1. Throttling and Rate Limiting for AI APIs on Azure

    Python

    Throttling and rate limiting are important aspects of API management that help to control the amount of traffic that your API can handle at any given time. These mechanisms protect your APIs from being overwhelmed by too many requests and ensure fair usage among consumers.

    On Azure, you can apply rate limiting and throttling policies to your APIs using Azure API Management (APIM). In APIM, policies are a powerful capability that allows you to change the behavior of the API through configuration. Policies are a collection of statements that are executed sequentially on the request or response of an API.

    To illustrate how to use Pulumi to apply rate limiting to an API on Azure, the following Pulumi program will perform three key tasks:

    1. Create an Azure API Management service instance.
    2. Define an API on the API Management service.
    3. Apply a rate limit policy to the API to throttle the number of requests.

    The rate limit policy will specify a rate limit of 1 call per 15 seconds, for example. Please note that this is just a demonstrative rate and interval; you should set these values based on your actual requirements.

    Here is a detailed Pulumi program in Python that sets up rate limiting for an API on Azure:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group for organizing related resources resource_group = azure_native.resources.ResourceGroup("api-rg") # Create an Azure API Management instance for managing APIs api_management_service = azure_native.apimanagement.ApiManagementService( "api-management-service", resource_group_name=resource_group.name, publisher_name="Demo Publisher", publisher_email="demo@example.com", sku=azure_native.apimanagement.SkuDescriptionArgs( name=azure_native.apimanagement.SkuType.DEVELOPER, capacity=1 ), location=resource_group.location, ) # Define an API on the Azure API Management service api = azure_native.apimanagement.Api( "demo-api", resource_group_name=resource_group.name, service_name=api_management_service.name, display_name="Demo API", path="demo", protocols=["https"] ) # Define a rate limit policy for the API # This XML policy string will configure the rate-limit (throttling) to 1 call per 15 seconds policy_xml_content = """ <policies> <inbound> <rate-limit calls="1" renewal-period="15" /> <base /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies> """ # Apply the rate limit policy to the API api_policy = azure_native.apimanagement.ApiPolicy( "demo-api-policy", resource_group_name=resource_group.name, api_id=api.name, service_name=api_management_service.name, value=policy_xml_content, format="xml" # The format of the policy content must be specified ) # Export the API endpoint URL pulumi.export("api_endpoint", pulumi.Output.concat("https://", api_management_service.name, ".azure-api.net/", api.path)) # Export the API Management service public IP address for reference pulumi.export("api_management_public_ip", api_management_service.public_ip_addresses)

    This Pulumi program creates a new Resource Group and an API Management instance on Azure where you can manage your APIs. It then defines an API with a specified display name and path and applies an inbound policy to it. The policy limits the API calls to 1 call per 15 seconds, as specified in the policy XML content.

    In the program, ApiManagementService creates the API management instance, Api defines the API that you want to expose, and ApiPolicy applies the rate limiting policy to the API. The pulumi.export statements are used to output the API endpoint and the public IP address of the API Management service after the deployment, which can be useful for debugging or for clients to access the API.

    By managing API policies as code, you gain the benefits of versioning, easy rollbacks, auditing, and teamwork that comes with source control.


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