bhirsz commented on a change in pull request #22311:
URL: https://github.com/apache/airflow/pull/22311#discussion_r832905948



##########
File path: tests/system/providers/google/bigquery/example_bigquery_operations.py
##########
@@ -0,0 +1,106 @@
+#
+# 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.
+
+"""
+Example Airflow DAG for Google BigQuery service local file upload and external 
table creation.
+"""
+import os
+from datetime import datetime
+from pathlib import Path
+
+from airflow import models
+from airflow.providers.google.cloud.operators.bigquery import (
+    BigQueryCreateEmptyDatasetOperator,
+    BigQueryCreateExternalTableOperator,
+    BigQueryDeleteDatasetOperator,
+)
+from airflow.providers.google.cloud.operators.gcs import 
GCSCreateBucketOperator, GCSDeleteBucketOperator
+from airflow.providers.google.cloud.transfers.local_to_gcs import 
LocalFilesystemToGCSOperator
+from airflow.utils.trigger_rule import TriggerRule
+from tests.system.utils.watcher import watcher
+
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
+DAG_ID = "bigquery_operations"
+
+DATASET_NAME = f"dataset_{DAG_ID}_{ENV_ID}"
+DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
+DATA_SAMPLE_GCS_OBJECT_NAME = "bigquery/us-states/us-states.csv"
+CSV_FILE_LOCAL_PATH = str(Path(__file__).parent / "resources" / 
"us-states.csv")
+
+
+with models.DAG(
+    DAG_ID,
+    schedule_interval="@once",
+    start_date=datetime(2021, 1, 1),
+    catchup=False,
+    tags=["example", "bigquery"],
+) as dag:
+    create_bucket = GCSCreateBucketOperator(task_id="create_bucket", 
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME)
+
+    create_dataset = 
BigQueryCreateEmptyDatasetOperator(task_id="create_dataset", 
dataset_id=DATASET_NAME)
+
+    upload_file = LocalFilesystemToGCSOperator(
+        task_id="upload_file_to_bucket",
+        src=CSV_FILE_LOCAL_PATH,
+        dst=DATA_SAMPLE_GCS_OBJECT_NAME,
+        bucket=DATA_SAMPLE_GCS_BUCKET_NAME,
+    )
+
+    # [START howto_operator_bigquery_create_external_table]
+    create_external_table = BigQueryCreateExternalTableOperator(
+        task_id="create_external_table",
+        destination_project_dataset_table=f"{DATASET_NAME}.external_table",
+        bucket=DATA_SAMPLE_GCS_BUCKET_NAME,
+        source_objects=[DATA_SAMPLE_GCS_OBJECT_NAME],
+        schema_fields=[
+            {"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
+            {"name": "salary", "type": "INTEGER", "mode": "NULLABLE"},
+        ],
+    )
+    # [END howto_operator_bigquery_create_external_table]
+
+    delete_dataset = BigQueryDeleteDatasetOperator(
+        task_id="delete_dataset",
+        dataset_id=DATASET_NAME,
+        delete_contents=True,
+        trigger_rule=TriggerRule.ALL_DONE,
+    )
+
+    delete_bucket = GCSDeleteBucketOperator(
+        task_id="delete_bucket", bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME, 
trigger_rule=TriggerRule.ALL_DONE
+    )
+
+    (
+        # TEST SETUP
+        [create_bucket, create_dataset]
+        # TEST BODY
+        >> upload_file
+        >> create_external_table
+        # TEST TEARDOWN
+        >> delete_dataset
+        >> delete_bucket
+    )
+
+    list(dag.tasks) >> watcher()

Review comment:
       Cluster policy looks nice but it's overkill in our case when after 
discussion in this PR we managed to store the watcher together in the test 
method. It's worth to remember though in case we will need any extra steps.




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