{"id":35781,"date":"2025-12-03T20:31:20","date_gmt":"2025-12-03T17:31:20","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/bolgeler-arasi-gecikme-cross-region-latency-nedir-ve-java-uygulamaniz-icin-neden-kritik-bir-sorundur\/"},"modified":"2025-12-03T20:31:20","modified_gmt":"2025-12-03T17:31:20","slug":"bolgeler-arasi-gecikme-cross-region-latency-nedir-ve-java-uygulamaniz-icin-neden-kritik-bir-sorundur","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/bolgeler-arasi-gecikme-cross-region-latency-nedir-ve-java-uygulamaniz-icin-neden-kritik-bir-sorundur\/","title":{"rendered":"B\u00f6lgeler Aras\u0131 Gecikme (Cross-Region Latency) Nedir ve Java Uygulaman\u0131z \u0130\u00e7in Neden Kritik Bir Sorundur?"},"content":{"rendered":"<h1>Java ve Azure Cosmos DB&#8217;de Gecikme ve Thread Pool Sorunlar\u0131 Tan\u0131lama<\/h1>\n<p>Modern Java uygulamalar\u0131n\u0131z ve Azure Cosmos DB aras\u0131ndaki b\u00f6lgeler aras\u0131 gecikme ile i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu (thread pool) t\u00fckenmesi sorunlar\u0131n\u0131 ad\u0131m ad\u0131m nas\u0131l te\u015fhis edece\u011finizi ve \u00e7\u00f6zece\u011finizi \u00f6\u011frenin. Bu kapsaml\u0131 rehber, performans ipu\u00e7lar\u0131, vaka analizleri ve pratik \u00e7\u00f6z\u00fcmlerle uygulaman\u0131z\u0131n performans\u0131n\u0131 maksimize etmenize yard\u0131mc\u0131 olacak.<\/p>\n<p>Her \u015feyden \u00f6nce, b\u00f6lgeler aras\u0131 gecikmenin ne oldu\u011funu ve uygulaman\u0131z i\u00e7in neden ciddi bir ba\u015f a\u011fr\u0131s\u0131 yaratabilece\u011fini anlamak \u00f6nemlidir. Temel olarak, uygulaman\u0131z\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 co\u011frafi b\u00f6lge (\u00f6rne\u011fin, Azure Sanal Makinesi veya Azure App Service) ile Azure Cosmos DB veri taban\u0131n\u0131z\u0131n konu\u015fland\u0131r\u0131ld\u0131\u011f\u0131 co\u011frafi b\u00f6lge aras\u0131ndaki fiziksel mesafeden kaynaklanan a\u011f gecikmesidir. Veri, sunucular aras\u0131nda \u0131\u015f\u0131k h\u0131z\u0131nda bile olsa, kat etti\u011fi her kilometreye g\u00f6re belirli bir gecikmeye sahiptir. Bu gecikme, \u0131\u015f\u0131\u011f\u0131n bo\u015fluktaki h\u0131z\u0131 yakla\u015f\u0131k olarak saniyede 300.000 kilometre olsa da, ger\u00e7ek a\u011flarda fiber optik kablolar, y\u00f6nlendiriciler ve di\u011fer a\u011f ekipmanlar\u0131 nedeniyle daha yava\u015f seyreder. Bu durum, veri iste\u011finin g\u00f6nderilip yan\u0131t\u0131n al\u0131nmas\u0131 aras\u0131ndaki toplam s\u00fcreyi (Round-Trip Time &#8211; RTT) art\u0131r\u0131r.<\/p>\n<p>Peki, bu durum Java uygulaman\u0131z\u0131 nas\u0131l etkiler? Bir e-ticaret uygulamas\u0131n\u0131 d\u00fc\u015f\u00fcn\u00fcn. Uygulama sunucular\u0131n\u0131z Avrupa&#8217;da (\u00f6rne\u011fin, Bat\u0131 Avrupa Azure b\u00f6lgesi) konu\u015fland\u0131r\u0131lm\u0131\u015fken, Azure Cosmos DB hesab\u0131n\u0131z Amerika&#8217;da (\u00f6rne\u011fin, Do\u011fu ABD Azure b\u00f6lgesi) olabilir. M\u00fc\u015fterileriniz Avrupa&#8217;dan \u00fcr\u00fcn listelerini g\u00f6r\u00fcnt\u00fclerken veya sipari\u015f verirken, her veri okuma ve yazma iste\u011fi binlerce kilometrelik bir yolculuk yapmak zorunda kal\u0131r. Her bir veritaban\u0131 i\u015flemi, bu uzun mesafeyi kat etmek i\u00e7in ek milisaniyeler gerektirecektir. Sayfada birden fazla veri taban\u0131 \u00e7a\u011fr\u0131s\u0131 oldu\u011funda (\u00f6rne\u011fin, \u00fcr\u00fcn detaylar\u0131, fiyatland\u0131rma, envanter durumu gibi), bu k\u00fc\u00e7\u00fck gecikmeler birikerek saniyeler s\u00fcren toplam yan\u0131t s\u00fcrelerine d\u00f6n\u00fc\u015febilir. Kullan\u0131c\u0131lar i\u00e7in bu, yava\u015f y\u00fcklenen sayfalar, tak\u0131lan i\u015flemler ve genel olarak k\u00f6t\u00fc bir deneyim anlam\u0131na gelir. Bu da m\u00fc\u015fteri kayb\u0131na ve i\u015f performans\u0131 d\u00fc\u015f\u00fc\u015f\u00fcne yol a\u00e7abilir.<\/p>\n<p>B\u00f6lgeler aras\u0131 gecikme sadece uygulaman\u0131z\u0131n bulundu\u011fu b\u00f6lge ile Cosmos DB&#8217;nin tek bir yazma b\u00f6lgesi aras\u0131ndaki mesafeden kaynaklanmaz. E\u011fer Cosmos DB hesab\u0131n\u0131z farkl\u0131 b\u00f6lgelere replike edilmi\u015fse ve uygulaman\u0131z en yak\u0131n okuma b\u00f6lgesine y\u00f6nlendirilse bile, tutarl\u0131l\u0131k modeli de gecikmeyi etkileyebilir. \u00d6rne\u011fin, &#8220;Strong&#8221; (G\u00fc\u00e7l\u00fc) tutarl\u0131l\u0131k seviyesi, verinin t\u00fcm b\u00f6lgelerde senkronize olmas\u0131n\u0131 gerektirdi\u011fi i\u00e7in yazma i\u015flemlerinde daha y\u00fcksek gecikmelere neden olabilir. &#8220;Session&#8221; veya &#8220;Eventual&#8221; gibi daha gev\u015fek tutarl\u0131l\u0131k modelleri ise daha d\u00fc\u015f\u00fck yazma gecikmeleri sunar ancak farkl\u0131 okuma b\u00f6lgelerinden yap\u0131lan okumalarda verinin biraz eski olma potansiyeli vard\u0131r. Bu nedenle, uygulaman\u0131z\u0131n gereksinimlerine en uygun tutarl\u0131l\u0131k modelini se\u00e7mek de \u00f6nemlidir.<\/p>\n<p>B\u00f6lgeler aras\u0131 gecikmeyi anlamak ve ele almak, y\u00fcksek performansl\u0131 ve da\u011f\u0131t\u0131k sistemler geli\u015ftirmenin temel ta\u015flar\u0131ndan biridir. Optimizasyon stratejileri, uygulaman\u0131z\u0131 ve veri taban\u0131n\u0131z\u0131 co\u011frafi olarak yak\u0131nla\u015ft\u0131rmak, Cosmos DB&#8217;nin \u00e7ok b\u00f6lgeli yazma (multi-region writes) yetene\u011fini kullanmak ve do\u011fru tutarl\u0131l\u0131k seviyesini se\u00e7mek \u00fczerine odaklanmal\u0131d\u0131r. \u0130lk a\u015famada, uygulaman\u0131z\u0131n ve Cosmos DB&#8217;nizin hangi b\u00f6lgelerde oldu\u011funu basit bir kontrol ile belirlemek, potansiyel gecikme kaynaklar\u0131n\u0131 belirlemede kritik bir ad\u0131md\u0131r.<\/p>\n<h2>Java Uygulamalar\u0131nda Thread Pool T\u00fckenmesi (Exhaustion) Neden Ger\u00e7ekle\u015fir ve Nas\u0131l Anla\u015f\u0131l\u0131r?<\/h2>\n<p>\u0130\u015f par\u00e7ac\u0131\u011f\u0131 havuzu (thread pool) t\u00fckenmesi, \u00f6zellikle y\u00fcksek y\u00fck alt\u0131nda veya hatal\u0131 programlama desenleri kullan\u0131ld\u0131\u011f\u0131nda Java uygulamalar\u0131nda s\u0131k\u00e7a rastlanan ve ciddi sorunlara yol a\u00e7abilen bir performans sorunudur. Bir i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu, uygulaman\u0131z\u0131n e\u015f zamanl\u0131 olarak i\u015fleyebilece\u011fi g\u00f6rev say\u0131s\u0131n\u0131 s\u0131n\u0131rlayan ve y\u00f6neten bir dizi \u00f6nceden olu\u015fturulmu\u015f i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131 (thread) i\u00e7erir. Her bir gelen istek (\u00f6rne\u011fin, bir HTTP iste\u011fi, bir mesaj kuyru\u011fu i\u015flemi veya bir veri taban\u0131 \u00e7a\u011fr\u0131s\u0131), havuzdan bir i\u015f par\u00e7ac\u0131\u011f\u0131 alarak i\u015flenir. Bu mekanizma, her istek i\u00e7in yeni bir i\u015f par\u00e7ac\u0131\u011f\u0131 olu\u015fturman\u0131n getirece\u011fi ek y\u00fck\u00fc (thread creation overhead) ortadan kald\u0131rarak performans\u0131 art\u0131rmay\u0131 hedefler.<\/p>\n<p>Ancak, e\u011fer veri taban\u0131 \u00e7a\u011fr\u0131lar\u0131 gibi G\/\u00c7 (I\/O) yo\u011fun i\u015flemler \u00e7ok uzun s\u00fcrerse, ya da i\u015f par\u00e7ac\u0131klar\u0131 yanl\u0131\u015fl\u0131kla bloke olursa (blocking I\/O), havuzdaki t\u00fcm i\u015f par\u00e7ac\u0131klar\u0131 me\u015fgul hale gelebilir ve yeni g\u00f6revler i\u00e7in i\u015f par\u00e7ac\u0131\u011f\u0131 kalmayabilir. Bu &#8220;t\u00fckenme&#8221; durumu, yeni gelen isteklerin i\u015flenememesine ve genellikle uygulama donmalar\u0131na, isteklerin kuyrukta beklemesine veya hata yan\u0131tlar\u0131na (\u00f6rne\u011fin, HTTP 503 Service Unavailable, Connection Timeout) yol a\u00e7ar. Bir i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n bloke olmas\u0131, di\u011fer gelen isteklerin de beklemeye al\u0131nmas\u0131na ve &#8220;kar topu etkisi&#8221; yaratarak t\u00fcm uygulaman\u0131n yava\u015flamas\u0131na veya durmas\u0131na neden olabilir.<\/p>\n<p>Thread pool t\u00fckenmesinin ba\u015fl\u0131ca nedenleri \u015funlard\u0131r:<\/p>\n<ol>\n<li><strong>Uzun S\u00fcreli Blocking I\/O \u0130\u015flemleri:<\/strong> Veri taban\u0131 sorgular\u0131, harici API \u00e7a\u011fr\u0131lar\u0131, dosya okuma\/yazma gibi I\/O i\u015flemleri, senkron (blocking) bir \u015fekilde yap\u0131ld\u0131\u011f\u0131nda, i\u015flem tamamlanana kadar i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131 me\u015fgul eder. E\u011fer bu i\u015flemler b\u00f6lgeler aras\u0131 gecikme nedeniyle uzarsa, i\u015f par\u00e7ac\u0131\u011f\u0131 daha uzun s\u00fcre bloke kal\u0131r.<\/li>\n<li><strong>Yetersiz Thread Pool Boyutu:<\/strong> Uygulaman\u0131z\u0131n ayn\u0131 anda i\u015fleyebilece\u011fi maksimum istek say\u0131s\u0131n\u0131 kar\u015f\u0131layacak kadar b\u00fcy\u00fck olmayan bir i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu boyutu, yo\u011fun y\u00fck alt\u0131nda h\u0131zla t\u00fckenmeye yol a\u00e7abilir.<\/li>\n<li><strong>\u0130\u015f Par\u00e7ac\u0131\u011f\u0131 S\u0131z\u0131nt\u0131lar\u0131 (Thread Leaks):<\/strong> Hatal\u0131 kodlama veya kaynak y\u00f6netimi nedeniyle i\u015f par\u00e7ac\u0131klar\u0131n\u0131n serbest b\u0131rak\u0131lamamas\u0131 (\u00f6rne\u011fin, bir hata durumunda kaynaklar\u0131n kapat\u0131lmamas\u0131), havuzdaki i\u015f par\u00e7ac\u0131klar\u0131n\u0131n zamanla t\u00fckenmesine neden olabilir.<\/li>\n<li><strong>Uzun S\u00fcreli CPU Yo\u011fun \u0130\u015flemler:<\/strong> Nadiren de olsa, a\u015f\u0131r\u0131 CPU yo\u011fun hesaplamalar da bir i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131 uzun s\u00fcre me\u015fgul edebilir, ancak I\/O blokajlar\u0131 daha yayg\u0131n bir nedendir.<\/li>\n<\/ol>\n<p>Peki, bu sorunu nas\u0131l anlars\u0131n\u0131z? Uygulaman\u0131z\u0131n loglar\u0131nda &#8220;thread pool exhausted&#8221;, &#8220;connection timeout&#8221; veya &#8220;too many open connections&#8221; gibi hata mesajlar\u0131n\u0131 aramal\u0131s\u0131n\u0131z. Ayr\u0131ca, izleme ara\u00e7lar\u0131 (\u00f6rne\u011fin, JMX metrikleri, Azure Application Insights) arac\u0131l\u0131\u011f\u0131yla aktif i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131n\u0131, kuyruktaki g\u00f6rev say\u0131s\u0131n\u0131 ve i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu boyutunu g\u00f6zlemlemek de \u00f6nemlidir. \u0130\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131n\u0131n maksimum limite ula\u015ft\u0131\u011f\u0131n\u0131 ve kuyruk boyutunun s\u00fcrekli artt\u0131\u011f\u0131n\u0131 g\u00f6r\u00fcyorsan\u0131z, bu durum thread pool t\u00fckenmesinin a\u00e7\u0131k bir i\u015faretidir. JVM&#8217;de JStack gibi ara\u00e7lar kullanarak i\u015f par\u00e7ac\u0131\u011f\u0131 d\u00f6k\u00fcm\u00fc almak, hangi i\u015f par\u00e7ac\u0131klar\u0131n\u0131n ne yapt\u0131\u011f\u0131n\u0131 ve nerede bloke oldu\u011funu anlaman\u0131za yard\u0131mc\u0131 olabilir. Bu d\u00f6k\u00fcmlerde &#8220;WAITING (on object monitor)&#8221; veya &#8220;BLOCKED (on object monitor)&#8221; durumundaki i\u015f par\u00e7ac\u0131klar\u0131n\u0131n say\u0131s\u0131n\u0131n y\u00fcksek olmas\u0131, bir darbo\u011faz\u0131n varl\u0131\u011f\u0131na i\u015faret eder.<\/p>\n<h2>Problemleri Te\u015fhis Etmeye Ba\u015flang\u0131\u00e7: \u0130lk Ad\u0131mlar ve G\u00f6zlem Ara\u00e7lar\u0131<\/h2>\n<p>Herhangi bir performans sorununu \u00e7\u00f6zmenin ilk ad\u0131m\u0131, sorunun nerede oldu\u011funu do\u011fru bir \u015fekilde te\u015fhis etmektir. B\u00f6lgeler aras\u0131 gecikme ve i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu t\u00fckenmesi sorunlar\u0131n\u0131 te\u015fhis etmek i\u00e7in bir dizi araca ve y\u00f6nteme ihtiyac\u0131m\u0131z var. Bu ad\u0131mlar, sorunun kayna\u011f\u0131n\u0131 belirlemenize ve do\u011fru \u00e7\u00f6z\u00fcme y\u00f6nelmenize yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n<h3>1. B\u00f6lgesel Konumland\u0131rmay\u0131 Kontrol Edin:<\/h3>\n<p>\u00d6ncelikle, uygulaman\u0131z\u0131n ve Azure Cosmos DB hesab\u0131n\u0131z\u0131n co\u011frafi konumlar\u0131n\u0131 kontrol edin. Azure Portal \u00fczerinden Cosmos DB hesab\u0131n\u0131z\u0131n &#8220;Replicate data globally&#8221; (Verileri k\u00fcresel olarak \u00e7o\u011falt) b\u00f6l\u00fcm\u00fcne giderek hangi b\u00f6lgelerde konu\u015fland\u0131r\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6rebilirsiniz. Ayn\u0131 \u015fekilde, Java uygulaman\u0131z\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 Azure Sanal Makinesi, App Service veya Kubernetes k\u00fcmesinin hangi Azure b\u00f6lgesinde oldu\u011funu da teyit edin. E\u011fer aralar\u0131nda b\u00fcy\u00fck bir co\u011frafi mesafe varsa (\u00f6rne\u011fin, uygulaman\u0131z Bat\u0131 Avrupa, Cosmos DB Do\u011fu ABD), bu durum gecikmenin ana kayna\u011f\u0131 olabilir.<\/p>\n<div class=\"expert-tip\">\n  Uzman \u0130pucu: Uygulaman\u0131z ve Cosmos DB hesab\u0131n\u0131z aras\u0131nda RTT (Round Trip Time) gecikmesini manuel olarak test etmek i\u00e7in basit bir ping veya traceroute komutu \u00e7al\u0131\u015ft\u0131rabilirsiniz. Ancak, do\u011frudan IP adresleri yerine Azure region to region latency test ara\u00e7lar\u0131n\u0131 kullanmak daha do\u011fru sonu\u00e7lar verecektir.\n<\/div>\n<h3>2. Uygulama Loglar\u0131n\u0131 \u0130nceleyin:<\/h3>\n<p>Uygulaman\u0131z\u0131n loglar\u0131, sorunlar\u0131n ilk belirtilerini g\u00f6steren en zengin kaynaklardan biridir. Yava\u015f \u00e7al\u0131\u015fan veri taban\u0131 sorgular\u0131, zaman a\u015f\u0131m\u0131 hatalar\u0131 (timeout errors), ba\u011flant\u0131 hatalar\u0131 veya i\u015f par\u00e7ac\u0131\u011f\u0131 havuzuyla ilgili uyar\u0131lar\/hatalar (\u00f6rne\u011fin, &#8220;Thread pool exhausted&#8221;, &#8220;Connection timed out&#8221;, &#8220;Request queue full&#8221;) i\u00e7in loglar\u0131n\u0131z\u0131 taray\u0131n. Log seviyenizi <code>DEBUG<\/code> veya <code>INFO<\/code> seviyesine \u00e7ekerek Cosmos DB SDK&#8217;s\u0131n\u0131n dahili mesajlar\u0131n\u0131 da g\u00f6rmeyi d\u00fc\u015f\u00fcnebilirsiniz. \u00d6zellikle, iste\u011fin ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc g\u00f6steren metrikleri loglaman\u0131z, yava\u015f \u00e7a\u011fr\u0131lar\u0131 tespit etmenize yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n<pre><code class=\"language-java\">\n\/\/ \u00d6rnek: Basit bir zaman \u00f6l\u00e7\u00fcm\u00fc ve loglama\nlong startTime = System.currentTimeMillis();\n\/\/ Cosmos DB i\u015flemi burada ger\u00e7ekle\u015fir\n\/\/ ... container.readItem(...)\nlong endTime = System.currentTimeMillis();\nlong duration = endTime - startTime;\nSystem.out.println(\"Cosmos DB okuma i\u015flemi \" + duration + \" ms s\u00fcrd\u00fc.\");\n<\/pre>\n<p><\/code><\/p>\n<h3>3. Azure Monit\u00f6r ve Application Insights Kullan\u0131m\u0131:<\/h3>\n<p>Azure Monitor ve Application Insights, Azure ortam\u0131ndaki uygulamalar\u0131n\u0131z i\u00e7in olmazsa olmaz izleme ara\u00e7lar\u0131d\u0131r. Bu ara\u00e7lar\u0131 kullanarak:<\/p>\n<ul>\n<li><strong>Cosmos DB Metrikleri:<\/strong> Azure Cosmos DB hesab\u0131n\u0131z i\u00e7in ortalama istek gecikmesi, RU\/s t\u00fcketimi, normalize edilmi\u015f RU t\u00fcketimi ve sunucu taraf\u0131 gecikmeleri gibi metrikleri izleyin. Ani art\u0131\u015flar veya s\u00fcrekli y\u00fcksek gecikmeler, veri taban\u0131 taraf\u0131nda bir soruna i\u015faret edebilir.<\/li>\n<li><strong>Uygulama Metrikleri:<\/strong> Application Insights arac\u0131l\u0131\u011f\u0131yla Java uygulaman\u0131z\u0131n HTTP istek gecikmeleri, ba\u011f\u0131ml\u0131l\u0131k \u00e7a\u011fr\u0131s\u0131 gecikmeleri (Cosmos DB \u00e7a\u011fr\u0131lar\u0131 dahil), CPU ve bellek kullan\u0131m\u0131, i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131 ve kuyruk uzunluklar\u0131 gibi metriklerini takip edin. \"Dependencies\" (Ba\u011f\u0131ml\u0131l\u0131klar) b\u00f6l\u00fcm\u00fcnde Cosmos DB \u00e7a\u011fr\u0131lar\u0131n\u0131n ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc g\u00f6rebilirsiniz.<\/li>\n<\/ul>\n<p>Application Insights, \u00e7a\u011fr\u0131 y\u0131\u011f\u0131nlar\u0131n\u0131 (call stacks) ve performans profillerini de toplayabilir, bu da hangi kod par\u00e7alar\u0131n\u0131n daha uzun s\u00fcrd\u00fc\u011f\u00fcn\u00fc ve potansiyel olarak i\u015f par\u00e7ac\u0131klar\u0131n\u0131 bloke etti\u011fini belirlemenize yard\u0131mc\u0131 olur. \u00d6zellikle uzun s\u00fcreli Cosmos DB \u00e7a\u011fr\u0131lar\u0131, thread pool t\u00fckenmesinin en b\u00fcy\u00fck tetikleyicilerinden biridir.<\/p>\n<h3>4. A\u011f Te\u015fhis Ara\u00e7lar\u0131:<\/h3>\n<p>Uygulaman\u0131z\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 sanal makineden veya kapsay\u0131c\u0131dan Cosmos DB u\u00e7 noktas\u0131na do\u011fru basit a\u011f te\u015fhisleri yap\u0131n. <code>ping<\/code> veya <code>traceroute<\/code> (Windows'ta <code>tracert<\/code>) gibi komutlar, paketlerin hedefe ula\u015fmas\u0131 i\u00e7in ge\u00e7en s\u00fcreyi ve aradaki atlamalar\u0131 (hops) g\u00f6stererek a\u011f gecikmesi hakk\u0131nda temel bir fikir verebilir. Y\u00fcksek RTT de\u011ferleri veya beklenmedik atlamalar, a\u011f taraf\u0131nda bir soruna i\u015faret edebilir. Ancak, bulut ortamlar\u0131nda g\u00fcvenlik duvarlar\u0131 ve NAT gibi fakt\u00f6rler nedeniyle bu ara\u00e7lar\u0131n etkinli\u011fi s\u0131n\u0131rl\u0131 olabilir.<\/p>\n<p>Bu ilk ad\u0131mlar, sorunun genel resmini \u00e7izmenize ve daha derinlemesine incelemeler i\u00e7in do\u011fru y\u00f6ne odaklanman\u0131za olanak tan\u0131r. Te\u015fhis s\u00fcrecinde sab\u0131rl\u0131 olmak ve her bir veri noktas\u0131n\u0131 dikkatle incelemek, do\u011fru \u00e7\u00f6z\u00fcme ula\u015fman\u0131n anahtar\u0131d\u0131r.<\/p>\n<h2>Azure Cosmos DB Taraf\u0131nda Gecikme Kaynaklar\u0131n\u0131 Anlamak ve Azaltmak<\/h2>\n<p>Cosmos DB taraf\u0131nda gecikmeyi azaltmak, do\u011fru konfig\u00fcrasyon, veri modellemesi ve tutarl\u0131l\u0131k modeli se\u00e7imi ile ba\u015flar. Uygulaman\u0131z\u0131n performans\u0131n\u0131 etkileyen en \u00f6nemli fakt\u00f6rlerden biri, veri taban\u0131 ile olan ileti\u015fim gecikmesidir. Bu b\u00f6l\u00fcm, Cosmos DB taraf\u0131ndaki ba\u015fl\u0131ca gecikme kaynaklar\u0131n\u0131 ve bunlar\u0131 nas\u0131l azaltabilece\u011finizi detayland\u0131racakt\u0131r.<\/p>\n<h3>1. B\u00f6lgesel Yak\u0131nl\u0131k ve \u00c7ok B\u00f6lgeli Yazma (Multi-Region Writes):<\/h3>\n<p>En temel ve etkili optimizasyon, uygulaman\u0131z\u0131n ve Cosmos DB hesab\u0131n\u0131z\u0131n co\u011frafi olarak m\u00fcmk\u00fcn oldu\u011funca yak\u0131n konumland\u0131r\u0131lmas\u0131d\u0131r. E\u011fer uygulaman\u0131z yaln\u0131zca tek bir Azure b\u00f6lgesinde \u00e7al\u0131\u015f\u0131yorsa, Cosmos DB hesab\u0131n\u0131z\u0131n da ayn\u0131 b\u00f6lgede birincil yazma b\u00f6lgesi olarak yap\u0131land\u0131r\u0131ld\u0131\u011f\u0131ndan emin olun. Ancak, uygulaman\u0131z\u0131n kullan\u0131c\u0131lar\u0131 veya hizmetleri farkl\u0131 co\u011frafi b\u00f6lgelerde bulunuyorsa, Cosmos DB'nin \"\u00e7ok b\u00f6lgeli yazma\" \u00f6zelli\u011fini etkinle\u015ftirmek oyun de\u011fi\u015ftirici olabilir. Bu \u00f6zellik, uygulaman\u0131z\u0131n en yak\u0131n veri taban\u0131 kopyas\u0131na hem okuma hem de yazma i\u015flemleri yapmas\u0131na olanak tan\u0131r. B\u00f6ylece, veri taban\u0131na her eri\u015fimde verinin co\u011frafi olarak yak\u0131n bir kopyas\u0131na y\u00f6nlendirilirsiniz, bu da b\u00f6lgeler aras\u0131 gecikmeyi \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n<h3>2. Tutarl\u0131l\u0131k Modeli Se\u00e7imi:<\/h3>\n<p>Azure Cosmos DB, be\u015f farkl\u0131 tutarl\u0131l\u0131k modeli sunar: Strong (G\u00fc\u00e7l\u00fc), Bounded Staleness (S\u0131n\u0131rl\u0131 Eskime), Session (Oturum), Consistent Prefix (Tutarl\u0131 \u00d6nek) ve Eventual (Nihai). Se\u00e7ti\u011finiz tutarl\u0131l\u0131k modeli, yazma ve okuma gecikmeleri \u00fczerinde do\u011frudan bir etkiye sahiptir:<\/p>\n<ul>\n<li><strong>Strong:<\/strong> En y\u00fcksek tutarl\u0131l\u0131\u011f\u0131 sa\u011flar ancak t\u00fcm b\u00f6lgelerde verinin senkronize olmas\u0131n\u0131 gerektirdi\u011fi i\u00e7in yazma i\u015flemlerinde en y\u00fcksek gecikmeyi sunar. Genellikle kritik i\u015f y\u00fckleri i\u00e7in tercih edilir.<\/li>\n<li><strong>Bounded Staleness:<\/strong> Belirli bir s\u00fcre veya belirli bir i\u015flem say\u0131s\u0131 kadar geride kalmaya izin veren bir ara yoldur. G\u00fc\u00e7l\u00fc tutarl\u0131l\u0131\u011fa g\u00f6re daha d\u00fc\u015f\u00fck gecikme sunar.<\/li>\n<li><strong>Session:<\/strong> En yayg\u0131n kullan\u0131lan modeldir. Belirli bir istemci oturumu i\u00e7in g\u00fc\u00e7l\u00fc tutarl\u0131l\u0131\u011f\u0131 garanti eder ancak di\u011fer oturumlar g\u00fcncel olmayan verileri g\u00f6rebilir. \u0130yi bir performans ve tutarl\u0131l\u0131k dengesi sunar.<\/li>\n<li><strong>Consistent Prefix ve Eventual:<\/strong> En d\u00fc\u015f\u00fck gecikmeyi ve en y\u00fcksek i\u015flem hacmini sunar, ancak verinin di\u011fer b\u00f6lgelerde g\u00fcncellenmesinin biraz zaman alabilece\u011fi anlam\u0131na gelir. Y\u00fcksek oranda okunabilir uygulamalar veya ger\u00e7ek zamanl\u0131 veri gerektirmeyen senaryolar i\u00e7in uygundur.<\/li>\n<\/ul>\n<p>Uygulaman\u0131z\u0131n gereksinimlerine g\u00f6re en gev\u015fek ama kabul edilebilir tutarl\u0131l\u0131k modelini se\u00e7mek, \u00f6zellikle yazma i\u015flemleri i\u00e7in gecikmeleri \u00f6nemli \u00f6l\u00e7\u00fcde azaltabilir.<\/p>\n<h3>3. Dizinleme Stratejileri (Indexing Strategies):<\/h3>\n<p>Yanl\u0131\u015f veya eksik dizinleme, sorgu performans\u0131n\u0131 d\u00fc\u015f\u00fcrerek gecikmeleri art\u0131rabilir. Cosmos DB, varsay\u0131lan olarak t\u00fcm \u00f6zellikler i\u00e7in otomatik dizinleme yapar, ancak bazen bu, performans i\u00e7in ideal olmayabilir. \u0130htiya\u00e7 duymad\u0131\u011f\u0131n\u0131z \u00f6zellikler i\u00e7in dizinlemeyi devre d\u0131\u015f\u0131 b\u0131rakarak yazma i\u015flemlerinin maliyetini ve gecikmesini azaltabilirsiniz. Ayr\u0131ca, s\u0131k\u00e7a sorgulad\u0131\u011f\u0131n\u0131z \u00f6zellikler \u00fczerinde \u00f6zel bile\u015fik dizinler olu\u015fturmak, karma\u015f\u0131k sorgular\u0131n daha h\u0131zl\u0131 \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayabilir. Dizinleme politikalar\u0131n\u0131z\u0131, uygulaman\u0131z\u0131n sorgu desenlerini analiz ederek optimize edin.<\/p>\n<h3>4. Sa\u011flanan \u0130\u015flem Hacmi (RU\/s) Y\u00f6netimi:<\/h3>\n<p>Cosmos DB'de her i\u015flem bir \u0130stek Birimi (Request Unit - RU) t\u00fcketir. Yetersiz RU\/s sa\u011fland\u0131\u011f\u0131nda, Cosmos DB istekleri k\u0131s\u0131tlayabilir (throttling), bu da uygulaman\u0131zda gecikmelere ve hata mesajlar\u0131na (HTTP 429) yol a\u00e7ar. Y\u00fcksek y\u00fck alt\u0131nda veya anl\u0131k yo\u011funluklarda bu durum s\u0131k\u00e7a ya\u015fan\u0131r. Auto-scale (Otomatik \u00d6l\u00e7eklendirme) \u00f6zelli\u011fi, i\u015f y\u00fck\u00fcn\u00fcze g\u00f6re RU\/s'yi dinamik olarak ayarlayarak k\u0131s\u0131tlamalar\u0131 minimize etmeye yard\u0131mc\u0131 olabilir. Ayr\u0131ca, en maliyetli sorgular\u0131n\u0131z\u0131 belirleyerek optimize etmek (\u00f6rne\u011fin, daha az veri okumak, daha verimli filtreler kullanmak), RU\/s t\u00fcketimini azalt\u0131r ve genel gecikmeyi d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n<h3>5. Cosmos DB SDK Ba\u011flant\u0131 Modu (Gateway vs. Direct):<\/h3>\n<p>Azure Cosmos DB Java SDK's\u0131, iki farkl\u0131 ba\u011flant\u0131 modu sunar: Gateway (A\u011f Ge\u00e7idi) ve Direct (Do\u011frudan). Her birinin kendine \u00f6zg\u00fc avantajlar\u0131 ve dezavantajlar\u0131 vard\u0131r:<\/p>\n<ul>\n<li><strong>Gateway Modu:<\/strong> T\u00fcm istekler tek bir a\u011f ge\u00e7idi hizmeti \u00fczerinden ge\u00e7er. Bu mod genellikle daha az ba\u011flant\u0131 y\u00f6netimi gerektirir ve g\u00fcvenlik duvar\u0131 kurallar\u0131n\u0131 basitle\u015ftirir. Ancak, istek ba\u015f\u0131na daha fazla a\u011f atlamas\u0131 oldu\u011fu i\u00e7in genellikle daha y\u00fcksek gecikme sunar.<\/li>\n<li><strong>Direct Modu:<\/strong> \u0130stemci, arka u\u00e7 b\u00f6lmelerine do\u011frudan ba\u011flan\u0131r. Bu, a\u011f atlamalar\u0131n\u0131 azalt\u0131r ve genellikle daha d\u00fc\u015f\u00fck gecikme ve daha iyi performans sunar. \u00d6zellikle d\u00fc\u015f\u00fck gecikmeli ve y\u00fcksek i\u015flem hacimli i\u015f y\u00fckleri i\u00e7in \u00f6nerilir. Ancak, daha fazla ba\u011flant\u0131 y\u00f6netimi ve g\u00fcvenlik duvar\u0131 yap\u0131land\u0131rmas\u0131 gerektirebilir.<\/li>\n<\/ul>\n<p>\u00c7o\u011fu modern Java uygulamas\u0131 i\u00e7in Direct mod daha iyi performans sa\u011flad\u0131\u011f\u0131ndan tercih edilmelidir. SDK'y\u0131 yap\u0131land\u0131r\u0131rken bu se\u00e7ene\u011fi do\u011fru bir \u015fekilde ayarlad\u0131\u011f\u0131n\u0131zdan emin olun.<\/p>\n<p>Bu stratejileri uygulayarak, Azure Cosmos DB taraf\u0131nda olu\u015fabilecek gecikmeleri \u00f6nemli \u00f6l\u00e7\u00fcde azaltabilir ve Java uygulaman\u0131z\u0131n genel performans\u0131n\u0131 art\u0131rabilirsiniz. S\u00fcrekli izleme ve proaktif optimizasyon, bu s\u00fcrecin kritik par\u00e7alar\u0131d\u0131r.<\/p>\n<h2>Java Uygulamas\u0131nda Thread Pool Y\u00f6netimi ve Asenkron Programlama Teknikleri<\/h2>\n<p>Java uygulamas\u0131ndaki i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu (thread pool) t\u00fckenmesi sorunlar\u0131n\u0131 gidermenin ve performans\u0131 art\u0131rman\u0131n en etkili yollar\u0131ndan biri, G\/\u00c7 (I\/O) i\u015flemlerini bloke etmeyen (non-blocking) veya asenkron yakla\u015f\u0131mlar kullanmakt\u0131r. Java'n\u0131n modern s\u00fcr\u00fcmleri (Java 8 ve sonras\u0131) ve reaktif programlama k\u00fct\u00fcphaneleri (\u00f6rne\u011fin Reactor, RxJava) bu konuda \u00f6nemli avantajlar sunar. Azure Cosmos DB Java SDK's\u0131 da reaktif API'ler sunarak bu yakla\u015f\u0131m\u0131 destekler.<\/p>\n<h3>1. Senkron (Blocking) vs. Asenkron (Non-Blocking) I\/O:<\/h3>\n<p>Geleneksel olarak, Java'da bir veri taban\u0131 \u00e7a\u011fr\u0131s\u0131 veya harici bir servis \u00e7a\u011fr\u0131s\u0131 yapt\u0131\u011f\u0131n\u0131zda, \u00e7a\u011fr\u0131 tamamlanana kadar o i\u015f par\u00e7ac\u0131\u011f\u0131 bloke olur. Bu, \u00e7a\u011fr\u0131 100 ms s\u00fcrse bile, o i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n ba\u015fka bir i\u015f yapmadan bo\u015fta beklemesi anlam\u0131na gelir. E\u011fer bu gecikme b\u00f6lgeler aras\u0131 mesafeden kaynakl\u0131 olarak artarsa, i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n bekleme s\u00fcresi de uzar. Bir\u00e7ok i\u015f par\u00e7ac\u0131\u011f\u0131 ayn\u0131 anda bloke oldu\u011funda, thread pool h\u0131zla t\u00fckenir.<\/p>\n<p>Asenkron programlama ise bu sorunu \u00e7\u00f6zer. Bir asenkron \u00e7a\u011fr\u0131 yapt\u0131\u011f\u0131n\u0131zda, iste\u011fi g\u00f6nderir ve i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131 serbest b\u0131rak\u0131rs\u0131n\u0131z. \u00c7a\u011fr\u0131 tamamland\u0131\u011f\u0131nda, sonu\u00e7 bir geri \u00e7a\u011f\u0131rma (callback) veya reaktif bir ak\u0131\u015f (reactive stream) arac\u0131l\u0131\u011f\u0131yla i\u015flenir. Bu sayede, ayn\u0131 say\u0131da i\u015f par\u00e7ac\u0131\u011f\u0131yla \u00e7ok daha fazla e\u015f zamanl\u0131 iste\u011fi i\u015fleyebilir ve thread pool t\u00fckenmesini engelleyebilirsiniz.<\/p>\n<h3>2. Azure Cosmos DB Java SDK's\u0131n\u0131n Reaktif API'leri:<\/h3>\n<p>Cosmos DB Java SDK v4 ve sonras\u0131, Project Reactor tabanl\u0131 reaktif API'leri kullan\u0131r. Bu API'ler, veri taban\u0131 i\u015flemlerini <code>Mono<\/code> (tek bir \u00f6\u011fe) veya <code>Flux<\/code> (bir\u00e7ok \u00f6\u011fe) yay\u0131nc\u0131lar\u0131 olarak d\u00f6nd\u00fcr\u00fcr. Bu yay\u0131nc\u0131lar\u0131 kullanarak, veri taban\u0131 isteklerini asenkron ve non-blocking bir \u015fekilde i\u015fleyebilirsiniz.<\/p>\n<div class=\"code-block\">\n<h3>Asenkron Cosmos DB Okuma \u00d6rne\u011fi (Project Reactor Kullan\u0131m\u0131)<\/h3>\n<pre><code class=\"language-java\">\nimport com.azure.cosmos.CosmosAsyncClient;\nimport com.azure.cosmos.CosmosAsyncContainer;\nimport com.azure.cosmos.models.CosmosQueryRequestOptions;\nimport com.azure.cosmos.util.CosmosPagedFlux;\nimport reactor.core.publisher.Flux;\n\npublic class AsyncCosmosExample {\n\n    \/\/ YourItemClass: Cosmos DB'den d\u00f6necek verinin modeli\n    static class YourItemClass {\n        private String id;\n        private String category;\n        \/\/ Di\u011fer alanlar ve getter\/setter'lar\n        public String getId() { return id; }\n        public void setId(String id) { this.id = id; }\n        public String getCategory() { return category; }\n        public void setCategory(String category) { this.category = category; }\n\n        @Override\n        public String toString() {\n            return \"Item ID: \" + id + \", Category: \" + category;\n        }\n    }\n\n    public void readItemsAsync(CosmosAsyncClient client, String databaseName, String containerName) {\n        CosmosAsyncContainer container = client.getDatabase(databaseName).getContainer(containerName);\n        CosmosQueryRequestOptions queryOptions = new CosmosQueryRequestOptions();\n\n        \/\/ Asenkron bir sorgu \u00e7al\u0131\u015ft\u0131rma. Flux<YourItemClass> d\u00f6ner.\n        \/\/ Bu \u00e7a\u011fr\u0131 hemen d\u00f6ner, bloklama yapmaz.\n        CosmosPagedFlux<YourItemClass> results = container.queryItems(\n                \"SELECT * FROM c WHERE c.category = 'Electronics'\", queryOptions, YourItemClass.class);\n\n        System.out.println(\"Sorgu ba\u015flat\u0131ld\u0131, ana i\u015f par\u00e7ac\u0131\u011f\u0131 engellenmedi.\");\n\n        \/\/ Abonelik ile sonu\u00e7lar\u0131 asenkron olarak i\u015fleyin.\n        \/\/ Her bir \u00f6\u011fe geldi\u011finde veya hata oldu\u011funda bu bloklar \u00e7al\u0131\u015f\u0131r.\n        results.byPage() \/\/ Sayfa sayfa i\u015flemek i\u00e7in (opsiyonel)\n               .flatMap(cosmosPagedResponse -> {\n                   cosmosPagedResponse.getResults().forEach(item -> System.out.println(\"Item received: \" + item));\n                   return Flux.just(item); \/\/ Ak\u0131\u015f\u0131 devam ettirir\n               })\n               .subscribe(\n                   item -> System.out.println(\"Processing item: \" + item.getId()), \/\/ Her \u00f6\u011fe i\u00e7in\n                   error -> System.err.println(\"Error: \" + error.getMessage()),   \/\/ Hata durumunda\n                   () -> System.out.println(\"Query completed.\")                 \/\/ Ak\u0131\u015f tamamland\u0131\u011f\u0131nda\n               );\n\n        \/\/ Ana i\u015f par\u00e7ac\u0131\u011f\u0131 burada ba\u015fka i\u015fler yapmaya devam edebilir.\n        System.out.println(\"Ana i\u015f par\u00e7ac\u0131\u011f\u0131 ba\u015fka i\u015fler yapmaya devam ediyor...\");\n    }\n}\n  <\/pre>\n<p><\/code><\/p>\n<p>Bu \u00f6rnek, Cosmos DB Java SDK's\u0131n\u0131n reaktif API'sini kullanarak asenkron bir sorgunun nas\u0131l \u00e7al\u0131\u015ft\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. <code>Flux<\/code>, sonu\u00e7lar\u0131 yava\u015f\u00e7a i\u015flerken ana i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n bloke olmamas\u0131n\u0131 sa\u011flar. <code>subscribe<\/code> metodundaki callback'ler, verinin i\u015flenmesini veya hatalar\u0131n y\u00f6netilmesini sa\u011flar.<\/p>\n<\/div>\n<h3>3. <code>CompletableFuture<\/code> Kullan\u0131m\u0131:<\/h3>\n<p>E\u011fer reaktif programlama k\u00fct\u00fcphanelerine ge\u00e7i\u015f yapmak istemiyorsan\u0131z, Java 8 ile gelen <code>CompletableFuture<\/code> s\u0131n\u0131f\u0131 da asenkron ve non-blocking i\u015flemler i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Kendi bloklama yapan kodunuzu <code>CompletableFuture.supplyAsync()<\/code> ile bir <code>ForkJoinPool<\/code> veya \u00f6zel bir <code>ExecutorService<\/code> \u00fczerinde \u00e7al\u0131\u015ft\u0131rarak ana i\u015f par\u00e7ac\u0131klar\u0131n\u0131z\u0131 serbest b\u0131rakabilirsiniz.<\/p>\n<h3>4. \u0130\u015f Par\u00e7ac\u0131\u011f\u0131 Havuzu Boyutland\u0131rmas\u0131:<\/h3>\n<p>Asenkron yakla\u015f\u0131mlar\u0131 kullanmak, i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131n\u0131 optimize etmenize yard\u0131mc\u0131 olsa da, yine de do\u011fru bir havuz boyutu belirlemek \u00f6nemlidir. \u00c7ok k\u00fc\u00e7\u00fck bir havuz, isteklerin kuyrukta beklemesine neden olur; \u00e7ok b\u00fcy\u00fck bir havuz ise a\u015f\u0131r\u0131 kaynak t\u00fcketimine (bellek, CPU ba\u011flam de\u011fi\u015fimi) yol a\u00e7abilir. Genellikle CPU yo\u011fun i\u015f y\u00fckleri i\u00e7in <code>Runtime.getRuntime().availableProcessors()<\/code> ile orant\u0131l\u0131 bir havuz boyutu (\u00f6rne\u011fin, 1x veya 2x) \u00f6nerilirken, G\/\u00c7 yo\u011fun i\u015f y\u00fckleri i\u00e7in daha b\u00fcy\u00fck havuz boyutlar\u0131 gerekebilir. Ancak asenkron I\/O kullan\u0131yorsan\u0131z, daha k\u00fc\u00e7\u00fck bir havuz bile y\u00fcksek performans sa\u011flayabilir.<\/p>\n<h3>5. Ba\u011flant\u0131 Havuzu (Connection Pooling) Optimizasyonu:<\/h3>\n<p>Cosmos DB SDK's\u0131 kendi dahili ba\u011flant\u0131 y\u00f6netimini yapsa da, uygulaman\u0131z\u0131n di\u011fer harici servislere veya veri tabanlar\u0131na olan ba\u011flant\u0131lar\u0131n\u0131 y\u00f6neten HTTP istemcileri (\u00f6rne\u011fin, Apache HttpClient, OkHttp) veya di\u011fer JDBC s\u00fcr\u00fcc\u00fcleri i\u00e7in ba\u011flant\u0131 havuzlar\u0131n\u0131 do\u011fru yap\u0131land\u0131rmak \u00f6nemlidir. Her istek i\u00e7in yeni bir ba\u011flant\u0131 a\u00e7mak yerine, ba\u011flant\u0131 havuzundan mevcut bir ba\u011flant\u0131y\u0131 yeniden kullanmak, gecikmeleri ve kaynak t\u00fcketimini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n<p>Asenkron programlama ve do\u011fru i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu y\u00f6netimi, Java uygulaman\u0131z\u0131n y\u00fcksek e\u015fzamanl\u0131l\u0131k ve d\u00fc\u015f\u00fck gecikme ile \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flaman\u0131n anahtar\u0131d\u0131r. \u00d6zellikle b\u00f6lgeler aras\u0131 gecikmelerin ka\u00e7\u0131n\u0131lmaz oldu\u011fu durumlarda, i\u015f par\u00e7ac\u0131klar\u0131n\u0131 bloke etmeyen yakla\u015f\u0131mlar, uygulaman\u0131z\u0131n yan\u0131t verme yetene\u011fini kritik derecede art\u0131r\u0131r.<\/p>\n<h2>Geli\u015fmi\u015f Te\u015fhis ve Optimizasyon Teknikleri: Uygulama \u0130\u00e7i Metrikler ve \u0130zleme<\/h2>\n<p>Performans\u0131 s\u00fcrekli olarak izlemek ve optimize etmek i\u00e7in geli\u015fmi\u015f te\u015fhis teknikleri ve uygulama i\u00e7i metrikler hayati \u00f6nem ta\u015f\u0131r. Statik konfig\u00fcrasyonlarla yetinmek yerine, uygulaman\u0131z\u0131n ger\u00e7ek zamanl\u0131 davran\u0131\u015f\u0131n\u0131 anlamak, darbo\u011fazlar\u0131 proaktif olarak tespit etmenize ve \u00e7\u00f6zmenize olanak tan\u0131r. Bu b\u00f6l\u00fcmde, daha derinlemesine izleme ve te\u015fhis yakla\u015f\u0131mlar\u0131n\u0131 inceleyece\u011fiz.<\/p>\n<h3>1. Uygulama Performans \u0130zleme (APM) Ara\u00e7lar\u0131:<\/h3>\n<p>Azure Application Insights, New Relic, Dynatrace veya Prometheus\/Grafana gibi APM ara\u00e7lar\u0131, uygulaman\u0131z\u0131n davran\u0131\u015f\u0131n\u0131 derinlemesine analiz etmenizi sa\u011flayan kapsaml\u0131 metrikler ve g\u00f6rselle\u015ftirmeler sunar. Bu ara\u00e7lar, a\u015fa\u011f\u0131daki gibi kritik verileri toplar ve analiz eder:<\/p>\n<ul>\n<li><strong>\u0130stek Gecikmeleri ve Hata Oranlar\u0131:<\/strong> Hangi HTTP u\u00e7 noktalar\u0131n\u0131n yava\u015f \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 veya hata verdi\u011fini g\u00f6sterir.<\/li>\n<li><strong>Ba\u011f\u0131ml\u0131l\u0131k \u00c7a\u011fr\u0131lar\u0131:<\/strong> Cosmos DB, di\u011fer mikro hizmetler veya harici API'lere yap\u0131lan \u00e7a\u011fr\u0131lar\u0131n gecikmelerini ve hata oranlar\u0131n\u0131 izler. Bu, \u00f6zellikle Cosmos DB \u00e7a\u011fr\u0131lar\u0131n\u0131n toplam gecikmeye ne kadar katk\u0131da bulundu\u011funu anlamak i\u00e7in kritik \u00f6neme sahiptir.<\/li>\n<li><strong>Sistem Metrikleri:<\/strong> CPU kullan\u0131m\u0131, bellek t\u00fcketimi, i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131 (aktif, beklemede, bloke), \u00e7\u00f6p toplama (garbage collection) s\u00fcreleri gibi JVM metriklerini takip eder. Thread pool t\u00fckenmesi durumunda aktif veya bloke i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131lar\u0131ndaki art\u0131\u015flar\u0131 h\u0131zl\u0131ca g\u00f6rebilirsiniz.<\/li>\n<li><strong>U\u00e7tan Uca \u0130zleme (End-to-End Tracing):<\/strong> Tek bir iste\u011fin uygulaman\u0131z\u0131n farkl\u0131 bile\u015fenleri ve ba\u011f\u0131ml\u0131l\u0131klar\u0131 aras\u0131nda nas\u0131l ilerledi\u011fini g\u00f6sterir. Bu, b\u00f6lgeler aras\u0131 \u00e7a\u011fr\u0131lar\u0131n neden oldu\u011fu bir gecikmeyi veya belirli bir veri taban\u0131 sorgusunun t\u00fcm i\u015flemi ne kadar yava\u015flatt\u0131\u011f\u0131n\u0131 net bir \u015fekilde ortaya koyar.<\/li>\n<\/ul>\n<div class=\"expert-tip\">\n  Uzman \u0130pucu: Uygulaman\u0131z\u0131n ve Cosmos DB'nizin metriklerini (\u00f6rne\u011fin, App Insights'ta) tek bir g\u00f6sterge panosunda birle\u015ftirerek korelasyon analizi yap\u0131n. \u00d6rne\u011fin, bir b\u00f6lgeler aras\u0131 veri merkezi ba\u011flant\u0131s\u0131nda ani bir gecikme art\u0131\u015f\u0131n\u0131n, ayn\u0131 anda uygulaman\u0131z\u0131n yan\u0131t s\u00fcrelerinde ve Cosmos DB istek gecikmelerinde paralel bir art\u0131\u015fa neden olup olmad\u0131\u011f\u0131n\u0131 g\u00f6rmek, sorunun kayna\u011f\u0131n\u0131 h\u0131zla daraltman\u0131za yard\u0131mc\u0131 olabilir.\n<\/div>\n<h3>2. Cosmos DB SDK Te\u015fhis Dizesi (Diagnostic String):<\/h3>\n<p>Azure Cosmos DB Java SDK's\u0131, her bir yan\u0131tla birlikte ayr\u0131nt\u0131l\u0131 bir te\u015fhis dizesi sa\u011flar. Bu dize, iste\u011fin istemci taraf\u0131nda ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc, a\u011f gecikmesini, i\u015flem g\u00f6ren RU miktar\u0131n\u0131, kullan\u0131lan a\u011f ge\u00e7idi bilgilerini ve daha fazlas\u0131n\u0131 i\u00e7eren zengin bilgiler bar\u0131nd\u0131r\u0131r. Bu te\u015fhis dizesini loglar\u0131n\u0131za dahil etmek, her bir Cosmos DB \u00e7a\u011fr\u0131s\u0131n\u0131n performans\u0131n\u0131 derinlemesine analiz etmenizi sa\u011flar.<\/p>\n<pre><code class=\"language-java\">\nimport com.azure.cosmos.CosmosClient;\nimport com.azure.cosmos.CosmosContainer;\nimport com.azure.cosmos.models.CosmosItemRequestOptions;\nimport com.azure.cosmos.models.CosmosItemResponse;\n\npublic class CosmosDiagnosticExample {\n\n    public void readItemWithDiagnostics(CosmosClient client, String databaseName, String containerName, String itemId, String partitionKey) {\n        CosmosContainer container = client.getDatabase(databaseName).getContainer(containerName);\n\n        try {\n            CosmosItemResponse<YourItemClass> response = container.readItem(itemId, new com.azure.cosmos.models.PartitionKey(partitionKey), YourItemClass.class);\n\n            System.out.println(\"Item okundu. RU T\u00fcketimi: \" + response.getRequestCharge());\n            System.out.println(\"Gecikme s\u00fcresi (MS): \" + response.getDuration().toMillis());\n            System.out.println(\"Te\u015fhis Dizesi: \" + response.getDiagnostics().toString());\n            \/\/ Te\u015fhis dizesini loglaman\u0131z, performans sorunlar\u0131n\u0131 izlemede kritik \u00f6neme sahiptir.\n\n        } catch (Exception e) {\n            System.err.println(\"\u00d6\u011fe okuma hatas\u0131: \" + e.getMessage());\n        }\n    }\n}\n<\/pre>\n<p><\/code><\/p>\n<h3>3. JVM \u0130\u015f Par\u00e7ac\u0131\u011f\u0131 D\u00f6k\u00fcmleri (Thread Dumps) ve Bellek D\u00f6k\u00fcmleri (Heap Dumps):<\/h3>\n<p>Uygulaman\u0131z dondu\u011funda veya performans d\u00fc\u015f\u00fc\u015f\u00fc ya\u015fad\u0131\u011f\u0131nda, <code>jstack<\/code> komutuyla al\u0131nan i\u015f par\u00e7ac\u0131\u011f\u0131 d\u00f6k\u00fcmleri, o an hangi i\u015f par\u00e7ac\u0131klar\u0131n\u0131n ne yapt\u0131\u011f\u0131n\u0131 g\u00f6sterir. Bloke olmu\u015f veya uzun s\u00fcre beklemede olan i\u015f par\u00e7ac\u0131klar\u0131, thread pool t\u00fckenmesinin ve performans darbo\u011fazlar\u0131n\u0131n do\u011frudan kan\u0131t\u0131d\u0131r. <code>jmap<\/code> veya <code>jcmd<\/code> ile al\u0131nan bellek d\u00f6k\u00fcmleri ise, bellek s\u0131z\u0131nt\u0131lar\u0131n\u0131 veya a\u015f\u0131r\u0131 bellek t\u00fcketimini tespit etmenize yard\u0131mc\u0131 olabilir, ki bu da dolayl\u0131 olarak performans\u0131 etkileyebilir.<\/p>\n<h3>4. \u00d6zel Metrikler ve Log Korelasyonu:<\/h3>\n<p>Uygulaman\u0131z\u0131n kritik i\u015f ak\u0131\u015flar\u0131 i\u00e7in \u00f6zel metrikler olu\u015fturun (\u00f6rne\u011fin, bir kullan\u0131c\u0131n\u0131n sepetine \u00fcr\u00fcn ekleme s\u00fcresi). Bu metrikleri APM ara\u00e7lar\u0131n\u0131za g\u00f6ndererek i\u015f a\u00e7\u0131s\u0131ndan kritik performans g\u00f6stergelerini izleyin. Ayr\u0131ca, istek kimliklerini (request IDs) kullanarak t\u00fcm log mesajlar\u0131n\u0131 ve metrikleri ili\u015fkilendirin. Bu, belirli bir kullan\u0131c\u0131 iste\u011finin uygulaman\u0131zdaki ve Cosmos DB'deki t\u00fcm yolculu\u011funu takip etmenizi sa\u011flayarak, performans d\u00fc\u015f\u00fc\u015f\u00fcn\u00fcn tam olarak nerede ba\u015flad\u0131\u011f\u0131n\u0131 belirlemenize yard\u0131mc\u0131 olur.<\/p>\n<h3>5. Verimli Sorgular ve \u0130ndeksleme Kontrol\u00fc:<\/h3>\n<p>Cosmos DB taraf\u0131nda, maliyetli (y\u00fcksek RU t\u00fcketen) sorgular\u0131 d\u00fczenli olarak g\u00f6zden ge\u00e7irin. Azure Portal'daki \"Metrics\" (Metrikler) b\u00f6l\u00fcm\u00fcnden ve Application Insights'taki ba\u011f\u0131ml\u0131l\u0131k \u00e7a\u011fr\u0131lar\u0131ndan en pahal\u0131 sorgular\u0131 tespit edebilirsiniz. Bu sorgular\u0131 optimize etmek (\u00f6rne\u011fin, daha az \u00f6zellik se\u00e7mek, <code>ORDER BY<\/code> veya <code>GROUP BY<\/code> kullanmaktan ka\u00e7\u0131nmak, do\u011fru dizinleri kullanmak), hem RU maliyetini hem de gecikmeyi d\u00fc\u015f\u00fcrecektir. \u0130ndeksleme politikan\u0131z\u0131n, sorgu desenlerinizle uyumlu oldu\u011fundan emin olun.<\/p>\n<p>Bu ileri d\u00fczey te\u015fhis ve optimizasyon tekniklerini kullanarak, Java uygulaman\u0131z\u0131n Azure Cosmos DB ile etkile\u015fimini s\u00fcrekli olarak iyile\u015ftirebilir, proaktif bir \u015fekilde sorunlar\u0131 tespit edebilir ve kullan\u0131c\u0131lar\u0131n\u0131za kesintisiz bir deneyim sunabilirsiniz.<\/p>\n<h2>Bir Vaka Analizi: E-ticaret Uygulamas\u0131nda Gecikme ve Thread Pool Krizi<\/h2>\n<p>\u015eimdi, \u00f6\u011frendi\u011fimiz teorik bilgileri ger\u00e7ek d\u00fcnya bir senaryo \u00fczerinden peki\u015ftirelim. Orta \u00f6l\u00e7ekli bir e-ticaret firmas\u0131, \u00fcr\u00fcn kataloglar\u0131n\u0131 Azure Cosmos DB'de tutan ve Java tabanl\u0131 Spring Boot mikro hizmetleri kullanan bir platforma sahipti. Uygulama sunucular\u0131 Bat\u0131 Avrupa'da, Cosmos DB ise ba\u015flang\u0131\u00e7ta tek bir Do\u011fu ABD b\u00f6lgesinde konumland\u0131r\u0131lm\u0131\u015ft\u0131. \u0130lk ba\u015fta her \u015fey yolundayken, b\u00fcy\u00fck bir indirim kampanyas\u0131 (Black Friday) s\u0131ras\u0131nda uygulama performans\u0131 aniden d\u00fc\u015fmeye ba\u015flad\u0131: m\u00fc\u015fteriler \u00fcr\u00fcn sayfalar\u0131na eri\u015fmekte zorlan\u0131yor, sepet i\u015flemleri yava\u015fl\u0131yor ve baz\u0131 kullan\u0131c\u0131lar zaman a\u015f\u0131m\u0131 hatalar\u0131yla kar\u015f\u0131la\u015f\u0131yordu. Geli\u015ftirme ekibi alarma ge\u00e7ti.<\/p>\n<h3>Sorunun Belirtileri:<\/h3>\n<ul>\n<li><strong>Kullan\u0131c\u0131 \u015eikayetleri:<\/strong> \"Sayfalar y\u00fcklenmiyor\", \"Sepetime \u00fcr\u00fcn ekleyemiyorum\", \"\u00d6deme yapam\u0131yorum\".<\/li>\n<li><strong>Uygulama Yan\u0131t S\u00fcreleri:<\/strong> Azure Application Insights metrikleri, HTTP istek yan\u0131t s\u00fcrelerinin 1 saniyeden 5-10 saniyeye f\u0131rlad\u0131\u011f\u0131n\u0131 g\u00f6steriyordu.<\/li>\n<li><strong>Hata Oranlar\u0131:<\/strong> \u00d6zellikle Cosmos DB ile etkile\u015fim kuran servislerde <code>Connection Timeout<\/code> ve <code>503 Service Unavailable<\/code> hatalar\u0131nda art\u0131\u015f.<\/li>\n<li><strong>Thread Metrikleri:<\/strong> JVM monit\u00f6r ara\u00e7lar\u0131 ve Application Insights, i\u015f par\u00e7ac\u0131\u011f\u0131 havuzlar\u0131ndaki (hem uygulama sunucusu hem de Cosmos DB SDK's\u0131 i\u00e7indeki) aktif i\u015f par\u00e7ac\u0131\u011f\u0131 say\u0131s\u0131n\u0131n maksimuma ula\u015ft\u0131\u011f\u0131n\u0131 ve kuyruklarda y\u00fczlerce iste\u011fin bekledi\u011fini g\u00f6steriyordu. CPU kullan\u0131m\u0131 nispeten normaldi, bu da CPU darbo\u011faz\u0131 olmad\u0131\u011f\u0131n\u0131 i\u015faret ediyordu.<\/li>\n<\/ul>\n<h3>Te\u015fhis S\u00fcreci:<\/h3>\n<ol>\n<li><strong>B\u00f6lgesel Kontrol:<\/strong> Ekip ilk olarak uygulaman\u0131n Bat\u0131 Avrupa'da, Cosmos DB'nin ise Do\u011fu ABD'de oldu\u011funu fark etti. Bu, do\u011frudan bir b\u00f6lgeler aras\u0131 gecikme kayna\u011f\u0131yd\u0131. Her bir Cosmos DB \u00e7a\u011fr\u0131s\u0131 i\u00e7in ~80-150ms RTT eklentisi vard\u0131.<\/li>\n<li><strong>Log Analizi:<\/strong> Loglarda, <code>CosmosDbClient<\/code> \u00e7a\u011fr\u0131lar\u0131n\u0131n normalden \u00e7ok daha uzun s\u00fcrd\u00fc\u011f\u00fc ve s\u0131k s\u0131k zaman a\u015f\u0131m\u0131 hatalar\u0131 f\u0131rlatt\u0131\u011f\u0131 g\u00f6r\u00fcld\u00fc. \u00d6zellikle \u00fcr\u00fcn detaylar\u0131 sayfalar\u0131nda, birden fazla \u00f6\u011fe i\u00e7in yap\u0131lan paralel Cosmos DB okumalar\u0131n\u0131n toplam s\u00fcreyi katlad\u0131\u011f\u0131 tespit edildi.<\/li>\n<li><strong>Thread Dump Analizi:<\/strong> <code>jstack<\/code> ile al\u0131nan i\u015f par\u00e7ac\u0131\u011f\u0131 d\u00f6k\u00fcmleri, y\u00fczlerce i\u015f par\u00e7ac\u0131\u011f\u0131n\u0131n <code>CosmosDbClient<\/code> taraf\u0131ndan ba\u015flat\u0131lan G\/\u00c7 i\u015flemlerini beklerken \"WAITING\" veya \"BLOCKED\" durumda oldu\u011funu g\u00f6sterdi. Bu, senkron (blocking) Cosmos DB \u00e7a\u011fr\u0131lar\u0131n\u0131n thread pool t\u00fckenmesine yol a\u00e7t\u0131\u011f\u0131n\u0131 do\u011frulad\u0131.<\/li>\n<li><strong>Cosmos DB Metrikleri:<\/strong> Cosmos DB metrikleri, sunucu taraf\u0131 gecikmelerin asl\u0131nda d\u00fc\u015f\u00fck oldu\u011funu ancak client taraf\u0131 gecikmelerin \u00e7ok y\u00fcksek oldu\u011funu g\u00f6sterdi. Bu durum, sorunun veri taban\u0131n\u0131n kendisinden de\u011fil, istemci ile veri taban\u0131 aras\u0131ndaki a\u011f gecikmesinden kaynakland\u0131\u011f\u0131n\u0131 teyit etti. RU\/s t\u00fcketimi de anl\u0131k olarak tavan yap\u0131yor, bu da baz\u0131 k\u0131s\u0131tlamalara yol a\u00e7\u0131yordu.<\/li>\n<\/ol>\n<h3>Uygulanan \u00c7\u00f6z\u00fcmler:<\/h3>\n<ol>\n<li><strong>Cosmos DB \u00c7ok B\u00f6lgeli Yazma ve B\u00f6lgesel Yak\u0131nla\u015ft\u0131rma:<\/strong>\n<ul>\n<li>Cosmos DB hesab\u0131, Bat\u0131 Avrupa b\u00f6lgesine bir okuma\/yazma b\u00f6lgesi olarak eklendi.<\/li>\n<li>Uygulama, Cosmos DB Java SDK's\u0131n\u0131n otomatik b\u00f6lge y\u00f6nlendirme (automatic region failover\/preferring closest region) \u00f6zelli\u011finden faydalanacak \u015fekilde yeniden yap\u0131land\u0131r\u0131ld\u0131. Bu sayede, uygulama art\u0131k Bat\u0131 Avrupa'daki yak\u0131n Cosmos DB kopyas\u0131na ba\u011flanarak b\u00f6lgeler aras\u0131 RTT'yi minimize etti.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Asenkron SDK Kullan\u0131m\u0131:<\/strong>\n<ul>\n<li>T\u00fcm Cosmos DB \u00e7a\u011fr\u0131lar\u0131, senkron API'lerden Project Reactor tabanl\u0131 asenkron API'lere (<code>CosmosAsyncClient<\/code>) ta\u015f\u0131nd\u0131. Bu, veri taban\u0131 \u00e7a\u011fr\u0131lar\u0131 beklerken i\u015f par\u00e7ac\u0131klar\u0131n\u0131n bloke olmamas\u0131n\u0131 sa\u011flad\u0131.<\/li>\n<li>\u00d6zellikle \u00fcr\u00fcn detaylar\u0131 sayfas\u0131ndaki paralel okumalar <code>Flux.merge()<\/code> veya <code>Flux.zip()<\/code> gibi reaktif operat\u00f6rlerle birle\u015ftirilerek tek bir ak\u0131\u015fta verimli bir \u015fekilde i\u015flendi.<\/li>\n<\/ul>\n<\/li>\n<li><strong>RU\/s Optimizasyonu:<\/strong>\n<ul>\n<li>Yo\u011fun d\u00f6nemlerdeki k\u0131s\u0131tlamalar\u0131 \u00f6nlemek i\u00e7in Cosmos DB koleksiyonlar\u0131 i\u00e7in Autoscale (Otomatik \u00d6l\u00e7eklendirme) etkinle\u015ftirildi.<\/li>\n<li>En maliyetli sorgular (Application Insights'tan tespit edildi) optimize edildi. \u00d6rne\u011fin, gereksiz <code>SELECT *<\/code> yerine sadece ihtiya\u00e7 duyulan alanlar \u00e7ekildi ve <code>WHERE<\/code> c\u00fcmlelerinde daha verimli indeksler kullan\u0131ld\u0131.<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u0130\u015f Par\u00e7ac\u0131\u011f\u0131 Havuzu Boyutland\u0131rmas\u0131:<\/strong>\n<ul>\n<li>Asenkron ge\u00e7i\u015fle birlikte, uygulama sunucusunun i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu boyutlar\u0131 daha makul seviyelere \u00e7ekildi, \u00e7\u00fcnk\u00fc art\u0131k i\u015f par\u00e7ac\u0131klar\u0131 G\/\u00c7 i\u00e7in bloke olmuyordu.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h3>Sonu\u00e7:<\/h3>\n<p>Yap\u0131lan bu de\u011fi\u015fiklikler sayesinde, indirim kampanyas\u0131n\u0131n ilerleyen d\u00f6nemlerinde uygulama performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde iyile\u015fti. Yan\u0131t s\u00fcreleri eski seviyelere d\u00fc\u015ft\u00fc, hata oranlar\u0131 azald\u0131 ve m\u00fc\u015fteriler kesintisiz bir al\u0131\u015fveri\u015f deneyimi ya\u015fad\u0131. Bu vaka analizi, hem b\u00f6lgeler aras\u0131 gecikmenin hem de thread pool t\u00fckenmesinin birbiriyle ili\u015fkili sorunlar oldu\u011funu ve b\u00fct\u00fcnc\u00fcl bir yakla\u015f\u0131mla \u00e7\u00f6z\u00fclmesi gerekti\u011fini a\u00e7\u0131k\u00e7a g\u00f6stermektedir.<\/p>\n<h2>Sonu\u00e7: Performans Sorunlar\u0131na Kar\u015f\u0131 Proaktif Olmak<\/h2>\n<p>Java uygulamalar\u0131n\u0131zda Azure Cosmos DB ile \u00e7al\u0131\u015f\u0131rken b\u00f6lgeler aras\u0131 gecikme ve i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu t\u00fckenmesi gibi performans sorunlar\u0131 ka\u00e7\u0131n\u0131lmaz olabilir. Ancak, bu makalede ele ald\u0131\u011f\u0131m\u0131z do\u011fru te\u015fhis ara\u00e7lar\u0131, kapsaml\u0131 izleme stratejileri ve modern programlama yakla\u015f\u0131mlar\u0131 sayesinde bu sorunlar\u0131n \u00fcstesinden gelmek ve uygulaman\u0131z\u0131n performans\u0131n\u0131 optimize etmek m\u00fcmk\u00fcnd\u00fcr. Unutmay\u0131n, performans optimizasyonu tek seferlik bir i\u015f de\u011fildir; s\u00fcrekli bir s\u00fcre\u00e7tir. Uygulaman\u0131z\u0131n mimarisini, veri modellemesini ve kodunu d\u00fczenli olarak g\u00f6zden ge\u00e7irerek ve izleyerek, kullan\u0131c\u0131lar\u0131n\u0131za kesintisiz ve h\u0131zl\u0131 bir deneyim sunabilirsiniz.<\/p>\n<p>Ba\u015far\u0131l\u0131 bir bulut tabanl\u0131 uygulama stratejisinin temelinde, gelecekteki potansiyel sorunlar\u0131 \u00f6ng\u00f6rmek ve bunlara kar\u015f\u0131 proaktif \u00e7\u00f6z\u00fcmler geli\u015ftirmek yatar. Gecikme ve kaynak t\u00fckenmesi gibi sorunlar\u0131 h\u0131zl\u0131 bir \u015fekilde tespit edip \u00e7\u00f6zmek, hem m\u00fc\u015fteri memnuniyetini art\u0131r\u0131r hem de operasyonel maliyetleri d\u00fc\u015f\u00fcr\u00fcr. Uygulaman\u0131z\u0131n her zaman en iyi performansta \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan emin olmak i\u00e7in izleme ara\u00e7lar\u0131n\u0131z\u0131 aktif tutun, loglar\u0131n\u0131z\u0131 d\u00fczenli olarak inceleyin ve performans testlerini (y\u00fck ve stres testleri) rutin geli\u015ftirme d\u00f6ng\u00fcn\u00fcz\u00fcn bir par\u00e7as\u0131 haline getirin. Bu sayede, beklenmedik y\u00fck art\u0131\u015flar\u0131 kar\u015f\u0131s\u0131nda bile uygulaman\u0131z\u0131n sa\u011flam kalmas\u0131n\u0131 sa\u011flayabilirsiniz.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ul>\n<li><strong>S: B\u00f6lgeler aras\u0131 gecikmeyi tamamen ortadan kald\u0131rmak m\u00fcmk\u00fcn m\u00fcd\u00fcr?<\/strong>\n<p>C: Fiziksel mesafeden kaynaklanan a\u011f gecikmesini tamamen ortadan kald\u0131rmak m\u00fcmk\u00fcn de\u011fildir. Ancak, uygulaman\u0131z ve veri taban\u0131n\u0131z\u0131n ayn\u0131 veya birbirine yak\u0131n co\u011frafi b\u00f6lgelerde konumland\u0131r\u0131lmas\u0131, Azure Cosmos DB'nin \u00e7ok b\u00f6lgeli yazma \u00f6zellikleri, istemci taraf\u0131 \u00f6nbellekleme ve a\u011f trafi\u011fini optimize eden di\u011fer y\u00f6ntemlerle bu gecikme en aza indirilebilir. Ama\u00e7, gecikmeyi kabul edilebilir seviyelere \u00e7ekmektir.<\/p>\n<\/li>\n<li><strong>S: Thread pool t\u00fckenmesi her zaman k\u00f6t\u00fc bir \u015fey midir?<\/strong>\n<p>C: Thread pool t\u00fckenmesi, genellikle uygulaman\u0131z\u0131n gelen y\u00fck\u00fc etkili bir \u015fekilde i\u015fleyemedi\u011finin bir i\u015faretidir ve bu da performans\u0131 ve kullan\u0131labilirli\u011fi ciddi \u015fekilde d\u00fc\u015f\u00fcrebilir. Bu durum genellikle bir performans darbo\u011faz\u0131na i\u015faret eder. Ancak, baz\u0131 durumlarda, a\u015f\u0131r\u0131 kaynak t\u00fcketimini \u00f6nlemek i\u00e7in bilin\u00e7li olarak k\u00fc\u00e7\u00fck bir havuz boyutu belirlemek m\u00fcmk\u00fcnd\u00fcr, ancak bu dikkatli bir planlama ve kapsaml\u0131 test gerektirir. \u00c7o\u011fu senaryoda, thread pool t\u00fckenmesi istenmeyen bir durumdur.<\/p>\n<\/li>\n<li><strong>S: Cosmos DB Java SDK's\u0131n\u0131n hangi modunu kullanmal\u0131y\u0131m: Gateway mi, Direct mi?<\/strong>\n<p>C: Genellikle Direct mod, daha az a\u011f atlamas\u0131 i\u00e7erdi\u011fi ve daha d\u00fc\u015f\u00fck gecikme ve daha iyi performans sundu\u011fu i\u00e7in tercih edilir. \u00d6zellikle d\u00fc\u015f\u00fck gecikmeli ve y\u00fcksek i\u015flem hacimli i\u015f y\u00fckleri i\u00e7in \u00f6nerilir. Gateway modu ise daha basit a\u011f yap\u0131land\u0131rmalar\u0131 ve g\u00fcvenlik duvar\u0131 kurallar\u0131 gerektirebilir. \u00c7o\u011fu modern uygulama i\u00e7in Direct modu \u00f6nerilir, ancak ortam\u0131n\u0131z\u0131n gereksinimlerine ve a\u011f yap\u0131land\u0131rmas\u0131na g\u00f6re test etmeniz ve uygun modu se\u00e7meniz \u00f6nemlidir.<\/p>\n<\/li>\n<li><strong>S: Uygulamam\u0131n performans\u0131n\u0131 izlemek i\u00e7in hangi ara\u00e7lar\u0131 kullanmal\u0131y\u0131m?<\/strong>\n<p>C: Azure Monitor ve Azure Application Insights, Azure ekosisteminde entegre ve g\u00fc\u00e7l\u00fc izleme ara\u00e7lar\u0131d\u0131r. Ayr\u0131ca, Java uygulamalar\u0131 i\u00e7in JMX, Prometheus\/Grafana, New Relic, Dynatrace gibi \u00fc\u00e7\u00fcnc\u00fc taraf Uygulama Performans \u0130zleme (APM) ara\u00e7lar\u0131 da derinlemesine metrikler, izleme ve te\u015fhis yetenekleri sunar. Se\u00e7iminiz, b\u00fct\u00e7enize, entegrasyon gereksinimlerinize ve ekibinizin deneyimine ba\u011fl\u0131 olacakt\u0131r.<\/p>\n<\/li>\n<li><strong>S: Asenkron programlamaya ge\u00e7i\u015f yapmak her zaman en iyi \u00e7\u00f6z\u00fcm m\u00fcd\u00fcr?<\/strong>\n<p>C: Asenkron programlama, \u00f6zellikle G\/\u00c7 yo\u011fun ve y\u00fcksek e\u015fzamanl\u0131l\u0131k gerektiren uygulamalar i\u00e7in harika bir \u00e7\u00f6z\u00fcmd\u00fcr, \u00e7\u00fcnk\u00fc i\u015f par\u00e7ac\u0131\u011f\u0131 t\u00fckenmesini b\u00fcy\u00fck \u00f6l\u00e7\u00fcde azalt\u0131r. Ancak, kod karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 art\u0131rabilir ve do\u011fru uygulanmazsa hata ay\u0131klamay\u0131 zorla\u015ft\u0131rabilir. Projenizin b\u00fcy\u00fckl\u00fc\u011f\u00fc, ekibinizin deneyimi ve mevcut mimarinin yap\u0131s\u0131 gibi fakt\u00f6rleri g\u00f6z \u00f6n\u00fcnde bulundurarak dikkatli bir de\u011ferlendirme yapmak \u00f6nemlidir. K\u00fc\u00e7\u00fck ve basit uygulamalarda senkron yakla\u015f\u0131m hala yeterli olabilirken, da\u011f\u0131t\u0131k ve y\u00fcksek performansl\u0131 sistemlerde asenkron yakla\u015f\u0131mlar genellikle ka\u00e7\u0131n\u0131lmazd\u0131r.<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Java ve Azure Cosmos DB&#8217;de Gecikme ve Thread Pool Sorunlar\u0131 Tan\u0131lama Modern Java uygulamalar\u0131n\u0131z ve Azure Cosmos DB&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":[1408],"tags":[],"class_list":{"0":"post-35781","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-java","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>B\u00f6lgeler Aras\u0131 Gecikme (Cross-Region Latency) Nedir ve Java Uygulaman\u0131z \u0130\u00e7in Neden Kritik Bir Sorundur?<\/title>\n<meta name=\"description\" content=\"Modern Java uygulamalar\u0131n\u0131z ve Azure Cosmos DB aras\u0131ndaki b\u00f6lgeler aras\u0131 gecikme ile i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu (thread pool) t\u00fckenmesi sorunlar\u0131n\u0131 ad\u0131m ad\u0131m nas\u0131l te\u015fhis edece\u011finizi ve \u00e7\u00f6zece\u011finizi \u00f6\u011frenin. Bu kapsaml\u0131 rehber, performans ipu\u00e7lar\u0131, vaka analizleri ve pratik \u00e7\u00f6z\u00fcmlerle uygulaman\u0131z\u0131n performans\u0131n\u0131 maksimize etmenize yard\u0131mc\u0131 olacak.\" \/>\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\/bolgeler-arasi-gecikme-cross-region-latency-nedir-ve-java-uygulamaniz-icin-neden-kritik-bir-sorundur\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"B\u00f6lgeler Aras\u0131 Gecikme (Cross-Region Latency) Nedir ve Java Uygulaman\u0131z \u0130\u00e7in Neden Kritik Bir Sorundur?\" \/>\n<meta property=\"og:description\" content=\"Modern Java uygulamalar\u0131n\u0131z ve Azure Cosmos DB aras\u0131ndaki b\u00f6lgeler aras\u0131 gecikme ile i\u015f par\u00e7ac\u0131\u011f\u0131 havuzu (thread pool) t\u00fckenmesi sorunlar\u0131n\u0131 ad\u0131m ad\u0131m nas\u0131l te\u015fhis edece\u011finizi ve \u00e7\u00f6zece\u011finizi \u00f6\u011frenin. 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