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#
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from __future__ import annotations
from datetime import datetime
from typing import TYPE_CHECKING
import boto3
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
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, prune_logs
if TYPE_CHECKING:
from botocore.client import BaseClient
# 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 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,
verbose=True, # prints glue job logs in airflow logs
)
# [END howto_sensor_glue]
wait_for_job.poke_interval = 5
delete_bucket = S3DeleteBucketOperator(
task_id="delete_bucket",
trigger_rule=TriggerRule.ALL_DONE,
bucket_name=bucket_name,
force_delete=True,
)
log_cleanup = prune_logs(
[
# Format: ('log group name', 'log stream prefix')
("/aws-glue/crawlers", glue_crawler_name),
("/aws-glue/jobs/logs-v2", submit_glue_job.output),
("/aws-glue/jobs/error", submit_glue_job.output),
("/aws-glue/jobs/output", submit_glue_job.output),
]
)
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,
log_cleanup,
)
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)
