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Concurrency & performance

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

5 matches

How to Time Code Performance with timeit in Python

Benchmark two implementations of the same logic using Python's timeit module and compare their execution speeds.

import timeit

# Implementation 1: Using a list comprehension
def list_comprehension_squares(n):
    return [i ** 2 for i in range(n)]

# Implementation 2: Using a for loop with append
def loop_squares(n):
    result = []
    for i in range(n):
        result.append(i ** 2)
    return result

if __name__ == "__main__"…

12 0 Open

Concurrency & performance easy

How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python

Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.

concurrency threadpool processpool

Python

import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math

numbers = list(range(1, 1000001))


def compute_square(n):
    return n * n


def compute_sqrt(n):
    return math.sqrt(n)


def run_executor(executor, func, data):
    start = time.perf_counter()
    results = list(executo…

15 0 Open

Concurrency & performance easy

How to Use bisect.insort in Python to Maintain a Sorted List

Insert items into an already sorted list using Python's bisect.insort to keep it sorted efficiently in O(n) time.

bisect sorted insertion

Python

import bisect

def maintain_sorted_list():
    data = [3, 1, 4, 1, 5, 9, 2, 6]
    sorted_list = []
    
    for num in data:
        bisect.insort(sorted_list, num)
    
    print("Original data:", data)
    print("Sorted list maintained with insort:", sorted_list)
    
    # Insert new values to maintain sorted orde…

13 0 Open

Concurrency & performance easy

How to set a timeout with asyncio.wait_for in Python

Use asyncio.wait_for to bound an async function with a timeout, catching TimeoutError when it exceeds the limit.

asyncio timeout concurrency

Python

import asyncio

async def slow_task():
    await asyncio.sleep(3)
    return "finished"

async def main():
    try:
        result = await asyncio.wait_for(slow_task(), timeout=1)
        print(result)
    except asyncio.TimeoutError:
        print("Task timed out")

if __name__ == "__main__":
    asyncio.run(main())

13 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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