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"""
Example Airflow DAG that shows interactions with Google Cloud Firestore.
Prerequisites
=============
This example uses two Google Cloud projects:
* ``GCP_PROJECT_ID`` - It contains a bucket and a firestore database.
* ``G_FIRESTORE_PROJECT_ID`` - it contains the Data Warehouse based on the BigQuery service.
Saving in a bucket should be possible from the ``G_FIRESTORE_PROJECT_ID`` project.
Reading from a bucket should be possible from the ``GCP_PROJECT_ID`` project.
The bucket and dataset should be located in the same region.
If you want to run this example, you must do the following:
1. Create Google Cloud project and enable the BigQuery API
2. Create the Firebase project
3. Create a bucket in the same location as the Firebase project
4. Grant Firebase admin account permissions to manage BigQuery. This is required to create a dataset.
5. Create a bucket in Firebase project and
6. Give read/write access for Firebase admin to bucket to step no. 5.
7. Create collection in the Firestore database.
"""
from __future__ import annotations
import os
from datetime import datetime
from urllib.parse import urlsplit
from airflow import models
from airflow.providers.google.cloud.operators.bigquery import (
BigQueryCreateEmptyDatasetOperator,
BigQueryCreateExternalTableOperator,
BigQueryDeleteDatasetOperator,
BigQueryInsertJobOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.firebase.operators.firestore import CloudFirestoreExportDatabaseOperator
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_google_firestore"
[docs]GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-gcp-project")
[docs]FIRESTORE_PROJECT_ID = os.environ.get("G_FIRESTORE_PROJECT_ID", "example-firebase-project")
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]DATASET_NAME = f"dataset_{DAG_ID}_{ENV_ID}"
[docs]EXPORT_DESTINATION_URL = os.environ.get("GCP_FIRESTORE_ARCHIVE_URL", "gs://INVALID BUCKET NAME/namespace/")
[docs]EXPORT_PREFIX = urlsplit(EXPORT_DESTINATION_URL).path
[docs]EXPORT_COLLECTION_ID = os.environ.get("GCP_FIRESTORE_COLLECTION_ID", "firestore_collection_id")
[docs]DATASET_LOCATION = os.environ.get("GCP_FIRESTORE_DATASET_LOCATION", "EU")
if BUCKET_NAME is None:
raise ValueError("Bucket name is required. Please set GCP_FIRESTORE_ARCHIVE_URL env variable.")
with models.DAG(
DAG_ID,
start_date=datetime(2021, 1, 1),
schedule="@once",
catchup=False,
tags=["example", "firestore"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME)
create_dataset = BigQueryCreateEmptyDatasetOperator(
task_id="create_dataset",
dataset_id=DATASET_NAME,
location=DATASET_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [START howto_operator_export_database_to_gcs]
export_database_to_gcs = CloudFirestoreExportDatabaseOperator(
task_id="export_database_to_gcs",
project_id=FIRESTORE_PROJECT_ID,
body={"outputUriPrefix": EXPORT_DESTINATION_URL, "collectionIds": [EXPORT_COLLECTION_ID]},
)
# [END howto_operator_export_database_to_gcs]
# [START howto_operator_create_external_table_multiple_types]
create_external_table_multiple_types = BigQueryCreateExternalTableOperator(
task_id="create_external_table",
bucket=BUCKET_NAME,
table_resource={
"tableReference": {
"projectId": GCP_PROJECT_ID,
"datasetId": DATASET_NAME,
"tableId": "firestore_data",
},
"schema": {
"fields": [
{"name": "name", "type": "STRING"},
{"name": "post_abbr", "type": "STRING"},
]
},
"externalDataConfiguration": {
"sourceFormat": "DATASTORE_BACKUP",
"compression": "NONE",
"csvOptions": {"skipLeadingRows": 1},
},
},
)
# [END howto_operator_create_external_table_multiple_types]
read_data_from_gcs_multiple_types = BigQueryInsertJobOperator(
task_id="execute_query",
configuration={
"query": {
"query": f"SELECT COUNT(*) FROM `{GCP_PROJECT_ID}.{DATASET_NAME}.firestore_data`",
"useLegacySql": False,
}
},
)
delete_dataset = BigQueryDeleteDatasetOperator(
task_id="delete_dataset",
dataset_id=DATASET_NAME,
project_id=GCP_PROJECT_ID,
delete_contents=True,
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
# TEST BODY
>> export_database_to_gcs
>> create_external_table_multiple_types
>> read_data_from_gcs_multiple_types
# TEST TEARDOWN
>> delete_dataset
>> 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)
[docs]test_run = get_test_run(dag)
