Python async patterns for payment processing

Processing payments at scale requires handling thousands of concurrent requests efficiently. Python's async/await syntax, combined with FastAPI, provides an excellent foundation for building high-performance payment APIs.

Why Async Matters

Synchronous payment processing blocks threads while waiting for external APIs (banks, payment processors, fraud checks). With async, we can handle many requests concurrently without blocking.

The Pattern

from fastapi import FastAPI
import httpx

app = FastAPI()

async def process_payment(payment_id: str):
    async with httpx.AsyncClient() as client:
        # These run concurrently
        fraud_check = await client.post("/fraud/check", json=payment_data)
        bank_validation = await client.post("/bank/validate", json=payment_data)
        
        if fraud_check.status_code == 200 and bank_validation.status_code == 200:
            return await execute_payment(payment_id)

Real-World Performance

At Mercury, moving to async patterns increased our payment processing throughput by 3x. The key was understanding when to use async (I/O-bound operations) versus when to use sync (CPU-bound calculations).

Gotchas

• Database connection pooling is critical
• Error handling becomes more complex
• Testing async code requires different patterns
• Monitoring needs to account for concurrent operations

Best Practices

1. Use async for I/O-bound operations
2. Keep CPU-bound work synchronous or use process pools
3. Implement proper connection pooling
4. Add comprehensive error handling and retries
5. Monitor concurrency metrics