Azure Functions Documentation

General Best Practices

• Keep Functions Small and Focused: Each function should ideally perform a single, well-defined task. This improves maintainability, testability, and can indirectly help with performance by reducing complexity.
• Avoid Blocking Operations: Use asynchronous programming patterns (async/await in C#, Promises in JavaScript) to prevent your function from blocking the execution thread.
• Handle State Externally: Azure Functions are designed to be stateless. Use external services like Azure Storage, Azure Cache for Redis, or Azure Cosmos DB to manage any state required between function invocations.
• Minimize Cold Starts: While not always entirely preventable, understand factors that contribute to cold starts and implement strategies to mitigate them.

Choosing the Right Hosting Plan

The hosting plan significantly impacts performance, scalability, and cost.

• Consumption Plan: Ideal for event-driven workloads with intermittent traffic. It scales automatically but can experience cold starts.
• Premium Plan: Offers pre-warmed instances to reduce cold starts, VNet connectivity, and longer run times. A good balance for applications requiring consistent performance and advanced features.
• Dedicated (App Service) Plan: Provides predictable performance and cost, no cold starts (unless scaling occurs), and full control over the underlying infrastructure. Best for predictable, high-throughput applications.

Tip: For latency-sensitive applications, consider the Premium or Dedicated plan to minimize cold start delays.

Optimizing Function Code

Write efficient code within your functions:

• Efficient Data Retrieval: Fetch only the data you need. Use projections and filters when querying databases or other data sources.
• Connection Pooling: Reuse database or service connections instead of creating new ones for each invocation. Many SDKs handle this automatically, but be aware of it.
• Resource Management: Properly dispose of resources like network connections, file handles, and large objects when they are no longer needed.
• Caching: Cache frequently accessed, relatively static data in memory or using a distributed cache (like Azure Cache for Redis) to reduce external calls.

Example: Caching with Application Insights

While not direct in-memory caching for function instances, you can leverage application insights for tracing and analysis, which indirectly helps in identifying performance bottlenecks.

// C# Example Snippet (Conceptual)
using Microsoft.Extensions.Caching.Memory;

public class MyFunction
{
    private readonly IMemoryCache _cache;

    public MyFunction(IMemoryCache cache)
    {
        _cache = cache;
    }

    public async Task Run(
        [HttpTrigger(AuthorizationLevel.Function, "get", "post")] HttpRequest req,
        ILogger log)
    {
        string cacheKey = "my_cached_data";
        if (!_cache.TryGetValue(cacheKey, out MyData data))
        {
            // Data not in cache, fetch it
            data = await FetchDataFromExternalService();

            // Set cache options
            var cacheEntryOptions = new MemoryCacheEntryOptions()
                .SetSlidingExpiration(TimeSpan.FromMinutes(5)); // Cache expires after 5 minutes of inactivity

            // Save data in cache
            _cache.Set(cacheKey, data, cacheEntryOptions);
            log.LogInformation("Data fetched and cached.");
        }
        else
        {
            log.LogInformation("Data retrieved from cache.");
        }

        return new OkObjectResult(data);
    }

    private async Task<MyData> FetchDataFromExternalService()
    {
        // Simulate fetching data
        await Task.Delay(1000); // Simulate latency
        return new MyData { Value = $"Data fetched at {DateTime.UtcNow}" };
    }
}

public class MyData
{
    public string Value { get; set; }
}

Managing Dependencies

• Keep Dependencies Lean: Only include libraries that are strictly necessary. Large dependency trees can increase deployment size and load times.
• Use Latest Versions: Keep your function runtime and library versions up-to-date. Newer versions often include performance improvements and bug fixes.
• Analyze Dependencies: Use tools to analyze your project's dependencies and remove any unused packages.

Handling Long-Running Operations

Azure Functions have execution time limits (especially on the Consumption plan). For operations that might exceed these limits, use patterns like:

• Durable Functions: A powerful extension that allows you to orchestrate stateful workflows using code. Ideal for long-running processes, fan-out/fan-in scenarios, and human interaction.
• Queue-Based Processing: For long-running tasks, trigger another function via a queue (e.g., Azure Queue Storage, Service Bus). The initial function completes quickly, and a separate function handles the heavy lifting asynchronously.

Leveraging Configuration

Externalize configuration settings instead of hardcoding them.

• Application Settings: Store connection strings, API keys, and other configurations in your function app's application settings. This makes them easily discoverable by your code and allows for easy updates without redeploying code.
• Azure App Configuration: For more advanced scenarios, consider Azure App Configuration for centralized management of feature flags, application settings, and more across multiple applications.

Monitoring and Scaling

Regularly monitor your function's performance and configure appropriate scaling behaviors.

• Application Insights: Essential for tracking function executions, performance metrics, exceptions, and dependencies. Set up alerts for critical metrics like latency and error rates.
• Autoscaling: On Consumption and Premium plans, Azure Functions scales automatically based on load. Understand the scaling behavior and consider setting minimum instances on Premium plans if needed.
• Performance Testing: Regularly conduct load and performance tests to identify bottlenecks before they impact users.