| :py:mod:`tests.system.providers.google.cloud.gcs.example_firestore` |
| =================================================================== |
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| .. py:module:: tests.system.providers.google.cloud.gcs.example_firestore |
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| .. autoapi-nested-parse:: |
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| Example Airflow DAG that shows interactions with Google Cloud Firestore. |
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| Prerequisites |
| ============= |
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| This example uses two Google Cloud projects: |
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| * ``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. |
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| 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. |
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| The bucket and dataset should be located in the same region. |
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| If you want to run this example, you must do the following: |
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| 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. |
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| Module Contents |
| --------------- |
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| .. py:data:: ENV_ID |
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| .. py:data:: DAG_ID |
| :annotation: = example_google_firestore |
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| .. py:data:: GCP_PROJECT_ID |
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| .. py:data:: FIRESTORE_PROJECT_ID |
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| .. py:data:: BUCKET_NAME |
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| .. py:data:: DATASET_NAME |
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| .. py:data:: EXPORT_DESTINATION_URL |
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| .. py:data:: EXPORT_PREFIX |
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| .. py:data:: EXPORT_COLLECTION_ID |
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| .. py:data:: DATASET_LOCATION |
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| .. py:data:: create_bucket |
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| .. py:data:: test_run |
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