Reference library

Concurrency & performance

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

4 matches

Benchmark list.append vs deque.append in Python

Measures and compares the performance of appending to a Python list versus a collections.deque using timeit.repeat, showing best and average timings.

benchmark performance list

Python

"""Benchmark list.append vs collections.deque.append."""

import timeit

def bench(stmt, setup, repeat=5, number=1_000_000):
    times = timeit.repeat(stmt, setup=setup, repeat=repeat, number=number)
    return min(times), sum(times) / len(times)

if __name__ == "__main__":
    number = 1_000_000
    list_best, list_a…

12 0 Open

Concurrency & performance medium

How to Share Memory Between Processes in Python with multiprocessing.Value and Array

Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.

multiprocessing shared-memory concurrency

Python

import multiprocessing

def worker(shared_value, shared_array, index):
    shared_value.value += 10
    shared_array[index] = shared_array[index] * 2

if __name__ == "__main__":
    shared_value = multiprocessing.Value("i", 5)
    shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])

    processes = []
    for i…

13 0 Open

Concurrency & performance medium

How to Share a Dict and List Between Processes with multiprocessing Manager in Python

This code demonstrates how to share a dictionary and a list between multiple processes using multiprocessing.Manager, enabling safe concurrent updates.

multiprocessing manager shared-state

Python

import multiprocessing as mp


def worker(shared_dict, shared_list, name):
    shared_dict[name] = name.upper()
    shared_list.append(name)
    print(f"{name} added to shared structures")


def main():
    with mp.Manager() as manager:
        shared_dict = manager.dict()
        shared_list = manager.list()

       …

13 0 Open

Concurrency & performance medium

Merge K Sorted Lists in Python with heapq

Merge k sorted lists into one sorted list in O(N log k) time using a min-heap of current elements.

heapq merge sorted-lists

Python

import heapq

def merge_k_sorted_lists(lists):
    heap = []
    for i, lst in enumerate(lists):
        if lst:  # only push non-empty lists
            heapq.heappush(heap, (lst[0], i, 0))
    result = []
    while heap:
        val, list_idx, elem_idx = heapq.heappop(heap)
        result.append(val)
        if elem…

13 0 Open

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