Convert Quicksight Sample DAG to System Test (AIP-47)

airflow/providers/amazon/aws/operators/quicksight.py

@@ -88,7 +88,7 @@ def execute(self, context: "Context"):
             aws_conn_id=self.aws_conn_id,
             region_name=self.region,
         )
-        self.log.info("Running the Amazon QuickSight SPICE Ingestion on Dataset ID: %s)", self.data_set_id)
+        self.log.info("Running the Amazon QuickSight SPICE Ingestion on Dataset ID: %s", self.data_set_id)
         return hook.create_ingestion(
             data_set_id=self.data_set_id,
             ingestion_id=self.ingestion_id,

airflow/providers/amazon/aws/sensors/quicksight.py

@@ -16,7 +16,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, Sequence
 
 from airflow.compat.functools import cached_property
 from airflow.exceptions import AirflowException
@@ -45,6 +45,8 @@ class QuickSightSensor(BaseSensorOperator):
          maintained on each worker node).
     """
 
+    template_fields: Sequence[str] = ('data_set_id', 'ingestion_id', 'aws_conn_id')
+
     def __init__(
         self,
         *,

docs/apache-airflow-providers-amazon/operators/quicksight.rst

@@ -19,11 +19,11 @@
 Amazon QuickSight
 =================
 
-`Amazon QuickSight <https://aws.amazon.com/quicksight/>`__ is a fast business analytics service to build visualizations,
-perform ad hoc analysis, and quickly get business insights from your data.
-Amazon QuickSight seamlessly discovers AWS data sources, enables organizations to scale to hundreds of thousands of
-users, and delivers fast and responsive query performance by using the Amazon QuickSight Super-fast,
-Parallel, In-Memory, Calculation Engine (SPICE).
+`Amazon QuickSight <https://aws.amazon.com/quicksight/>`__ is a fast business analytics service
+to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.
+Amazon QuickSight seamlessly discovers AWS data sources, enables organizations to scale to hundreds
+of thousands of users, and delivers fast and responsive query performance by using the Amazon
+QuickSight Super-fast, Parallel, In-Memory, Calculation Engine (SPICE).
 
 Prerequisite Tasks
 ------------------
@@ -41,7 +41,7 @@ Amazon QuickSight create ingestion
 The ``QuickSightCreateIngestionOperator`` creates and starts a new SPICE ingestion for a dataset.
 The operator also refreshes existing SPICE datasets.
 
-.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_quicksight.py
+.. exampleinclude:: /../../tests/system/providers/amazon/aws/example_quicksight.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_quicksight_create_ingestion]
@@ -57,7 +57,7 @@ Amazon QuickSight ingestion sensor
 
 The ``QuickSightSensor`` waits for an Amazon QuickSight create ingestion until it reaches a terminal state.
 
-.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_quicksight.py
+.. exampleinclude:: /../../tests/system/providers/amazon/aws/example_quicksight.py
     :language: python
     :dedent: 4
     :start-after: [START howto_sensor_quicksight]

tests/system/providers/amazon/aws/example_quicksight.py

@@ -0,0 +1,226 @@
+# 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.
+import json
+from datetime import datetime
+
+import boto3
+
+from airflow import DAG
+from airflow.decorators import task
+from airflow.models.baseoperator import chain
+from airflow.providers.amazon.aws.operators.quicksight import QuickSightCreateIngestionOperator
+from airflow.providers.amazon.aws.operators.s3 import (
+    S3CreateBucketOperator,
+    S3CreateObjectOperator,
+    S3DeleteBucketOperator,
+)
+from airflow.providers.amazon.aws.sensors.quicksight import QuickSightSensor
+from airflow.utils.trigger_rule import TriggerRule
+from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder
+
+"""
+Prerequisites:
+1. The account which runs this test must manually be activated in Quicksight here:
+https://quicksight.aws.amazon.com/sn/console/signup?#
+2. The activation process creates an IAM Role called `aws-quicksight-service-role-v0`.
+ You have to add a policy named 'AWSQuickSightS3Policy' with the S3 access permissions.
+ The policy name is enforced, and the permissions json can be copied from `AmazonS3FullAccess`.
+
+NOTES:  If Create Ingestion fails for any reason, that ingestion name will remain in use and
+ future runs will stall with the sensor returning a status of QUEUED "forever".  If you run
+ into this behavior, changing the template for the ingestion name or the ENV_ID and re-running
+ the test should resolve the issue.
+"""
+
+DAG_ID = 'example_quicksight'
+
+sys_test_context_task = SystemTestContextBuilder().build()
+
+SAMPLE_DATA_COLUMNS = ['Project', 'Year']
+SAMPLE_DATA = """'Airflow','2015'
+    'OpenOffice','2012'
+    'Subversion','2000'
+    'NiFi','2006'
+"""
+
+
+@task
+def get_aws_account_id() -> int:
+    return boto3.client('sts').get_caller_identity()['Account']
+
+
+@task
+def create_quicksight_data_source(
+    aws_account_id: str, datasource_name: str, bucket: str, manifest_key: str
+) -> str:
+    response = boto3.client('quicksight').create_data_source(
+        AwsAccountId=aws_account_id,
+        DataSourceId=datasource_name,
+        Name=datasource_name,
+        Type='S3',
+        DataSourceParameters={
+            'S3Parameters': {'ManifestFileLocation': {'Bucket': bucket, 'Key': manifest_key}}
+        },
+    )
+    return response['Arn']
+
+
+@task
+def create_quicksight_dataset(aws_account_id: int, dataset_name: str, data_source_arn: str) -> None:
+    table_map = {
+        'default': {
+            'S3Source': {
+                'DataSourceArn': data_source_arn,
+                'InputColumns': [{'Name': name, 'Type': 'STRING'} for name in SAMPLE_DATA_COLUMNS],
+            }
+        }
+    }
+
+    boto3.client('quicksight').create_data_set(
+        AwsAccountId=aws_account_id,
+        DataSetId=dataset_name,
+        Name=dataset_name,
+        PhysicalTableMap=table_map,
+        ImportMode='SPICE',
+    )
+
+
+@task(trigger_rule=TriggerRule.ALL_DONE)
+def delete_quicksight_data_source(aws_account_id: str, datasource_name: str):
+    boto3.client('quicksight').delete_data_source(AwsAccountId=aws_account_id, DataSourceId=datasource_name)
+
+
+@task(trigger_rule=TriggerRule.ALL_DONE)
+def delete_dataset(aws_account_id: str, dataset_name: str):
+    boto3.client('quicksight').delete_data_set(AwsAccountId=aws_account_id, DataSetId=dataset_name)
+
+
+@task(trigger_rule=TriggerRule.ALL_DONE)
+def delete_ingestion(aws_account_id: str, dataset_name: str, ingestion_name: str) -> None:
+    client = boto3.client('quicksight')
+    try:
+        client.cancel_ingestion(
+            AwsAccountId=aws_account_id,
+            DataSetId=dataset_name,
+            IngestionId=ingestion_name,
+        )
+    except client.exceptions.ResourceNotFoundException:
+        # Ingestion has already terminated on its own.
+        pass
+
+
+with DAG(
+    dag_id=DAG_ID,
+    schedule_interval='@once',
+    start_date=datetime(2021, 1, 1),
+    tags=["example"],
+    catchup=False,
+) as dag:
+    test_context = sys_test_context_task()
+    account_id = get_aws_account_id()
+
+    env_id = test_context[ENV_ID_KEY]
+    bucket_name = f'{env_id}-quicksight-bucket'
+    data_filename = 'sample_data.csv'
+    dataset_id = f'{env_id}-data-set'
+    datasource_id = f'{env_id}-data-source'
+    ingestion_id = f'{env_id}-ingestion'
+    manifest_filename = f'{env_id}-manifest.json'
+    manifest_contents = {'fileLocations': [{'URIs': [f's3://{bucket_name}/{data_filename}']}]}
+
+    create_s3_bucket = S3CreateBucketOperator(task_id='create_s3_bucket', bucket_name=bucket_name)
+
+    upload_manifest_file = S3CreateObjectOperator(
+        task_id='upload_manifest_file',
+        s3_bucket=bucket_name,
+        s3_key=manifest_filename,
+        data=json.dumps(manifest_contents),
+        replace=True,
+    )
+
+    upload_sample_data = S3CreateObjectOperator(
+        task_id='upload_sample_data',
+        s3_bucket=bucket_name,
+        s3_key=data_filename,
+        data=SAMPLE_DATA,
+        replace=True,
+    )
+
+    data_source = create_quicksight_data_source(
+        aws_account_id=account_id,
+        datasource_name=datasource_id,
+        bucket=bucket_name,
+        manifest_key=manifest_filename,
+    )
+
+    create_dataset = create_quicksight_dataset(account_id, dataset_id, data_source)
+
+    # [START howto_operator_quicksight_create_ingestion]
+    create_ingestion = QuickSightCreateIngestionOperator(
+        task_id='create_ingestion',
+        data_set_id=dataset_id,
+        ingestion_id=ingestion_id,
+        # Waits by default, setting as False to demonstrate the Sensor below.
+        wait_for_completion=False,
+    )
+    # [END howto_operator_quicksight_create_ingestion]
+
+    # If this sensor appears to freeze with a "QUEUED" status, see note above.
+    # [START howto_sensor_quicksight]
+    await_job = QuickSightSensor(
+        task_id='await_job',
+        data_set_id=dataset_id,
+        ingestion_id=ingestion_id,
+    )
+    # [END howto_sensor_quicksight]
+
+    delete_bucket = S3DeleteBucketOperator(
+        task_id='delete_s3_bucket',
+        trigger_rule=TriggerRule.ALL_DONE,
+        bucket_name=bucket_name,
+        force_delete=True,
+    )
+
+    chain(
+        # TEST SETUP
+        test_context,
+        account_id,
+        create_s3_bucket,
+        upload_manifest_file,
+        upload_sample_data,
+        data_source,
+        create_dataset,
+        # TEST BODY
+        create_ingestion,
+        await_job,
+        # TEST TEARDOWN
+        delete_dataset(account_id, dataset_id),
+        delete_quicksight_data_source(account_id, datasource_id),
+        delete_ingestion(account_id, dataset_id, ingestion_id),
+        delete_bucket,
+    )
+
+    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)
+test_run = get_test_run(dag)