Loading data from Datastore exports

BigQuery supports loading data from Datastore exports created using the Datastore managed import and export service. You can use the managed import and export service to export Datastore entities into a Cloud Storage bucket. You can then load the export into BigQuery as a table.

To learn how to create a Datastore export file, see Exporting and importing entities in the Datastore documentation. For information on scheduling exports, see Scheduling an export.

Note: If you intend to load a Datastore export into BigQuery, you must specify an entity filter in your export command. Data exported without specifying an entity filter cannot be loaded into BigQuery.

You can control which properties BigQuery should load by setting the projectionFields property in the API or by using the --projection_fields flag in the bq command-line tool.

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.

When you load data from Cloud Storage into a BigQuery table, the dataset that contains the table must be in the same regional or multi- regional location as the Cloud Storage bucket.

Limitations

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

• You cannot use a wildcard in the Cloud Storage URI when you specify a Datastore export file.
• You can specify only one Cloud Storage URI when loading data from Datastore exports.
• You cannot append Datastore export data to an existing table with a defined schema.
• For a Datastore export to load correctly, entities in the export data must share a consistent schema with fewer than 10,000 unique property names.
• Data exported without specifying an entity filter cannot be loaded into BigQuery. The export request must include one or more kind names in the entity filter.
• The maximum field size for Datastore exports is 64 KB. When you load a Datastore export, any field larger than 64 KB is truncated.

Required permissions

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

BigQuery permissions

At a minimum, the following permissions are required to load data into BigQuery. These permissions are required if you are loading data into a new table or partition, or if you are appending or overwriting a table or partition.

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

The following predefined IAM roles include both bigquery.tables.create and bigquery.tables.updateData permissions:

• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.admin

The following predefined IAM roles include bigquery.jobs.create permissions:

• bigquery.user
• bigquery.jobUser
• bigquery.admin

In addition, if a user has bigquery.datasets.create permissions, when that user creates a dataset, they are granted bigquery.dataOwner access to it. bigquery.dataOwner access lets the user create and update tables in the dataset by using a load job.

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

Cloud Storage permissions

To load data from a Cloud Storage bucket, you must be granted storage.objects.get permissions. If you are using a URI wildcard, you must also have storage.objects.list permissions.

The predefined IAM role storage.objectViewer can be granted to provide both storage.objects.get and storage.objects.list permissions.

Loading Datastore export service data

To load data from a Datastore export metadata file:

Console

1. In the Cloud Console, open the BigQuery page.

   Go to BigQuery

2. In the Explorer panel, expand your project and select a dataset.

   Note: The default experience is the Preview Cloud Console. If you clicked Hide preview features to go to the Generally Available Cloud Console, then perform the following step instead: In the navigation panel, in the Resources section, expand your project and select a dataset.

3. In the details panel, click Create table.

4. On the Create table page, in the Source section:

   • For Create table from, select Cloud Storage.
   • In the source field, enter the Cloud Storage URI. The Cloud Storage bucket must be in the same location as the dataset that contains the table you're creating. The URI for your Datastore export file should end with [KIND_NAME].export_metadata or export[NUM].export_metadata. For example: default_namespace_kind_Book.export_metadata. In this example, Book is the kind name, and default_namespace_kind_Book is the filename generated by Datastore.
   • For File format, select Datastore Backup

5. On the Create table page, in the Destination section:

   • For Dataset name, choose the appropriate dataset.

     

   • In the Table name field, enter the name of the table you're creating in BigQuery.

   • Verify that Table type is set to Native table.

6. In the Schema section, no action is necessary. The schema is inferred from a Datastore export.

7. Select applicable items in the Advanced options section and then click Create table. For information on the available options, see Datastore options.

bq

Use the bq load command with source_format set to DATASTORE_BACKUP. Supply the --location flag and set the value to your location.

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

Replace the following:

• LOCATION: your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location by using the .bigqueryrc file.
• FORMAT: DATASTORE_BACKUP.
• 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 does not 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 Datastore 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 Datastore export data using the API.

1. Create a load job 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 source URIs must be fully qualified, in the format gs://[BUCKET]/[OBJECT]. The file (object) name must end in [KIND_NAME].export_metadata. Only one URI is allowed for Datastore exports, and you cannot use a wildcard.

4. Specify the data format by setting the configuration.load.sourceFormat property to DATASTORE_BACKUP.

Datastore Admin backups

Warning: The Datastore Admin backups feature is deprecated and will be shut down on February 28, 2019. It is replaced by the managed export and import service.

If you use the Datastore Admin backup feature to export your Datastore data, note that the file extension will be .backup_info instead of .export_metadata. When you import your data into BigQuery, you can use either a .backup_info file or a .export_metadata file until the Datastore Admin backups service becomes unavailable.

Appending to or overwriting a table with Datastore data

When you load Datastore export data into BigQuery, you can create a new table to store the data, or you can overwrite an existing table. You cannot append Datastore export data to an existing table.

If you attempt to append Datastore export data to an existing table, the following error results: Cannot append a datastore backup to a table that already has a schema. Try using the WRITE_TRUNCATE write disposition to replace the existing table.

To overwrite an existing table with Datastore export data:

Console

1. In the Cloud Console, open the BigQuery page.

   Go to BigQuery

2. In the Explorer panel, expand your project and select a dataset.

   Note: The default experience is the Preview Cloud Console. If you clicked Hide preview features to go to the Generally Available Cloud Console, then perform the following step instead: In the navigation panel, in the Resources section, expand your project and select a dataset.

3. In the details panel, click Create table.

4. On the Create table page, in the Source section:

   • For Create table from, select Cloud Storage.

   • In the source field, enter the Cloud Storage URI. The Cloud Storage bucket must be in the same location as the dataset that contains the table you're creating. The URI for your Datastore export file should end with [KIND_NAME].export_metadata. For example: default_namespace_kind_Book.export_metadata. In this example, Book is the kind name, and default_namespace_kind_Book is the filename generated by Datastore.

     Verify [KIND_NAME] is specified in your Cloud Storage URI. If you specify the URI without [KIND_NAME], you receive the following error: does not contain valid backup metadata. (error code: invalid).

   • For File format, select Datastore Backup

5. On the Create table page, in the Destination section:

   • For Dataset name, choose the appropriate dataset.

     

   • In the Table name field, enter the name of the table you're creating in BigQuery.

   • Verify that Table type is set to Native table.

6. In the Schema section, no action is necessary. The schema is inferred from a Datastore export.

7. In the Advanced options section, for Write preference, select Overwrite table.

8. Click Create table.

bq

Use the bq load command with the --replace flag and with source_format set to DATASTORE_BACKUP. Supply the --location flag and set the value to your location.

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

Replace the following:

• LOCATION: your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location by using the .bigqueryrc file.
• FORMAT: DATASTORE_BACKUP.
• DATASET: the dataset containing the table into which you're loading data.
• TABLE: the table you're overwriting.
• 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 Datastore export file and overwrites a table named book_data:

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

API

Set the following properties to load data from the API.

1. Create a load job 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 source URIs must be fully qualified, in the format gs://[BUCKET]/[OBJECT]. The file (object) name must end in [KIND_NAME].export_metadata. Only one URI is allowed for Datastore exports, and you cannot use a wildcard.

4. Specify the data format by setting the configuration.load.sourceFormat property to DATASTORE_BACKUP.

5. Specify the write disposition by setting the configuration.load.writeDisposition property to WRITE_TRUNCATE.

Datastore options

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

Console option	bq tool flag	BigQuery API property	Description
Not available	--projection_fields	projectionFields	A comma-separated list that indicates which entity properties to load into BigQuery from a Datastore export. Property names are case sensitive and must be top-level properties. If no properties are specified, BigQuery loads all properties. If any named property isn't found in the Datastore export, an invalid error is returned in the job result. The default value is ''.

Data type conversion

BigQuery converts data from each entity in Datastore export files to BigQuery data types. The following table describes the conversion between data types.

Datastore data type	BigQuery data type
Blob	BYTES
Blobstore key	STRING
Boolean	BOOLEAN
Category	STRING
Datastore key	RECORD
Date and time	TIMESTAMP
Email	STRING
Embedded entity	RECORD
Floating-point number	FLOAT
Geographical point	RECORD [{"lat","DOUBLE"}, {"long","DOUBLE"}]
IM handle	STRING
Integer	INTEGER
Link	STRING
Phone number	STRING
Postal address	STRING
Rating	INTEGER
Short blob	BYTES
String	STRING (truncated to 64 KB)
User	RECORD [{"email","STRING"} {"userid","STRING"}]

Datastore key properties

Each entity in Datastore has a unique key that contains information such as the namespace and the path. BigQuery creates a RECORD data type 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 Datastore app name.	STRING
__key__.id	The entity's ID, or null if __key__.name is set.	INTEGER
__key__.kind	The entity's kind.	STRING
__key__.name	The entity's name, or null if __key__.id is set.	STRING
__key__.namespace	If the Datastore app uses a custom namespace, the entity's namespace. Else, the default namespace is represented by an empty string.	STRING
__key__.path	The flattened ancestral path of the entity, consisting of the sequence of kind-identifier pairs from the root entity to the entity itself. For example: "Country", "USA", "PostalCode", 10011, "Route", 1234.	STRING