Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Use a Weakref Cache to Avoid Memory Leaks in Python
This code demonstrates building a value cache with weakref.WeakValueDictionary so objects can be garbage collected when no longer referenced, preventing memory leaks.
import weakref
import gc
class ExpensiveObject:
def __init__(self, name):
self.name = name
def __repr__(self):
return f"ExpensiveObject('{self.name}')"
class ObjectCache:
def __init__(self):
self._cache = weakref.WeakValueDictionary()
def get_or_create(self, name):
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Concurrency & performance — Python code examples
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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.
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