Loading data from Firestore exports

BigQuery supports loading data from Firestore exports created using the Firestore managed import and export service. The managed import and export service exports Firestore documents into a Cloud Storage bucket. You can then load the exported data into a BigQuery table.

Note: Not all Firestore exports can be loaded. Reference the BigQuery limitations to create a Firestore export which can be loaded into a BigQuery table.

Limitations

When you load data into BigQuery from a Firestore export, note the following restrictions:

• Your dataset must be in the same location as the Cloud Storage bucket containing your export files.
• You can specify only one Cloud Storage URI, and you cannot use a URI wildcard.
• For a Firestore export to load correctly, documents in the export data must share a consistent schema with fewer than 10,000 unique field names.
• You can create a new table to store the data, or you can overwrite an existing table. You cannot append Firestore export data to an existing table.
• Your export command must specify a collection-ids filter. Data exported without specifying a collection ID filter cannot be loaded into BigQuery.

Before you begin

Grant Identity and Access Management (IAM) roles that give users the necessary permissions to perform each task in this document.

Required permissions

To load data into BigQuery, you need IAM permissions to run a load job and load data into BigQuery tables and partitions. If you are loading data from Cloud Storage, you also need IAM permissions to access the bucket that contains your data.

Permissions to load data into BigQuery

To load data into a new BigQuery table or partition or to append or overwrite an existing table or partition, you need the following IAM permissions:

• bigquery.tables.create
• bigquery.tables.updateData
• bigquery.tables.update
• bigquery.jobs.create

Each of the following predefined IAM roles includes the permissions that you need in order to load data into a BigQuery table or partition:

• roles/bigquery.dataEditor
• roles/bigquery.dataOwner
• roles/bigquery.admin (includes the bigquery.jobs.create permission)
• bigquery.user (includes the bigquery.jobs.create permission)
• bigquery.jobUser (includes the bigquery.jobs.create permission)

Additionally, if you have the bigquery.datasets.create permission, you can create and update tables using a load job in the datasets that you create.

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Permissions to load data from Cloud Storage

To get the permissions that you need to load data from a Cloud Storage bucket, ask your administrator to grant you the Storage Admin (roles/storage.admin) IAM role on the bucket. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to load data from a Cloud Storage bucket. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to load data from a Cloud Storage bucket:

• storage.buckets.get
• storage.objects.get
• storage.objects.list (required if you are using a URI wildcard)

You might also be able to get these permissions with custom roles or other predefined roles.

Loading Firestore export service data

You can load data from a Firestore export metadata file by using the Google Cloud console, bq command-line tool, or API.

Sometimes Datastore terminology is used in the Google Cloud console and the bq command-line tool, but the following procedures are compatible with Firestore export files. Firestore and Datastore share an export format.

Note: You can load specific fields by using the --projection_fields flag in the bq command-line tool or by setting the projectionFields property in the load job configuration.

Console

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, expand your project, and then select a dataset.
3. In the Dataset info section, click Create table.
4. In the Create table panel, specify the following details:
1. In the Source section, select Google Cloud Storage in the Create table from list. Then, do the following:
   1. Select a file from the Cloud Storage bucket, or enter the Cloud Storage URI. You cannot include multiple URIs in the Google Cloud console, but wildcards are supported. The Cloud Storage bucket must be in the same location as the dataset that contains the table you want to create, append, or overwrite.
      The URI for your Firestore export file must end with KIND_COLLECTION_ID.export_metadata. For example, in default_namespace_kind_Book.export_metadata, Book is the collection ID, and default_namespace_kind_Book is the file name generated by Firestore. If the URI doesn't end with KIND_COLLECTION_ID.export_metadata, you receive the following error message: does not contain valid backup metadata. (error code: invalid).
      Note: Do not use the file ending in overall_export_metadata. This file is not usable by BigQuery.
      
   2. For File format, select Cloud Datastore Backup. Firestore and Datastore share the export format.
2. In the Destination section, specify the following details:
   1. For Dataset, select the dataset in which you want to create the table.
   2. In the Table field, enter the name of the table that you want to create.
   3. Verify that the Table type field is set to Native table.
3. In the Schema section, no action is necessary. The schema is inferred for a Firestore export.
4. Optional: Specify Partition and cluster settings. For more information, see Creating partitioned tables and Creating and using clustered tables.
5. Click Advanced options and do the following:
   • For Write preference, leave Write if empty selected. This option creates a new table and loads your data into it.
   • If you want to ignore values in a row that are not present in the table's schema, then select Unknown values.
   • For Encryption, click Customer-managed key to use a Cloud Key Management Service key. If you leave the Google-managed key setting, BigQuery encrypts the data at rest.
6. Click Create table.

bq

Use the bq load command with source_format set to DATASTORE_BACKUP. Supply the --location flag and set the value to your location. If you are overwiting an existing table, add the --replace flag.

To load only specific fields, use the --projection_fields flag.

bq --location=LOCATION load \
--source_format=FORMAT \
DATASET.TABLE \
PATH_TO_SOURCE

Replace the following:

• LOCATION: your location. The --location flag is optional.
• FORMAT: DATASTORE_BACKUP. Datastore Backup is the correct option for Firestore. Firestore and Datastore share an export format.
• DATASET: the dataset that contains the table into which you're loading data.
• TABLE: the table into which you're loading data. If the table doesn't exist, it is created.
• PATH_TO_SOURCE: the Cloud Storage URI.

For example, the following command loads the gs://mybucket/20180228T1256/default_namespace/kind_Book/default_namespace_kind_Book.export_metadata Firestore export file into a table named book_data. mybucket and mydataset were created in the US multi-region location.

bq --location=US load \
--source_format=DATASTORE_BACKUP \
mydataset.book_data \
gs://mybucket/20180228T1256/default_namespace/kind_Book/default_namespace_kind_Book.export_metadata

API

Set the following properties to load Firestore export data using the API.

1. Create a load job configuration that points to the source data in Cloud Storage.

2. Specify your location in the location property in the jobReference section of the job resource.

3. The sourceUris must be fully qualified, in the format gs://BUCKET/OBJECT in the load job configuration. The file (object) name must end in KIND_NAME.export_metadata. Only one URI is allowed for Firestore exports, and you cannot use a wildcard.

4. Specify the data format by setting the sourceFormat property to DATASTORE_BACKUP in the load job configuration. Datastore Backup is the correct option for Firestore. Firestore and Datastore share an export format.

5. To load only specific fields, set the projectionFields property.

6. If you are overwriting an existing table, specify the write disposition by setting the writeDisposition property to WRITE_TRUNCATE.

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

# TODO(developer): Set table_id to the ID of the table to create.
table_id = "your-project.your_dataset.your_table_name"

# TODO(developer): Set uri to the path of the kind export metadata
uri = (
    "gs://cloud-samples-data/bigquery/us-states"
    "/2021-07-02T16:04:48_70344/all_namespaces/kind_us-states"
    "/all_namespaces_kind_us-states.export_metadata"
)

# TODO(developer): Set projection_fields to a list of document properties
#                  to import. Leave unset or set to `None` for all fields.
projection_fields = ["name", "post_abbr"]

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

job_config = bigquery.LoadJobConfig(
    source_format=bigquery.SourceFormat.DATASTORE_BACKUP,
    projection_fields=projection_fields,
)

load_job = client.load_table_from_uri(
    uri, table_id, job_config=job_config
)  # Make an API request.

load_job.result()  # Waits for the job to complete.

destination_table = client.get_table(table_id)
print("Loaded {} rows.".format(destination_table.num_rows))

Note: If you prefer to skip the loading process, you can query the export directly by setting it up as an external data source. For more information, see External data sources.

Firestore options

To change how BigQuery parses Firestore export data, specify the following option:

Google Cloud console option	`bq` flag	BigQuery API property	Description
Not available	--projection_fields	projectionFields (Java, Python)	(Optional) A comma-separated list that indicates which document fields to load from a Firestore export. By default, BigQuery loads all fields. Field names are case-sensitive and must be present in the export. You cannot specify field paths within a map field such as map.foo.

Data type conversion

BigQuery converts data from each document in Firestore export files to BigQuery data types. The following table describes the conversion between supported data types.

Firestore data type	BigQuery data type
Array	RECORD
Boolean	BOOLEAN
Reference	RECORD
Date and time	TIMESTAMP
Map	RECORD
Floating-point number	FLOAT
Geographical point	RECORD [{"lat","FLOAT"}, {"long","FLOAT"}]
Integer	INTEGER
String	STRING (truncated to 64 KB)

Firestore key properties

Each document in Firestore has a unique key that contains information such as the document ID and the document path. BigQuery creates a RECORD data type (also known as a STRUCT) for the key, with nested fields for each piece of information, as described in the following table.

Key property	Description	BigQuery data type
__key__.app	The Firestore app name.	STRING
__key__.id	The document's ID, or null if __key__.name is set.	INTEGER
__key__.kind	The document's collection ID.	STRING
__key__.name	The document's name, or null if __key__.id is set.	STRING
__key__.namespace	Firestore does not support custom namespaces. The default namespace is represented by an empty string.	STRING
__key__.path	The path of the document: the sequence of the document and the collection pairs from the root collection. For example: "Country", "USA", "PostalCode", 10011, "Route", 1234.	STRING