Introduction
Asyncio is Python core async library, but many developers only know basic usage. This article explores advanced patterns for maximum performance.
1. Semaphore for Concurrency Control
import asyncio
class RateLimiter:
def __init__(self, max_concurrent=10):
self.semaphore = asyncio.Semaphore(max_concurrent)
async def __aenter__(self):
await self.semaphore.acquire()
return self
async def __aexit__(self, *args):
self.semaphore.release()
2. Queue Processing Pattern
class AsyncWorkerPool:
def __init__(self, worker_count, processor):
self.queue = asyncio.Queue()
self.worker_count = worker_count
self.processor = processor
3. Mixing CPU-Intensive Tasks
from concurrent.futures import ProcessPoolExecutor
process_pool = ProcessPoolExecutor()
async def hybrid_processing(items):
loop = asyncio.get_running_loop()
raw_data = await asyncio.gather(*[fetch_data(i) for i in items])
futures = [loop.run_in_executor(process_pool, cpu_task, d) for d in raw_data]
return await asyncio.gather(*futures)
Summary
Asyncio advanced patterns help you build high-performance applications.
Tool Recommendation: Try PySecToolkit - a Python security toolkit built on asyncio.
Originally published on WD Tech Blog
