Regions

Google Cloud uses regions, subdivided into zones, to define the geographic location of physical computing resources.

Key concepts

You specify a location for storing your Cloud Healthcare API data when you create a dataset. After you create the dataset, the location cannot be changed. Data within the dataset is stored at rest in the chosen location.

The location is tied to the dataset's identity and is a permanent part of the dataset's resource name. All data stores within the dataset are assigned to the same region as the dataset.

There are two types of locations:

  • A regional location is a specific geographic place, such as Tokyo. For more information, see Regional resources on the Geography and Regions page.

  • A multi-regional location is a large geographic area, such as the United States, that contains at least two regional locations. For more information, see Multi-regional resources on the Geography and Regions page.

Available regions

The Cloud Healthcare API supports a subset of the full list of Google Cloud locations.

The Cloud Healthcare API is available in the following regions:

Regional locations

Region name Region description
Americas
northamerica-northeast1 Montréal
northamerica-northeast2 Toronto
us-central1 Iowa
us-east1 South Carolina
us-east4 North Virginia
us-west1 Oregon
us-west2 Los Angeles
us-west3 Salt Lake City
southamerica-east1 São Paulo
Asia Pacific
asia-east1 Taiwan
asia-east2 Hong Kong
asia-northeast1 Tokyo
asia-northeast2 Osaka
asia-northeast3 Seoul
asia-south1 Mumbai
asia-southeast1 Singapore
asia-southeast2 Jakarta
australia-southeast1 Sydney
Europe
europe-west2 London
europe-west3 Frankfurt
europe-west4 Netherlands
europe-west6 Zurich
Middle East
me-west1 Tel Aviv

Multi-regional locations

Multi-region name Multi-region description
us Data centers in the United States
eu Data centers in Europe

Location considerations

When you choose a location for your data, you might want to consider factors such as:

  • Regulatory requirements about where to store your data
  • Latency
  • Resiliency
  • Cost
  • Colocation with other Google Cloud services

For example, Google manages multi-regional locations to be redundant and distributed within and across regions. These services optimize availability, performance, and resource efficiency. As a result, these services require a trade-off on either latency or the consistency model.

Consider doing the following when choosing a location for your data:

  • Colocate your dataset and your external data source.

  • Colocate your dataset with your Cloud Storage buckets when importing data.

  • Colocate your dataset with your Cloud Storage buckets and BigQuery datasets when exporting data.

Moving Cloud Healthcare API data between locations

You cannot change the location of a dataset after it is created. Also, you cannot move a dataset from one location to another. If you need to move data from one location to another, complete one of the following processes:

FHIR data

  1. Export the data from your FHIR stores to a regional or multi-regional Cloud Storage bucket. When you export the data, the operation only exports the current version of each resource. The operation does not export version history; there is no bulk export operation for version history.

    There are charges for exporting FHIR data to Cloud Storage. You also incur charges for storing the exported data in Cloud Storage.

  2. After you transfer the data to a Cloud Storage bucket, create a new dataset in the new location. Create any FHIR stores in the new dataset that you require for storing your data. Then, import your data from Cloud Storage into the new FHIR stores.

DICOM data

  1. Export the data from your DICOM stores to a regional or multi-regional Cloud Storage bucket.

    There are charges for exporting DICOM data to Cloud Storage. You also incur charges for storing the exported data in Cloud Storage.

  2. After you transfer the data to a Cloud Storage bucket, create a new dataset in the new location. Create any DICOM stores in the new dataset that you require for storing your data. Then, import your data from Cloud Storage into the new DICOM stores.

HL7v2 data

  1. Export the HL7v2 messages from your HL7v2 store to a regional or multi-regional Cloud Storage bucket.

    There are charges for exporting HL7v2 messages to Cloud Storage. You also incur charges for storing the exported data in Cloud Storage.

  2. After you transfer the data to a Cloud Storage bucket, create a new dataset in the new location. Create any HL7v2 stores in the new dataset that you require for storing your data. Then, import your messages from Cloud Storage into the new HL7v2 stores.