{"id":33465,"date":"2025-11-03T06:01:02","date_gmt":"2025-11-03T03:01:02","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/python-ile-saniyede-20-000-istek-gonderme-gucune-ulasmak\/"},"modified":"2025-11-03T06:01:02","modified_gmt":"2025-11-03T03:01:02","slug":"python-ile-saniyede-20-000-istek-gonderme-gucune-ulasmak","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/python-ile-saniyede-20-000-istek-gonderme-gucune-ulasmak\/","title":{"rendered":"Python ile Saniyede 20.000 \u0130stek G\u00f6nderme G\u00fcc\u00fcne Ula\u015fmak"},"content":{"rendered":"<p>Python&#8217;\u0131n y\u00fcksek performans gerektiren modern web uygulamalar\u0131 ve mikroservis mimarileri i\u00e7in yetersiz oldu\u011fu d\u00fc\u015f\u00fcncesi yayg\u0131nd\u0131r, ancak do\u011fru stratejiler ve ara\u00e7larla Python ile saniyede 20.000 istek (RPS) gibi hedeflere ula\u015fmak m\u00fcmk\u00fcnd\u00fcr. Bu makale, Python&#8217;\u0131n performans s\u0131n\u0131rlar\u0131n\u0131 zorlayarak y\u00fcksek verimli sistemler in\u015fa etmenin anahtarlar\u0131n\u0131, pratik \u00f6rneklerle ve ger\u00e7ek d\u00fcnya senaryolar\u0131yla ad\u0131m ad\u0131m a\u00e7\u0131kl\u0131yor.<\/p>\n<p>Python, geli\u015ftirme kolayl\u0131\u011f\u0131 ve geni\u015f k\u00fct\u00fcphane deste\u011fi sayesinde bir\u00e7ok alanda tercih edilen bir dil olsa da, y\u00fcksek e\u015fzamanl\u0131 istek i\u015fleme kapasitesi s\u00f6z konusu oldu\u011funda baz\u0131 do\u011fal s\u0131n\u0131rlamalarla kar\u015f\u0131la\u015f\u0131r. Bu s\u0131n\u0131rlamalar\u0131n ba\u015f\u0131nda Global Interpreter Lock (GIL) gelir. GIL, CPython yorumlay\u0131c\u0131s\u0131nda ayn\u0131 anda yaln\u0131zca bir i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n Python bayt kodunu y\u00fcr\u00fctmesine izin veren bir mekanizmad\u0131r. Bu durum, \u00e7ok \u00e7ekirdekli i\u015flemcilerde bile Python&#8217;\u0131n \u00e7oklu i\u015f par\u00e7ac\u0131\u011f\u0131 (multithreading) kullanarak ger\u00e7ek paralel hesaplama yapmas\u0131n\u0131 engeller. Dolay\u0131s\u0131yla, CPU yo\u011fun g\u00f6revlerde, birden fazla i\u015f par\u00e7ac\u0131\u011f\u0131 olu\u015fturmak performans\u0131 art\u0131rmak yerine d\u00fc\u015f\u00fcrebilir, \u00e7\u00fcnk\u00fc i\u015f par\u00e7ac\u0131klar\u0131 GIL i\u00e7in rekabet etmek zorunda kal\u0131r.<\/p>\n<p>Ancak, her g\u00f6rev CPU yo\u011fun de\u011fildir. Bir\u00e7ok modern uygulama, veri taban\u0131 sorgular\u0131, a\u011f istekleri (API \u00e7a\u011fr\u0131lar\u0131), dosya okuma\/yazma gibi I\/O (Giri\u015f\/\u00c7\u0131k\u0131\u015f) yo\u011fun i\u015flemlerle doludur. I\/O i\u015flemleri genellikle CPU&#8217;nun beklemede kalmas\u0131na neden olur. \u0130\u015fte bu noktada senkron ve asenkron programlama aras\u0131ndaki fark \u00f6nem kazan\u0131r. Senkron programlamada, bir I\/O i\u015flemi tamamlanana kadar program duraklar ve bekler. Bu bekleme s\u00fcresi, di\u011fer g\u00f6revlerin i\u015flenmesini engeller. \u00d6rne\u011fin, bir web sunucusu senkron \u00e7al\u0131\u015f\u0131yorsa, bir kullan\u0131c\u0131n\u0131n iste\u011fi uzun s\u00fcren bir veritaban\u0131 sorgusu tetikledi\u011finde, sunucu o sorgu tamamlanana kadar ba\u015fka hi\u00e7bir iste\u011fi i\u015fleyemez. Bu, do\u011fal olarak saniyede i\u015flenebilecek istek say\u0131s\u0131n\u0131 ciddi \u015fekilde s\u0131n\u0131rlar.<\/p>\n<p>\u00d6te yandan, asenkron programlama, I\/O i\u015flemlerinin tamamlanmas\u0131n\u0131 beklerken CPU&#8217;nun ba\u015fka g\u00f6revleri yapmas\u0131na olanak tan\u0131r. Python&#8217;da <code>asyncio<\/code> k\u00fct\u00fcphanesi ve <code>async\/await<\/code> s\u00f6zdizimi, bu paradigman\u0131n temelini olu\u015fturur. Asenkron yakla\u015f\u0131m, bir g\u00f6revin I\/O beklemeye ba\u015flad\u0131\u011f\u0131nda, yorumlay\u0131c\u0131n\u0131n ba\u015fka bir g\u00f6reve ge\u00e7mesini ve o g\u00f6revin I\/O&#8217;su da beklemeye girdi\u011finde bir sonrakine ge\u00e7mesini sa\u011flar. Bu, tek bir i\u015f par\u00e7ac\u0131\u011f\u0131 \u00fczerinde bile binlerce e\u015fzamanl\u0131 I\/O yo\u011fun g\u00f6revin etkili bir \u015fekilde y\u00f6netilmesini m\u00fcmk\u00fcn k\u0131lar. Dolay\u0131s\u0131yla, 20.000 RPS hedefine ula\u015fmak i\u00e7in Python&#8217;\u0131n GIL k\u0131s\u0131tlamas\u0131n\u0131 a\u015fman\u0131n yollar\u0131ndan biri, I\/O yo\u011fun i\u015flemlerde asenkron programlamay\u0131 benimsemektir. Bu sayede, ayn\u0131 anda \u00e7ok say\u0131da ba\u011flant\u0131y\u0131 etkin bir \u015fekilde y\u00f6netebilir ve sistem kaynaklar\u0131n\u0131 daha verimli kullanabilirsiniz.<\/p>\n<h2>Asenkron Programlama ile Performans Patlamas\u0131 Nas\u0131l Sa\u011flan\u0131r?<\/h2>\n<p>Y\u00fcksek performansl\u0131 Python uygulamalar\u0131 geli\u015ftirmek, \u00f6zellikle I\/O yo\u011fun g\u00f6revlerde, asenkron programlaman\u0131n sundu\u011fu potansiyeli anlamak ve kullanmaktan ge\u00e7er. Python&#8217;\u0131n yerle\u015fik <code>asyncio<\/code> k\u00fct\u00fcphanesi, e\u015fzamanl\u0131 ancak paralel olmayan kod yazman\u0131n temelini olu\u015fturur. Bu k\u00fct\u00fcphane ile birlikte, web istekleri i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015f <code>aiohttp<\/code> gibi k\u00fct\u00fcphaneler, saniyede binlerce hatta on binlerce iste\u011fi kolayca y\u00f6netebilir. <code>asyncio<\/code>, bir event loop (olay d\u00f6ng\u00fcs\u00fc) mekanizmas\u0131 kullanarak tek bir i\u015f par\u00e7ac\u0131\u011f\u0131 \u00fczerinde birden fazla g\u00f6revi s\u0131rayla \u00e7al\u0131\u015ft\u0131r\u0131r. Bir g\u00f6rev I\/O beklemeye girdi\u011finde, event loop di\u011fer bekleyen g\u00f6revlere ge\u00e7er ve I\/O i\u015flemi tamamland\u0131\u011f\u0131nda orijinal g\u00f6reve geri d\u00f6ner. Bu model, CPU kaynaklar\u0131n\u0131 bo\u015f yere beklemeyle harcamadan maksimum verim almay\u0131 sa\u011flar.<\/p>\n<p>Bir senaryo d\u00fc\u015f\u00fcnelim: Bir veri toplama servisi, farkl\u0131 API&#8217;lerden e\u015fzamanl\u0131 olarak veri \u00e7ekmek zorunda. Geleneksel senkron bir yakla\u015f\u0131mla, her API \u00e7a\u011fr\u0131s\u0131 di\u011ferlerini bloke eder ve toplam bekleme s\u00fcresi, t\u00fcm API \u00e7a\u011fr\u0131lar\u0131n\u0131n toplam bekleme s\u00fcresine e\u015fit olurdu. Ancak <code>aiohttp<\/code> kullanarak, t\u00fcm bu API \u00e7a\u011fr\u0131lar\u0131n\u0131 e\u015fzamanl\u0131 olarak ba\u015flatabilir ve ilk d\u00f6nen veriyi i\u015flemeye ba\u015flayabiliriz. Bu, servisin yan\u0131t s\u00fcresini \u00f6nemli \u00f6l\u00e7\u00fcde k\u0131salt\u0131r ve ayn\u0131 anda daha fazla iste\u011fi i\u015flemesini sa\u011flar.<\/p>\n<p>A\u015fa\u011f\u0131da, <code>aiohttp<\/code> kullanarak birden fazla URL&#8217;ye e\u015fzamanl\u0131 olarak HTTP GET istekleri g\u00f6nderen basit bir \u00f6rnek g\u00f6rebilirsiniz:<\/p>\n<pre><code>\nimport asyncio\nimport aiohttp\nimport time\n\nasync def fetch(session, url):\n    async with session.get(url) as response:\n        return await response.text()\n\nasync def main(urls):\n    async with aiohttp.ClientSession() as session:\n        tasks = [fetch(session, url) for url in urls]\n        responses = await asyncio.gather(*tasks)\n        return responses\n\nif __name__ == \"__main__\":\n    start_time = time.time()\n    target_urls = [\n        \"https:\/\/jsonplaceholder.typicode.com\/todos\/1\",\n        \"https:\/\/jsonplaceholder.typicode.com\/todos\/2\",\n        \"https:\/\/jsonplaceholder.typicode.com\/todos\/3\",\n        \"https:\/\/jsonplaceholder.typicode.com\/todos\/4\",\n        \"https:\/\/jsonplaceholder.typicode.com\/todos\/5\",\n        # Ger\u00e7ek senaryoda \u00e7ok daha fazla URL eklenebilir\n    ]\n    \n    # Python 3.7+ i\u00e7in asyncio.run() kullan\u0131l\u0131r\n    results = asyncio.run(main(target_urls))\n    \n    end_time = time.time()\n    print(f\"Toplam {len(target_urls)} URL'den veri \u00e7ekme s\u00fcresi: {end_time - start_time:.2f} saniye\")\n    # for i, res in enumerate(results):\n    #     print(f\"URL {i+1} yan\u0131t\u0131: {res[:50]}...\") # Yan\u0131t\u0131n ilk 50 karakterini g\u00f6ster\n<\/pre>\n<p><\/code><\/p>\n<p>Bu kod blo\u011fu, <code>aiohttp.ClientSession<\/code> kullanarak bir oturum olu\u015fturur ve bu oturum arac\u0131l\u0131\u011f\u0131yla belirtilen URL'lere e\u015fzamanl\u0131 istekler g\u00f6nderir. <code>asyncio.gather(*tasks)<\/code> fonksiyonu, t\u00fcm bu asenkron g\u00f6revlerin tamamlanmas\u0131n\u0131 bekler ve sonu\u00e7lar\u0131 bir liste olarak d\u00f6nd\u00fcr\u00fcr. Senkron bir yakla\u015f\u0131mla bu kadar k\u0131sa s\u00fcrede bu kadar \u00e7ok iste\u011fi i\u015flemek, her bir iste\u011fin bekleme s\u00fcresinin toplam bekleme s\u00fcresine eklenmesi nedeniyle m\u00fcmk\u00fcn olmazd\u0131. Ancak asenkron programlama sayesinde, I\/O beklemeleri s\u0131ras\u0131nda di\u011fer g\u00f6revler i\u015flenebildi\u011fi i\u00e7in performans katlanarak artar. \u00d6zellikle API Gateway veya mikroservis ileti\u015fimi gibi I\/O yo\u011fun bir yap\u0131ya sahip sistemlerde, <code>aiohttp<\/code> gibi k\u00fct\u00fcphanelerle saniyede on binlerce istek i\u015flemek, do\u011fru optimizasyonlarla ula\u015f\u0131labilir bir hedeftir. \u00d6rne\u011fin, bir fintech \u015firketi, anl\u0131k borsa verilerini almak i\u00e7in y\u00fczlerce farkl\u0131 API'ye ba\u011flanmak zorundad\u0131r. Burada <code>aiohttp<\/code> kullanarak t\u00fcm bu ba\u011flant\u0131lar\u0131 e\u015fzamanl\u0131 hale getirmek, veri gecikmesini minimuma indirir ve kritik anl\u0131k kararlar\u0131n al\u0131nabilmesini sa\u011flar. Ayr\u0131ca, connection pooling (ba\u011flant\u0131 havuzu) kullan\u0131m\u0131 da performans\u0131 art\u0131ran \u00f6nemli bir fakt\u00f6rd\u00fcr.<\/p>\n<div class=\"expert-tip\">\n<h3>Uzman \u0130pucu: Connection Pooling'i G\u00f6z Ard\u0131 Etmeyin!<\/h3>\n<p>Her HTTP iste\u011fi i\u00e7in yeni bir TCP ba\u011flant\u0131s\u0131 kurmak ve kapatmak ciddi bir overhead (ek y\u00fck) olu\u015fturur. <code>aiohttp.ClientSession<\/code> varsay\u0131lan olarak ba\u011flant\u0131 havuzlama (connection pooling) yapar, ancak veritaban\u0131 ba\u011flant\u0131lar\u0131 gibi di\u011fer kaynaklar i\u00e7in de bu stratejiyi uygulamak \u00e7ok \u00f6nemlidir. Ba\u011flant\u0131 havuzlar\u0131, mevcut ba\u011flant\u0131lar\u0131 yeniden kullanarak hem gecikmeyi azalt\u0131r hem de kaynak t\u00fcketimini optimize eder. Bu teknikle, \u00f6zellikle y\u00fcksek y\u00fck alt\u0131nda, %40'\u0131n \u00fczerinde performans art\u0131\u015f\u0131 sa\u011flamak m\u00fcmk\u00fcnd\u00fcr.<\/p>\n<\/div>\n<h2>\u00c7ok \u00c7ekirdekli Sistemlerde Python'\u0131n G\u00fcc\u00fcn\u00fc Tam Kullanmak: Multiprocessing mi, Yoksa Da\u011f\u0131t\u0131k Sistemler mi?<\/h2>\n<p>Python'daki GIL, tek bir Python yorumlay\u0131c\u0131s\u0131 i\u00e7inde ger\u00e7ek paralel i\u015fleme yapmay\u0131 engellese de, bu, Python'\u0131n \u00e7ok \u00e7ekirdekli sistemlerin g\u00fcc\u00fcnden faydalanamayaca\u011f\u0131 anlam\u0131na gelmez. Bu noktada <code>multiprocessing<\/code> mod\u00fcl\u00fc devreye girer. <code>multiprocessing<\/code>, her biri kendi Python yorumlay\u0131c\u0131s\u0131na ve dolay\u0131s\u0131yla kendi GIL'ine sahip ayr\u0131 i\u015flemler (process) olu\u015fturarak, CPU yo\u011fun g\u00f6revlerin birden fazla \u00e7ekirdek \u00fczerinde paralel olarak \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131n\u0131 sa\u011flar. Her i\u015flem ba\u011f\u0131ms\u0131z \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in, GIL k\u0131s\u0131tlamas\u0131 ortadan kalkar ve CPU yo\u011fun hesaplamalarda \u00f6nemli performans art\u0131\u015flar\u0131 elde edilebilir.<\/p>\n<p>\u00d6rne\u011fin, b\u00fcy\u00fck veri setleri \u00fczerinde karma\u015f\u0131k matematiksel hesaplamalar yapan bir uygulama d\u00fc\u015f\u00fcnelim. Bu t\u00fcr bir g\u00f6rev, <code>asyncio<\/code>'nun odakland\u0131\u011f\u0131 I\/O bekleme yerine yo\u011fun CPU kullan\u0131m\u0131 gerektirir. Burada <code>multiprocessing.Pool<\/code> kullanarak, veri setini par\u00e7alara ay\u0131rabilir ve her par\u00e7ay\u0131 ayr\u0131 bir i\u015flemde i\u015fleyebiliriz. B\u00f6ylece, t\u00fcm \u00e7ekirdeklerimizi etkin bir \u015fekilde kullanarak hesaplama s\u00fcresini drastik bir \u015fekilde azaltabiliriz. A\u015fa\u011f\u0131da basit bir <code>multiprocessing<\/code> \u00f6rne\u011fi bulunmaktad\u0131r:<\/p>\n<pre><code>\nimport multiprocessing\nimport time\n\ndef cpu_bound_task(number):\n    # \u00c7ok say\u0131da hesaplama yapan bir g\u00f6rev\n    result = sum(i*i for i in range(number))\n    return result\n\nif __name__ == \"__main__\":\n    start_time = time.time()\n    inputs = [10_000_000, 10_000_000, 10_000_000, 10_000_000] # 4 adet yo\u011fun g\u00f6rev\n\n    # \u0130\u015flem havuzu olu\u015fturma\n    # processes=None, \u00e7ekirdek say\u0131s\u0131 kadar i\u015flem kullan\u0131r\n    with multiprocessing.Pool(processes=None) as pool:\n        results = pool.map(cpu_bound_task, inputs) # G\u00f6revleri havuza da\u011f\u0131t\n    \n    end_time = time.time()\n    print(f\"Multiprocessing ile toplam s\u00fcre: {end_time - start_time:.2f} saniye\")\n    # print(\"Sonu\u00e7lar:\", results)\n\n    # Kar\u015f\u0131la\u015ft\u0131rma i\u00e7in tek \u00e7ekirdekli senkron versiyon\n    start_time_sync = time.time()\n    results_sync = [cpu_bound_task(num) for num in inputs]\n    end_time_sync = time.time()\n    print(f\"Senkron (tek \u00e7ekirdek) toplam s\u00fcre: {end_time_sync - start_time_sync:.2f} saniye\")\n<\/pre>\n<p><\/code><\/p>\n<p>Bu \u00f6rnekte, d\u00f6rt adet CPU yo\u011fun g\u00f6rev, multiprocessing havuzu sayesinde e\u015fzamanl\u0131 olarak i\u015flenir ve tek \u00e7ekirdekli senkron versiyona g\u00f6re \u00e7ok daha k\u0131sa s\u00fcrede tamamlan\u0131r. Dolay\u0131s\u0131yla, Python ile y\u00fcksek RPS hedeflerine ula\u015fmak i\u00e7in, I\/O yo\u011fun k\u0131s\u0131mlar\u0131 <code>asyncio<\/code> ve <code>aiohttp<\/code> ile asenkron hale getirirken, CPU yo\u011fun k\u0131s\u0131mlar\u0131 <code>multiprocessing<\/code> ile paralel hale getirmek ak\u0131ll\u0131ca bir stratejidir.<\/p>\n<p>Ancak, saniyede 20.000 istek gibi \u00e7ok y\u00fcksek hedefler genellikle tek bir sunucunun kapasitesini a\u015far. Bu noktada <strong>da\u011f\u0131t\u0131k sistem mimarileri<\/strong> ka\u00e7\u0131n\u0131lmaz hale gelir. Da\u011f\u0131t\u0131k sistemler, i\u015f y\u00fck\u00fcn\u00fc birden fazla sunucuya veya makineye yayarak \u00f6l\u00e7eklenebilirlik sa\u011flar. Temel bile\u015fenler \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>Y\u00fck Dengeleyiciler (Load Balancers):<\/strong> Gelen istekleri birden fazla uygulama sunucusuna da\u011f\u0131tarak tek bir noktadaki darbo\u011faz\u0131 \u00f6nler. Nginx, HAProxy veya bulut sa\u011flay\u0131c\u0131lar\u0131n\u0131n (AWS ELB, GCP Load Balancer) hizmetleri bu i\u015flevi g\u00f6r\u00fcr. Bu sayede, on binlerce iste\u011fi birden fazla Python uygulamas\u0131n\u0131 \u00e7al\u0131\u015ft\u0131ran sunucuya y\u00f6nlendirerek toplam kapasiteyi art\u0131rabiliriz.<\/li>\n<li><strong>Mesaj Kuyruklar\u0131 (Message Queues):<\/strong> Y\u00fcksek oranda \u00fcretici\/t\u00fcketici modeli gerektiren asenkron g\u00f6revleri y\u00f6netmek i\u00e7in kullan\u0131l\u0131r. RabbitMQ, Apache Kafka, Redis Streams gibi ara\u00e7lar, iste\u011fin an\u0131nda i\u015flenmesi gerekmeyen durumlarda (\u00f6rne\u011fin, e-posta g\u00f6nderme, b\u00fcy\u00fck veri i\u015fleme) istekleri s\u0131raya alarak sistemin stabil kalmas\u0131n\u0131 sa\u011flar. Gelen 20.000 iste\u011fin hepsi an\u0131nda i\u015flem gerektirmeyebilir; baz\u0131lar\u0131 bir kuyru\u011fa at\u0131l\u0131p daha sonra i\u015flenebilir, bu da \u00f6n uca h\u0131zl\u0131 yan\u0131t verilmesini sa\u011flar.<\/li>\n<li><strong>\u00d6nbellekleme (Caching):<\/strong> S\u0131k\u00e7a eri\u015filen verileri h\u0131zl\u0131 bellek katmanlar\u0131nda (\u00f6rn: Redis, Memcached) saklayarak veritaban\u0131 veya di\u011fer yava\u015f kaynaklara olan ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 azalt\u0131r. Bir e-ticaret sitesinin anl\u0131k indirim kampanyas\u0131nda, \u00fcr\u00fcn bilgilerini veya kategori listelerini \u00f6nbelle\u011fe almak, veritaban\u0131 y\u00fck\u00fcn\u00fc d\u00fc\u015f\u00fcrerek sunucular\u0131n gelen on binlerce iste\u011fe daha h\u0131zl\u0131 yan\u0131t vermesini sa\u011flar.<\/li>\n<\/ul>\n<p>Ger\u00e7ek d\u00fcnya senaryolar\u0131nda, \u00f6rne\u011fin bir e-ticaret platformunun Black Friday gibi y\u00fcksek trafik d\u00f6nemlerinde saniyede y\u00fcz binlerce istek almas\u0131 beklenir. Bu durumda, tek bir Python uygulamas\u0131yla ba\u015fa \u00e7\u0131kmak imkans\u0131zd\u0131r. Bir y\u00fck dengeleyici \u00f6n\u00fcne konulmu\u015f, her biri <code>asyncio<\/code> ve <code>aiohttp<\/code> kullanan birden fazla Python uygulamas\u0131 (\u00f6rne\u011fin, Gunicorn veya Uvicorn ile), veritaban\u0131 \u00f6nbelleklemesi i\u00e7in Redis ve asenkron g\u00f6revler i\u00e7in Celery (Redis veya RabbitMQ ile) kullan\u0131lan bir mimari, bu t\u00fcr \u00f6l\u00e7eklenirlik ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131layabilir. Gelen t\u00fcm istekleri do\u011frudan ana sunuculara g\u00f6ndermek yerine, ilk olarak bir \u00f6nbellek katman\u0131ndan yan\u0131t aramak, mevcut verinin h\u0131zl\u0131 bir \u015fekilde geri d\u00f6nmesini sa\u011flar. E\u011fer veri \u00f6nbellekte yoksa, istek uygulama sunucular\u0131na iletilir. Bu sunucular, asenkron yetenekleri sayesinde veritaban\u0131na veya harici servislere e\u015fzamanl\u0131 istekler g\u00f6nderir. Uzun s\u00fcren arka plan i\u015flemleri ise bir mesaj kuyru\u011funa at\u0131l\u0131r ve ayr\u0131 i\u015f\u00e7i s\u00fcre\u00e7leri (worker processes) taraf\u0131ndan i\u015flenir.<\/p>\n<div class=\"expert-tip\">\n<h3>Uzman \u0130pucu: Mimari Tasar\u0131m\u0131 Kilit Nokta<\/h3>\n<p>20.000 RPS hedefine ula\u015fmak sadece kod optimizasyonuyla de\u011fil, ayn\u0131 zamanda sistemin b\u00fct\u00fcn\u00fcyle nas\u0131l tasarland\u0131\u011f\u0131yla da ilgilidir. Uygulaman\u0131z\u0131 mikroservislere ay\u0131rmak, y\u00fck dengeleyiciler kullanmak, \u00f6nbellekleme katmanlar\u0131 eklemek ve mesaj kuyruklar\u0131 ile asenkron ileti\u015fimi sa\u011flamak, Python'\u0131n performans s\u0131n\u0131rlar\u0131n\u0131 a\u015fmada en b\u00fcy\u00fck yard\u0131mc\u0131n\u0131z olacakt\u0131r. Her bir bile\u015fenin rol\u00fcn\u00fc ve etkile\u015fimini do\u011fru planlamak, sistemin genel verimlili\u011fini %50'den fazla art\u0131rabilir.<\/p>\n<\/div>\n<h2>Optimizasyon ve Test \u0130\u00e7in \u0130leri D\u00fczey \u0130pu\u00e7lar\u0131 Nelerdir?<\/h2>\n<p>Python ile saniyede 20.000 istek gibi iddial\u0131 hedeflere ula\u015fmak, sadece asenkron programlama ve da\u011f\u0131t\u0131k mimarilerle s\u0131n\u0131rl\u0131 de\u011fildir; sistemin her katman\u0131nda detayl\u0131 optimizasyon ve s\u00fcrekli test gerektirir. Y\u00fcksek performans, ince ayar yap\u0131lm\u0131\u015f veritaban\u0131 etkile\u015fimlerinden verimli a\u011f protokollerine kadar bir\u00e7ok farkl\u0131 alandan gelir.<\/p>\n<h3>Veritaban\u0131 ve A\u011f Optimizasyonlar\u0131 Neden \u00d6nemlidir?<\/h3>\n<p>Bir uygulaman\u0131n performans\u0131 genellikle en yava\u015f halkas\u0131n\u0131n h\u0131z\u0131yla belirlenir ve \u00e7o\u011fu zaman bu yava\u015f halka veritaban\u0131d\u0131r. Veritaban\u0131 ba\u011flant\u0131 havuzlar\u0131 (connection pooling) kullanmak, her istek i\u00e7in yeni bir veritaban\u0131 ba\u011flant\u0131s\u0131 a\u00e7\u0131p kapatman\u0131n getirdi\u011fi ek y\u00fck\u00fc ortadan kald\u0131r\u0131r. <code>asyncpg<\/code> (PostgreSQL i\u00e7in) veya <code>aiomysql<\/code> (MySQL i\u00e7in) gibi asenkron veritaban\u0131 s\u00fcr\u00fcc\u00fcleri, veritaban\u0131 sorgular\u0131n\u0131n I\/O bekleme s\u00fcresi boyunca di\u011fer g\u00f6revlerin \u00e7al\u0131\u015fmas\u0131na izin vererek performans\u0131 art\u0131r\u0131r. Ayr\u0131ca, N+1 sorgu sorununu \u00e7\u00f6zmek, sorgular\u0131 optimize etmek (do\u011fru indeksler kullanmak, sorgular\u0131 birle\u015ftirmek), s\u0131k\u00e7a eri\u015filen verileri Redis gibi h\u0131zl\u0131 \u00f6nbellek sistemlerinde tutmak, veritaban\u0131 y\u00fck\u00fcn\u00fc \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n<p>A\u011f taraf\u0131nda ise, HTTP\/1.1 yerine HTTP\/2 veya gRPC gibi daha modern ve verimli protokoller kullanmak, \u00f6zellikle mikroservisler aras\u0131 ileti\u015fimde performans\u0131 art\u0131rabilir. HTTP\/2'nin multiplexing \u00f6zelli\u011fi, tek bir TCP ba\u011flant\u0131s\u0131 \u00fczerinden birden fazla iste\u011fin e\u015fzamanl\u0131 olarak g\u00f6nderilmesine olanak tan\u0131rken, gRPC daha k\u00fc\u00e7\u00fck, ikili mesaj formatlar\u0131yla daha d\u00fc\u015f\u00fck gecikme ve daha y\u00fcksek verim sunar.<\/p>\n<h3>Y\u00fck Testi ve \u0130zleme Nas\u0131l Yap\u0131l\u0131r?<\/h3>\n<p>Sisteminizin 20.000 RPS'yi ger\u00e7ekten kald\u0131r\u0131p kald\u0131ramayaca\u011f\u0131n\u0131 anlaman\u0131n tek yolu, onu bu y\u00fck alt\u0131nda test etmektir. Locust, JMeter, k6 gibi y\u00fck testi ara\u00e7lar\u0131, uygulaman\u0131z\u0131n farkl\u0131 y\u00fck seviyelerinde nas\u0131l davrand\u0131\u011f\u0131n\u0131 sim\u00fcle etmenizi sa\u011flar. Bu testler s\u0131ras\u0131nda:<\/p>\n<ul>\n<li>Uygulaman\u0131n yan\u0131t s\u00fcrelerini \u00f6l\u00e7\u00fcn.<\/li>\n<li>Hata oranlar\u0131n\u0131 takip edin.<\/li>\n<li>CPU, bellek, disk I\/O ve a\u011f kullan\u0131m\u0131 gibi sunucu kaynaklar\u0131n\u0131 izleyin.<\/li>\n<\/ul>\n<p>APM (Application Performance Monitoring) ara\u00e7lar\u0131 (\u00f6rne\u011fin, Datadog, New Relic, Prometheus + Grafana), uygulaman\u0131z\u0131n \u00e7al\u0131\u015fma zaman\u0131ndaki davran\u0131\u015f\u0131n\u0131 s\u00fcrekli olarak izlemenizi sa\u011flar. Bu ara\u00e7lar, performans darbo\u011fazlar\u0131n\u0131, anormal davran\u0131\u015flar\u0131 ve potansiyel sorunlar\u0131 proaktif olarak tespit etmenize yard\u0131mc\u0131 olur. Metrik toplama (\u00f6rne\u011fin, Prometheus ile) ve g\u00f6rselle\u015ftirme (\u00f6rne\u011fin, Grafana ile) sayesinde, sisteminizin anl\u0131k durumunu g\u00f6rebilir ve performans e\u011filimlerini analiz edebilirsiniz. Bu s\u00fcrekli geri bildirim d\u00f6ng\u00fcs\u00fc, optimizasyon \u00e7abalar\u0131n\u0131z\u0131n etkinli\u011fini \u00f6l\u00e7mek ve iyile\u015ftirmeler yapmak i\u00e7in kritik \u00f6neme sahiptir.<\/p>\n<h3>Bellek Y\u00f6netimi ve Mobil Uyumlu Tasar\u0131m \u0130\u00e7in \u0130pu\u00e7lar\u0131<\/h3>\n<p>Python'\u0131n otomatik bellek y\u00f6netimi (garbage collection) genellikle sorunsuz \u00e7al\u0131\u015fsa da, y\u00fcksek performansl\u0131 uygulamalarda bellek s\u0131z\u0131nt\u0131lar\u0131 veya gereksiz bellek t\u00fcketimi ciddi sorunlara yol a\u00e7abilir. \u00d6zellikle b\u00fcy\u00fck veri yap\u0131lar\u0131yla \u00e7al\u0131\u015f\u0131rken veya uzun \u00f6m\u00fcrl\u00fc uygulamalarda, bellek kullan\u0131m\u0131n\u0131 d\u00fczenli olarak profillemek (\u00f6rne\u011fin, <code>memory_profiler<\/code> gibi ara\u00e7larla) ve gereksiz referanslar\u0131 serbest b\u0131rakmak \u00f6nemlidir. Baz\u0131 ekstrem durumlarda, C uzant\u0131lar\u0131 (\u00f6rn: Cython) veya PyPy gibi JIT (Just-In-Time) derleyicili Python yorumlay\u0131c\u0131lar\u0131 kullanmak, CPU yo\u011fun g\u00f6revlerde \u00f6nemli performans art\u0131\u015flar\u0131 sa\u011flayabilir, ancak bu daha karma\u015f\u0131k bir kurulum gerektirebilir.<\/p>\n<p>Son olarak, modern web uygulamalar\u0131 sadece h\u0131zl\u0131 olmakla kalmay\u0131p, farkl\u0131 cihazlarda da sorunsuz bir deneyim sunmal\u0131d\u0131r. Mobil uyumlu HTML, CSS media query'leri kullan\u0131larak kolayca sa\u011flanabilir. Bu, uygulaman\u0131z\u0131n hem masa\u00fcst\u00fc hem de mobil kullan\u0131c\u0131lar i\u00e7in optimize edilmi\u015f olmas\u0131n\u0131 garanti eder:<\/p>\n<pre><code>\n<style>\n  \/* Varsay\u0131lan stil: Geni\u015f ekranlar i\u00e7in *\/\n  .container {\n    width: 960px;\n    margin: 0 auto;\n    padding: 20px;\n  }\n  .column {\n    float: left;\n    width: 33.33%;\n    box-sizing: border-box;\n    padding: 10px;\n  }\n\n  \/* Mobil cihazlar i\u00e7in medya sorgusu *\/\n  @media (max-width: 768px) {\n    .container {\n      width: 100%;\n      padding: 10px;\n    }\n    .column {\n      width: 100%; \/* Mobil cihazlarda s\u00fctunlar alt alta *\/\n      float: none;\n    }\n  }\n<\/style>\n<\/pre>\n<p><\/code><\/p>\n<p>Yukar\u0131daki CSS kodu, taray\u0131c\u0131 penceresi 768 pikselden daha dar oldu\u011funda, <code>.column<\/code> elementlerinin geni\u015fli\u011fini %100'e \u00e7\u0131kararak alt alta s\u0131ralanmas\u0131n\u0131 sa\u011flar. Bu, i\u00e7eri\u011fin mobil cihazlarda daha okunabilir ve kullan\u0131labilir olmas\u0131n\u0131 garanti eder. Performans optimizasyonu ve mobil uyumluluk, birbiriyle do\u011frudan ili\u015fkili olmasa da, modern bir web uygulamas\u0131n\u0131n ba\u015far\u0131s\u0131 i\u00e7in her ikisi de vazge\u00e7ilmezdir.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>Python'\u0131n saniyede 20.000 istek gibi y\u00fcksek performans hedeflerine ula\u015fmas\u0131, ba\u015flang\u0131\u00e7ta kula\u011fa zor gelse de, do\u011fru stratejiler ve ara\u00e7larla tamamen m\u00fcmk\u00fcnd\u00fcr. Global Interpreter Lock (GIL) k\u0131s\u0131tlamas\u0131na ra\u011fmen, asenkron programlama (<code>asyncio<\/code>, <code>aiohttp<\/code>) sayesinde I\/O yo\u011fun g\u00f6revlerde ola\u011fan\u00fcst\u00fc performans elde edilebilirken, <code>multiprocessing<\/code> mod\u00fcl\u00fc CPU yo\u011fun g\u00f6revler i\u00e7in ger\u00e7ek paralellik sa\u011flar. Daha da \u00f6nemlisi, y\u00fck dengeleyiciler, mesaj kuyruklar\u0131 ve \u00f6nbellekleme gibi da\u011f\u0131t\u0131k sistem mimarisi bile\u015fenleri, tek bir sunucunun kapasitesini a\u015fan \u00f6l\u00e7eklenebilirlik ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lamak i\u00e7in kritik rol oynar.<\/p>\n<p>Makale boyunca de\u011findi\u011fimiz gibi, performans optimizasyonu s\u00fcrekli bir s\u00fcre\u00e7tir. Veritaban\u0131 ba\u011flant\u0131 havuzlar\u0131, optimize edilmi\u015f sorgular, modern a\u011f protokolleri, d\u00fczenli y\u00fck testleri ve s\u00fcrekli izleme (APM ara\u00e7lar\u0131) bu yolculu\u011fun vazge\u00e7ilmez par\u00e7alar\u0131d\u0131r. Python'\u0131n esnekli\u011fi ve zengin ekosistemi, bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar sunar. Dolay\u0131s\u0131yla, Python'\u0131n y\u00fcksek performansl\u0131 uygulamalar geli\u015ftirmek i\u00e7in yeterince g\u00fc\u00e7l\u00fc olmad\u0131\u011f\u0131n\u0131 d\u00fc\u015f\u00fcnenler i\u00e7in bu makale, asl\u0131nda do\u011fru yakla\u015f\u0131mla s\u0131n\u0131rlar\u0131n ne kadar zorlanabilece\u011fini g\u00f6stermektedir. \u00d6nemli olan, sorunun do\u011fas\u0131n\u0131 anlamak ve Python'\u0131n sundu\u011fu en uygun \u00e7\u00f6z\u00fcmleri ustaca birle\u015ftirmektir. Unutmay\u0131n, en iyi performans, her zaman en iyi mimari ve s\u00fcrekli iyile\u015ftirmeyle elde edilir.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n<p><strong>1. Python'\u0131n GIL k\u0131s\u0131tlamas\u0131n\u0131 a\u015fman\u0131n en iyi yolu nedir?<\/strong><\/p>\n<p>Cevap: GIL, tek bir Python yorumlay\u0131c\u0131s\u0131 i\u00e7inde ger\u00e7ek multi-threading paralelli\u011fini engeller. Bunu a\u015fman\u0131n en iyi yollar\u0131, I\/O yo\u011fun g\u00f6revler i\u00e7in <code>asyncio<\/code> kullanarak e\u015fzamanl\u0131l\u0131\u011f\u0131 art\u0131rmak ve CPU yo\u011fun g\u00f6revler i\u00e7in <code>multiprocessing<\/code> mod\u00fcl\u00fc ile ayr\u0131 i\u015flemler olu\u015fturarak paralel \u00e7al\u0131\u015fma sa\u011flamakt\u0131r.<\/p>\n<p><strong>2. 20.000 RPS hedefi i\u00e7in tek bir Python uygulamas\u0131 yeterli olur mu?<\/strong><\/p>\n<p>Cevap: Genellikle hay\u0131r. Tek bir Python uygulamas\u0131, asenkron olsa bile, bu kadar y\u00fcksek bir y\u00fck\u00fc tek ba\u015f\u0131na kald\u0131ramayabilir. Bu hedefe ula\u015fmak i\u00e7in birden fazla Python uygulamas\u0131 \u00e7al\u0131\u015ft\u0131ran sunucular, y\u00fck dengeleyiciler, \u00f6nbellekleme (Redis gibi) ve mesaj kuyruklar\u0131 (RabbitMQ, Kafka gibi) i\u00e7eren da\u011f\u0131t\u0131k bir mimari kullanmak \u015fartt\u0131r.<\/p>\n<p><strong>3. Hangi k\u00fct\u00fcphaneler Python ile y\u00fcksek performansl\u0131 HTTP istekleri g\u00f6ndermek i\u00e7in \u00f6nerilir?<\/strong><\/p>\n<p>Cevap: Asenkron HTTP istekleri i\u00e7in <code>aiohttp<\/code> k\u00fct\u00fcphanesi en pop\u00fcler ve etkili se\u00e7eneklerden biridir. Y\u00fcksek e\u015fzamanl\u0131l\u0131k ve d\u00fc\u015f\u00fck gecikme sa\u011flamak i\u00e7in tasarlanm\u0131\u015ft\u0131r. Geleneksel senkron istekler i\u00e7in <code>requests<\/code> k\u00fct\u00fcphanesi kullan\u0131lsa da, <code>aiohttp<\/code> \u00e7ok daha y\u00fcksek verim sunar.<\/p>\n<p><strong>4. Y\u00fcksek RPS hedeflerine ula\u015fmak i\u00e7in sadece kod optimizasyonu yeterli midir?<\/strong><\/p>\n<p>Cevap: Hay\u0131r, sadece kod optimizasyonu yeterli de\u011fildir. Sistem mimarisi, altyap\u0131 (sunucular, a\u011f), veritaban\u0131 optimizasyonlar\u0131, \u00f6nbellekleme stratejileri, y\u00fck dengeleme ve s\u00fcrekli izleme\/test etme gibi bir\u00e7ok fakt\u00f6r bir araya geldi\u011finde y\u00fcksek RPS hedeflerine ula\u015f\u0131labilir.<\/p>\n<p><strong>5. Python uygulamalar\u0131n\u0131n performans\u0131n\u0131 test etmek i\u00e7in hangi ara\u00e7lar kullan\u0131lmal\u0131d\u0131r?<\/strong><\/p>\n<p>Cevap: Python uygulamalar\u0131n\u0131n performans\u0131n\u0131 y\u00fck alt\u0131nda test etmek i\u00e7in Locust, Apache JMeter veya k6 gibi ara\u00e7lar \u00f6nerilir. Bu ara\u00e7lar, uygulaman\u0131z\u0131n farkl\u0131 y\u00fck seviyelerinde nas\u0131l davrand\u0131\u011f\u0131n\u0131 sim\u00fcle etmenize ve performans darbo\u011fazlar\u0131n\u0131 tespit etmenize yard\u0131mc\u0131 olur.<\/p>\n","protected":false},"excerpt":{"rendered":"Python&#8217;\u0131n y\u00fcksek performans gerektiren modern web uygulamalar\u0131 ve mikroservis mimarileri i\u00e7in yetersiz oldu\u011fu d\u00fc\u015f\u00fcncesi yayg\u0131nd\u0131r, ancak do\u011fru stratejiler&hellip;","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-33465","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-python","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python ile Saniyede 20.000 \u0130stek G\u00f6nderme G\u00fcc\u00fcne Ula\u015fmak<\/title>\n<meta name=\"description\" content=\"Python&#039;\u0131n y\u00fcksek performans gerektiren modern web uygulamalar\u0131 ve mikroservis mimarileri i\u00e7in yetersiz oldu\u011fu d\u00fc\u015f\u00fcncesi yayg\u0131nd\u0131r, ancak do\u011fru stratejiler ve ara\u00e7larla Python ile saniyede 20.000 istek (RPS) gibi hedeflere ula\u015fmak m\u00fcmk\u00fcnd\u00fcr. Bu makale, Python&#039;\u0131n performans s\u0131n\u0131rlar\u0131n\u0131 zorlayarak y\u00fcksek verimli sistemler in\u015fa etmenin anahtarlar\u0131n\u0131, pratik \u00f6rneklerle ve ger\u00e7ek d\u00fcnya senaryolar\u0131yla ad\u0131m ad\u0131m a\u00e7\u0131kl\u0131yor.\" \/>\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-ile-saniyede-20-000-istek-gonderme-gucune-ulasmak\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python ile Saniyede 20.000 \u0130stek G\u00f6nderme G\u00fcc\u00fcne Ula\u015fmak\" \/>\n<meta property=\"og:description\" content=\"Python&#039;\u0131n y\u00fcksek performans gerektiren modern web uygulamalar\u0131 ve mikroservis mimarileri i\u00e7in yetersiz oldu\u011fu d\u00fc\u015f\u00fcncesi yayg\u0131nd\u0131r, ancak do\u011fru stratejiler ve ara\u00e7larla Python ile saniyede 20.000 istek (RPS) gibi hedeflere ula\u015fmak m\u00fcmk\u00fcnd\u00fcr. 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