FHIR import options

This page describes options for storing large batches of FHIR data in the Cloud Healthcare API.

Import FHIR resources

Use the fhirStores.import method to load FHIR resources from Cloud Storage into the Cloud Healthcare API. The method performs best when loading data into an empty FHIR store without interference from other applications.

To call fhirStores.import, see Importing and exporting FHIR resources using Cloud Storage.

Consider the following properties of fhirStores.import method when deciding whether to use it. If fhirStores.import isn't suitable for your application, consider using the fhir.executeBundle method to load data. For information about how to call fhir.executeBundle, see Managing FHIR resources using FHIR bundles.

  • The fhirStores.import method does not have limits on the size of FHIR resources resource. If your FHIR bundle exceeds the maximum bundle size of 50 MB, use fhirStores.import instead of fhir.executeBundle.

  • Using fhirStores.import removes the complexities of executing large FHIR bundles, such as the following:

    • Breaking up FHIR bundles into smaller bundles
    • Managing multiple bundle schedules
    • Managing transient errors that can be retried at the resource or bundle level

    Often, these advantages outweigh the advantages from using bundles.

  • Each resource in the input must contain a client-supplied ID. Each resource is stored using the provided ID regardless of the enableUpdateCreate setting on the FHIR store.

  • The import process doesn't enforce referential integrity, regardless of the disableReferentialIntegrity setting on the FHIR store. Not enforcing referential integrity lets you import resources with arbitrary interdependencies without considering grouping or ordering. If the input data contains invalid references or if some resources fail to import, the state of the FHIR store might violate referential integrity.

  • If a resource with a given ID already exists in the store, the most recent version of the resource is overwritten without creating a new historical version. The overwriting occurs regardless of the disableResourceVersioning setting on the FHIR store. If transient failures occur during the import, a successfully imported resource could be overwritten more than once.

  • The import operation is idempotent unless the input data contains multiple valid resources with the same ID but different contents. In that case, after the import completes, the store contains exactly one resource with each ID, but the duplicate entries could contain any version of the contents. For example, importing a million resources with the same ID writes only one resource to the store.

  • The operation result counters don't count duplicate IDs as an error. Each resource in the input counts as one success. This could result in a success count larger than the number of resources in the FHIR store. This often occurs when importing data organized in bundles produced by Patient-everything where each bundle contains its own copy of a resource, such as Practitioner, that might be referenced by many Patient resources.

  • If some resources fail to import, such as due to parsing errors, successfully imported resources aren't rolled back. For example, if 5 of 100 resources fail to import, the remaining 95 resources are imported into the FHIR store.

  • When using the BUNDLE format, the import method rejects bundles with Bundle.type of history. The import method doesn't apply the bundle processing semantics for batch or transaction bundles. Unlike in fhir.executeBundle, transaction bundles aren't executed as a single transaction and bundle-internal references aren't rewritten. The bundle is treated as a collection of resources to be written as provided in Bundle.entry.resource, ignoring Bundle.entry.request. For example, this allows the import of searchset bundles produced by a FHIR search or Patient-everything operation.

Use FHIR bundles

See FHIR bundles for an overview of FHIR bundles.

When to use FHIR bundles

Consider the following characteristics and advantages of using the fhir.executeBundle method when deciding whether to use it to store FHIR resources:

  • If it is too costly, either in terms of billing costs or network bandwidth, to build a pipeline that stores data in Cloud Storage and then imports the data using fhirStores.import, use fhir.executeBundle.
  • When executing bundles, transaction integrity can be enforced.
  • When executing bundles, FHIR profile validation can be enforced.
  • If you need to send Pub/Sub notifications when FHIR create, update, or delete operations occur, use fhir.executeBundle. Pub/Sub notifications are not sent when FHIR resources are imported using fhirStores.import.
  • If the time at which a particular FHIR resource must be processed is in in seconds or minutes, use fhir.executeBundle. If the time at which a particular FHIR resource must be processed is in hours or days, use fhirStores.import.
  • If your Google Cloud project has many existing long-running operations (LRO) performing other tasks, you might see better performance with fhir.executeBundle over fhirStores.import.

  • If the application managing the fhirStores.import operation doesn't have a good strategy for the following, use fhir.executeBundle:

    • Handling bulk errors
    • Addressing failures on a subset of FHIR resources or entire batches

When not to use FHIR bundles

Consider the following limitations of fhir.executeBundle when determining whether to use it to store FHIR resources:

  • Bundles have the equivalent quota and billing applied to the operations inside the bundle as if the operations were executed outside of the bundle. For example, if a bundle has 10 POST operations, 5 GET operations, and 1 DELETE operation, the quota and billing applied to the bundle is the same as if those operations were executed independently.

    As a result, aiming to lower quota limits and FHIR operation costs are not reasons to use bundles instead of fhirStores.import.

  • Large transaction bundles might be more likely to have transaction conflicts which leads to data contention and failed operations. For information on how these issues can occur, and how to resolve them, see Prevent 429 Resource Exhausted operation_too_costly errors.

  • You can achieve and maintain high data throughput using batch bundles, which helps you to avoid data contention. However, batch bundles do not have transactional consistency capabilities, such as referential integrity

  • If a bundle is large, even if it's a batch bundle, you might see reduced data throughput. For more information, see Avoid large transaction bundles.