),
("/aws-glue/jobs/output", job_id),
]
purge_logs(generated_log_groups)
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def glue_cleanup(crawler_name: str, job_name: str, db_name: str) -> None:
client: BaseClient = boto3.client("glue")
client.delete_crawler(Name=crawler_name)
client.delete_job(JobName=job_name)
client.delete_database(Name=db_name)
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]
glue_crawler_name = f"{env_id}_crawler"
glue_db_name = f"{env_id}_glue_db"
glue_job_name = f"{env_id}_glue_job"
bucket_name = f"{env_id}-bucket"
role_name = get_role_name(role_arn)
glue_crawler_config = {
"Name": glue_crawler_name,
"Role": role_arn,
"DatabaseName": glue_db_name,
"Targets": {"S3Targets": [{"Path": f"{bucket_name}/input"}]},
}
create_bucket = S3CreateBucketOperator(
task_id="create_bucket",
bucket_name=bucket_name,
)
upload_csv = S3CreateObjectOperator(
task_id="upload_csv",
s3_bucket=bucket_name,
s3_key="input/input.csv",
data=EXAMPLE_CSV,
replace=True,
)
upload_script = S3CreateObjectOperator(
task_id="upload_script",
s3_bucket=bucket_name,
s3_key="etl_script.py",
data=EXAMPLE_SCRIPT.format(db_name=glue_db_name, bucket_name=bucket_name),
replace=True,
)
# [START howto_operator_glue_crawler]
crawl_s3 = GlueCrawlerOperator(
task_id="crawl_s3",
config=glue_crawler_config,
)
# [END howto_operator_glue_crawler]
# GlueCrawlerOperator waits by default, setting as False to test the Sensor below.
crawl_s3.wait_for_completion = False
# [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]
submit_glue_job = GlueJobOperator(
task_id="submit_glue_job",
job_name=glue_job_name,
script_location=f"s3://{bucket_name}/etl_script.py",
s3_bucket=bucket_name,
iam_role_name=role_name,
create_job_kwargs={"GlueVersion": "3.0", "NumberOfWorkers": 2, "WorkerType": "G.1X"},
)
# [END howto_operator_glue]
# GlueJobOperator waits by default, setting as False to test the Sensor below.
submit_glue_job.wait_for_completion = False
# [START howto_sensor_glue]
wait_for_job = GlueJobSensor(
task_id="wait_for_job",
job_name=glue_job_name,
# Job ID extracted from previous Glue Job Operator task
run_id=submit_glue_job.output,
)
# [END howto_sensor_glue]
delete_bucket = S3DeleteBucketOperator(
task_id="delete_bucket",
trigger_rule=TriggerRule.ALL_DONE,
bucket_name=bucket_name,
force_delete=True,
)
chain(
# TEST SETUP
test_context,
create_bucket,
upload_csv,
upload_script,
# TEST BODY
crawl_s3,
wait_for_crawl,
submit_glue_job,
wait_for_job,
# TEST TEARDOWN
glue_cleanup(glue_crawler_name, glue_job_name, glue_db_name),
delete_bucket,
delete_logs(submit_glue_job.output, glue_crawler_name),
)
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)