#
# 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 testing tables.
"""
from __future__ import annotations
import os
import time
from datetime import datetime
from pathlib import Path
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.bigquery import (
BigQueryCreateEmptyDatasetOperator,
BigQueryCreateEmptyTableOperator,
BigQueryDeleteDatasetOperator,
BigQueryDeleteTableOperator,
BigQueryGetDatasetTablesOperator,
BigQueryUpdateDatasetOperator,
BigQueryUpdateTableOperator,
BigQueryUpdateTableSchemaOperator,
BigQueryUpsertTableOperator,
)
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
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "bigquery_tables"
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]DATASET_NAME = f"dataset_{DAG_ID}_{ENV_ID}"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT")
[docs]SCHEMA_JSON_LOCAL_SRC = str(Path(__file__).parent / "resources" / "update_table_schema.json")
[docs]SCHEMA_JSON_DESTINATION = "update_table_schema.json"
[docs]GCS_PATH_TO_SCHEMA_JSON = f"gs://{BUCKET_NAME}/{SCHEMA_JSON_DESTINATION}"
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "bigquery"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket", bucket_name=BUCKET_NAME, project_id=PROJECT_ID
)
upload_schema_json = LocalFilesystemToGCSOperator(
task_id="upload_schema_json",
src=SCHEMA_JSON_LOCAL_SRC,
dst=SCHEMA_JSON_DESTINATION,
bucket=BUCKET_NAME,
)
create_dataset = BigQueryCreateEmptyDatasetOperator(task_id="create_dataset", dataset_id=DATASET_NAME)
# [START howto_operator_bigquery_create_table]
create_table = BigQueryCreateEmptyTableOperator(
task_id="create_table",
dataset_id=DATASET_NAME,
table_id="test_table",
schema_fields=[
{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
{"name": "salary", "type": "INTEGER", "mode": "NULLABLE"},
],
)
# [END howto_operator_bigquery_create_table]
# [START howto_operator_bigquery_create_view]
create_view = BigQueryCreateEmptyTableOperator(
task_id="create_view",
dataset_id=DATASET_NAME,
table_id="test_view",
view={
"query": f"SELECT * FROM `{PROJECT_ID}.{DATASET_NAME}.test_table`",
"useLegacySql": False,
},
)
# [END howto_operator_bigquery_create_view]
# [START howto_operator_bigquery_create_materialized_view]
create_materialized_view = BigQueryCreateEmptyTableOperator(
task_id="create_materialized_view",
dataset_id=DATASET_NAME,
table_id="test_materialized_view",
materialized_view={
"query": f"SELECT SUM(salary) AS sum_salary FROM `{PROJECT_ID}.{DATASET_NAME}.test_table`",
"enableRefresh": True,
"refreshIntervalMs": 2000000,
},
)
# [END howto_operator_bigquery_create_materialized_view]
# [START howto_operator_bigquery_delete_view]
delete_view = BigQueryDeleteTableOperator(
task_id="delete_view",
deletion_dataset_table=f"{PROJECT_ID}.{DATASET_NAME}.test_view",
)
# [END howto_operator_bigquery_delete_view]
# [START howto_operator_bigquery_update_table]
update_table = BigQueryUpdateTableOperator(
task_id="update_table",
dataset_id=DATASET_NAME,
table_id="test_table",
fields=["friendlyName", "description"],
table_resource={
"friendlyName": "Updated Table",
"description": "Updated Table",
},
)
# [END howto_operator_bigquery_update_table]
# [START howto_operator_bigquery_upsert_table]
upsert_table = BigQueryUpsertTableOperator(
task_id="upsert_table",
dataset_id=DATASET_NAME,
table_resource={
"tableReference": {"tableId": "test_table_id"},
"expirationTime": (int(time.time()) + 300) * 1000,
},
)
# [END howto_operator_bigquery_upsert_table]
# [START howto_operator_bigquery_update_table_schema]
update_table_schema = BigQueryUpdateTableSchemaOperator(
task_id="update_table_schema",
dataset_id=DATASET_NAME,
table_id="test_table",
schema_fields_updates=[
{"name": "emp_name", "description": "Name of employee"},
{"name": "salary", "description": "Monthly salary in USD"},
],
)
# [END howto_operator_bigquery_update_table_schema]
# [START howto_operator_bigquery_create_table_schema_json]
update_table_schema_json = BigQueryCreateEmptyTableOperator(
task_id="update_table_schema_json",
dataset_id=DATASET_NAME,
table_id="test_table",
gcs_schema_object=GCS_PATH_TO_SCHEMA_JSON,
)
# [END howto_operator_bigquery_create_table_schema_json]
# [START howto_operator_bigquery_delete_materialized_view]
delete_materialized_view = BigQueryDeleteTableOperator(
task_id="delete_materialized_view",
deletion_dataset_table=f"{PROJECT_ID}.{DATASET_NAME}.test_materialized_view",
)
# [END howto_operator_bigquery_delete_materialized_view]
# [START howto_operator_bigquery_get_dataset_tables]
get_dataset_tables = BigQueryGetDatasetTablesOperator(
task_id="get_dataset_tables", dataset_id=DATASET_NAME
)
# [END howto_operator_bigquery_get_dataset_tables]
update_dataset = BigQueryUpdateDatasetOperator(
task_id="update_dataset",
dataset_id=DATASET_NAME,
dataset_resource={"description": "Updated dataset"},
)
# [START howto_operator_bigquery_delete_table]
delete_table = BigQueryDeleteTableOperator(
task_id="delete_table",
deletion_dataset_table=f"{PROJECT_ID}.{DATASET_NAME}.test_table",
)
# [END howto_operator_bigquery_delete_table]
delete_dataset = BigQueryDeleteDatasetOperator(
task_id="delete_dataset", dataset_id=DATASET_NAME, delete_contents=True
)
delete_dataset.trigger_rule = TriggerRule.ALL_DONE
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE
)
(
# TEST SETUP
create_bucket
>> create_dataset
>> upload_schema_json
# TEST BODY
>> update_dataset
>> create_table
>> create_view
>> create_materialized_view
>> [
get_dataset_tables,
delete_view,
]
>> update_table
>> upsert_table
>> update_table_schema
>> update_table_schema_json
>> delete_materialized_view
>> delete_table
# TEST TEARDOWN
>> delete_bucket
>> delete_dataset
)
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)
[docs]test_run = get_test_run(dag)
