Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Implement a Token Bucket Rate Limiter with asyncio in Python
This code implements a thread-safe token bucket rate limiter for asyncio, allowing you to limit the rate of async tasks or API calls.
import asyncio
import time
class TokenBucket:
def __init__(self, rate_per_second, capacity):
self.rate = rate_per_second
self.capacity = capacity
self.tokens = capacity
self.last_refill = time.monotonic()
self.lock = asyncio.Lock()
async def acquire(self):
asy…Limit Concurrency with asyncio.Semaphore in Python
Use asyncio.Semaphore to cap how many async tasks run at once, throttling a batch of coroutines to a set concurrency limit.
import asyncio
import random
async def fetch_data(i: int, semaphore: asyncio.Semaphore) -> str:
async with semaphore:
print(f"Task {i} starts")
await asyncio.sleep(random.uniform(0.1, 0.5))
print(f"Task {i} finishes")
return f"Result {i}"
async def main() -> None:
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Concurrency & performance — Python code examples
What you will find here
This page collects concurrency & performance snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.