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Concurrency & performance

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

3 matches

How to Test HTTPX Async Client Pool Reuse with Mocks in Python

Mock an httpx.AsyncClient to verify connection pool reuse by asserting GET calls share a single client instance across concurrent async requests.

httpx async-await mock

Python

import asyncio
import httpx
from unittest.mock import AsyncMock, patch, Mock

async def fetch_with_pool(client, url, n_reuses=3):
    results = []
    for i in range(n_reuses):
        resp = await client.get(url)
        results.append(resp.status_code)
        await asyncio.sleep(0)  # yield to loop to mimic real us…

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

Concurrency & performance easy

Using a Python Generator Instead of a List to Save Memory

Compare a list approach with a generator to stream values lazily, avoiding memory-heavy storage of large sequences.

generator lazy-evaluation memory

Python

def fibonacci_generator(limit):
    a, b = 0, 1
    count = 0
    while count < limit:
        yield a
        a, b = b, a + b
        count += 1


def sum_first_n(generator, n):
    total = 0
    for i, value in enumerate(generator):
        if i >= n:
            break
        total += value
    return total


if __…

12 0 Open

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