{"id":37672,"date":"2026-01-12T05:40:48","date_gmt":"2026-01-12T02:40:48","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=37672"},"modified":"2026-01-12T05:40:48","modified_gmt":"2026-01-12T02:40:48","slug":"python-multiprocessing-paralel-islem-gucuyle-performansi-artirma","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/","title":{"rendered":"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma"},"content":{"rendered":"

<\/p>\n

Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma<\/h2>\n

Python, basitli\u011fi ve geni\u015f k\u00fct\u00fcphane deste\u011fiyle pop\u00fcler bir programlama dilidir. Ancak, CPU yo\u011fun g\u00f6revlerde tek \u00e7ekirdekli performans s\u0131n\u0131rlar\u0131na tak\u0131labilir. Bu noktada, Python’\u0131n multiprocessing<\/code> mod\u00fcl\u00fc devreye girerek programlar\u0131n birden fazla CPU \u00e7ekirde\u011fini ayn\u0131 anda kullanarak paralel i\u015flem yapmas\u0131n\u0131 ve b\u00f6ylece performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rmas\u0131n\u0131 sa\u011flar. Bu makalede, Python multiprocessing<\/code> mod\u00fcl\u00fcn\u00fcn temellerini, neden \u00f6nemli oldu\u011funu, ana bile\u015fenlerini ve \u00e7e\u015fitli kullan\u0131m \u00f6rneklerini detayl\u0131 bir \u015fekilde inceleyece\u011fiz.<\/p>\n

Giri\u015f: Neden Paralel \u0130\u015flem?<\/h3>\n

Modern bilgisayarlar genellikle birden fazla i\u015flemci \u00e7ekirde\u011fine sahiptir. Ancak, varsay\u0131lan olarak Python programlar\u0131 tek bir \u00e7ekirdek \u00fczerinde \u00e7al\u0131\u015f\u0131r. Bu durum, \u00f6zellikle yo\u011fun hesaplama gerektiren g\u00f6revlerde (\u00f6rne\u011fin, b\u00fcy\u00fck veri analizi, bilimsel sim\u00fclasyonlar, g\u00f6r\u00fcnt\u00fc i\u015fleme) program\u0131n potansiyel performans\u0131n\u0131n alt\u0131nda kalmas\u0131na neden olur. Paralel i\u015flem, bir g\u00f6revi daha k\u00fc\u00e7\u00fck, ba\u011f\u0131ms\u0131z par\u00e7alara b\u00f6lerek bu par\u00e7alar\u0131n e\u015f zamanl\u0131 olarak farkl\u0131 i\u015flemci \u00e7ekirdeklerinde \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131 prensibine dayan\u0131r. Bu sayede, ayn\u0131 i\u015f daha k\u0131sa s\u00fcrede tamamlanabilir.<\/p>\n

Python’da paralellik denince akla genellikle iki ana kavram gelir:
\n* \u00c7oklu \u0130\u015f Par\u00e7ac\u0131\u011f\u0131 (Multithreading):<\/strong> Ayn\u0131 s\u00fcre\u00e7 i\u00e7inde birden fazla i\u015f par\u00e7ac\u0131\u011f\u0131 olu\u015fturarak e\u015f zamanl\u0131l\u0131k sa\u011flar. Ancak Python’daki Global Interpreter Lock (GIL) nedeniyle, ayn\u0131 anda yaln\u0131zca bir i\u015f par\u00e7ac\u0131\u011f\u0131 Python bytecode’unu \u00e7al\u0131\u015ft\u0131rabilir. Bu durum, CPU yo\u011fun g\u00f6revlerde multithreading’in ger\u00e7ek paralellik sa\u011flamas\u0131n\u0131 engeller. Daha \u00e7ok I\/O (giri\u015f\/\u00e7\u0131k\u0131\u015f) yo\u011fun g\u00f6revler i\u00e7in uygundur.
\n* \u00c7oklu S\u00fcre\u00e7 (Multiprocessing):<\/strong> Her biri kendi bellek alan\u0131na ve Python yorumlay\u0131c\u0131s\u0131na sahip birden fazla ba\u011f\u0131ms\u0131z s\u00fcre\u00e7 olu\u015fturur. Bu sayede GIL k\u0131s\u0131tlamas\u0131n\u0131 a\u015farak birden fazla CPU \u00e7ekirde\u011fini ayn\u0131 anda kullanabilir ve ger\u00e7ek paralel i\u015flem g\u00fcc\u00fc elde edebilir. CPU yo\u011fun g\u00f6revler i\u00e7in idealdir.<\/p>\n

Bu makale, multiprocessing<\/code> mod\u00fcl\u00fcne odaklanarak Python programlar\u0131n\u0131zda ger\u00e7ek paralelli\u011fi nas\u0131l uygulayabilece\u011finizi g\u00f6sterecektir.<\/p>\n

Multiprocessing Neden \u00d6nemlidir?<\/h3>\n

multiprocessing<\/code> mod\u00fcl\u00fc, Python programlar\u0131n\u0131n performans\u0131n\u0131 art\u0131rmak i\u00e7in kritik bir ara\u00e7t\u0131r, \u00f6zellikle a\u015fa\u011f\u0131daki senaryolarda:<\/p>\n

CPU Yo\u011fun G\u00f6revler<\/h4>\n

CPU yo\u011fun g\u00f6revler, i\u015flemci \u00fczerinde yo\u011fun hesaplamalar gerektiren ve genellikle I\/O beklemeyen g\u00f6revlerdir (\u00f6rne\u011fin, matematiksel hesaplamalar, \u015fifreleme\/\u015fifre \u00e7\u00f6zme, veri s\u0131k\u0131\u015ft\u0131rma). Python’\u0131n Global Interpreter Lock (GIL) k\u0131s\u0131tlamas\u0131 nedeniyle, threading<\/code> mod\u00fcl\u00fc bu t\u00fcr g\u00f6revlerde ger\u00e7ek paralellik sa\u011flayamaz. \u00c7\u00fcnk\u00fc GIL, ayn\u0131 anda yaln\u0131zca bir Python i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n yorumlay\u0131c\u0131y\u0131 kullanmas\u0131na izin verir. multiprocessing<\/code> ise her s\u00fcrece kendi Python yorumlay\u0131c\u0131s\u0131n\u0131 ve bellek alan\u0131n\u0131 atayarak bu k\u0131s\u0131tlamay\u0131 a\u015far. Bu, her s\u00fcrecin farkl\u0131 bir CPU \u00e7ekirde\u011finde ba\u011f\u0131ms\u0131z olarak \u00e7al\u0131\u015fabilece\u011fi anlam\u0131na gelir.<\/p>\n

\u00c7ok \u00c7ekirdekli \u0130\u015flemcilerden Yararlanma<\/h4>\n

G\u00fcn\u00fcm\u00fcz bilgisayarlar\u0131n\u0131n \u00e7o\u011fu \u00e7ok \u00e7ekirdekli i\u015flemcilere sahiptir. multiprocessing<\/code> mod\u00fcl\u00fc, bu \u00e7ekirdeklerin t\u00fcm potansiyelini kullanarak programlar\u0131n\u0131z\u0131n birden fazla i\u015flemi ayn\u0131 anda y\u00fcr\u00fctmesini sa\u011flar. Bu, \u00f6zellikle b\u00fcy\u00fck veri setlerinin i\u015flenmesi, karma\u015f\u0131k algoritmalar\u0131n \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131 veya birden fazla ba\u011f\u0131ms\u0131z g\u00f6revin e\u015f zamanl\u0131 olarak y\u00fcr\u00fct\u00fclmesi gereken durumlarda performans\u0131 katlayabilir.<\/p>\n

Daha \u0130yi Kaynak Kullan\u0131m\u0131<\/h4>\n

Paralel i\u015flem, sistem kaynaklar\u0131n\u0131n daha verimli kullan\u0131lmas\u0131na olanak tan\u0131r. \u0130\u015flemci \u00e7ekirdekleri bo\u015fta beklemek yerine aktif olarak g\u00f6revleri i\u015fler. Bu, genel sistem verimlili\u011fini art\u0131r\u0131r ve programlar\u0131n daha h\u0131zl\u0131 yan\u0131t vermesini sa\u011flar.<\/p>\n

Python’\u0131n multiprocessing<\/code> Mod\u00fcl\u00fc<\/h3>\n

multiprocessing<\/code> mod\u00fcl\u00fc, s\u00fcre\u00e7 tabanl\u0131 paralellik i\u00e7in zengin bir API sunar. Bu mod\u00fcl, s\u00fcre\u00e7 olu\u015fturma, s\u00fcre\u00e7ler aras\u0131 ileti\u015fim (IPC), senkronizasyon ve s\u00fcre\u00e7 havuzlar\u0131 gibi temel bile\u015fenleri i\u00e7erir. Ba\u015fl\u0131ca s\u0131n\u0131flar\u0131 ve fonksiyonlar\u0131 \u015funlard\u0131r:<\/p>\n

* Process<\/code>:<\/strong> Yeni bir s\u00fcre\u00e7 olu\u015fturmak ve y\u00f6netmek i\u00e7in temel s\u0131n\u0131f.
\n* Queue<\/code>:<\/strong> S\u00fcre\u00e7ler aras\u0131 g\u00fcvenli veri ileti\u015fimi i\u00e7in FIFO (\u0130lk Giren \u0130lk \u00c7\u0131kar) kuyruk.
\n* Pipe<\/code>:<\/strong> \u0130ki s\u00fcre\u00e7 aras\u0131nda tek veya \u00e7ift y\u00f6nl\u00fc ileti\u015fim i\u00e7in boru.
\n* Lock<\/code>, Semaphore<\/code>, Event<\/code>:<\/strong> S\u00fcre\u00e7ler aras\u0131 senkronizasyon mekanizmalar\u0131.
\n* Value<\/code>, Array<\/code>:<\/strong> S\u00fcre\u00e7ler aras\u0131nda payla\u015f\u0131lan bellek alanlar\u0131.
\n* Pool<\/code>:<\/strong> Bir grup i\u015f\u00e7i s\u00fcrecini y\u00f6netmek i\u00e7in bir havuz. G\u00f6revleri bu havuzdaki s\u00fcre\u00e7lere da\u011f\u0131tarak daha kolay paralel i\u015flem yapmay\u0131 sa\u011flar.<\/p>\n

Temel Multiprocessing Kavramlar\u0131 ve Process<\/code> S\u0131n\u0131f\u0131<\/h3>\n

multiprocessing<\/code> mod\u00fcl\u00fcn\u00fcn kalbinde Process<\/code> s\u0131n\u0131f\u0131 yer al\u0131r. Bu s\u0131n\u0131f, yeni bir i\u015fletim sistemi s\u00fcreci olu\u015fturman\u0131za ve bu s\u00fcre\u00e7 i\u00e7inde belirli bir fonksiyonu \u00e7al\u0131\u015ft\u0131rman\u0131za olanak tan\u0131r.<\/p>\n

Bir S\u00fcre\u00e7 Olu\u015fturma ve Y\u00f6netme<\/h4>\n

Bir Process<\/code> nesnesi olu\u015fturmak i\u00e7in genellikle target<\/code> parametresi ile \u00e7al\u0131\u015ft\u0131r\u0131lacak fonksiyonu ve args<\/code> parametresi ile bu fonksiyona ge\u00e7irilecek arg\u00fcmanlar\u0131 belirtirsiniz.<\/p>\n

import multiprocessing\nimport os\nimport time\n\ndef worker_function(name):\n    \"\"\"Her s\u00fcrecin \u00e7al\u0131\u015ft\u0131raca\u011f\u0131 fonksiyon.\"\"\"\n    print(f\"S\u00fcre\u00e7 {name} ba\u015flad\u0131. PID: {os.getpid()}\")\n    time.sleep(2) # G\u00f6revi sim\u00fcle etmek i\u00e7in 2 saniye bekler\n    print(f\"S\u00fcre\u00e7 {name} bitti.\")\n\nif __name__ == '__main__':\n    print(f\"Ana s\u00fcre\u00e7 ba\u015flad\u0131. PID: {os.getpid()}\")\n\n    # Bir Process nesnesi olu\u015fturma\n    process1 = multiprocessing.Process(target=worker_function, args=(\"Worker-1\",))\n    process2 = multiprocessing.Process(target=worker_function, args=(\"Worker-2\",))\n\n    # S\u00fcre\u00e7leri ba\u015flatma\n    process1.start()\n    process2.start()\n\n    # S\u00fcre\u00e7lerin bitmesini bekleme\n    # join() metodu, ana s\u00fcrecin alt s\u00fcrecin tamamlanmas\u0131n\u0131 beklemesini sa\u011flar.\n    process1.join()\n    process2.join()\n\n    print(\"Ana s\u00fcre\u00e7 t\u00fcm alt s\u00fcre\u00e7lerin bitmesini bekledi ve tamamland\u0131.\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Process(target=worker_function, args=(\"Worker-1\",))<\/code>: Yeni bir s\u00fcre\u00e7 olu\u015fturur. target<\/code> parametresi, yeni s\u00fcrecin \u00e7al\u0131\u015ft\u0131raca\u011f\u0131 fonksiyonu belirtir. args<\/code> parametresi ise target<\/code> fonksiyona arg\u00fcman olarak ge\u00e7irilecek bir demet (tuple) al\u0131r.
\n*   process.start()<\/code>: S\u00fcreci ba\u015flat\u0131r. Bu noktada worker_function<\/code> yeni bir i\u015fletim sistemi s\u00fcrecinde \u00e7al\u0131\u015fmaya ba\u015flar.
\n*   process.join()<\/code>: Ana s\u00fcrecin, ilgili alt s\u00fcrecin tamamlanmas\u0131n\u0131 beklemesini sa\u011flar. E\u011fer join()<\/code> \u00e7a\u011fr\u0131lmazsa, ana s\u00fcre\u00e7 alt s\u00fcre\u00e7ler hen\u00fcz bitmeden kendi i\u015fine devam edebilir ve hatta sonlanabilir.
\n*   if __name__ == '__main__':<\/code>: Bu blok kritik \u00f6neme sahiptir. \u00d6zellikle Windows i\u015fletim sisteminde, alt s\u00fcre\u00e7ler ana mod\u00fcl\u00fc yeniden i\u00e7e aktar\u0131r. Bu blok, alt s\u00fcre\u00e7lerin multiprocessing.Process()<\/code> \u00e7a\u011fr\u0131s\u0131n\u0131 tekrar y\u00fcr\u00fctmesini engelleyerek sonsuz bir s\u00fcre\u00e7 d\u00f6ng\u00fcs\u00fcne girmesini \u00f6nler. Unix tabanl\u0131 sistemlerde fork<\/code> ba\u015flang\u0131\u00e7 y\u00f6ntemi kullan\u0131ld\u0131\u011f\u0131nda bu o kadar kritik de\u011fildir, ancak ta\u015f\u0131nabilirlik i\u00e7in her zaman kullan\u0131lmas\u0131 \u00f6nerilir.<\/p>\n
S\u00fcre\u00e7ler Aras\u0131 \u0130leti\u015fim (Inter-Process Communication – IPC)<\/h3>\n
S\u00fcre\u00e7ler kendi ayr\u0131 bellek alanlar\u0131na sahip olduklar\u0131 i\u00e7in do\u011frudan birbirlerinin verilerine eri\u015femezler. Bu nedenle, s\u00fcre\u00e7ler aras\u0131nda veri payla\u015f\u0131m\u0131 veya ileti\u015fim kurmak i\u00e7in \u00f6zel mekanizmalara ihtiya\u00e7 duyulur. multiprocessing<\/code> mod\u00fcl\u00fc, bu ama\u00e7la \u00e7e\u015fitli IPC ara\u00e7lar\u0131 sunar.<\/p>\n
Kuyruklar (Queue<\/code>)<\/h4>\n
Queue<\/code>, s\u00fcre\u00e7ler aras\u0131 g\u00fcvenli veri ileti\u015fimi i\u00e7in en yayg\u0131n kullan\u0131lan ara\u00e7lardan biridir. FIFO (First-In, First-Out) prensibine g\u00f6re \u00e7al\u0131\u015f\u0131r ve birden fazla \u00fcreticinin veri koyup birden fazla t\u00fcketicinin veri alabilece\u011fi bir yap\u0131 sa\u011flar.<\/p>\n
import multiprocessing\nimport time\nimport random\n\ndef producer(queue):\n    \"\"\"Veri \u00fcreten s\u00fcre\u00e7.\"\"\"\n    for i in range(5):\n        item = f\"Veri-{i}\"\n        print(f\"\u00dcretici: {item} ekliyor.\")\n        queue.put(item)\n        time.sleep(random.uniform(0.1, 0.5))\n    queue.put(None) # T\u00fcketiciye i\u015fin bitti\u011fini bildirmek i\u00e7in sentinel de\u011feri\n\ndef consumer(queue):\n    \"\"\"Veriyi t\u00fcketen s\u00fcre\u00e7.\"\"\"\n    while True:\n        item = queue.get()\n        if item is None: # Sentinel de\u011feri al\u0131nd\u0131\u011f\u0131nda d\u00f6ng\u00fcden \u00e7\u0131k\n            break\n        print(f\"T\u00fcketici: {item} ald\u0131.\")\n        time.sleep(random.uniform(0.5, 1.0))\n    print(\"T\u00fcketici: \u0130\u015flem tamamland\u0131.\")\n\nif __name__ == '__main__':\n    my_queue = multiprocessing.Queue()\n\n    p1 = multiprocessing.Process(target=producer, args=(my_queue,))\n    c1 = multiprocessing.Process(target=consumer, args=(my_queue,))\n\n    p1.start()\n    c1.start()\n\n    p1.join()\n    c1.join()\n\n    print(\"Ana s\u00fcre\u00e7: T\u00fcm i\u015flemler bitti.\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Queue()<\/code>: Yeni bir kuyruk nesnesi olu\u015fturur.
\n*   queue.put(item)<\/code>: \u00d6\u011feyi kuyru\u011fa ekler.
\n*   queue.get()<\/code>: Kuyruktan bir \u00f6\u011fe al\u0131r. Kuyruk bo\u015fsa, yeni bir \u00f6\u011fe gelene kadar bekler.
\n*   None<\/code> sentinel de\u011feri: \u00dcretici, i\u015fi bitti\u011finde kuyru\u011fa \u00f6zel bir None<\/code> de\u011feri ekleyerek t\u00fcketicinin de i\u015finin bitti\u011fini anlamas\u0131n\u0131 sa\u011flar.<\/p>\n
Borular (Pipe<\/code>)<\/h4>\n
Pipe<\/code>, iki s\u00fcre\u00e7 aras\u0131nda tek veya \u00e7ift y\u00f6nl\u00fc ileti\u015fim kurmak i\u00e7in kullan\u0131l\u0131r. Bir boru, bir u\u00e7tan veri g\u00f6nderip di\u011fer u\u00e7tan almak i\u00e7in iki ba\u011flant\u0131 nesnesi d\u00f6nd\u00fcr\u00fcr.<\/p>\n
import multiprocessing\n\ndef sender_process(conn):\n    \"\"\"Veri g\u00f6nderen s\u00fcre\u00e7.\"\"\"\n    message = \"Merhaba Boru!\"\n    print(f\"G\u00f6nderici: '{message}' g\u00f6nderiyor.\")\n    conn.send(message)\n    conn.close()\n\ndef receiver_process(conn):\n    \"\"\"Veri alan s\u00fcre\u00e7.\"\"\"\n    message = conn.recv()\n    print(f\"Al\u0131c\u0131: '{message}' ald\u0131.\")\n    conn.close()\n\nif __name__ == '__main__':\n    parent_conn, child_conn = multiprocessing.Pipe()\n\n    p_sender = multiprocessing.Process(target=sender_process, args=(child_conn,))\n    p_receiver = multiprocessing.Process(target=receiver_process, args=(parent_conn,))\n\n    p_sender.start()\n    p_receiver.start()\n\n    p_sender.join()\n    p_receiver.join()\n\n    print(\"Ana s\u00fcre\u00e7: Boru ileti\u015fimi tamamland\u0131.\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Pipe()<\/code>: \u0130ki ba\u011flant\u0131 nesnesi d\u00f6nd\u00fcr\u00fcr: parent_conn<\/code> ve child_conn<\/code>. Bu nesneler, borunun iki ucunu temsil eder.
\n*   conn.send(data)<\/code>: Ba\u011flant\u0131 \u00fczerinden veri g\u00f6nderir.
\n*   conn.recv()<\/code>: Ba\u011flant\u0131dan veri al\u0131r. Veri yoksa, veri gelene kadar bekler.
\n*   conn.close()<\/code>: Ba\u011flant\u0131y\u0131 kapat\u0131r.<\/p>\n
Payla\u015f\u0131lan Bellek (Value<\/code> ve Array<\/code>)<\/h4>\n
Value<\/code> ve Array<\/code> s\u0131n\u0131flar\u0131, s\u00fcre\u00e7ler aras\u0131nda basit Python veri tiplerini (say\u0131lar, karakterler) veya C tipi dizileri do\u011frudan bellekte payla\u015fmak i\u00e7in kullan\u0131l\u0131r. Ancak, payla\u015f\u0131lan belle\u011fe eri\u015fimde senkronizasyon sorunlar\u0131 (yar\u0131\u015f ko\u015fullar\u0131) ortaya \u00e7\u0131kabilir, bu y\u00fczden genellikle Lock<\/code> ile birlikte kullan\u0131l\u0131rlar.<\/p>\n
import multiprocessing\nimport time\n\ndef increment_counter(counter, lock):\n    \"\"\"Payla\u015f\u0131lan sayac\u0131 art\u0131ran s\u00fcre\u00e7.\"\"\"\n    for _ in range(100000):\n        # Kilidi al\n        lock.acquire()\n        try:\n            counter.value += 1\n        finally:\n            # Kilidi serbest b\u0131rak\n            lock.release()\n\nif __name__ == '__main__':\n    # 'i' tipi (integer) ve ba\u015flang\u0131\u00e7 de\u011feri 0 olan payla\u015f\u0131lan bir Value olu\u015ftur\n    shared_counter = multiprocessing.Value('i', 0)\n    # Payla\u015f\u0131lan kayna\u011fa eri\u015fimi senkronize etmek i\u00e7in bir kilit olu\u015ftur\n    counter_lock = multiprocessing.Lock()\n\n    processes = []\n    for i in range(5):\n        p = multiprocessing.Process(target=increment_counter, args=(shared_counter, counter_lock))\n        processes.append(p)\n        p.start()\n\n    for p in processes:\n        p.join()\n\n    print(f\"Beklenen sonu\u00e7: 500000\")\n    print(f\"Ger\u00e7ek sonu\u00e7: {shared_counter.value}\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Value('i', 0)<\/code>: ‘i’ C tipi bir i\u015faretsiz tamsay\u0131y\u0131 (signed integer) temsil eder ve ba\u015flang\u0131\u00e7 de\u011feri 0’d\u0131r. Di\u011fer C tipleri i\u00e7in ‘d’ (double), ‘c’ (char) vb. kullan\u0131labilir.
\n*   multiprocessing.Lock()<\/code>: Bir kilit nesnesi olu\u015fturur.
\n*   lock.acquire()<\/code>: Kilidi al\u0131r. Ba\u015fka bir s\u00fcre\u00e7 kilidi tutuyorsa, bu \u00e7a\u011fr\u0131 kilit serbest b\u0131rak\u0131lana kadar engellenir.
\n*   lock.release()<\/code>: Kilidi serbest b\u0131rak\u0131r.
\n*   try...finally<\/code>: acquire()<\/code> sonras\u0131 her durumda release()<\/code> \u00e7a\u011fr\u0131lmas\u0131n\u0131 garanti eder, b\u00f6ylece kilitin tak\u0131l\u0131 kalmas\u0131 \u00f6nlenir.
\n*   Bu \u00f6rnekte, Lock<\/code> kullan\u0131lmasayd\u0131 shared_counter.value<\/code> de\u011feri muhtemelen 500000’den daha az olurdu, \u00e7\u00fcnk\u00fc yar\u0131\u015f ko\u015fullar\u0131 nedeniyle baz\u0131 art\u0131rma i\u015flemleri kaybolurdu.<\/p>\n
S\u00fcre\u00e7 Havuzu (Pool<\/code>) ile Kolay Y\u00f6netim<\/h3>\n
Bir\u00e7ok durumda, belirli say\u0131da i\u015f\u00e7i s\u00fcrecini y\u00f6netmek ve onlara g\u00f6revler da\u011f\u0131tmak istersiniz. multiprocessing.Pool<\/code> s\u0131n\u0131f\u0131, bu t\u00fcr senaryolar i\u00e7in y\u00fcksek seviyeli bir aray\u00fcz sa\u011flar. Bir havuz, belirli say\u0131da i\u015f\u00e7i s\u00fcrecini \u00f6nceden olu\u015fturur ve bu s\u00fcre\u00e7leri g\u00f6revleri i\u015flemek i\u00e7in kullan\u0131r.<\/p>\n
Pool<\/code> Kullan\u0131m\u0131n\u0131n Avantajlar\u0131<\/h4>\n
*   Otomatik S\u00fcre\u00e7 Y\u00f6netimi:<\/strong> S\u00fcre\u00e7 olu\u015fturma, sonland\u0131rma ve yeniden kullanma gibi detaylar\u0131 otomatik olarak y\u00f6netir.
\n*   G\u00f6rev Da\u011f\u0131t\u0131m\u0131:<\/strong> G\u00f6revleri mevcut i\u015f\u00e7i s\u00fcre\u00e7lerine otomatik olarak da\u011f\u0131t\u0131r.
\n*   Basit API:<\/strong> map<\/code>, apply<\/code>, apply_async<\/code> gibi kullan\u0131m\u0131 kolay metodlar sunar.<\/p>\n
import multiprocessing\nimport time\n\ndef expensive_function(x):\n    \"\"\"Yo\u011fun hesaplama yapan bir fonksiyon.\"\"\"\n    time.sleep(0.1) # \u0130\u015flem s\u00fcresini sim\u00fcle eder\n    return x * x\n\nif __name__ == '__main__':\n    data = range(10) # \u0130\u015flenecek veriler\n    num_processes = 4 # Kullan\u0131lacak i\u015flemci \u00e7ekirde\u011fi say\u0131s\u0131\n\n    print(f\"Ana s\u00fcre\u00e7: {num_processes} \u00e7ekirdekli bir havuz olu\u015fturuluyor.\")\n\n    # Bir s\u00fcre\u00e7 havuzu olu\u015fturma\n    # processes parametresi belirtilmezse, os.cpu_count() kullan\u0131l\u0131r.\n    with multiprocessing.Pool(processes=num_processes) as pool:\n        # map() metodu: Bir fonksiyonu bir iterable \u00fczerindeki t\u00fcm \u00f6\u011felere paralel olarak uygular.\n        # Sonu\u00e7lar, orijinal iterable'daki s\u0131rayla d\u00f6nd\u00fcr\u00fcl\u00fcr.\n        results = pool.map(expensive_function, data)\n\n    print(f\"Ana s\u00fcre\u00e7: Hesaplama sonu\u00e7lar\u0131: {results}\")\n\n    print(\"\\nAsenkron g\u00f6rev da\u011f\u0131t\u0131m\u0131 \u00f6rne\u011fi (apply_async):\")\n\n    # apply_async ile asenkron g\u00f6rev da\u011f\u0131t\u0131m\u0131\n    with multiprocessing.Pool(processes=num_processes) as pool:\n        async_results = []\n        for item in data:\n            # apply_async, g\u00f6revi havuza g\u00f6nderir ve bir AsyncResult nesnesi d\u00f6nd\u00fcr\u00fcr.\n            # Ana s\u00fcre\u00e7, alt s\u00fcre\u00e7lerin bitmesini beklemeden devam edebilir.\n            async_results.append(pool.apply_async(expensive_function, (item,)))\n\n        # T\u00fcm asenkron g\u00f6revlerin sonu\u00e7lar\u0131n\u0131 toplama\n        final_results = [res.get() for res in async_results]\n\n    print(f\"Ana s\u00fcre\u00e7: Asenkron hesaplama sonu\u00e7lar\u0131: {final_results}\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Pool(processes=num_processes)<\/code>: Belirtilen say\u0131da i\u015f\u00e7i s\u00fcrecini i\u00e7eren bir havuz olu\u015fturur. with<\/code> ifadesi, havuzun i\u015fi bitti\u011finde otomatik olarak kapat\u0131lmas\u0131n\u0131 ve s\u00fcre\u00e7lerin sonland\u0131r\u0131lmas\u0131n\u0131 sa\u011flar.
\n*   pool.map(function, iterable)<\/code>: function<\/code>‘\u0131 iterable<\/code>‘daki her \u00f6\u011feye paralel olarak uygular. Sonu\u00e7lar\u0131, giri\u015f s\u0131ras\u0131na g\u00f6re bir liste olarak d\u00f6nd\u00fcr\u00fcr. Bu bir bloklama \u00e7a\u011fr\u0131s\u0131d\u0131r, yani t\u00fcm g\u00f6revler bitene kadar ana s\u00fcre\u00e7 bekler.
\n*   pool.apply_async(function, args)<\/code>: function<\/code>‘\u0131 args<\/code> ile havuza g\u00f6nderir ve hemen bir AsyncResult<\/code> nesnesi d\u00f6nd\u00fcr\u00fcr. Bu, ana s\u00fcrecin alt s\u00fcre\u00e7lerin tamamlanmas\u0131n\u0131 beklemeden ba\u015fka i\u015fler yapmas\u0131na olanak tan\u0131r.
\n*   res.get()<\/code>: AsyncResult<\/code> nesnesinden g\u00f6revin sonucunu al\u0131r. G\u00f6rev hen\u00fcz tamamlanmad\u0131ysa, tamamlanana kadar bekler.<\/p>\n
Senkronizasyon Mekanizmalar\u0131<\/h3>\n
Birden fazla s\u00fcrecin ayn\u0131 payla\u015f\u0131lan kayna\u011fa (\u00f6rne\u011fin, payla\u015f\u0131lan bellek, dosya) e\u015f zamanl\u0131 olarak eri\u015fmeye \u00e7al\u0131\u015fmas\u0131 durumunda “yar\u0131\u015f ko\u015fullar\u0131” (race conditions) ortaya \u00e7\u0131kabilir. Bu, beklenmeyen veya yanl\u0131\u015f sonu\u00e7lara yol a\u00e7abilir. Senkronizasyon mekanizmalar\u0131, bu t\u00fcr sorunlar\u0131 \u00f6nlemek ve s\u00fcre\u00e7lerin payla\u015f\u0131lan kaynaklara d\u00fczenli bir \u015fekilde eri\u015fmesini sa\u011flamak i\u00e7in kullan\u0131l\u0131r.<\/p>\n
Kilitler (Lock<\/code>)<\/h4>\n
Lock<\/code> (kilit), bir kayna\u011fa ayn\u0131 anda yaln\u0131zca bir s\u00fcrecin eri\u015fmesine izin veren en temel senkronizasyon ilkelidir. Bir s\u00fcre\u00e7 kayna\u011f\u0131 kullanmak istedi\u011finde kilidi al\u0131r; i\u015fi bitti\u011finde kilidi serbest b\u0131rak\u0131r.<\/p>\n
import multiprocessing\nimport time\n\ndef safe_increment(shared_value, lock):\n    for _ in range(100000):\n        with lock: # Kilidi almak ve i\u015f bitince otomatik serbest b\u0131rakmak i\u00e7in 'with' kullan\u0131l\u0131r.\n            shared_value.value += 1\n\nif __name__ == '__main__':\n    counter = multiprocessing.Value('i', 0)\n    lock = multiprocessing.Lock()\n\n    processes = []\n    for _ in range(5):\n        p = multiprocessing.Process(target=safe_increment, args=(counter, lock))\n        processes.append(p)\n        p.start()\n\n    for p in processes:\n        p.join()\n\n    print(f\"Beklenen de\u011fer: 500000\")\n    print(f\"Son de\u011fer: {counter.value}\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   with lock:<\/code> ifadesi, lock.acquire()<\/code> ve lock.release()<\/code> \u00e7a\u011fr\u0131lar\u0131n\u0131 otomatik olarak y\u00f6neten bir ba\u011flam y\u00f6neticisidir. Bu, kilidin her zaman do\u011fru bir \u015fekilde serbest b\u0131rak\u0131lmas\u0131n\u0131 sa\u011flar, hata olu\u015fsa bile.<\/p>\n
Semaphorlar (Semaphore<\/code>)<\/h4>\n
Bir semafor, belirli bir kayna\u011fa ayn\u0131 anda eri\u015febilecek s\u00fcre\u00e7 say\u0131s\u0131n\u0131 s\u0131n\u0131rlamak i\u00e7in kullan\u0131l\u0131r. \u00d6rne\u011fin, bir veritaban\u0131 ba\u011flant\u0131 havuzuna ayn\u0131 anda sadece belirli say\u0131da s\u00fcrecin eri\u015fmesine izin vermek isteyebilirsiniz.<\/p>\n
import multiprocessing\nimport time\nimport random\n\ndef worker_with_semaphore(semaphore, worker_id):\n    print(f\"\u0130\u015f\u00e7i {worker_id}: Kayna\u011fa eri\u015fim izni bekliyor.\")\n    with semaphore: # Semaforu al (izin varsa devam et, yoksa bekle)\n        print(f\"\u0130\u015f\u00e7i {worker_id}: Kayna\u011fa eri\u015fti ve \u00e7al\u0131\u015f\u0131yor.\")\n        time.sleep(random.uniform(1, 3)) # Kayna\u011f\u0131 kullanma s\u00fcresi\n        print(f\"\u0130\u015f\u00e7i {worker_id}: Kayna\u011f\u0131 serbest b\u0131rakt\u0131.\")\n\nif __name__ == '__main__':\n    # Ayn\u0131 anda 2 s\u00fcrecin kayna\u011fa eri\u015fmesine izin veren bir semafor\n    resource_semaphore = multiprocessing.Semaphore(2)\n    processes = []\n\n    for i in range(5):\n        p = multiprocessing.Process(target=worker_with_semaphore, args=(resource_semaphore, i + 1))\n        processes.append(p)\n        p.start()\n\n    for p in processes:\n        p.join()\n\n    print(\"Ana s\u00fcre\u00e7: T\u00fcm i\u015f\u00e7iler tamamland\u0131.\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Semaphore(2)<\/code>: Ayn\u0131 anda en fazla 2 s\u00fcrecin with semaphore:<\/code> blo\u011funa girmesine izin veren bir semafor olu\u015fturur.<\/p>\n
Olaylar (Event<\/code>)<\/h4>\n
Bir Event<\/code> (olay), bir s\u00fcrecin di\u011fer s\u00fcre\u00e7lere bir olay\u0131n ger\u00e7ekle\u015fti\u011fini bildirmesi i\u00e7in kullan\u0131l\u0131r. Bir veya daha fazla s\u00fcre\u00e7, bir olay\u0131n ger\u00e7ekle\u015fmesini bekleyebilir.<\/p>\n
import multiprocessing\nimport time\n\ndef waiter_process(event, process_id):\n    print(f\"Bekleyici {process_id}: Olay\u0131n ger\u00e7ekle\u015fmesini bekliyor...\")\n    event.wait() # Olay\u0131n ayarlanmas\u0131n\u0131 bekler\n    print(f\"Bekleyici {process_id}: Olay ger\u00e7ekle\u015fti! Devam ediyor.\")\n    time.sleep(1)\n    print(f\"Bekleyici {process_id}: \u0130\u015flem tamamland\u0131.\")\n\ndef signaler_process(event):\n    print(\"Sinyalci: 3 saniye sonra olay\u0131 ayarlayacak.\")\n    time.sleep(3)\n    event.set() # Olay\u0131 ayarlar, bekleyen s\u00fcre\u00e7leri uyand\u0131r\u0131r\n    print(\"Sinyalci: Olay ayarland\u0131.\")\n\nif __name__ == '__main__':\n    event = multiprocessing.Event()\n    waiters = []\n\n    for i in range(3):\n        p = multiprocessing.Process(target=waiter_process, args=(event, i + 1))\n        waiters.append(p)\n        p.start()\n\n    signaler = multiprocessing.Process(target=signaler_process, args=(event,))\n    signaler.start()\n\n    signaler.join()\n    for p in waiters:\n        p.join()\n\n    print(\"Ana s\u00fcre\u00e7: T\u00fcm s\u00fcre\u00e7ler tamamland\u0131.\")<\/code><\/pre>\n
A\u00e7\u0131klama:<\/strong>
\n*   multiprocessing.Event()<\/code>: Yeni bir olay nesnesi olu\u015fturur. Ba\u015flang\u0131\u00e7ta olay “clear” (temiz) durumdad\u0131r.
\n*   event.wait()<\/code>: Olay “set” (ayarland\u0131) durumuna gelene kadar bekler.
\n*   event.set()<\/code>: Olay\u0131 “set” durumuna getirir, wait()<\/code> \u00e7a\u011fr\u0131s\u0131 yapan t\u00fcm s\u00fcre\u00e7leri uyand\u0131r\u0131r.
\n*   event.clear()<\/code>: Olay\u0131 tekrar “clear” durumuna getirir.<\/p>\n
Multiprocessing Kullan\u0131m Senaryolar\u0131<\/h3>\n
multiprocessing<\/code> mod\u00fcl\u00fc, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya uygulamalar\u0131nda performans\u0131 art\u0131rmak i\u00e7in kullan\u0131labilir:<\/p>\n
*   B\u00fcy\u00fck Veri \u0130\u015fleme ve Analizi:<\/strong> \u00c7ok b\u00fcy\u00fck veri setlerini paralel olarak okuma, d\u00f6n\u00fc\u015ft\u00fcrme ve analiz etme.
\n*   Web Kaz\u0131ma (Web Scraping):<\/strong> Birden fazla web sayfas\u0131n\u0131 veya API \u00e7a\u011fr\u0131s\u0131n\u0131 ayn\u0131 anda yaparak veri toplama h\u0131z\u0131n\u0131 art\u0131rma.
\n*   G\u00f6r\u00fcnt\u00fc ve Video \u0130\u015fleme:<\/strong> G\u00f6r\u00fcnt\u00fc filtreleme, yeniden boyutland\u0131rma veya video karelerini paralel olarak i\u015fleme.
\n*   Bilimsel Hesaplamalar ve Sim\u00fclasyonlar:<\/strong> Karma\u015f\u0131k matematiksel modelleri veya sim\u00fclasyonlar\u0131 paralel olarak \u00e7al\u0131\u015ft\u0131rma.
\n*   Paralel Test \u00c7al\u0131\u015ft\u0131rma:<\/strong> Yaz\u0131l\u0131m testlerini birden fazla s\u00fcre\u00e7te ayn\u0131 anda y\u00fcr\u00fcterek test s\u00fcresini k\u0131saltma.
\n*   Makine \u00d6\u011frenimi:<\/strong> Model e\u011fitimi veya hiperparametre optimizasyonu gibi yo\u011fun g\u00f6revleri paralel hale getirme.<\/p>\n
Multiprocessing Kullan\u0131rken Dikkat Edilmesi Gerekenler<\/h3>\n
multiprocessing<\/code> g\u00fc\u00e7l\u00fc bir ara\u00e7 olsa da, kullan\u0131m\u0131nda baz\u0131 \u00f6nemli noktalar g\u00f6z \u00f6n\u00fcnde bulundurulmal\u0131d\u0131r:<\/p>\n
*   S\u00fcre\u00e7 Olu\u015fturma Maliyeti (Overhead):<\/strong> Yeni bir s\u00fcre\u00e7 olu\u015fturmak, bir i\u015f par\u00e7ac\u0131\u011f\u0131 olu\u015fturmaktan daha maliyetlidir. Her s\u00fcre\u00e7 kendi bellek alan\u0131na ve yorumlay\u0131c\u0131s\u0131na sahip oldu\u011fu i\u00e7in daha fazla sistem kayna\u011f\u0131 t\u00fcketir. Bu nedenle, \u00e7ok k\u0131sa s\u00fcren veya \u00e7ok fazla s\u00fcre\u00e7 olu\u015fturan g\u00f6revler i\u00e7in multiprocessing<\/code> uygun olmayabilir.
\n*   Veri Serile\u015ftirme (Serialization):<\/strong> S\u00fcre\u00e7ler aras\u0131 ileti\u015fimde (Queue, Pipe vb.), veriler bir s\u00fcre\u00e7ten di\u011ferine g\u00f6nderilirken serile\u015ftirilir (pickle). Bu serile\u015ftirme ve seri durumdan \u00e7\u0131karma i\u015flemi de ek bir maliyet getirir. B\u00fcy\u00fck veya karma\u015f\u0131k nesnelerin s\u0131k s\u0131k s\u00fcre\u00e7ler aras\u0131nda aktar\u0131lmas\u0131 performans d\u00fc\u015f\u00fc\u015f\u00fcne neden olabilir.
\n*   Bellek T\u00fcketimi:<\/strong> Her s\u00fcre\u00e7 kendi bellek alan\u0131na sahip oldu\u011fundan, \u00e7ok say\u0131da s\u00fcre\u00e7 olu\u015fturmak sistem belle\u011fini h\u0131zla t\u00fcketebilir. Bu durum, \u00f6zellikle bellek k\u0131s\u0131tl\u0131 sistemlerde sorunlara yol a\u00e7abilir.
\n*   Hata Ay\u0131klama (Debugging):<\/strong> Paralel programlarda hata ay\u0131klama, tek i\u015f par\u00e7ac\u0131kl\u0131 programlara g\u00f6re daha zordur. Yar\u0131\u015f ko\u015fullar\u0131, kilitlenmeler (deadlock) ve senkronizasyon sorunlar\u0131 gibi problemlerin tespiti ve \u00e7\u00f6z\u00fcm\u00fc karma\u015f\u0131kt\u0131r.
\n*   Ba\u015flatma Y\u00f6ntemleri (start_method<\/code>):<\/strong> multiprocessing<\/code> mod\u00fcl\u00fc, s\u00fcre\u00e7leri ba\u015flatmak i\u00e7in farkl\u0131 y\u00f6ntemler sunar:
\n    *   'fork'<\/code> (Unix varsay\u0131lan\u0131):<\/strong> Ana s\u00fcrecin bellek alan\u0131n\u0131n bir kopyas\u0131n\u0131 olu\u015fturur. En h\u0131zl\u0131d\u0131r ancak bazen ana s\u00fcre\u00e7teki a\u00e7\u0131k dosya tan\u0131mlay\u0131c\u0131lar\u0131 veya kilitler gibi kaynaklar\u0131 alt s\u00fcre\u00e7lere ta\u015f\u0131r.
\n    *   'spawn'<\/code> (Windows varsay\u0131lan\u0131, macOS’ta \u00f6nerilen):<\/strong> Tamamen yeni ve bo\u015f bir Python yorumlay\u0131c\u0131 s\u00fcreci ba\u015flat\u0131r. Ana s\u00fcre\u00e7teki kaynaklar\u0131 do\u011frudan kopyalamaz, bu y\u00fczden daha g\u00fcvenli ve ta\u015f\u0131nabilirdir ancak daha yava\u015ft\u0131r.
\n    *   'forkserver'<\/code> (Unix):<\/strong> Bir sunucu s\u00fcreci ba\u015flat\u0131r ve bu sunucu s\u00fcreci yeni alt s\u00fcre\u00e7leri \u00e7atalland\u0131r\u0131r.
\n    Kullan\u0131lan i\u015fletim sistemine ve uygulaman\u0131n gereksinimlerine g\u00f6re do\u011fru ba\u015flatma y\u00f6ntemini se\u00e7mek \u00f6nemlidir. multiprocessing.set_start_method()<\/code> fonksiyonu ile bu y\u00f6ntem de\u011fi\u015ftirilebilir.
\n*   Ana S\u00fcre\u00e7 Koruma (if __name__ == '__main__':<\/code>):<\/strong> Daha \u00f6nce belirtildi\u011fi gibi, bu blok \u00f6zellikle spawn<\/code> ba\u015flatma y\u00f6ntemini kullanan veya Windows’ta \u00e7al\u0131\u015fan uygulamalar i\u00e7in hayati \u00f6neme sahiptir. S\u00fcre\u00e7lerin sonsuz bir d\u00f6ng\u00fcye girmesini engeller.<\/p>\n
Multiprocessing vs. Multithreading: K\u0131sa Bir Kar\u015f\u0131la\u015ft\u0131rma<\/h3>\n
| \u00d6zellik           | Multiprocessing                                  | Multithreading                                   |
\n| :—————- | :———————————————– | :———————————————– |
\n| GIL Etkisi<\/strong>    | GIL k\u0131s\u0131tlamas\u0131n\u0131 a\u015far; ger\u00e7ek CPU paralelli\u011fi.  | GIL nedeniyle CPU yo\u011fun g\u00f6revlerde ger\u00e7ek paralellik sa\u011flamaz. |
\n| Bellek Payla\u015f\u0131m\u0131<\/strong> | Her s\u00fcrecin kendi ayr\u0131 bellek alan\u0131 vard\u0131r; IPC mekanizmalar\u0131 gereklidir. | Ayn\u0131 s\u00fcre\u00e7 i\u00e7inde bellek payla\u015f\u0131m\u0131 kolayd\u0131r.      |
\n| G\u00f6rev Tipi<\/strong>    | CPU yo\u011fun g\u00f6revler i\u00e7in idealdir.                | I\/O yo\u011fun g\u00f6revler (a\u011f, disk eri\u015fimi) i\u00e7in daha uygundur. |
\n| S\u00fcre\u00e7 Olu\u015fturma<\/strong> | Daha a\u011f\u0131r ve maliyetlidir.                      | Daha hafif ve daha h\u0131zl\u0131d\u0131r.                     |
\n| G\u00fcvenlik\/\u0130zolasyon<\/strong> | S\u00fcre\u00e7ler birbirinden izole oldu\u011fundan daha g\u00fcvenlidir. | Ayn\u0131 bellek alan\u0131n\u0131 payla\u015ft\u0131\u011f\u0131ndan daha fazla senkronizasyon gerektirir. |
\n| Hata Ay\u0131klama<\/strong> | Daha zordur.                                    | Multiprocessing’e g\u00f6re daha kolayd\u0131r.            |<\/p>\n
Sonu\u00e7<\/h3>\n
Python’\u0131n multiprocessing<\/code> mod\u00fcl\u00fc, CPU yo\u011fun g\u00f6revlerde performans darbo\u011fazlar\u0131n\u0131 a\u015fmak ve modern \u00e7ok \u00e7ekirdekli i\u015flemcilerin g\u00fcc\u00fcnden tam olarak yararlanmak i\u00e7in vazge\u00e7ilmez bir ara\u00e7t\u0131r. Process<\/code> s\u0131n\u0131f\u0131 ile temel s\u00fcre\u00e7 olu\u015fturmadan, Queue<\/code> ve Pipe<\/code> ile s\u00fcre\u00e7ler aras\u0131 g\u00fcvenli ileti\u015fime, Pool<\/code> ile i\u015f\u00e7i s\u00fcre\u00e7lerini kolayca y\u00f6netmeye ve Lock<\/code>, Semaphore<\/code>, Event<\/code> gibi senkronizasyon mekanizmalar\u0131yla payla\u015f\u0131lan kaynaklara g\u00fcvenli eri\u015fim sa\u011flamaya kadar geni\u015f bir yelpazede yetenekler sunar.<\/p>\n
Her ne kadar s\u00fcre\u00e7 olu\u015fturma maliyeti ve hata ay\u0131klama zorluklar\u0131 gibi baz\u0131 dezavantajlar\u0131 olsa da, do\u011fru kullan\u0131ld\u0131\u011f\u0131nda multiprocessing<\/code>, Python uygulamalar\u0131n\u0131z\u0131n h\u0131z\u0131n\u0131 ve verimlili\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir. B\u00fcy\u00fck veri i\u015fleme, bilimsel hesaplamalar ve paralel testler gibi alanlarda Python geli\u015ftiricileri i\u00e7in kritik bir beceridir. Paralel programlaman\u0131n prensiplerini anlamak ve multiprocessing<\/code> mod\u00fcl\u00fcn\u00fc etkin bir \u015fekilde kullanmak, daha performansl\u0131 ve \u00f6l\u00e7eklenebilir Python uygulamalar\u0131 geli\u015ftirmenize olanak tan\u0131r.
\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma\nPython, basitli\u011fi ve geni\u015f k\u00fct\u00fcphane deste\u011fiyle pop\u00fcler bir programlam","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1403],"tags":[],"class_list":{"0":"post-37672","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-python","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nPython Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma\" \/>\n<meta property=\"og:description\" content=\"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma Python, basitli\u011fi ve geni\u015f k\u00fct\u00fcphane deste\u011fiyle pop\u00fcler bir programlam\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-12T02:40:48+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"20 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma\",\"datePublished\":\"2026-01-12T02:40:48+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\"},\"wordCount\":2824,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"articleSection\":[\"Python\"],\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/python-multiprocessing-paralel-islem-gucuyle-performansi-artirma\/\",\"name\":\"Python Multiprocessing: Paralel \u0130\u015flem G\u00fcc\u00fcyle Performans\u0131 Art\u0131rma - 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