Source code for tests.system.amazon.aws.example_glue_databrew

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from __future__ import annotations

import boto3
import pendulum

from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.providers.amazon.aws.operators.glue_databrew import (
    GlueDataBrewStartJobOperator,
)
from airflow.providers.amazon.aws.operators.s3 import (
    S3CreateBucketOperator,
    S3CreateObjectOperator,
    S3DeleteBucketOperator,
)
from airflow.utils.trigger_rule import TriggerRule

from providers.tests.system.amazon.aws.utils import SystemTestContextBuilder

[docs]DAG_ID = "example_glue_databrew"
# Externally fetched variables:
[docs]ROLE_ARN_KEY = "ROLE_ARN"
[docs]sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()
[docs]EXAMPLE_JSON = "{}"
@task
[docs]def create_dataset(dataset_name: str, bucket_name: str, object_key: str): client = boto3.client("databrew") client.create_dataset( Name=dataset_name, Format="JSON", FormatOptions={ "Json": {"MultiLine": False}, }, Input={ "S3InputDefinition": { "Bucket": bucket_name, "Key": object_key, }, }, )
@task
[docs]def create_job( dataset_name: str, job_name: str, bucket_output_name: str, object_output_key: str, role_arn: str ): client = boto3.client("databrew") client.create_profile_job( DatasetName=dataset_name, Name=job_name, LogSubscription="ENABLE", OutputLocation={ "Bucket": bucket_output_name, "Key": object_output_key, }, RoleArn=role_arn, )
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_dataset(dataset_name: str): client = boto3.client("databrew") client.delete_dataset(Name=dataset_name)
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_job(job_name: str): client = boto3.client("databrew") client.delete_job(Name=job_name)
with DAG(DAG_ID, schedule="@once", start_date=pendulum.datetime(2023, 1, 1, tz="UTC"), catchup=False) as dag:
[docs] test_context = sys_test_context_task()
env_id = test_context["ENV_ID"] role_arn = test_context[ROLE_ARN_KEY] bucket_name = f"{env_id}-bucket-databrew" output_bucket_name = f"{env_id}-output-bucket-databrew" file_name = "data.json" dataset_name = f"{env_id}-dataset" job_name = f"{env_id}-databrew-job" create_bucket = S3CreateBucketOperator( task_id="create_bucket", bucket_name=bucket_name, ) create_output_bucket = S3CreateBucketOperator( task_id="create_output_bucket", bucket_name=output_bucket_name, ) upload_file = S3CreateObjectOperator( task_id="upload_file", s3_bucket=bucket_name, s3_key=file_name, data=EXAMPLE_JSON, replace=True, ) # [START howto_operator_glue_databrew_start] start_job = GlueDataBrewStartJobOperator(task_id="startjob", job_name=job_name, waiter_delay=15) # [END howto_operator_glue_databrew_start] delete_bucket = S3DeleteBucketOperator( task_id="delete_bucket", trigger_rule=TriggerRule.ALL_DONE, bucket_name=bucket_name, force_delete=True, ) delete_output_bucket = S3DeleteBucketOperator( task_id="delete_output_bucket", trigger_rule=TriggerRule.ALL_DONE, bucket_name=output_bucket_name, force_delete=True, ) chain( # TEST SETUP test_context, create_bucket, create_output_bucket, upload_file, create_dataset(dataset_name, bucket_name, file_name), create_job(dataset_name, job_name, output_bucket_name, "output.json", role_arn), # TEST BODY start_job, # TEST TEARDOWN delete_job(job_name), delete_dataset(dataset_name), delete_bucket, delete_output_bucket, ) from dev.tests_common.test_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 dev.tests_common.test_utils.system_tests 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)

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