Async Recipes: Mastering Asynchronous Programming

What Are Async Recipes?

Asynchronous programming is the cornerstone of modern web and backend development. This post provides reusable patterns and optimized techniques for handling concurrency in JavaScript, Rust, Python, and other modern languages. We'll explore common use cases, performance considerations, and best practices for writing clean, maintainable async code.

Key Concepts in Async Programming

Concurrency Models

• Event loops in JavaScript (Node.js)
• Async/await in Python
• Green threads in Rust (Tokio)

Error Handling

• Try/catch for async functions
• Graceful fallback patterns
• Circuit breakers for service resilience

JavaScript Async Recipe Example


// Parallel API calls with concurrency limit
async function batchFetch(urls) {
  const results = [];
  for (const url of urls) {
    try {
      const response = await fetch(url);
      results.push(await response.json());
    } catch (err) {
      results.push({ error: err.message });
    }
  }
  return results;
}

// Usage pattern
const data = await batchFetch([
  'https://api.example.com/data1',
  'https://api.example.com/data2'
]);

This pattern enables controlled parallelism with proper error isolation in browser and Node.js environments.

Async Best Practices

✓ Use timeouts for long-running operations
✓ Isolate database queries in async transactions
✓ Implement rate limiting for external API calls
✓ Use stream processing for large datasets

Performance Optimization

CPU-bound Workloads

1. Use Web Workers in browser clients
2. Leverage Rust WASM for heavy computations

IO-bound Workloads

1. Batch database operations
2. Use connection pooling