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#
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#
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from __future__ import annotations
import boto3
import pendulum
from airflow.providers.amazon.aws.operators.glue_databrew import (
GlueDataBrewStartJobOperator,
)
from airflow.providers.amazon.aws.operators.s3 import (
S3CreateBucketOperator,
S3CreateObjectOperator,
S3DeleteBucketOperator,
)
from tests_common.test_utils.version_compat import AIRFLOW_V_3_0_PLUS
if AIRFLOW_V_3_0_PLUS:
from airflow.sdk import DAG, chain, task
else:
# Airflow 2 path
from airflow.decorators import task # type: ignore[attr-defined,no-redef]
from airflow.models.baseoperator import chain # type: ignore[attr-defined,no-redef]
from airflow.models.dag import DAG # type: ignore[attr-defined,no-redef,assignment]
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
from system.amazon.aws.utils import SystemTestContextBuilder
DAG_ID = "example_glue_databrew"
# Externally fetched variables:
ROLE_ARN_KEY = "ROLE_ARN"
sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()
EXAMPLE_JSON = "{}"
@task
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
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)
def delete_dataset(dataset_name: str):
client = boto3.client("databrew")
client.delete_dataset(Name=dataset_name)
@task(trigger_rule=TriggerRule.ALL_DONE)
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:
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 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 tests_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)