Multithreading va multiprocessing

Zamonaviy dasturlashda parallelism va concurrency kabi atamalar ko'p uchraydi. Python'da bir vaqtning o'zida bir nechta ishni bajarishning ikki asosiy yo'li bor: multithreading va multiprocessing.

Qaysi birini tanlash vazifangiz turiga bog'liq: u I/O Boundmi yoki CPU Boundmi.

1. I/O Bound va CPU Bound

• I/O Bound: dastur ko'proq input/output kutadi (misol: network so'rovlar, diskdan o'qish, database query). Bu paytda CPU ko'pincha bo'sh turadi.
• CPU Bound: dastur ko'proq hisob-kitob (matematika, og'ir data processing) qiladi. CPU 100% ishlaydi.

2. Multithreading (I/O Bound uchun)

Threading bitta process ichida bir nechta thread ishlatadi. Thread'lar xotirani bo'lishadi (shared memory).

Lekin Python (CPython) da GIL (Global Interpreter Lock) bor: u bir vaqtning o'zida ikki thread'ning bitta CPU core'da Python bytecode bajarishini cheklaydi. Shuning uchun Python multithreading CPU-bound ishlarni tezlashtirmaydi (hatto overhead sabab sekinlashishi ham mumkin).

Ammo I/O bound ishlar uchun multithreading juda foydali: bitta thread kutayotgan paytda (masalan web javobini), boshqasi ishlashi mumkin.

import threading
import time

def download_page(url):
    print(f"Start downloading {url}...")
    time.sleep(2) # Simulate network delay
    print(f"Finished downloading {url}")

start = time.time()

threads = []
urls = ["web1", "web2", "web3"]

for url in urls:
    t = threading.Thread(target=download_page, args=(url,))
    threads.append(t)
    t.start()

# Barcha thread'lar tugashini kutish
for t in threads:
    t.join()

end = time.time()
print(f"Umumiy vaqt: {end - start:.2f} soniya")
# Natija taxminan 2 soniya, 6 soniya emas!

3. Multiprocessing (CPU Bound uchun)

Multiprocessing alohida Python process'larni yaratadi. Har bir process'ning o'z Python interpreter'i va xotira maydoni bo'ladi. Bu GIL'ni chetlab o'tadi va multi-core CPU'dan maksimal foydalanishga imkon beradi.

Og'ir hisob-kitobli vazifalar uchun shuni ishlating.

import multiprocessing
import time

def heavy_square_calculation(number):
    print(f"Process {number} starts...")
    result = sum(i * i for i in range(10**7)) # Heavy calculation
    print(f"Process {number} finished.")
    return result

if __name__ == "__main__":
    start = time.time()

    # Turli CPU core'larda parallel ishlaydigan 2 ta process yaratish
    p1 = multiprocessing.Process(target=heavy_square_calculation, args=(1,))
    p2 = multiprocessing.Process(target=heavy_square_calculation, args=(2,))

    p1.start()
    p2.start()

    p1.join()
    p2.join()

    end = time.time()
    print(f"Total time: {end - start:.2f} seconds")

Eslatma: Windows'da multiprocessing ishlatganda asosiy kodni if __name__ == "__main__": bilan himoyalash kerak.

4. Concurrent Futures (zamonaviy usul)

Python concurrent.futures modulini beradi. U Threading va Multiprocessing uchun yuqori darajali va ishlatish oson interfeys.

from concurrent.futures import ThreadPoolExecutor
import time

def task(n):
    time.sleep(1)
    return f"Task {n} finished"

start = time.time()

with ThreadPoolExecutor(max_workers=3) as executor:
    results = executor.map(task, [1, 2, 3])

    for result in results:
        print(result)

print(f"Time: {time.time() - start:.2f} seconds")

Multiprocessing'ga o'tmoqchi bo'lsangiz ThreadPoolExecutor o'rniga ProcessPoolExecutor ishlating.

Xulosa