Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Parse JSON Files in Parallel with Python ThreadPoolExecutor
Load and transform JSON records from multiple files concurrently using ThreadPoolExecutor for faster I/O-bound parsing.
import time
from concurrent.futures import ThreadPoolExecutor
import json
def load_json_file(path):
with open(path, 'r') as f:
return json.load(f)
def transform_record(record):
record['full_name'] = f"{record.pop('first_name', '')} {record.pop('last_name', '')}".strip()
record['score'] = int(reco…How to Use uvloop Faster Event Loop
Install uvloop at startup to replace asyncio's default event loop with a faster libuv-based one, with a graceful fallback when it's unavailable.
import asyncio
try:
import uvloop
uvloop.install()
USING_UVLOOP = True
except ImportError:
USING_UVLOOP = False
async def fetch_data(index):
await asyncio.sleep(0.01)
return f"data-{index}"
async def main():
tasks = [fetch_data(i) for i in range(10)]
results = await asyncio.gather(*…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.