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Copy file name to clipboardExpand all lines: articles/healthcare-apis/fhir/fhir-service-autoscale.md
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Azure Health Data Services provides a managed service for persisting FHIR-compliant healthcare data and interacting with it securely through the API service endpoint.
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Autoscaling is a capability to dynamically scale your FHIR service based on the load reported. The FHIR service in Azure Health Data Services provides the built-in autoscale capability and the process is automated. The capability provides elasticity and enables provisioning of additional instances for customers FHIR service on demand.
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The autoscaling feature adjusts computing resources automatically to optimize service scalability. There's no action required from customers.
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Autoscaling is a capability to dynamically scale FHIR service based on the load reported. The FHIR service in Azure Health Data Services provides the built-in autoscaling capability and the process is automated. This capability provides elasticity and enables provisioning of additional instances for FHIR service customers on demand.
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The autoscaling feature for FHIR service is available in all regions where the FHIR service is supported.
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> [!NOTE]
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> Autoscaling feature is subject to the resources availability in Azure regions.
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## Autoscaling Policies at Compute level
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The autoscaling feature adjusts computing resources automatically to optimize service scalability. There's no action required from customers.
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## Autoscale at Compute level
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* Scaling Trigger
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Scaling Trigger describes when scaling of the service will be performed. Conditions that are defined in the trigger are checked periodically to determine if a service should be scaled or not. All triggers that are currently supported are Average CPU, Max Worker Thread, Average LogWrite, Average data IO.
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Scaling Trigger describes when scaling of the service is performed. Conditions that are defined in the trigger are checked periodically to determine if a service should be scaled or not. All triggers that are currently supported are Average CPU, Max Worker Thread, Average LogWrite, Average data IO.
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* Scaling mechanism
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Scaling Mechanism describes how scaling will be performed when it's triggered. Mechanism is only applied when the conditions from the trigger are met.
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There are three factors that determine when the service will be scaled:
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** Lower load threshold is a value that determines when the service will be scaled in. If the average load of all instances is lower than 20% of CPU usage then the service will be scaled in.
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** Upper load threshold is a value that determines when the service will be scaled out. If the average load of all instances is higher than 70% of CPU usage then the service will be scaled out.
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** Scaling Interval is used to determine how often the trigger will be checked. Once the trigger is checked, if scaling is needed the mechanism will be applied. Scaling trigger will not be checked before scaling interval expires, which is to set to 1 minute for FHIR service.
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The scaling mechanism is applied if the trigger check determines that scaling is necessary. Additionally, the scaling trigger won't be evaluated again until the scaling interval has expired, which is set to one minute for FHIR service.
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To ensure the best possible outcome, we recommend customers to gradually increase their request rate to match the expected push rate, rather than pushing all requests at once.
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## Next steps
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In this article, you've learned about the FHIR service autoscaling feature in Azure Health Data Services, for more information about the FHIR service supported features, see
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In this article, you've learned about the FHIR service autoscaling feature in Azure Health Data Services.
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For more information about the FHIR service supported features, see
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