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Concurrency & performance

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

4 matches

How to Run Coroutines Concurrently with asyncio.gather in Python

Run multiple async coroutines concurrently and collect their results in the order they were passed.

import asyncio


async def fetch_data(name: str, delay: float) -> str:
    """Simulate an async operation (e.g., API call) with a delay."""
    await asyncio.sleep(delay)
    return f"{name} data (after {delay}s)"


async def main() -> None:
    """Run multiple coroutines concurrently with asyncio.gather."""
    resul…

15 0 Open

Concurrency & performance medium

How to Use Thread Pool Executor map for IO-Bound Tasks in Python

Run multiple I/O-bound tasks concurrently with ThreadPoolExecutor map and collect their results in order.

threadpool concurrency io-bound

Python

import time
from concurrent.futures import ThreadPoolExecutor

def io_bound_task(task_id: int) -> str:
    time.sleep(0.2)  # mock I/O wait
    return f"Task {task_id} completed"

def main() -> None:
    task_ids = [1, 2, 3, 4, 5]
    with ThreadPoolExecutor(max_workers=3) as executor:
        results = list(executor.…

12 0 Open

Concurrency & performance easy

How to Use ThreadPoolExecutor in Python for Parallel Processing

Use ThreadPoolExecutor with executor.map to run a function over many inputs concurrently and collect ordered results.

concurrency threadpoolexecutor parallel

Python

def worker(item):
    return item * item

if __name__ == "__main__":
    from concurrent.futures import ThreadPoolExecutor
    numbers = list(range(1, 11))
    with ThreadPoolExecutor(max_workers=4) as executor:
        results = list(executor.map(worker, numbers))
    print("Input:  ", numbers)
    print("Results:", …

13 0 Open

Concurrency & performance medium

How to Use as_completed to Process Futures in Order of Completion

Submit multiple tasks to a ThreadPoolExecutor and process each result as soon as it finishes using as_completed.

concurrency threads futures

Python

from concurrent.futures import ThreadPoolExecutor, as_completed
import time


def fetch_data(item_id):
    time.sleep(1)
    return f"item-{item_id}"


def main():
    with ThreadPoolExecutor(max_workers=3) as executor:
        future_map = {executor.submit(fetch_data, i): i for i in range(1, 6)}
        for future in…

14 0 Open

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