Source code for

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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 import GlueJobOperator
from import GlueCrawlerOperator
from import (
from import GlueJobSensor
from import GlueCrawlerSensor
from airflow.utils.trigger_rule import TriggerRule
from import ENV_ID_KEY, SystemTestContextBuilder, prune_logs

[docs]DAG_ID = "example_glue"
# Externally fetched variables: # Role needs S3 putobject/getobject access as well as the glue service role, # see docs here:
[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') """
[docs]def get_role_name(arn: str) -> str: return arn.split("/")[-1]
[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="", 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}/", 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/
[docs]test_run = get_test_run(dag)

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