{"id":31969,"date":"2025-10-16T05:40:56","date_gmt":"2025-10-16T02:40:56","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=31969"},"modified":"2025-10-16T05:40:56","modified_gmt":"2025-10-16T02:40:56","slug":"elasticsearch-fluentd-ve-kibana-acik-kaynak-log-arama-ve-gorsellestirme-cozumu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/elasticsearch-fluentd-ve-kibana-acik-kaynak-log-arama-ve-gorsellestirme-cozumu\/","title":{"rendered":"Elasticsearch, Fluentd ve Kibana: A\u00e7\u0131k Kaynak Log Arama ve G\u00f6rselle\u015ftirme \u00c7\u00f6z\u00fcm\u00fc"},"content":{"rendered":"

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Elasticsearch, Fluentd ve Kibana: A\u00e7\u0131k Kaynak Log Arama ve G\u00f6rselle\u015ftirme \u00c7\u00f6z\u00fcm\u00fc<\/h2>\n

Giri\u015f: Log Y\u00f6netiminin \u00d6nemi ve Geli\u015fen \u0130htiya\u00e7lar<\/h3>\n

G\u00fcn\u00fcm\u00fcz\u00fcn h\u0131zla dijitalle\u015fen d\u00fcnyas\u0131nda, yaz\u0131l\u0131m sistemleri giderek daha karma\u015f\u0131k hale gelmekte ve i\u015fletmelerin operasyonel s\u00fcreklili\u011fi, g\u00fcvenli\u011fi ve performans\u0131 i\u00e7in kritik bir rol oynamaktad\u0131r. Bu sistemlerin \u00fcretti\u011fi loglar (g\u00fcnl\u00fckler), bir uygulaman\u0131n veya altyap\u0131n\u0131n i\u00e7 i\u015fleyi\u015fine dair paha bi\u00e7ilmez bilgiler sunar. Hata ay\u0131klama, g\u00fcvenlik ihlallerinin tespiti, performans darbo\u011fazlar\u0131n\u0131n belirlenmesi, kullan\u0131c\u0131 davran\u0131\u015flar\u0131n\u0131n analizi ve uyumluluk denetimleri gibi pek \u00e7ok alanda loglar, karar verme s\u00fcre\u00e7leri i\u00e7in temel bir veri kayna\u011f\u0131d\u0131r.<\/p>\n

Ancak, modern sistemler, \u00f6zellikle mikroservis mimarileri ve da\u011f\u0131t\u0131k ortamlar, gigabaytlarca, hatta terabaytlarca log verisi \u00fcretebilir. Bu devasa veri y\u0131\u011f\u0131n\u0131n\u0131 manuel olarak incelemek, anlamland\u0131rmak ve bunlardan de\u011ferli i\u00e7g\u00f6r\u00fcler elde etmek neredeyse imkans\u0131zd\u0131r. Geleneksel log y\u00f6netimi yakla\u015f\u0131mlar\u0131 (\u00f6rne\u011fin, SSH ile sunuculara ba\u011flan\u0131p grep<\/code> komutlar\u0131 \u00e7al\u0131\u015ft\u0131rmak) bu \u00f6l\u00e7ekte yetersiz kal\u0131r. \u0130\u015fte bu noktada, merkezi, \u00f6l\u00e7eklenebilir ve ger\u00e7ek zamanl\u0131 log y\u00f6netim \u00e7\u00f6z\u00fcmlerine olan ihtiya\u00e7 ortaya \u00e7\u0131kar.<\/p>\n

A\u00e7\u0131k kaynak d\u00fcnyas\u0131, bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in g\u00fc\u00e7l\u00fc ve esnek ara\u00e7lar sunmaktad\u0131r. Bu ara\u00e7lardan \u00fc\u00e7\u00fc, bir araya geldi\u011finde “EFK Stack” olarak bilinen, g\u00fc\u00e7l\u00fc bir log toplama, depolama, arama ve g\u00f6rselle\u015ftirme \u00e7\u00f6z\u00fcm\u00fc olu\u015fturur: Fluentd, Elasticsearch ve Kibana. Bu makale, bu \u00fc\u00e7 bile\u015fenin ne oldu\u011funu, nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 ve birlikte nas\u0131l entegre bir \u00e7\u00f6z\u00fcm sundu\u011funu detayl\u0131 bir \u015fekilde inceleyecektir.<\/p>\n

Fluentd: Birle\u015fik Loglama Katman\u0131<\/h2>\n

Fluentd, \u00e7e\u015fitli kaynaklardan gelen log verilerini toplayan, ayr\u0131\u015ft\u0131ran, d\u00f6n\u00fc\u015ft\u00fcren ve farkl\u0131 hedeflere y\u00f6nlendiren a\u00e7\u0131k kaynakl\u0131 bir veri toplay\u0131c\u0131d\u0131r. 2011 y\u0131l\u0131nda Treasure Data taraf\u0131ndan geli\u015ftirilen Fluentd, y\u00fcksek performans, esneklik ve zengin eklenti ekosistemi sayesinde modern log y\u00f6netiminin temel ta\u015flar\u0131ndan biri haline gelmi\u015ftir. “Birle\u015fik Loglama Katman\u0131” felsefesiyle, farkl\u0131 formatlardaki ve kaynaklardaki loglar\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f, JSON tabanl\u0131 bir formata d\u00f6n\u00fc\u015ft\u00fcrerek standartla\u015ft\u0131r\u0131r.<\/p>\n

Fluentd’nin Temel \u00d6zellikleri<\/h3>\n

* Birle\u015fik Loglama:<\/strong> Fluentd, metin dosyalar\u0131, syslog, HTTP, TCP\/UDP, Kafka, Kubernetes ve Docker gibi \u00e7ok say\u0131da kaynaktan log toplayabilir. Toplad\u0131\u011f\u0131 bu verileri standart bir yap\u0131ya (genellikle JSON) d\u00f6n\u00fc\u015ft\u00fcrerek, farkl\u0131 sistemlerden gelen loglar\u0131n ayn\u0131 formatta i\u015flenmesini sa\u011flar. Bu, loglar\u0131n daha kolay aranabilir ve analiz edilebilir olmas\u0131n\u0131 sa\u011flar.
\n* Esneklik ve Geni\u015fletilebilirlik:<\/strong> Fluentd’nin en g\u00fc\u00e7l\u00fc y\u00f6nlerinden biri, mod\u00fcler yap\u0131s\u0131d\u0131r. Giri\u015f (input), ayr\u0131\u015ft\u0131r\u0131c\u0131 (parser), filtre (filter) ve \u00e7\u0131k\u0131\u015f (output) eklentileri sayesinde neredeyse her t\u00fcrl\u00fc veri kayna\u011f\u0131na ve hedefine entegre olabilir. Binlerce topluluk taraf\u0131ndan geli\u015ftirilmi\u015f eklenti mevcuttur.
\n* G\u00fcvenilirlik:<\/strong> Veri kayb\u0131n\u0131 \u00f6nlemek i\u00e7in dahili arabellekleme (buffering) mekanizmalar\u0131na sahiptir. Bellek i\u00e7i (in-memory) veya disk tabanl\u0131 arabellekler kullanarak, hedefe veri g\u00f6nderilemedi\u011fi durumlarda (\u00f6rne\u011fin, Elasticsearch’\u00fcn ge\u00e7ici olarak kapal\u0131 olmas\u0131) loglar\u0131 g\u00fcvenli bir \u015fekilde saklar ve hedef tekrar eri\u015filebilir oldu\u011funda g\u00f6nderimi yeniden dener.
\n* Performans:<\/strong> C diliyle yaz\u0131lm\u0131\u015f \u00e7ekirde\u011fi ve optimize edilmi\u015f yap\u0131s\u0131 sayesinde y\u00fcksek i\u015flem hacimlerini d\u00fc\u015f\u00fck kaynak t\u00fcketimiyle y\u00f6netebilir. Saniyede on binlerce olay\u0131 i\u015fleyebilir.
\n* \u00d6l\u00e7eklenebilirlik:<\/strong> K\u00fc\u00e7\u00fck bir sistemden b\u00fcy\u00fck, da\u011f\u0131t\u0131k bir altyap\u0131ya kadar her \u00f6l\u00e7ekte kullan\u0131labilir. Agent olarak her sunucuya kurulabilir veya Kubernetes gibi ortamlarda DaemonSet olarak da\u011f\u0131t\u0131labilir.<\/p>\n

Fluentd Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n

Fluentd’nin \u00e7al\u0131\u015fma prensibi bir veri ak\u0131\u015f hatt\u0131na benzer:
\n1. Giri\u015f (Input):<\/strong> Belirli bir kaynaktan veri al\u0131r. Bu bir dosya (tail<\/code> eklentisi), bir a\u011f portu (syslog<\/code>, http<\/code>), bir kuyruk (kafka<\/code>) veya bir konteyner runtime’\u0131 (docker<\/code>, kubernetes<\/code>) olabilir.
\n2. Ayr\u0131\u015ft\u0131r\u0131c\u0131 (Parser):<\/strong> Gelen ham veriyi yap\u0131land\u0131r\u0131lm\u0131\u015f bir formata (genellikle JSON) d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Log sat\u0131rlar\u0131n\u0131 zaman damgas\u0131, seviye, mesaj gibi alanlara ay\u0131r\u0131r. Regex, Labeled TSV, Apache gibi haz\u0131r ayr\u0131\u015ft\u0131r\u0131c\u0131lar mevcuttur.
\n3. Filtre (Filter):<\/strong> Veri ak\u0131\u015f\u0131ndaki olaylar\u0131 de\u011fi\u015ftirmek, zenginle\u015ftirmek veya filtrelemek i\u00e7in kullan\u0131l\u0131r. Belirli alanlar\u0131 ekleyebilir, kald\u0131rabilir, yeniden adland\u0131rabilir veya ko\u015fullu mant\u0131k uygulayarak belirli olaylar\u0131 atlayabilir. \u00d6rne\u011fin, IP adreslerinden co\u011frafi konum bilgisi eklemek veya hassas verileri maskelemek i\u00e7in kullan\u0131labilir.
\n4. \u00c7\u0131k\u0131\u015f (Output):<\/strong> \u0130\u015flenmi\u015f veriyi bir hedefe g\u00f6nderir. Bu hedef bir dosya, ba\u015fka bir Fluentd \u00f6rne\u011fi, bir mesaj kuyru\u011fu (Kafka), bir bulut depolama hizmeti (S3) veya EFK y\u0131\u011f\u0131n\u0131m\u0131z\u0131n ana depolama bile\u015feni olan Elasticsearch olabilir.<\/p>\n

Fluentd, bu esnek yap\u0131s\u0131 sayesinde, loglar\u0131n kayna\u011f\u0131ndan hedefine kadar t\u00fcm ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc y\u00f6netmek i\u00e7in ideal bir ara\u00e7t\u0131r. \u00d6zellikle Kubernetes gibi dinamik ve konteyner tabanl\u0131 ortamlarda, log toplama ve y\u00f6nlendirme konusunda vazge\u00e7ilmez bir rol oynar.<\/p>\n

Elasticsearch: Da\u011f\u0131t\u0131k Arama ve Analiz Motoru<\/h2>\n

Elasticsearch, Apache Lucene \u00fczerine in\u015fa edilmi\u015f, da\u011f\u0131t\u0131k, RESTful, a\u00e7\u0131k kaynakl\u0131 bir arama ve analiz motorudur. B\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde h\u0131zl\u0131 ve \u00f6l\u00e7eklenebilir arama, analiz ve g\u00f6rselle\u015ftirme yetenekleri sunar. Log y\u00f6netiminde, Fluentd taraf\u0131ndan toplanan ve i\u015flenen yap\u0131land\u0131r\u0131lm\u0131\u015f log verilerini depolamak, indekslemek ve \u00fczerinde karma\u015f\u0131k sorgular \u00e7al\u0131\u015ft\u0131rmak i\u00e7in merkezi bir depolama ve analiz katman\u0131 olarak g\u00f6rev yapar.<\/p>\n

Elasticsearch’\u00fcn Temel Kavramlar\u0131<\/h3>\n

* Belge (Document):<\/strong> Elasticsearch’teki en temel veri birimidir. JSON format\u0131nda bir veri kayd\u0131n\u0131 temsil eder. \u00d6rne\u011fin, bir log sat\u0131r\u0131, bir belge olarak depolan\u0131r. Her belge benzersiz bir kimli\u011fe sahiptir.
\n* Dizin (Index):<\/strong> Benzer \u00f6zelliklere sahip belgelerin bir koleksiyonudur. \u0130li\u015fkisel veritabanlar\u0131ndaki “tablo” kavram\u0131na benzer, ancak \u00e7ok daha esnektir. Log y\u00f6netiminde, genellikle g\u00fcnl\u00fck veya haftal\u0131k olarak yeni dizinler olu\u015fturulur (\u00f6rn: logs-2023-10-27<\/code>).
\n* Alan (Field):<\/strong> Bir belgedeki anahtar-de\u011fer \u00e7iftleridir. \u00d6rne\u011fin, bir log belgesinde timestamp<\/code>, level<\/code>, message<\/code>, hostname<\/code> gibi alanlar bulunabilir.
\n* Shardlar (Shards):<\/strong> Bir dizinin yatay olarak b\u00f6l\u00fcmlere ayr\u0131lm\u0131\u015f halidir. Her shard, tam te\u015fekk\u00fcll\u00fc ve ba\u011f\u0131ms\u0131z bir Lucene dizinidir. Bu, Elasticsearch’\u00fcn b\u00fcy\u00fck veri k\u00fcmelerini da\u011f\u0131t\u0131k bir \u015fekilde saklamas\u0131na ve sorgulamas\u0131na olanak tan\u0131r. Shardlar, bir k\u00fcmedeki farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131t\u0131larak \u00f6l\u00e7eklenebilirlik sa\u011flan\u0131r.
\n* Replika (Replicas):<\/strong> Bir shard’\u0131n kopyalar\u0131d\u0131r. Veri kayb\u0131n\u0131 \u00f6nlemek i\u00e7in y\u00fcksek eri\u015filebilirlik sa\u011flar ve arama sorgular\u0131n\u0131n performans\u0131n\u0131 art\u0131r\u0131r, \u00e7\u00fcnk\u00fc sorgular hem birincil shard’dan hem de replikalardan paralel olarak \u00e7al\u0131\u015ft\u0131r\u0131labilir.<\/p>\n

Elasticsearch’\u00fcn Temel \u00d6zellikleri<\/h3>\n

* Da\u011f\u0131t\u0131k Mimari:<\/strong> Verileri birden fazla d\u00fc\u011f\u00fcme da\u011f\u0131tarak yatay \u00f6l\u00e7eklenebilirlik ve y\u00fcksek eri\u015filebilirlik sunar. Bir d\u00fc\u011f\u00fcm ar\u0131zalansa bile, k\u00fcme \u00e7al\u0131\u015fmaya devam eder.
\n* RESTful API:<\/strong> T\u00fcm i\u015flemler (indeksleme, arama, g\u00fcncelleme, silme) HTTP REST API’si \u00fczerinden ger\u00e7ekle\u015ftirilir. Bu, farkl\u0131 programlama dilleri ve ara\u00e7larla kolay entegrasyon sa\u011flar.
\n* Yak\u0131n Ger\u00e7ek Zamanl\u0131 (Near Real-time) \u0130ndeksleme:<\/strong> Veriler indekslendikten saniyeler sonra aranabilir hale gelir. Bu, log analizi gibi h\u0131zl\u0131 tepki gerektiren durumlar i\u00e7in kritiktir.
\n* G\u00fc\u00e7l\u00fc Arama Yetenekleri:<\/strong> Tam metin arama, bulan\u0131k arama, \u00e7oklu alan arama, co\u011frafi konum tabanl\u0131 arama ve karma\u015f\u0131k sorgular gibi geli\u015fmi\u015f arama \u00f6zellikleri sunar.
\n* Analitik Yetenekler (Aggregations):<\/strong> B\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde karma\u015f\u0131k istatistiksel analizler yapmaya olanak tan\u0131r. Ortalama, toplam, minimum, maksimum, benzersiz de\u011ferler, histogramlar gibi metrikleri hesaplayarak log verilerinden anlaml\u0131 i\u00e7g\u00f6r\u00fcler \u00e7\u0131kar\u0131lmas\u0131n\u0131 sa\u011flar. \u00d6rne\u011fin, belirli bir zaman dilimindeki hata say\u0131s\u0131n\u0131, en \u00e7ok hata veren sunucuyu veya en s\u0131k eri\u015filen URL’leri bulmak i\u00e7in kullan\u0131labilir.<\/p>\n

Elasticsearch Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n

Fluentd’den gelen log belgeleri, Elasticsearch’e JSON format\u0131nda g\u00f6nderilir. Elasticsearch, bu belgeleri al\u0131r, analiz eder ve ilgili dizinlere indeksler. \u0130ndeksleme s\u0131ras\u0131nda, belgelerdeki metin alanlar\u0131 token’lara ayr\u0131l\u0131r ve tersine indeksler olu\u015fturularak h\u0131zl\u0131 arama i\u00e7in optimize edilir.<\/p>\n

Kullan\u0131c\u0131lar veya uygulamalar, Elasticsearch’\u00fcn REST API’sini kullanarak dizinlerde arama sorgular\u0131 \u00e7al\u0131\u015ft\u0131r\u0131r. Elasticsearch, sorguyu al\u0131r, ilgili shard’larda arama yapar, sonu\u00e7lar\u0131 birle\u015ftirir ve kullan\u0131c\u0131ya d\u00f6nd\u00fcr\u00fcr. Aggregation sorgular\u0131 ise, b\u00fcy\u00fck veri k\u00fcmelerinden \u00f6zet bilgiler \u00e7\u0131kararak dashboard’larda g\u00f6rselle\u015ftirme i\u00e7in temel olu\u015fturur.<\/p>\n

Kibana: Veri G\u00f6rselle\u015ftirme ve Ke\u015fif Arac\u0131<\/h2>\n

Kibana, Elasticsearch’te depolanan verileri ke\u015ffetmek, analiz etmek ve g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131lan a\u00e7\u0131k kaynakl\u0131 bir web aray\u00fcz\u00fcd\u00fcr. Log y\u00f6netiminde, Fluentd ve Elasticsearch taraf\u0131ndan toplanan ve depolanan log verilerini kullan\u0131c\u0131 dostu grafikler, tablolar ve dashboard’lar arac\u0131l\u0131\u011f\u0131yla sunarak, log analistlerinin ve operasyon ekiplerinin sistem davran\u0131\u015flar\u0131n\u0131 kolayca anlamalar\u0131n\u0131 sa\u011flar.<\/p>\n

Kibana’n\u0131n Temel \u00d6zellikleri<\/h3>\n

* Discover (Ke\u015ffet):<\/strong> Elasticsearch’teki ham log verilerini zaman bazl\u0131 olarak incelemek, filtrelemek ve sorgulamak i\u00e7in kullan\u0131l\u0131r. Belirli bir zaman aral\u0131\u011f\u0131ndaki t\u00fcm loglar\u0131 g\u00f6rmek, belirli anahtar kelimelerle arama yapmak veya alan de\u011ferlerine g\u00f6re filtreleme uygulamak m\u00fcmk\u00fcnd\u00fcr.
\n* Visualize (G\u00f6rselle\u015ftir):<\/strong> Farkl\u0131 t\u00fcrlerde grafikler, tablolar ve haritalar olu\u015fturmak i\u00e7in kullan\u0131l\u0131r. \u00c7ubuk grafikler, pasta grafikler, \u00e7izgi grafikler, metrikler, \u0131s\u0131 haritalar\u0131 gibi bir\u00e7ok g\u00f6rselle\u015ftirme se\u00e7ene\u011fi sunar. Bu g\u00f6rselle\u015ftirmeler, log verilerindeki e\u011filimleri, anormallikleri ve kal\u0131plar\u0131 ortaya \u00e7\u0131karmaya yard\u0131mc\u0131 olur.
\n* Dashboard (Kontrol Paneli):<\/strong> Birden fazla g\u00f6rselle\u015ftirmeyi tek bir ekranda birle\u015ftirerek kapsaml\u0131 bir genel bak\u0131\u015f sunar. Farkl\u0131 sistemlerin veya uygulamalar\u0131n loglar\u0131n\u0131 tek bir yerden izlemek i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, sunucu durumu, hata oranlar\u0131, kullan\u0131c\u0131 etkinli\u011fi gibi metrikleri i\u00e7eren bir dashboard olu\u015fturulabilir.
\n* Dev Tools (Geli\u015ftirici Ara\u00e7lar\u0131):<\/strong> Elasticsearch’\u00fcn REST API’si ile do\u011frudan etkile\u015fim kurmak i\u00e7in bir konsol sa\u011flar. Kullan\u0131c\u0131lar\u0131n Elasticsearch sorgular\u0131n\u0131 ve API \u00e7a\u011fr\u0131lar\u0131n\u0131 kolayca test etmesine olanak tan\u0131r.
\n* Maps (Haritalar):<\/strong> Co\u011frafi konum verilerini haritalar \u00fczerinde g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131l\u0131r. Loglardaki IP adreslerinden elde edilen co\u011frafi bilgileri haritalarda g\u00f6stererek, kullan\u0131c\u0131lar\u0131n nereden geldi\u011fini veya sald\u0131r\u0131lar\u0131n co\u011frafi da\u011f\u0131l\u0131m\u0131n\u0131 anlamaya yard\u0131mc\u0131 olur.
\n* Alerting ve Machine Learning (X-Pack):<\/strong> Kibana’n\u0131n ticari eklentileri olan X-Pack ile geli\u015fmi\u015f \u00f6zellikler sunulur. Alerting, belirli log kal\u0131plar\u0131 veya metrik e\u015fikleri a\u015f\u0131ld\u0131\u011f\u0131nda bildirimler g\u00f6ndermeyi sa\u011flar. Machine Learning ise anormallik tespiti ve tahmin modelleri olu\u015fturarak proaktif izlemeye olanak tan\u0131r.<\/p>\n

Kibana Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n

Kibana, web taray\u0131c\u0131s\u0131 \u00fczerinden eri\u015filen bir Node.js uygulamas\u0131d\u0131r. Kullan\u0131c\u0131 aray\u00fcz\u00fc, Elasticsearch’e API \u00e7a\u011fr\u0131lar\u0131 yaparak veri sorgular\u0131n\u0131 g\u00f6nderir. Elasticsearch, bu sorgular\u0131 i\u015fler ve sonu\u00e7lar\u0131 Kibana’ya JSON format\u0131nda d\u00f6nd\u00fcr\u00fcr. Kibana, bu JSON verilerini al\u0131r ve kullan\u0131c\u0131 tan\u0131ml\u0131 g\u00f6rselle\u015ftirmeler ve dashboard’lar arac\u0131l\u0131\u011f\u0131yla anlaml\u0131 bir \u015fekilde sunar. Kibana, kendisi veri depolamaz; sadece Elasticsearch’teki verilere bir pencere g\u00f6revi g\u00f6r\u00fcr.<\/p>\n

EFK Stack’in Birlikte \u00c7al\u0131\u015fmas\u0131: Entegre Bir Log \u00c7\u00f6z\u00fcm\u00fc<\/h2>\n

Fluentd, Elasticsearch ve Kibana’n\u0131n bir araya gelmesiyle olu\u015fan EFK Stack, log y\u00f6netimi i\u00e7in g\u00fc\u00e7l\u00fc, esnek ve \u00f6l\u00e7eklenebilir bir \u00e7\u00f6z\u00fcm sunar. Bu \u00fc\u00e7 bile\u015fen, loglar\u0131n kayna\u011f\u0131ndan son kullan\u0131c\u0131ya ula\u015fana kadar olan t\u00fcm ak\u0131\u015f\u0131 y\u00f6netir:<\/p>\n

1. Log Toplama (Fluentd):<\/strong> \u00c7e\u015fitli kaynaklardan (uygulamalar, sunucular, konteynerler, a\u011f cihazlar\u0131 vb.) loglar \u00fcretilir. Fluentd agent’lar\u0131 bu loglar\u0131 toplar, ham veriyi ayr\u0131\u015ft\u0131r\u0131r, yap\u0131land\u0131r\u0131lm\u0131\u015f bir JSON format\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcr ve gerekti\u011finde filtreleyip zenginle\u015ftirir.
\n2. Log Depolama ve \u0130ndeksleme (Elasticsearch):<\/strong> Fluentd taraf\u0131ndan i\u015flenen yap\u0131land\u0131r\u0131lm\u0131\u015f log verileri, Elasticsearch k\u00fcmesine g\u00f6nderilir. Elasticsearch, bu verileri al\u0131r, dizinler (index) olu\u015fturur ve belgeler (document) olarak depolar. Veriler, h\u0131zl\u0131 arama ve analitik sorgular i\u00e7in optimize edilir.
\n3. Log G\u00f6rselle\u015ftirme ve Analiz (Kibana):<\/strong> Son kullan\u0131c\u0131lar, Kibana’n\u0131n web aray\u00fcz\u00fcne eri\u015ferek Elasticsearch’te depolanan log verilerini ke\u015ffeder. Belirli zaman aral\u0131klar\u0131nda loglar\u0131 arayabilir, filtreleyebilir, farkl\u0131 g\u00f6rselle\u015ftirmelerle (grafikler, tablolar) analiz edebilir ve \u00f6zelle\u015ftirilmi\u015f dashboard’lar olu\u015fturarak sistemlerinin genel sa\u011fl\u0131k durumunu ve performans\u0131n\u0131 izleyebilirler.<\/p>\n

Bu entegre ak\u0131\u015f, i\u015fletmelere a\u015fa\u011f\u0131daki temel faydalar\u0131 sa\u011flar:<\/p>\n

* Merkezi Log Y\u00f6netimi:<\/strong> Da\u011f\u0131t\u0131k sistemlerden gelen t\u00fcm loglar\u0131 tek bir merkezi konumda toplar ve eri\u015filebilir k\u0131lar.
\n* Ger\u00e7ek Zamanl\u0131 Analiz:<\/strong> Loglar \u00fcretildikten saniyeler sonra analiz edilebilir hale gelir, bu da sorunlar\u0131 h\u0131zl\u0131 bir \u015fekilde tespit etmeye ve \u00e7\u00f6zmeye yard\u0131mc\u0131 olur.
\n* \u00d6l\u00e7eklenebilirlik:<\/strong> Her bir bile\u015fen ba\u011f\u0131ms\u0131z olarak \u00f6l\u00e7eklenebilir, bu da log hacmi artt\u0131k\u00e7a sistemin kolayca geni\u015fletilebilmesini sa\u011flar.
\n* Esneklik:<\/strong> A\u00e7\u0131k kaynakl\u0131 yap\u0131s\u0131 ve zengin eklenti ekosistemi sayesinde, farkl\u0131 ihtiya\u00e7lara g\u00f6re \u00f6zelle\u015ftirilebilir.
\n* Maliyet Etkinli\u011fi:<\/strong> T\u00fcm bile\u015fenler a\u00e7\u0131k kaynakl\u0131 oldu\u011fundan, lisans maliyeti olmaks\u0131z\u0131n g\u00fc\u00e7l\u00fc bir \u00e7\u00f6z\u00fcm sunar.<\/p>\n

Kurulum ve Yap\u0131land\u0131rmaya Genel Bak\u0131\u015f<\/h2>\n

EFK Stack’in kurulumu, her bir bile\u015fenin ayr\u0131 ayr\u0131 kurulmas\u0131n\u0131 ve ard\u0131ndan birbirleriyle ileti\u015fim kuracak \u015fekilde yap\u0131land\u0131r\u0131lmas\u0131n\u0131 i\u00e7erir. Genellikle, her sunucuda bir Fluentd agent’\u0131, bir veya daha fazla sunucuda Elasticsearch d\u00fc\u011f\u00fcmleri ve ayr\u0131 bir sunucuda Kibana \u00e7al\u0131\u015f\u0131r.<\/p>\n

Fluentd Kurulumu<\/h3>\n

Fluentd, \u00e7e\u015fitli i\u015fletim sistemlerine paket y\u00f6neticileri (apt, yum), Ruby gem’i olarak veya Docker konteyneri arac\u0131l\u0131\u011f\u0131yla kurulabilir. Genellikle, td-agent<\/code> ad\u0131 verilen \u00f6nceden paketlenmi\u015f da\u011f\u0131t\u0131m\u0131 tercih edilir.<\/p>\n

Basit bir fluent.conf<\/code> \u00f6rne\u011fi:<\/strong><\/p>\n

\n  @type tail\n  path \/var\/log\/nginx\/access.log\n  pos_file \/var\/log\/td-agent\/nginx_access.pos\n  tag nginx.access\n  \n    @type nginx\n  <\/parse>\n<\/source>\n\n\n  @type elasticsearch\n  host YOUR_ELASTICSEARCH_HOST\n  port 9200\n  logstash_format true\n  logstash_prefix nginx-access\n  flush_interval 5s\n<\/match><\/code><\/pre>\n
Bu yap\u0131land\u0131rma, Nginx eri\u015fim loglar\u0131n\u0131 \/var\/log\/nginx\/access.log<\/code> dosyas\u0131ndan okur, Nginx format\u0131na g\u00f6re ayr\u0131\u015ft\u0131r\u0131r ve nginx.access<\/code> etiketiyle Elasticsearch’e g\u00f6nderir. logstash_format true<\/code> ayar\u0131, Elasticsearch’e g\u00f6nderilen dizin adlar\u0131n\u0131n Logstash format\u0131nda (\u00f6rn: nginx-access-YYYY.MM.DD<\/code>) olmas\u0131n\u0131 sa\u011flar.<\/p>\n
Elasticsearch Kurulumu<\/h3>\n
Elasticsearch, Java tabanl\u0131 bir uygulama oldu\u011fu i\u00e7in bir Java Runtime Environment (JRE) gerektirir. OpenJDK genellikle tercih edilen se\u00e7enektir. Elasticsearch, .deb<\/code> veya .rpm<\/code> paketleri, tarball veya Docker konteyneri olarak kurulabilir.<\/p>\n
Temel elasticsearch.yml<\/code> yap\u0131land\u0131rmas\u0131:<\/strong><\/p>\n
cluster.name: efk-cluster\nnode.name: node-1\nnetwork.host: 0.0.0.0 # T\u00fcm a\u011f aray\u00fczlerinden eri\u015fime izin verir\nhttp.port: 9200\ndiscovery.seed_hosts: [\"node-1-ip\", \"node-2-ip\"] # K\u00fcmedeki di\u011fer d\u00fc\u011f\u00fcmler<\/code><\/pre>\n
\u00dcretim ortamlar\u0131nda, \u00f6zellikle network.host<\/code> ayar\u0131 dikkatlice yap\u0131lmal\u0131 ve g\u00fcvenlik duvar\u0131 kurallar\u0131 ile eri\u015fim k\u0131s\u0131tlanmal\u0131d\u0131r. Ayr\u0131ca, JVM heap size ayar\u0131 (jvm.options<\/code> dosyas\u0131nda) sistem belle\u011finin yakla\u015f\u0131k yar\u0131s\u0131 kadar, ancak 30-32 GB’\u0131 ge\u00e7meyecek \u015fekilde ayarlanmal\u0131d\u0131r.<\/p>\n
Kibana Kurulumu<\/h3>\n
Kibana da .deb<\/code> veya .rpm<\/code> paketleri, tarball veya Docker konteyneri olarak kurulabilir.<\/p>\n
Temel kibana.yml<\/code> yap\u0131land\u0131rmas\u0131:<\/strong><\/p>\n
server.port: 5601\nserver.host: \"0.0.0.0\" # T\u00fcm a\u011f aray\u00fczlerinden eri\u015fime izin verir\nelasticsearch.hosts: [\"http:\/\/YOUR_ELASTICSEARCH_HOST:9200\"]<\/code><\/pre>\n
Kibana’n\u0131n da \u00fcretim ortamlar\u0131nda server.host<\/code> ayar\u0131 ve g\u00fcvenlik duvar\u0131 kurallar\u0131 ile eri\u015fimi k\u0131s\u0131tlanmal\u0131d\u0131r.<\/p>\n
\u0130pu\u00e7lar\u0131 ve En \u0130yi Uygulamalar<\/h3>\n
*   G\u00fcvenlik:<\/strong> \u00dcretim ortamlar\u0131nda, Elasticsearch ve Kibana’ya eri\u015fim i\u00e7in SSL\/TLS \u015fifrelemesi, kullan\u0131c\u0131 kimlik do\u011frulamas\u0131 ve yetkilendirme (X-Pack Security veya alternatifleri ile) mutlaka yap\u0131land\u0131r\u0131lmal\u0131d\u0131r. Fluentd ile Elasticsearch aras\u0131ndaki ileti\u015fimin de g\u00fcvenli olmas\u0131 \u00f6nemlidir.
\n*   Performans ve Kaynak Y\u00f6netimi:<\/strong> Elasticsearch, \u00f6zellikle disk I\/O, CPU ve RAM a\u00e7\u0131s\u0131ndan yo\u011fun kaynak t\u00fcketebilir. Yeterli donan\u0131m kaynaklar\u0131n\u0131n tahsis edildi\u011finden ve JVM heap size gibi ayarlar\u0131n do\u011fru yap\u0131ld\u0131\u011f\u0131ndan emin olunmal\u0131d\u0131r. Shard ve replika say\u0131lar\u0131, veri hacmine ve sorgu desenlerine g\u00f6re optimize edilmelidir.
\n*   Veri Ya\u015fam D\u00f6ng\u00fcs\u00fc Y\u00f6netimi (ILM):<\/strong> Log verileri h\u0131zla b\u00fcy\u00fcr. Elasticsearch’\u00fcn Index Lifecycle Management (ILM) \u00f6zelli\u011fi, eski dizinleri otomatik olarak kapatmak, s\u0131k\u0131\u015ft\u0131rmak veya silmek i\u00e7in politikalar tan\u0131mlamaya olanak tan\u0131r. Bu, depolama maliyetlerini d\u00fc\u015f\u00fcr\u00fcr ve performans\u0131 art\u0131r\u0131r.
\n*   Monitoring:<\/strong> EFK Stack’in kendi performans\u0131n\u0131 izlemek i\u00e7in Elastic Stack Monitoring veya Prometheus\/Grafana gibi ara\u00e7lar kullan\u0131labilir.
\n*   Konteyner Ortamlar\u0131:<\/strong> Docker ve Kubernetes gibi konteynerize edilmi\u015f ortamlarda, EFK Stack’in da\u011f\u0131t\u0131m\u0131 i\u00e7in Helm grafikleri veya operat\u00f6rler kullan\u0131labilir. Fluentd, Kubernetes’te DaemonSet olarak her d\u00fc\u011f\u00fcme da\u011f\u0131t\u0131larak loglar\u0131 toplar.<\/p>\n
EFK Stack’in Avantajlar\u0131<\/h2>\n
*   A\u00e7\u0131k Kaynak ve Maliyet Etkinli\u011fi:<\/strong> T\u00fcm bile\u015fenlerin a\u00e7\u0131k kaynak olmas\u0131, i\u015fletmelerin y\u00fcksek lisans maliyetlerinden ka\u00e7\u0131narak g\u00fc\u00e7l\u00fc bir log y\u00f6netim \u00e7\u00f6z\u00fcm\u00fc kurmas\u0131n\u0131 sa\u011flar.
\n*   Esneklik ve Geni\u015fletilebilirlik:<\/strong> Fluentd’nin zengin eklenti ekosistemi ve Elasticsearch’\u00fcn esnek veri modeli, farkl\u0131 veri kaynaklar\u0131 ve analiz ihtiya\u00e7lar\u0131na kolayca uyum sa\u011flamay\u0131 m\u00fcmk\u00fcn k\u0131lar.
\n*   Da\u011f\u0131t\u0131k ve \u00d6l\u00e7eklenebilir Mimari:<\/strong> Her bir bile\u015fenin da\u011f\u0131t\u0131k yap\u0131s\u0131, artan log hacmi ve kullan\u0131c\u0131 y\u00fck\u00fc kar\u015f\u0131s\u0131nda sistemin kolayca yatay olarak \u00f6l\u00e7eklenmesini sa\u011flar.
\n*   Ger\u00e7ek Zamanl\u0131 Analiz:<\/strong> Loglar \u00fcretildikten saniyeler sonra analiz edilebilir hale gelir, bu da operasyonel sorunlar\u0131n h\u0131zl\u0131 tespiti ve \u00e7\u00f6z\u00fcm\u00fc i\u00e7in kritiktir.
\n*   Zengin G\u00f6rselle\u015ftirme Yetenekleri:<\/strong> Kibana, log verilerinden anlaml\u0131 i\u00e7g\u00f6r\u00fcler \u00e7\u0131karmak i\u00e7in \u00e7e\u015fitli g\u00f6rselle\u015ftirme ve dashboard se\u00e7enekleri sunar.
\n*   Aktif Topluluk Deste\u011fi:<\/strong> Geni\u015f ve aktif bir toplulu\u011fa sahip olmas\u0131, sorun giderme ve yeni \u00f6zellikler konusunda s\u00fcrekli destek anlam\u0131na gelir.<\/p>\n
EFK Stack’in Zorluklar\u0131 ve Dikkat Edilmesi Gerekenler<\/h2>\n
*   Kaynak T\u00fcketimi:<\/strong> \u00d6zellikle Elasticsearch, CPU, RAM ve disk I\/O a\u00e7\u0131s\u0131ndan yo\u011fun kaynak t\u00fcketebilir. B\u00fcy\u00fck \u00f6l\u00e7ekli da\u011f\u0131t\u0131mlar i\u00e7in \u00f6nemli donan\u0131m yat\u0131r\u0131mlar\u0131 gerekebilir.
\n*   Kurulum ve Y\u00f6netim Karma\u015f\u0131kl\u0131\u011f\u0131:<\/strong> \u00dc\u00e7 farkl\u0131 bile\u015fenin kurulmas\u0131, yap\u0131land\u0131r\u0131lmas\u0131 ve optimize edilmesi, \u00f6zellikle yeni ba\u015flayanlar i\u00e7in karma\u015f\u0131k olabilir. Da\u011f\u0131t\u0131k bir k\u00fcmenin y\u00f6netimi uzmanl\u0131k gerektirir.
\n*   G\u00fcvenlik Yap\u0131land\u0131rmas\u0131:<\/strong> A\u00e7\u0131k kaynak varsay\u0131lanlar\u0131 genellikle g\u00fcvenli de\u011fildir. \u00dcretim ortamlar\u0131nda g\u00fcvenlik (kimlik do\u011frulama, yetkilendirme, \u015fifreleme) i\u00e7in ek \u00e7aba ve yap\u0131land\u0131rma gerekir.
\n*   Veri B\u00fcy\u00fcmesi ve Saklama Maliyetleri:<\/strong> Log verileri h\u0131zla birikir. Uzun s\u00fcreli saklama, depolama maliyetlerini art\u0131rabilir. Veri ya\u015fam d\u00f6ng\u00fcs\u00fc y\u00f6netimi stratejileri (ILM) bu konuda kritik \u00f6neme sahiptir.
\n*   Y\u00fckseltmeler ve Uyumluluk:<\/strong> Bile\u015fenlerin farkl\u0131 s\u00fcr\u00fcmleri aras\u0131nda uyumluluk sorunlar\u0131 ya\u015fanabilir. Y\u00fckseltme s\u00fcre\u00e7leri dikkatlice planlanmal\u0131 ve test edilmelidir.<\/p>\n
Alternatifler ve Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n
EFK Stack, log y\u00f6netimi alan\u0131ndaki tek \u00e7\u00f6z\u00fcm de\u011fildir. Piyasa, hem a\u00e7\u0131k kaynak hem de ticari bir\u00e7ok alternatif sunmaktad\u0131r:<\/p>\n
*   ELK Stack (Elasticsearch, Logstash, Kibana):<\/strong> EFK’nin en yak\u0131n kuzenidir. Logstash, Fluentd gibi bir veri toplay\u0131c\u0131 ve i\u015flemcidir. Logstash, Ruby tabanl\u0131 olmas\u0131 nedeniyle Fluentd’ye g\u00f6re daha fazla kaynak t\u00fcketebilir ve genellikle daha yava\u015f olabilir. Ancak, daha zengin filtreleme yetenekleri sunabilir. Fluentd, \u00f6zellikle konteynerize edilmi\u015f ortamlarda ve performans odakl\u0131 senaryolarda Logstash’e g\u00f6re daha hafif ve verimli bir alternatif olarak \u00f6ne \u00e7\u0131kar.
\n*   Splunk, Sumo Logic, Datadog:<\/strong> Bunlar, log y\u00f6netimi, metrik izleme ve APM (Uygulama Performans Y\u00f6netimi) sunan ticari \u00e7\u00f6z\u00fcmlerdir. Genellikle daha kolay kurulum, kapsaml\u0131 \u00f6zellik setleri ve profesyonel destek sunarlar, ancak y\u00fcksek lisans maliyetleri vard\u0131r.
\n*   Prometheus ve Grafana:<\/strong> Metrik odakl\u0131 izleme i\u00e7in pop\u00fcler bir a\u00e7\u0131k kaynak \u00e7\u00f6z\u00fcm\u00fcd\u00fcr. Log y\u00f6netimi i\u00e7in tasarlanmam\u0131\u015ft\u0131r, ancak sistem metriklerini izlemek ve g\u00f6rselle\u015ftirmek i\u00e7in EFK ile birlikte kullan\u0131labilir.
\n*   Loki ve Grafana:<\/strong> Grafana Labs taraf\u0131ndan geli\u015ftirilen Loki, Prometheus’tan ilham alan, ancak loglara odaklanan daha hafif bir log toplama ve depolama \u00e7\u00f6z\u00fcm\u00fcd\u00fcr. Elasticsearch’e g\u00f6re daha az kaynak t\u00fcketir ve daha basit bir mimariye sahiptir, ancak Elasticsearch’\u00fcn geli\u015fmi\u015f arama ve analitik yeteneklerine sahip de\u011fildir.<\/p>\n
EFK Stack, Logstash yerine Fluentd’nin se\u00e7ilmesiyle, \u00f6zellikle mikroservisler ve Kubernetes gibi modern, da\u011f\u0131t\u0131k ortamlarda, daha hafif, daha performansl\u0131 ve daha esnek bir log toplama katman\u0131 sunar. Bu, kaynak verimlili\u011fi ve kolay entegrasyon arayan i\u015fletmeler i\u00e7in \u00f6nemli bir avantajd\u0131r.<\/p>\n
Sonu\u00e7<\/h2>\n
G\u00fcn\u00fcm\u00fcz\u00fcn karma\u015f\u0131k ve da\u011f\u0131t\u0131k sistemlerinde log y\u00f6netimi, operasyonel g\u00f6r\u00fcn\u00fcrl\u00fck, g\u00fcvenlik duru\u015fu ve performans optimizasyonu i\u00e7in vazge\u00e7ilmez bir unsurdur. Geleneksel y\u00f6ntemlerin yetersiz kald\u0131\u011f\u0131 bu ortamda, Elasticsearch, Fluentd ve Kibana’dan olu\u015fan EFK Stack, g\u00fc\u00e7l\u00fc, esnek ve \u00f6l\u00e7eklenebilir bir a\u00e7\u0131k kaynak \u00e7\u00f6z\u00fcm\u00fc sunar.<\/p>\n
Fluentd’nin \u00e7ok \u00e7e\u015fitli kaynaklardan log toplama ve yap\u0131land\u0131rma yetene\u011fi, Elasticsearch’\u00fcn devasa veri k\u00fcmeleri \u00fczerinde h\u0131zl\u0131 arama ve analitik g\u00fcc\u00fc ile Kibana’n\u0131n sezgisel g\u00f6rselle\u015ftirme ara\u00e7lar\u0131 birle\u015fti\u011finde, i\u015fletmeler log verilerinden de\u011ferli i\u00e7g\u00f6r\u00fcler elde edebilir. Bu sayede, sorunlar daha h\u0131zl\u0131 tespit edilir, g\u00fcvenlik tehditleri proaktif olarak izlenir ve sistem performans\u0131 s\u00fcrekli olarak iyile\u015ftirilir.<\/p>\n
EFK Stack’in kurulum ve y\u00f6netimi belirli bir \u00f6\u011frenme e\u011frisi gerektirse de, sundu\u011fu maliyet etkinli\u011fi, esneklik ve \u00f6l\u00e7eklenebilirlik, onu modern IT altyap\u0131lar\u0131 i\u00e7in cazip bir se\u00e7enek haline getirmektedir. Do\u011fru planlama, yap\u0131land\u0131rma ve optimizasyon ile EFK, herhangi bir \u00f6l\u00e7ekteki kurulu\u015fun log y\u00f6netimi ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131layabilecek sa\u011flam bir temel olu\u015fturabilir ve dijital d\u00f6n\u00fc\u015f\u00fcm yolculu\u011funda kritik bir destek sa\u011flayabilir. Loglar\u0131n\u0131z\u0131 yaln\u0131zca toplamakla kalmay\u0131n, onlardan \u00f6\u011frenin ve sistemlerinizi daha ak\u0131ll\u0131 hale getirin.<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Elasticsearch, Fluentd ve Kibana: A\u00e7\u0131k Kaynak Log Arama ve G\u00f6rselle\u015ftirme \u00c7\u00f6z\u00fcm\u00fc\nGiri\u015f: Log Y\u00f6netiminin \u00d6nemi ve Geli\u015fen \u0130htiya\u00e7lar\nG\u00fc","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":[1],"tags":[],"class_list":{"0":"post-31969","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nElasticsearch, Fluentd ve Kibana: A\u00e7\u0131k Kaynak Log Arama ve G\u00f6rselle\u015ftirme \u00c7\u00f6z\u00fcm\u00fc - 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