Microsoft Docs

Scaling Azure Functions

Azure Functions offers automatic scaling based on incoming event data. Understanding how this scaling works and how to optimize it is crucial for building efficient and cost-effective serverless applications.

Consumption Plan vs. Premium Plan vs. App Service Plan

Azure Functions can be hosted on different plans, each with distinct scaling characteristics:

• Consumption Plan: Functions automatically scale from zero to thousands of instances based on the number of events. You pay only for the compute time you consume.
• Premium Plan: Provides pre-warmed instances to eliminate cold starts, advanced networking features, and more predictable scaling.
• App Service Plan: Functions run on dedicated VMs, allowing you to scale manually or automatically based on metrics like CPU or memory. This is ideal for predictable workloads.

Scaling Triggers

The scaling of your Functions is primarily driven by the triggers associated with your functions. Different triggers have different scaling behaviors:

Event-driven Scaling

Triggers like Azure Queue Storage, Azure Service Bus, and Event Hubs are designed for event-driven scaling. The runtime monitors the queue depth or event stream and automatically scales out the number of function instances to process events concurrently.

• Queue Triggers: Scales based on the number of messages in the queue.
• Event Hubs Triggers: Scales based on partitions in the event stream.
• Service Bus Triggers: Scales based on the number of messages in topics or queues.

HTTP Triggers

HTTP-triggered functions are typically invoked by HTTP requests. While not directly scaling based on a queue, the underlying infrastructure (especially on Consumption and Premium plans) handles incoming requests and scales instances as needed to meet demand.

Scaling Out and In

Azure Functions dynamically scales out by adding more instances to handle increased load and scales in by reducing instances when the load decreases. This is managed by the Functions host runtime.

Best Practices for Scaling

To optimize your Azure Functions' scalability:

• Choose the Right Hosting Plan: Select a plan that aligns with your workload's predictability and performance requirements.
• Optimize Function Code: Minimize execution time, handle dependencies efficiently, and avoid blocking operations.
• Use Asynchronous Patterns: For long-running tasks, consider using durable functions or integrating with other Azure services like Logic Apps.
• Monitor Scale Controller: Observe the Functions host's scale controller logs to understand how your functions are scaling.
• Batching and Concurrency: Configure batch size and concurrency settings for triggers like Event Hubs and Service Bus to fine-tune processing.

Event Hubs Concurrency and Batching

For Event Hubs triggers, you can configure IsBatched and MaxBatchSize. Increasing MaxBatchSize allows a single function instance to process multiple events, potentially reducing the number of instances needed but increasing the processing time per instance. Adjusting concurrency settings per partition also impacts how many instances are active for a given consumer group.

{
  "scriptFile": "__init__.py",
  "bindings": [
    {
      "type": "eventHubTrigger",
      "name": "myEventHubMessage",
      "direction": "in",
      "eventHubName": "myEventHub",
      "connection": "EventHubConnection",
      "consumerGroup": "$Default",
      "isBatched": true,
      "maxBatchSize": 100
    }
  ]
}
                    

Cold Starts

On the Consumption plan, functions may experience a "cold start" if an instance hasn't been used recently. This adds latency to the first invocation. Premium and App Service plans can mitigate cold starts with pre-warmed instances or always-on configurations.

For more detailed information on specific triggers and advanced scaling configurations, please refer to the Azure Functions triggers and bindings documentation.