Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
Thread Pool Map for IO Bound Tasks in Python
Run IO-bound mock tasks concurrently with ThreadPoolExecutor.map and measure total elapsed time in Python.
import concurrent.futures
import time
from pathlib import Path
def mock_io_task(filename):
"""Simulate an IO-bound task by creating a small file and measuring its latency."""
path = Path(filename)
path.write_text("data")
time.sleep(0.1) # Simulate slow disk/network
return f"{filename} written in …asyncio Condition wait notify pattern in Python
Coordinate coroutines with asyncio.Condition: workers wait for notifications and the main task notifies one or all of them.
import asyncio
async def worker(condition, name):
async with condition:
print(f"{name} waiting...")
await condition.wait()
print(f"{name} notified!")
async def main():
condition = asyncio.Condition()
tasks = [asyncio.create_task(worker(condition, f"worker-{i}")) for i in range(3…Browse by section
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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.