# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
from datetime import datetime
from airflow.providers.amazon.aws.hooks.glue import GlueDataQualityHook
from airflow.providers.amazon.aws.operators.athena import AthenaOperator
from airflow.providers.amazon.aws.operators.glue import (
GlueDataQualityRuleRecommendationRunOperator,
GlueDataQualityRuleSetEvaluationRunOperator,
)
from airflow.providers.amazon.aws.operators.s3 import (
S3CreateBucketOperator,
S3CreateObjectOperator,
S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.glue import (
GlueDataQualityRuleRecommendationRunSensor,
GlueDataQualityRuleSetEvaluationRunSensor,
)
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, task_group
else:
# Airflow 2 path
from airflow.decorators import task, task_group # 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
ROLE_ARN_KEY = "ROLE_ARN"
sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()
DAG_ID = "example_glue_data_quality_with_recommendation"
SAMPLE_DATA = """"Alice",20
"Bob",25
"Charlie",30"""
SAMPLE_FILENAME = "airflow_sample.csv"
@task_group
def glue_data_quality_recommendation_workflow():
# [START howto_operator_glue_data_quality_rule_recommendation_run]
recommendation_run = GlueDataQualityRuleRecommendationRunOperator(
task_id="recommendation_run",
datasource={
"GlueTable": {
"TableName": athena_table,
"DatabaseName": athena_database,
}
},
role=test_context[ROLE_ARN_KEY],
recommendation_run_kwargs={"CreatedRulesetName": rule_set_name},
)
# [END howto_operator_glue_data_quality_rule_recommendation_run]
recommendation_run.wait_for_completion = False
# [START howto_sensor_glue_data_quality_rule_recommendation_run]
await_recommendation_run_sensor = GlueDataQualityRuleRecommendationRunSensor(
task_id="await_recommendation_run_sensor",
recommendation_run_id=recommendation_run.output,
)
# [END howto_sensor_glue_data_quality_rule_recommendation_run]
start_evaluation_run = GlueDataQualityRuleSetEvaluationRunOperator(
task_id="start_evaluation_run",
datasource={
"GlueTable": {
"TableName": athena_table,
"DatabaseName": athena_database,
}
},
role=test_context[ROLE_ARN_KEY],
rule_set_names=[rule_set_name],
# The ruleset is generated dynamically by Glue's recommendation engine and is
# non-deterministic, so recommended rules may not always pass against the sample
# data. This system test only needs to exercise the operators/sensors end-to-end,
# not assert that every recommended rule passes, so do not fail on rule failures.
verify_result_status=False,
)
start_evaluation_run.wait_for_completion = False
await_evaluation_run_sensor = GlueDataQualityRuleSetEvaluationRunSensor(
task_id="await_evaluation_run_sensor",
evaluation_run_id=start_evaluation_run.output,
# The ruleset is generated dynamically by Glue's recommendation engine and is
# non-deterministic, so recommended rules may not always pass against the sample
# data. This system test only needs to exercise the operators/sensors end-to-end,
# not assert that every recommended rule passes, so do not fail on rule failures.
verify_result_status=False,
)
chain(
recommendation_run, await_recommendation_run_sensor, start_evaluation_run, await_evaluation_run_sensor
)
@task(trigger_rule=TriggerRule.ALL_DONE)
def delete_ruleset(ruleset_name):
hook = GlueDataQualityHook()
hook.conn.delete_data_quality_ruleset(Name=ruleset_name)
with DAG(
dag_id=DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as dag:
test_context = sys_test_context_task()
env_id = test_context["ENV_ID"]
rule_set_name = f"{env_id}-recommendation-ruleset"
s3_bucket = f"{env_id}-glue-dq-athena-bucket"
athena_table = f"{env_id}_test_glue_dq_table"
athena_database = f"{env_id}_glue_dq_default"
query_create_database = f"CREATE DATABASE IF NOT EXISTS {athena_database}"
query_create_table = f"""CREATE EXTERNAL TABLE IF NOT EXISTS {athena_database}.{athena_table}
( `name` string, `age` int )
ROW FORMAT SERDE "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"
WITH SERDEPROPERTIES ( "serialization.format" = ",", "field.delim" = "," )
LOCATION "s3://{s3_bucket}//{athena_table}"
TBLPROPERTIES ("has_encrypted_data"="false")
"""
query_read_table = f"SELECT * from {athena_database}.{athena_table}"
query_drop_table = f"DROP TABLE IF EXISTS {athena_database}.{athena_table}"
query_drop_database = f"DROP DATABASE IF EXISTS {athena_database}"
create_s3_bucket = S3CreateBucketOperator(task_id="create_s3_bucket", bucket_name=s3_bucket)
upload_sample_data = S3CreateObjectOperator(
task_id="upload_sample_data",
s3_bucket=s3_bucket,
s3_key=f"{athena_table}/{SAMPLE_FILENAME}",
data=SAMPLE_DATA,
replace=True,
)
create_database = AthenaOperator(
task_id="create_database",
query=query_create_database,
database=athena_database,
output_location=f"s3://{s3_bucket}/",
sleep_time=1,
)
create_table = AthenaOperator(
task_id="create_table",
query=query_create_table,
database=athena_database,
output_location=f"s3://{s3_bucket}/",
sleep_time=1,
)
drop_table = AthenaOperator(
task_id="drop_table",
query=query_drop_table,
database=athena_database,
output_location=f"s3://{s3_bucket}/",
trigger_rule=TriggerRule.ALL_DONE,
sleep_time=1,
)
drop_database = AthenaOperator(
task_id="drop_database",
query=query_drop_database,
database=athena_database,
output_location=f"s3://{s3_bucket}/",
trigger_rule=TriggerRule.ALL_DONE,
sleep_time=1,
)
delete_s3_bucket = S3DeleteBucketOperator(
task_id="delete_s3_bucket",
bucket_name=s3_bucket,
force_delete=True,
trigger_rule=TriggerRule.ALL_DONE,
)
chain(
# TEST SETUP
test_context,
create_s3_bucket,
upload_sample_data,
create_database,
create_table,
# TEST BODY
glue_data_quality_recommendation_workflow(),
# TEST TEARDOWN
delete_ruleset(rule_set_name),
drop_table,
drop_database,
delete_s3_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)