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Concurrency & performance

asyncio, threading, multiprocessing, and profiling-friendly performance patterns.

3 matches

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.

asyncio lru_cache memoization

Python

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…

13 0 Open

Concurrency & performance easy

How to Send and Receive Messages Between Processes with multiprocessing.Pipe in Python

Use multiprocessing.Pipe to create a two-way connection between two processes, send a message from parent to child, and receive a reply back.

multiprocessing pipe interprocess-communication

Python

import multiprocessing


def child_process(conn):
    """Receive from parent and send back a response."""
    message = conn.recv()
    print(f"Child received: {message}")
    conn.send("Hello from child!")


if __name__ == "__main__":
    parent_conn, child_conn = multiprocessing.Pipe()

    process = multiprocessing…

14 0 Open

Concurrency & performance easy

How to use ThreadPoolExecutor for concurrent tasks in Python

Run blocking functions in parallel with ThreadPoolExecutor and as_completed, cutting total runtime from 5 sequential sleeps to about 1 second.

concurrency threadpoolexecutor parallel

Python

import time
from concurrent.futures import ThreadPoolExecutor, as_completed


def fetch_data(item):
    """Simulate a slow operation with a fixed delay."""
    time.sleep(0.2)
    return item * 2


def main():
    items = [1, 2, 3, 4, 5]
    start = time.perf_counter()

    with ThreadPoolExecutor(max_workers=3) as ex…

14 0 Open

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