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BigQuery locations

This page explains the concept of location and the different regions where data can be stored and processed. Pricing for storage and analysis is also defined by location of data and reservations. For more information about pricing for locations, see BigQuery pricing. To learn how to set the location for your dataset, see Create datasets. For information about reservation locations, see Managing reservations in different regions.

For more information about how the BigQuery Data Transfer Service uses location, see Data location and transfers.

Locations and regions

BigQuery provides two types of data and compute 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 regions. Multi-region locations can provide larger quotas than single regions.

For either location type, BigQuery automatically stores copies of your data in two different Google Cloud zones within a single region in the selected location. For more information about data availability and durability, see Reliability: Disaster planning.

Supported locations

BigQuery datasets can be stored in the following regions and multi-regions. For more information about regions and zones, see Geography and regions.

Regions

The following table lists the regions in the Americas where BigQuery is available.
Region description Region name Details
Columbus, Ohio us-east5
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
The following table lists the regions in Asia Pacific where BigQuery is available.
Region description Region name Details
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
The following table lists the regions in Europe where BigQuery is available.
Region description Region name Details
Belgium europe-west1 leaf icon Low CO2
Finland europe-north1 leaf icon Low CO2
Frankfurt europe-west3
London europe-west2
Madrid europe-southwest1 leaf icon Low CO2
Milan europe-west8
Netherlands europe-west4
Paris europe-west9 leaf icon Low CO2
Warsaw europe-central2
Zürich europe-west6 leaf icon Low CO2

Multi-regions

The following table lists the multi-regions where BigQuery is available.
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.

BigQuery Omni locations

BigQuery Omni processes queries in the same location as the dataset that contains the tables you're querying. After you create the dataset, the location cannot be changed. Your data resides within your own AWS or Azure account.
Region description Region name
AWS
AWS - US East (N. Virginia) aws-us-east-1
Azure
Azure - East US 2 azure-eastus2

Specify locations

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. Flat-rate commitments are tied to a location, such as US or EU. If you run a job outside the location of your flat-rate capacity, pricing for that job automatically shifts to on-demand pricing.

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

  • When you query data using the Google Cloud console in the query editor, click More > Query settings, expand Advanced options, and then select your Data 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 contained within 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.

Locations, reservations, and jobs

Capacity commitments are a regional resource. When you buy slots, those slots are limited to a specific region or multi-region. If your only capacity commitment is in the EU then you can't create a reservation in the US. When you create a reservation, you specify a location (region) and a number of slots. Those slots are pulled from your capacity commitment in that region.

Likewise, when you run a job in a region, it only uses a reservation if the location of the job matches the location of a reservation. For example, if you assign a reservation to a project in the EU and run a query in that project on a dataset located in the US, then that query is not run on your EU reservation. In the absence of any US reservation, the job is run as on-demand.

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 colocated with your BigQuery dataset. For example:
      • Single region bucket: If your BigQuery dataset is in the Warsaw (europe-central2) region, the corresponding Cloud Storage bucket must also be in the Warsaw region because there is currently no Cloud Storage dual-region that includes Warsaw.
      • If your BigQuery dataset is in one of the multi-regions, then using a single region Cloud Storage bucket is not supported, even if the bucket is in a location that is contained within the multi-region of the dataset. For example, if the external tables are in the EU multi-region and the Cloud Storage bucket is in europe-central2, the job will fail.
      • Dual-region bucket: 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 in the ASIA1 dual-region (which includes Tokyo).
      • Multi-region bucket: 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.
      View the supported Cloud Storage locations.
    • Bigtable: When you query data in Bigtable through a BigQuery external table, your Bigtable instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Bigtable instance must be in the Belgium region.
      • Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations is not recommended for external tables on Bigtable.
      View the supported Bigtable locations.
    • Google Drive: Location considerations do not apply to Google Drive external data sources.
    • Cloud SQL: When you query data in Cloud SQL through a BigQuery federated query, your Cloud SQL instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Cloud SQL instance must be in the Belgium region.
      • Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Cloud SQL instance must be in single region in the US geographic area.
      View the supported Cloud SQL locations.
    • Cloud Spanner: When you query data in Spanner through a BigQuery federated query, your Cloud Spanner instance must be in the same location as your BigQuery dataset.
      • Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Spanner instance must be in the Belgium region.
      • Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Spanner instance must be in single region in the US geographic area.
      View the supported Spanner locations.
  • Colocate your BigQuery dataset with your analysis tools.
  • Colocate your Cloud Storage buckets for loading data.
    • If your BigQuery dataset is in a multi-region, the Cloud Storage bucket containing the data you're loading must be in the same multi-region or in a location that is contained within the multi-region. For example, if your BigQuery dataset is in the `EU` multi-region, the Cloud Storage bucket can be located in the `europe-west1` Belgium region, which is within the EU.
    • If your dataset is in a region, your Cloud Storage bucket must be in the same region. For example, if your dataset is in the `asia-northeast1` Tokyo region, your Cloud Storage bucket cannot be in the `ASIA` multi-region.
    • Exception: If your dataset is in the `US` multi-region, you can load data from a Cloud Storage bucket in any location.
  • Colocate your Cloud Storage buckets for exporting data.
    • If your BigQuery dataset is in a multi-region, the Cloud Storage bucket containing the data you're exporting must be in the same multi-region or in a location that is contained within the multi-region. For example, if your BigQuery dataset is in the EU multi-region, the Cloud Storage bucket can be located in the europe-west1 Belgium region, which is within the EU.
    • If your dataset is in a region, your Cloud Storage bucket must be in the same region. For example, if your dataset is in the asia-northeast1 Tokyo region, your Cloud Storage bucket cannot be in the ASIA multi-region.
    • Exception: If your dataset is in the US multi-region, you can export data into a Cloud Storage bucket in any location.
  • Develop a data management plan.

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

Restrict 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