Dataset locations

This page explains the concept of data location and the different locations where you can create datasets. To learn how to set the location for your dataset, see Creating datasets.

For information on regional pricing for BigQuery, see the Pricing page.

Key concepts

Locations or region types

There are two types of locations:

  • A region is a specific geographic place, such as London.

  • A multi-region is a large geographic area, such as the United States, that contains two or more geographic places.

Dataset location

You specify a location for storing your BigQuery data when you create a dataset. After you create the dataset, the location cannot be changed, but you can copy the dataset to a different location, or manually move (recreate) the dataset in a different location.

BigQuery processes queries in the same location as the dataset that contains the tables you're querying.

BigQuery stores your data in the selected location in accordance with the Service Specific Terms.

Supported regions

Regional locations

Region description Region name Notes
Americas
Iowa us-central1 leaf icon Low CO2
Las Vegas us-west4
Los Angeles us-west2
Montréal northamerica-northeast1 leaf icon Low CO2
Northern Virginia us-east4
Oregon us-west1 leaf icon Low CO2
Salt Lake City us-west3
São Paulo southamerica-east1 leaf icon Low CO2
Santiago southamerica-west1
South Carolina us-east1
Toronto northamerica-northeast2
Europe
Belgium europe-west1 leaf icon Low CO2
Finland europe-north1 leaf icon Low CO2
Frankfurt europe-west3
London europe-west2
Netherlands europe-west4
Warsaw europe-central2
Zürich europe-west6 leaf icon Low CO2
Asia Pacific
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1

Multi-regional locations

Multi-region description Multi-region name
Data centers within member states of the European Union1 EU
Data centers in the United States US

1 Data located in the EU multi-region is not stored in the europe-west2 (London) or europe-west6 (Zürich) data centers.

Specifying your location

When loading data, querying data, or exporting data, BigQuery determines the location to run the job based on the datasets referenced in the request. For example, if a query references a table in a dataset stored in the asia-northeast1 region, the query job will run in that region. If a query does not reference any tables or other resources contained within datasets, and no destination table is provided, the query job will run in the US multi-region. If the project has a flat-rate reservation in a region other than the US and the query does not reference any tables or other resources contained within datasets, then you must explicitly specify the location of the flat-rate reservation when submitting the job.

You can specify the location to run a job explicitly in the following ways:

  • When you query data using the Cloud Console, click More > Query settings, and for Processing Location, click Auto-select and choose your data's location.
  • When you use the bq command-line tool, supply the --location global flag and set the value to your location.
  • When you use the API, specify your region in the location property in the jobReference section of the job resource.

BigQuery returns an error if the specified location does not match the location of the datasets in the request. The location of every dataset involved in the request, including those read from and those written to, must match the location of the job as inferred or specified.

Single-region locations do not match multi-region locations, even when the single-region location is associated with the multi-region location. Therefore, a job will always fail if the set of associated locations includes both a single-region location and a multi-region location. For example, if a job's location is set to US, the job will fail if it references a dataset in us-central1. Likewise, a job that references one dataset in US and another dataset in us-central1 will fail.

Location considerations

When you choose a location for your data, consider the following:

  • Colocate your BigQuery dataset when using external data sources.
    • Cloud Storage: When you query data in Cloud Storage through a BigQuery external table, the data you query must be in the same location as your BigQuery dataset, in either a regional or dual-region bucket. For example:
      • Single region: If your BigQuery dataset is in the Warsaw (EUROPE-CENTRAL2) regional location, the corresponding Cloud Storage bucket must also be in the Warsaw region because there is currently no Cloud Storage dual-region location that includes Warsaw.
      • Dual-region: If your BigQuery dataset is in the Tokyo (ASIA-NORTHEAST1) region, the corresponding Cloud Storage bucket must be a bucket in the Tokyo region or the ASIA1 dual-region (which includes Tokyo).
      • Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations with multi-region Cloud Storage buckets is not recommended for external tables.
    • Cloud Bigtable: If your external data source is in Cloud Bigtable, your dataset must be in either the US or the EU multi-regional location. Your Cloud Bigtable data must be in one of the supported Cloud Bigtable locations.
    • Google Drive: Location considerations do not apply to Google Drive external data sources.
  • Colocate your Cloud Storage buckets for loading data.
    • If your BigQuery dataset is in a multi-regional location, the Cloud Storage bucket containing the data you're loading must be in a regional or multi-regional bucket in the same location. For example, if your BigQuery dataset is in the EU, the Cloud Storage bucket must be in a regional or multi-regional bucket in the EU.
    • If your dataset is in a regional location, your Cloud Storage bucket must be a regional bucket in the same location. For example, if your dataset is in the Tokyo region, your Cloud Storage bucket must be a regional bucket in Tokyo.
    • Exception: If your dataset is in the US multi-regional location, you can load data from a Cloud Storage bucket in any regional or multi-regional location.
  • Colocate your Cloud Storage buckets for exporting data.
    • When you export data, the regional or multi-regional Cloud Storage bucket must be in the same location as the BigQuery dataset. For example, if your BigQuery dataset is in the EU multi-regional location, the Cloud Storage bucket containing the data you're exporting must be in a regional or multi-regional location in the EU.
    • If your dataset is in a regional location, your Cloud Storage bucket must be a regional bucket in the same location. For example, if your dataset is in the Tokyo region, your Cloud Storage bucket must be a regional bucket in Tokyo.
    • Exception: If your dataset is in the US multi-regional location, you can export data into a Cloud Storage bucket in any regional or multi-regional location.
  • Develop a data management plan.

For more information on Cloud Storage locations, see Bucket locations in the Cloud Storage documentation.

Restrict dataset locations

You can restrict the locations in which your datasets can be created by using the Organization Policy Service. For more information, see Restricting resource locations and Resource locations supported services.

Dataset security

To control access to datasets in BigQuery, see Controlling access to datasets. For information about data encryption, see Encryption at rest.

Next steps