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Concurrency & performance

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

4 matches

How to Convert Data in Parallel with ThreadPoolExecutor in Python

This example demonstrates converting a list of items in parallel using ThreadPoolExecutor, showing performance gains over serial processing.

concurrency threadpoolexecutor parallelism

Python

import time
from concurrent.futures import ThreadPoolExecutor


def convert_data(item):
    """Simulate a CPU/IO-bound conversion task."""
    time.sleep(0.05)  # simulate work
    return item.upper()


if __name__ == "__main__":
    items = [f"item_{i}" for i in range(20)]

    start = time.perf_counter()
    serial_…

16 0 Open

Concurrency & performance medium

How to Speed Up Data Filtering with Python ThreadPoolExecutor

This code compares sequential filtering of even numbers with a threaded version using ThreadPoolExecutor, showing a measurable speedup for I/O-bound work.

threadpoolexecutor concurrency filtering

Python

import time
from concurrent.futures import ThreadPoolExecutor
import random


def is_even(number):
    time.sleep(0.001)  # simulate work
    return number % 2 == 0


def filter_even_sequential(numbers):
    return [n for n in numbers if is_even(n)]


def filter_even_threaded(numbers):
    with ThreadPoolExecutor(max_…

14 0 Open

Concurrency & performance medium

How to Use threading.local for Per-Thread Data in Python

Use threading.local to keep thread-specific data — each thread gets its own copy of the attribute, so values don't leak between threads.

threading thread-local concurrency

Python

import threading
import time

local_storage = threading.local()

def worker(name):
    local_storage.name = name
    time.sleep(0.1)
    print(f"Thread {threading.current_thread().name}: {local_storage.name}")

if __name__ == "__main__":
    threads = []
    for i in range(3):
        t = threading.Thread(target=worke…

14 0 Open

Concurrency & performance easy

How to Validate Data with ThreadPoolExecutor in Python

This code shows how to validate a list of numbers concurrently using ThreadPoolExecutor, dramatically speeding up slow validation tasks by running them in parallel threads.

concurrency threadpool validation

Python

import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass


@dataclass
class Result:
    is_valid: bool
    value: int


def validate(value: int) -> Result:
    time.sleep(0.1)  # simulate slow validation (API call, DB check)
    return Result(is_valid=0 < value < 100, value=value…

12 0 Open

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