{"id":34125,"date":"2025-11-11T13:31:10","date_gmt":"2025-11-11T10:31:10","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/"},"modified":"2025-11-11T13:31:10","modified_gmt":"2025-11-11T10:31:10","slug":"opentelemetry-ve-falcon-izleme-neden-onemli","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/","title":{"rendered":"OpenTelemetry ve Falcon \u0130zleme Neden \u00d6nemli?"},"content":{"rendered":"<style>\n    \/* Basic mobile-first responsive design *\/\n    body {\n        font-family: Arial, sans-serif;\n        line-height: 1.6;\n        color: #333;\n        margin: 0 auto;\n        max-width: 960px; \/* Default desktop max width *\/\n        padding: 20px;\n        background-color: #fdfdfd;\n    }\n    h2, h3 {\n        color: #2c3e50;\n        margin-top: 30px;\n        margin-bottom: 15px;\n        border-bottom: 1px solid #eee;\n        padding-bottom: 10px;\n    }\n    h2 {\n        font-size: 2em;\n    }\n    h3 {\n        font-size: 1.5em;\n    }\n    p {\n        margin-bottom: 1em;\n    }\n    pre {\n        background-color: #f4f4f4;\n        padding: 15px;\n        border-left: 5px solid #007bff;\n        overflow-x: auto;\n        border-radius: 5px;\n        margin: 20px 0;\n    }\n    code {\n        font-family: 'Courier New', Courier, monospace;\n        background-color: #e9e9e9;\n        padding: 2px 4px;\n        border-radius: 3px;\n    }\n    pre code {\n        background-color: transparent;\n        padding: 0;\n    }\n    ul, ol {\n        margin-left: 20px;\n        margin-bottom: 1em;\n    }\n    li {\n        margin-bottom: 0.5em;\n    }\n    table {\n        width: 100%;\n        border-collapse: collapse;\n        margin: 20px 0;\n    }\n    table, th, td {\n        border: 1px solid #ddd;\n    }\n    th, td {\n        padding: 12px;\n        text-align: left;\n    }\n    th {\n        background-color: #f2f2f2;\n        font-weight: bold;\n    }\n    .expert-tip {\n        background-color: #e6f7ff;\n        border-left: 4px solid #3399ff;\n        padding: 15px;\n        margin: 20px 0;\n        font-style: italic;\n        border-radius: 5px;\n    }\n    .article-title {\n        font-size: 2.2em;\n        font-weight: bold;\n        text-align: center;\n        margin-bottom: 40px;\n        color: #2c3e50;\n        padding-bottom: 15px;\n        border-bottom: 2px solid #007bff;\n    }\n    \/* Mobile-specific adjustments *\/\n    @media (max-width: 768px) {\n        body {\n            padding: 15px;\n        }\n        h2 {\n            font-size: 1.8em;\n        }\n        h3 {\n            font-size: 1.3em;\n        }\n        .article-title {\n            font-size: 1.8em;\n        }\n        table, thead, tbody, th, td, tr {\n            display: block;\n        }\n        thead tr {\n            position: absolute;\n            top: -9999px;\n            left: -9999px;\n        }\n        tr {\n            border: 1px solid #ccc;\n            margin-bottom: 10px;\n        }\n        td {\n            border: none;\n            border-bottom: 1px solid #eee;\n            position: relative;\n            padding-left: 50%;\n            text-align: right;\n        }\n        td:before {\n            position: absolute;\n            top: 6px;\n            left: 6px;\n            width: 45%;\n            padding-right: 10px;\n            white-space: nowrap;\n            text-align: left;\n            font-weight: bold;\n        }\n        td:nth-of-type(1):before { content: \"Veri Tipi\"; }\n        td:nth-of-type(2):before { content: \"A\u00e7\u0131klama\"; }\n        td:nth-of-type(3):before { content: \"Neden Kullan\u0131l\u0131r?\"; }\n    }\n    @media (max-width: 480px) {\n        body {\n            padding: 10px;\n        }\n        h2 {\n            font-size: 1.5em;\n        }\n        h3 {\n            font-size: 1.1em;\n        }\n        .article-title {\n            font-size: 1.5em;\n        }\n    }\n<\/style>\n<p class=\"article-title\">Falcon Uygulamalar\u0131n\u0131 OpenTelemetry ile \u0130zleme: Derinlemesine Bir K\u0131lavuz<\/p>\n<p>Modern da\u011f\u0131t\u0131k sistemler giderek karma\u015f\u0131k hale gelirken, uygulamalar\u0131n\u0131z\u0131n ger\u00e7ek zamanl\u0131 performans\u0131n\u0131 ve davran\u0131\u015f\u0131n\u0131 anlamak hayati \u00f6nem ta\u015f\u0131yor. \u00d6zellikle Python ekosisteminde pop\u00fcler bir web \u00e7er\u00e7evesi olan Falcon ile geli\u015ftirilen API&#8217;lerinizi izlemek, olas\u0131 darbo\u011fazlar\u0131 tespit etmek ve kullan\u0131c\u0131 deneyimini optimize etmek i\u00e7in kritik bir ihtiya\u00e7 haline gelmi\u015ftir. Bu makale, Falcon uygulamalar\u0131n\u0131z\u0131 OpenTelemetry ile entegre ederek kapsaml\u0131 bir g\u00f6zlemlenebilirlik (observability) kazanman\u0131n ad\u0131mlar\u0131n\u0131 ve ileri d\u00fczey tekniklerini derinlemesine inceliyor.<\/p>\n<p>G\u00fcn\u00fcm\u00fcz\u00fcn mikroservis tabanl\u0131 mimarilerinde, bir iste\u011fin farkl\u0131 servisler aras\u0131nda dola\u015f\u0131m\u0131 s\u0131ras\u0131nda ne oldu\u011funu anlamak olduk\u00e7a zordur. Geleneksel izleme ara\u00e7lar\u0131 genellikle tekil bir uygulaman\u0131n metriklerini toplarken, da\u011f\u0131t\u0131k sistemlerdeki u\u00e7tan uca ak\u0131\u015f\u0131 takip etmekte yetersiz kal\u0131r. Bu durum, performans sorunlar\u0131n\u0131n, hatalar\u0131n ve gecikmelerin k\u00f6k nedenini bulmay\u0131 adeta bir i\u011fne aray\u0131\u015f\u0131na d\u00f6n\u00fc\u015ft\u00fcrebilir. \u0130\u015fte tam da bu noktada OpenTelemetry devreye giriyor.<\/p>\n<p>OpenTelemetry, bulut yerel yaz\u0131l\u0131mlar\u0131 i\u00e7in y\u00fcksek kaliteli telemetri verisi (izleme, metrikler ve loglar) toplamak, d\u00f6n\u00fc\u015ft\u00fcrmek ve d\u0131\u015fa aktarmak i\u00e7in sat\u0131c\u0131dan ba\u011f\u0131ms\u0131z ve a\u00e7\u0131k kaynakl\u0131 bir \u00e7er\u00e7evedir. Bu, geli\u015ftiricilerin kodlar\u0131na \u00f6zel izleme mant\u0131\u011f\u0131 eklemeleri ve bu verileri Prometheus, Jaeger, Grafana gibi \u00e7e\u015fitli izleme ve analiz ara\u00e7lar\u0131na g\u00f6ndermeleri i\u00e7in standart bir yol sunar. Falcon gibi hafif ve performans odakl\u0131 bir framework ile \u00e7al\u0131\u015f\u0131rken, uygulaman\u0131z\u0131n her bir iste\u011fi nas\u0131l i\u015fledi\u011fini, hangi bile\u015fenlerin ne kadar s\u00fcre harcad\u0131\u011f\u0131n\u0131 ve olas\u0131 hatalar\u0131 an\u0131nda g\u00f6rmek, hem geli\u015ftirme s\u00fcrecini h\u0131zland\u0131r\u0131r hem de \u00fcretim ortam\u0131nda kar\u015f\u0131la\u015f\u0131lacak sorunlar\u0131n \u00f6n\u00fcne ge\u00e7meye yard\u0131mc\u0131 olur. Ayr\u0131ca, bu entegrasyon sayesinde performans d\u00fc\u015f\u00fc\u015flerini proaktif olarak tespit edebilir, m\u00fc\u015fteri \u015fikayetleri gelmeden \u00f6nce m\u00fcdahale edebilirsiniz.<\/p>\n<h3>Geleneksel \u0130zleme Y\u00f6ntemlerinin S\u0131n\u0131rl\u0131l\u0131klar\u0131 Nelerdir?<\/h3>\n<p>Geleneksel izleme y\u00f6ntemleri genellikle sunucu seviyesi metrikler (CPU, bellek kullan\u0131m\u0131), uygulama loglar\u0131 ve bazen de temel HTTP istek\/cevap s\u00fcreleri gibi verilere odaklan\u0131r. Ancak, bu yakla\u015f\u0131mlar\u0131n da\u011f\u0131t\u0131k sistemlerde ve karma\u015f\u0131k uygulama mant\u0131klar\u0131nda \u00f6nemli s\u0131n\u0131rl\u0131l\u0131klar\u0131 vard\u0131r:<\/p>\n<ul>\n<li><strong>Kapsaml\u0131 Ba\u011flam Eksikli\u011fi:<\/strong> Bir iste\u011fin farkl\u0131 servisler aras\u0131nda nas\u0131l hareket etti\u011fini, hangi servislerin hangi s\u0131rayla \u00e7a\u011fr\u0131ld\u0131\u011f\u0131n\u0131 veya bir hatan\u0131n tam olarak hangi serviste ve i\u015flemde meydana geldi\u011fini anlamak zordur.<\/li>\n<li><strong>Silolanm\u0131\u015f Veri:<\/strong> Metrikler, loglar ve izlemeler genellikle farkl\u0131 ara\u00e7lar taraf\u0131ndan toplan\u0131r ve birbiriyle ili\u015fkilendirilmez, bu da sorun gidermeyi karma\u015f\u0131kla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>Sat\u0131c\u0131 Ba\u011f\u0131ml\u0131l\u0131\u011f\u0131:<\/strong> Ticari APM (Uygulama Performans Y\u00f6netimi) \u00e7\u00f6z\u00fcmleri genellikle belirli bir sat\u0131c\u0131ya kilitlenmeye neden olur, bu da esnekli\u011fi azalt\u0131r ve maliyetleri art\u0131r\u0131r.<\/li>\n<li><strong>Manuel Korelasyon:<\/strong> Farkl\u0131 servislerdeki loglar\u0131 ve metrikleri manuel olarak birle\u015ftirmeye \u00e7al\u0131\u015fmak zaman al\u0131c\u0131 ve hataya a\u00e7\u0131k bir s\u00fcre\u00e7tir.<\/li>\n<\/ul>\n<p>Bu s\u0131n\u0131rlamalar, \u00f6zellikle Falcon gibi mikroservis ortamlar\u0131nda h\u0131zl\u0131 ve verimli \u00e7al\u0131\u015fmas\u0131 beklenen uygulamalar i\u00e7in ciddi operasyonel zorluklar yarat\u0131r. \u0130\u015fte bu y\u00fczden OpenTelemetry gibi standartla\u015ft\u0131r\u0131lm\u0131\u015f, a\u00e7\u0131k kaynakl\u0131 bir \u00e7\u00f6z\u00fcm, modern web uygulamalar\u0131 i\u00e7in vazge\u00e7ilmez bir ara\u00e7 haline gelmi\u015ftir.<\/p>\n<h3>OpenTelemetry Nedir ve Hangi Sorunlar\u0131 \u00c7\u00f6zer?<\/h3>\n<p>OpenTelemetry (OTel), CNCF (Cloud Native Computing Foundation) b\u00fcnyesinde geli\u015ftirilen, uygulamalar\u0131n\u0131zdan telemetri verilerini (izleme, metrikler, loglar) standart bir \u015fekilde toplamak, i\u015flemek ve d\u0131\u015fa aktarmak i\u00e7in tek bir dizi API, SDK ve ara\u00e7 sunan bir projedir. Temel amac\u0131, g\u00f6zlemlenebilirli\u011fi kolayla\u015ft\u0131rmak ve sat\u0131c\u0131 ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131 sa\u011flamakt\u0131r.<\/p>\n<p>OTel&#8217;in temel bile\u015fenleri \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>Tracing (\u0130zleme):<\/strong> Bir iste\u011fin veya i\u015flemin sisteminizdeki ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc g\u00f6rselle\u015ftirmenizi sa\u011flar. Bir izleme (trace), birden fazla span&#8217;dan (alt i\u015flem) olu\u015fur. Her span, bir i\u015flemin belirli bir b\u00f6l\u00fcm\u00fcn\u00fc, \u00f6rne\u011fin bir veritaban\u0131 sorgusunu veya bir HTTP \u00e7a\u011fr\u0131s\u0131n\u0131 temsil eder.<\/li>\n<li><strong>Metrics (Metrikler):<\/strong> Uygulaman\u0131z\u0131n performans\u0131n\u0131n nicel \u00f6l\u00e7\u00fcmlerini toplar. \u00d6rne\u011fin, istek say\u0131s\u0131, hata oran\u0131, ortalama gecikme s\u00fcresi gibi veriler metrikler arac\u0131l\u0131\u011f\u0131yla takip edilir.<\/li>\n<li><strong>Logging (Loglama):<\/strong> Uygulama olaylar\u0131n\u0131 kaydetme mekanizmas\u0131d\u0131r. OpenTelemetry, loglar\u0131 izlemelerle ili\u015fkilendirerek daha zengin bir ba\u011flam sunmay\u0131 hedefler.<\/li>\n<\/ul>\n<p>OTel bu bile\u015fenleri standartla\u015ft\u0131rarak, Falcon gibi bir uygulamadan toplanan verilerin Jaeger gibi bir izleme sisteminde, Prometheus\/Grafana gibi bir metrik g\u00f6rselle\u015ftirme arac\u0131nda ve hatta Logstash gibi bir log toplama platformunda sorunsuz bir \u015fekilde kullan\u0131labilmesini sa\u011flar. Bu sayede, &#8220;\u0130stek X neden yava\u015f?&#8221; gibi sorulara \u00e7ok daha h\u0131zl\u0131 ve do\u011fru cevaplar bulabilirsiniz, \u00e7\u00fcnk\u00fc t\u00fcm ilgili telemetri verileri tek bir tutarl\u0131 \u00e7er\u00e7evede toplanm\u0131\u015f olur.<\/p>\n<div class=\"expert-tip\">\n    Uzman \u0130pucu: OpenTelemetry&#8217;nin sa\u011flad\u0131\u011f\u0131 standartla\u015fma, sat\u0131c\u0131 kilitlenmesini ortadan kald\u0131r\u0131r. Bu, izleme altyap\u0131n\u0131z\u0131 istedi\u011finiz zaman farkl\u0131 ara\u00e7larla de\u011fi\u015ftirebilece\u011finiz anlam\u0131na gelir, b\u00f6ylece gelecekteki esnekli\u011finiz garanti alt\u0131na al\u0131n\u0131r.\n<\/div>\n<h2>Falcon Uygulamas\u0131n\u0131 OpenTelemetry ile Nas\u0131l Entegre Ederiz?<\/h2>\n<p>Falcon uygulaman\u0131z\u0131 OpenTelemetry ile izlemeye ba\u015flamak, birka\u00e7 temel ad\u0131m ve biraz kod de\u011fi\u015fikli\u011fi gerektirir. Bu b\u00f6l\u00fcmde, s\u0131f\u0131rdan bir Falcon uygulamas\u0131 olu\u015fturup OpenTelemetry enstr\u00fcmantasyonunu nas\u0131l ekleyece\u011finizi ad\u0131m ad\u0131m inceleyece\u011fiz. Bu s\u00fcre\u00e7, uygulaman\u0131z\u0131n performans\u0131n\u0131 ve davran\u0131\u015f\u0131n\u0131 daha yak\u0131ndan g\u00f6zlemlemenize olanak tan\u0131r.<\/p>\n<h3>Gerekli K\u00fct\u00fcphanelerin Kurulumu ve Temel Yap\u0131land\u0131rma<\/h3>\n<p>\u0130lk olarak, Falcon uygulaman\u0131z i\u00e7in gerekli Python k\u00fct\u00fcphanelerini kurman\u0131z gerekiyor. Bu k\u00fct\u00fcphaneler OpenTelemetry SDK&#8217;s\u0131n\u0131, Falcon i\u00e7in otomatik enstr\u00fcmantasyon eklentisini ve Prometheus&#8217;a metrikleri d\u0131\u015fa aktarmak i\u00e7in bir exporter&#8217;\u0131 i\u00e7erecektir. A\u015fa\u011f\u0131daki komutlarla kurulumu ger\u00e7ekle\u015ftirebilirsiniz:<\/p>\n<pre><code>\npip install falcon opentelemetry-sdk opentelemetry-instrumentation-falcon opentelemetry-exporter-prometheus\n<\/pre>\n<p><\/code><\/p>\n<p>Kurulum tamamland\u0131ktan sonra, OpenTelemetry'yi uygulaman\u0131zda yap\u0131land\u0131rman\u0131z gerekir. Bu yap\u0131land\u0131rma, izlemelerin ve metriklerin nas\u0131l toplanaca\u011f\u0131n\u0131 ve nereye g\u00f6nderilece\u011fini belirler. Genellikle, uygulaman\u0131z\u0131n ba\u015flang\u0131\u00e7 noktas\u0131nda bir OpenTelemetry TracerProvider ve MeterProvider ayarlars\u0131n\u0131z.<\/p>\n<pre><code>\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor\n\nfrom opentelemetry import metrics\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.sdk.metrics.export import (\n    ConsoleMetricExporter,\n    PeriodicExportingMetricReader,\n)\nfrom opentelemetry.exporter.prometheus import PrometheusMetricReader\n\nimport falcon\n\n# 1. Kaynak Tan\u0131mlama (Application Name)\nresource = Resource.create({\n    \"service.name\": \"falcon-api-service\",\n    \"service.version\": \"1.0.0\",\n    \"environment\": \"development\"\n})\n\n# 2. \u0130zleme (Tracing) Yap\u0131land\u0131rmas\u0131\ntrace_provider = TracerProvider(resource=resource)\n# Span'lar\u0131 konsola yazd\u0131ran basit bir exporter. Ger\u00e7ek uygulamada JaegerExporter kullan\u0131l\u0131r.\nspan_processor = SimpleSpanProcessor(ConsoleSpanExporter())\ntrace_provider.add_span_processor(span_processor)\ntrace.set_tracer_provider(trace_provider)\n\n# 3. Metrik (Metrics) Yap\u0131land\u0131rmas\u0131\n# Prometheus i\u00e7in MetricReader. Varsay\u0131lan olarak 8000 portunda endpoint a\u00e7ar.\nmetric_reader = PeriodicExportingMetricReader(PrometheusMetricReader())\nmetric_provider = MeterProvider(metric_readers=[metric_reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\n# Tracer ve Meter nesnelerini al\ntracer = trace.get_tracer(__name__)\nmeter = metrics.get_meter(__name__)\n\n# \u00d6rnek bir say\u0131c\u0131 metri\u011fi olu\u015fturma\nrequest_counter = meter.create_counter(\n    \"api_requests_total\",\n    description=\"Toplam API istek say\u0131s\u0131\",\n    unit=\"1\",\n)\n<\/pre>\n<p><\/code><\/p>\n<p>Bu kod blo\u011fu, uygulaman\u0131z i\u00e7in bir kaynak tan\u0131mlar, izleme verilerini (span'lar\u0131) konsola yazacak bir TracerProvider ayarlar ve metrikleri Prometheus uyumlu bir endpoint \u00fczerinden d\u0131\u015fa aktaracak bir MeterProvider yap\u0131land\u0131r\u0131r. Ger\u00e7ek bir senaryoda, <code>ConsoleSpanExporter<\/code> yerine <code>JaegerExporter<\/code> gibi bir d\u0131\u015fa aktar\u0131c\u0131 kullan\u0131rs\u0131n\u0131z. PrometheusMetricReader, uygulaman\u0131zda otomatik olarak <code>\/metrics<\/code> ad\u0131nda bir endpoint a\u00e7ar ve Prometheus sunucunuz bu endpoint'ten metrikleri \u00e7ekebilir (scrape edebilir).<\/p>\n<h3>Tracer ve Span Olu\u015fturma: Kod \u0130\u00e7inde Ad\u0131m Ad\u0131m \u0130zleme<\/h3>\n<p>OpenTelemetry'nin Falcon i\u00e7in sundu\u011fu otomatik enstr\u00fcmantasyon, \u00e7o\u011fu temel HTTP iste\u011fini ve cevab\u0131n\u0131 otomatik olarak izler. Ancak, uygulaman\u0131z\u0131n i\u00e7indeki \u00f6zel i\u015f mant\u0131\u011f\u0131n\u0131 veya kritik i\u015flem ad\u0131mlar\u0131n\u0131 izlemek i\u00e7in manuel enstr\u00fcmantasyon eklemeniz gerekebilir. Bu, daha detayl\u0131 performans analizi yapman\u0131z\u0131 sa\u011flar.<\/p>\n<p>A\u015fa\u011f\u0131da, bir Falcon uygulamas\u0131n\u0131n otomatik enstr\u00fcmantasyonla nas\u0131l ba\u015flat\u0131laca\u011f\u0131n\u0131 ve \u00f6zel bir kaynakta (resource) manuel bir span'\u0131n nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6steren basit bir \u00f6rnek verilmi\u015ftir:<\/p>\n<pre><code>\nimport falcon\nfrom opentelemetry.instrumentation.falcon import FalconInstrumentor\n\n# ... \u00f6nceki OpenTelemetry yap\u0131land\u0131rma kodlar\u0131 buraya gelir ...\n\n# Otomatik enstr\u00fcmantasyonu etkinle\u015ftirme\nFalconInstrumentor().instrument()\n\nclass QuoteResource:\n    def on_get(self, req, resp):\n        # Manuel bir span olu\u015fturma\n        with tracer.start_as_current_span(\"get-quote-operation\") as span:\n            # Span'a \u00f6znitelikler ekleme\n            span.set_attribute(\"http.method\", req.method)\n            span.set_attribute(\"request.path\", req.path)\n            \n            # Request counter'\u0131 art\u0131rma\n            request_counter.add(1, {\"method\": req.method, \"path\": req.path})\n\n            # Uzun s\u00fcren bir i\u015flem veya veritaban\u0131 \u00e7a\u011fr\u0131s\u0131 sim\u00fclasyonu\n            import time\n            time.sleep(0.05) \n\n            # Bir alt span (child span) olu\u015fturma \u00f6rne\u011fi\n            with tracer.start_as_current_span(\"simulate-db-call\"):\n                time.sleep(0.02)\n                resp.status = falcon.HTTP_200\n                resp.media = {\n                    'quote': 'Hello, Falcon and OpenTelemetry!',\n                    'author': 'OpenTelemetry Community'\n                }\n\napp = falcon.App()\nquotes = QuoteResource()\napp.add_route('\/quote', quotes)\n\n# Uygulamay\u0131 \u00e7al\u0131\u015ft\u0131rmak i\u00e7in: gunicorn --worker-class gevent -w 4 your_app_module:app\n<\/pre>\n<p><\/code><\/p>\n<p>Yukar\u0131daki \u00f6rnekte:<\/p>\n<ul>\n<li><code>FalconInstrumentor().instrument()<\/code> \u00e7a\u011fr\u0131s\u0131, Falcon uygulamas\u0131na gelen t\u00fcm HTTP istekleri i\u00e7in otomatik olarak span'lar olu\u015fturur.<\/li>\n<li><code>with tracer.start_as_current_span(\"get-quote-operation\") as span:<\/code> yap\u0131s\u0131, kod blo\u011fu i\u00e7inde \u00f6zel bir izleme span'\u0131 olu\u015fturur. Bu span, HTTP iste\u011fi span'\u0131n\u0131n alt span'\u0131 olacakt\u0131r.<\/li>\n<li><code>span.set_attribute(...)<\/code> ile span'a \u00f6zel bilgiler ekleyebilirsiniz. Bu \u00f6znitelikler, izleme sisteminizde detayl\u0131 filtreleme ve arama yapman\u0131z\u0131 sa\u011flar.<\/li>\n<li><code>request_counter.add(...)<\/code> ile tan\u0131mlad\u0131\u011f\u0131m\u0131z metri\u011fi her istekte art\u0131r\u0131yoruz. Metriklere de \u00f6znitelikler ekleyerek daha detayl\u0131 segmentasyon yapabilirsiniz.<\/li>\n<\/ul>\n<p>Bu sayede, uygulaman\u0131za gelen bir iste\u011fin ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc, bu s\u00fcrenin hangi \u00f6zel i\u015flemler aras\u0131nda b\u00f6l\u00fcnd\u00fc\u011f\u00fcn\u00fc ve her bir i\u015flem ad\u0131m\u0131n\u0131n detaylar\u0131n\u0131 g\u00f6rselle\u015ftirebilirsiniz. Bu, \u00f6zellikle performans darbo\u011fazlar\u0131n\u0131 tespit etmede veya karma\u015f\u0131k i\u015f mant\u0131\u011f\u0131ndaki hatalar\u0131 ay\u0131klamada paha bi\u00e7ilmez bir ara\u00e7t\u0131r.<\/p>\n<h3>Metriklerin Toplanmas\u0131 ve \u00d6zel Metrikler<\/h3>\n<p>\u0130zlemeler, bir iste\u011fin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc anlamak i\u00e7in harikayken, metrikler sistemin genel sa\u011fl\u0131\u011f\u0131 ve performans\u0131 hakk\u0131nda toplu, nicel veriler sa\u011flar. OpenTelemetry, \u00e7e\u015fitli metrik t\u00fcrlerini destekler:<\/p>\n<ul>\n<li><strong>Counter (Say\u0131c\u0131):<\/strong> Sadece artan de\u011ferleri takip eder (\u00f6rne\u011fin, toplam istek say\u0131s\u0131, hata say\u0131s\u0131).<\/li>\n<li><strong>UpDownCounter:<\/strong> Hem artan hem de azalan de\u011ferleri takip eder (\u00f6rne\u011fin, aktif ba\u011flant\u0131 say\u0131s\u0131).<\/li>\n<li><strong>Histogram:<\/strong> G\u00f6zlemlenen de\u011ferlerin bir da\u011f\u0131l\u0131m\u0131n\u0131 ve istatistiklerini (min, max, ortalama, persentiller) tutar (\u00f6rne\u011fin, istek gecikme s\u00fcreleri).<\/li>\n<li><strong>Gauge:<\/strong> Mevcut bir de\u011feri anl\u0131k olarak g\u00f6sterir (\u00f6rne\u011fin, anl\u0131k CPU kullan\u0131m\u0131, bellek t\u00fcketimi).<\/li>\n<\/ul>\n<p>Yukar\u0131daki \u00f6rnekte <code>request_counter<\/code> adl\u0131 bir say\u0131c\u0131 metri\u011fi olu\u015fturduk ve her istek geldi\u011finde bu metri\u011fi art\u0131rd\u0131k. Benzer \u015fekilde, di\u011fer performans g\u00f6stergelerini veya uygulaman\u0131za \u00f6zel i\u015f metriklerini de toplayabilirsiniz. \u00d6rne\u011fin, belirli bir i\u015f i\u015fleminin ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc \u00f6l\u00e7mek i\u00e7in bir Histogram metri\u011fi kullanabilirsiniz:<\/p>\n<pre><code>\nimport time\n\n# ... \u00f6nceki OpenTelemetry metrik yap\u0131land\u0131rma kodlar\u0131 buraya gelir ...\n\nresponse_time_histogram = meter.create_histogram(\n    \"api_response_time_seconds\",\n    description=\"API yan\u0131t s\u00fcreleri da\u011f\u0131l\u0131m\u0131\",\n    unit=\"s\",\n)\n\nclass AnotherResource:\n    def on_post(self, req, resp):\n        start_time = time.time()\n        with tracer.start_as_current_span(\"process-data-operation\"):\n            # Uzun s\u00fcren bir veri i\u015fleme sim\u00fclasyonu\n            time.sleep(0.1 + (len(req.media.get(\"data\", \"\")) \/ 1000)) \n            resp.status = falcon.HTTP_200\n            resp.media = {\"status\": \"processed\", \"received_data_length\": len(req.media.get(\"data\", \"\"))}\n        \n        end_time = time.time()\n        duration = end_time - start_time\n        response_time_histogram.record(duration, {\"path\": req.path, \"status_code\": resp.status})\n\napp.add_route('\/process', AnotherResource())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu \u00f6rnekte, <code>\/process<\/code> endpoint'ine gelen isteklerin i\u015flenme s\u00fcresini \u00f6l\u00e7en bir histogram metri\u011fi tan\u0131mlad\u0131k. <code>response_time_histogram.record(duration, ...)<\/code> \u00e7a\u011fr\u0131s\u0131 ile her iste\u011fin i\u015flenme s\u00fcresini bu histograma kaydediyoruz. Bu metrikler, Prometheus gibi bir izleme sistemi taraf\u0131ndan toplanacak ve Grafana gibi bir ara\u00e7ta g\u00f6rselle\u015ftirilerek uygulaman\u0131z\u0131n genel performans e\u011filimlerini izlemenizi sa\u011flayacakt\u0131r. Metrikleri Falcon ile entegre etmek, sisteminizdeki anl\u0131k de\u011fi\u015fimleri ve uzun vadeli trendleri g\u00f6zlemlemek i\u00e7in g\u00fc\u00e7l\u00fc bir yol sunar.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Senaryolar\u0131nda Falcon ve OpenTelemetry Kullan\u0131m\u0131<\/h2>\n<p>OpenTelemetry ve Falcon entegrasyonu, sadece basit izleme verileri toplamakla kalmaz, ayn\u0131 zamanda karma\u015f\u0131k ger\u00e7ek d\u00fcnya senaryolar\u0131nda derinlemesine g\u00f6zlemlenebilirlik sa\u011flamak i\u00e7in de kullan\u0131labilir. \u00d6zellikle da\u011f\u0131t\u0131k sistemler, mikroservis mimarileri ve y\u00fcksek trafikli API'lerde bu entegrasyonun de\u011feri daha da artar. \u015eimdi, bu teknolojilerin ger\u00e7ek d\u00fcnya problemlerini nas\u0131l \u00e7\u00f6zd\u00fc\u011f\u00fcne dair baz\u0131 senaryolara ve ileri d\u00fczey kullan\u0131mlara bakal\u0131m.<\/p>\n<h3>Da\u011f\u0131t\u0131k \u0130zleme ve Hata Ay\u0131klama Nas\u0131l Yap\u0131l\u0131r?<\/h3>\n<p>Bir Falcon API'sinin ba\u015fka bir mikroservisi \u00e7a\u011f\u0131rd\u0131\u011f\u0131 bir senaryo d\u00fc\u015f\u00fcnelim. Sorun giderme a\u015famas\u0131nda, iste\u011fin hangi serviste tak\u0131ld\u0131\u011f\u0131n\u0131 veya bir hata olu\u015ftu\u011funda nerede patlad\u0131\u011f\u0131n\u0131 bulmak kritik \u00f6nem ta\u015f\u0131r. OpenTelemetry'nin da\u011f\u0131t\u0131k izleme yetene\u011fi burada parlar.<\/p>\n<p>Bir istek geldi\u011finde, OpenTelemetry otomatik olarak bir \"trace context\" olu\u015fturur. Bu ba\u011flam, HTTP ba\u015fl\u0131klar\u0131 (\u00f6rne\u011fin, <code>traceparent<\/code> ve <code>tracestate<\/code>) arac\u0131l\u0131\u011f\u0131yla downstream servislere yay\u0131l\u0131r. Downstream servisler de OpenTelemetry ile enstr\u00fcmante edilmi\u015fse, bu ba\u011flam\u0131 al\u0131p kendi span'lar\u0131n\u0131 mevcut trace'e eklerler. B\u00f6ylece, u\u00e7tan uca tek bir izleme (trace) elde edilir ve bu izleme, t\u00fcm servislerdeki ilgili span'lar\u0131 i\u00e7erir.<\/p>\n<p>Bu t\u00fcr da\u011f\u0131t\u0131k izleme verilerini g\u00f6rselle\u015ftirmek i\u00e7in Jaeger veya Zipkin gibi da\u011f\u0131t\u0131k izleme backend'leri kullan\u0131l\u0131r. OpenTelemetry Python SDK's\u0131, bu sistemlere veri g\u00f6ndermek i\u00e7in \u00f6zel exporter'lar sunar:<\/p>\n<pre><code>\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.jaeger.proto.grpc import JaegerExporter # veya ZipkinExporter\n\n# ... \u00f6nceki trace_provider yap\u0131land\u0131rmas\u0131ndan sonra ...\n\n# Jaeger Exporter'\u0131 yap\u0131land\u0131rma (varsay\u0131lan 14250 portunda grpc kullan\u0131r)\njaeger_exporter = JaegerExporter(\n    agent_host_name=\"localhost\", # Jaeger agent host\n    agent_port=14250,\n)\nspan_processor = BatchSpanProcessor(jaeger_exporter)\ntrace_provider.add_span_processor(span_processor)\n\n# ... Falcon uygulamas\u0131n\u0131 ba\u015flatma ...\n<\/pre>\n<p><\/code><\/p>\n<p>Bu yap\u0131land\u0131rma ile, Falcon uygulaman\u0131zdan toplanan t\u00fcm izleme verileri Jaeger agent'\u0131na g\u00f6nderilir ve oradan da Jaeger Collector'a ula\u015farak g\u00f6rselle\u015ftirilebilir. Jaeger UI'da, tek bir iste\u011fin hangi mikroservislerden ge\u00e7ti\u011fini, her serviste ne kadar zaman harcand\u0131\u011f\u0131n\u0131 ve olas\u0131 hatalar\u0131 bir \u015felale grafi\u011fi (waterfall graph) \u015feklinde g\u00f6rebilirsiniz. Bu, \u00f6zellikle karma\u015f\u0131k mikroservis mimarilerinde hata ay\u0131klamay\u0131 ve performans sorunlar\u0131n\u0131 \u00e7\u00f6zmeyi inan\u0131lmaz derecede h\u0131zland\u0131r\u0131r. Da\u011f\u0131t\u0131k izleme, sistemdeki t\u00fcm par\u00e7alar\u0131n birbirleriyle nas\u0131l etkile\u015fimde bulundu\u011funu \u015feffaf hale getirerek, \"kim kimle konu\u015fuyor, ne kadar s\u00fcr\u00fcyor?\" gibi sorulara net yan\u0131tlar verir.<\/p>\n<h3>Performans Metriklerini G\u00f6rselle\u015ftirme (Prometheus\/Grafana)<\/h3>\n<p>Metrikler, sistemin sa\u011fl\u0131\u011f\u0131n\u0131 ve performans\u0131n\u0131 anl\u0131k olarak izlemek i\u00e7in kullan\u0131l\u0131r. OpenTelemetry'nin PrometheusExporter'\u0131 sayesinde, Falcon uygulaman\u0131zdaki metrikleri kolayca Prometheus taraf\u0131ndan toplanabilir hale getirebilirsiniz. Prometheus, zaman serisi verilerini depolayan ve sorgulayan g\u00fc\u00e7l\u00fc bir a\u00e7\u0131k kaynak izleme sistemidir. Toplanan bu metrikler, Grafana gibi bir g\u00f6rselle\u015ftirme arac\u0131yla anlaml\u0131 panolara d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir.<\/p>\n<pre><code>\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry.exporter.prometheus import PrometheusMetricReader\n\n# ... \u00f6nceki MeterProvider yap\u0131land\u0131rmas\u0131ndan sonra ...\n\n# PrometheusMetricReader, uygulaman\u0131zda otomatik olarak \/metrics endpoint'i a\u00e7ar.\nmetric_reader = PeriodicExportingMetricReader(PrometheusMetricReader())\nmetric_provider = MeterProvider(metric_readers=[metric_reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\n# ... \u00d6rnek metrikler olu\u015fturma (\u00f6rne\u011fin, request_counter, response_time_histogram) ...\n\n# Falcon uygulamas\u0131n\u0131 ba\u015flatma\n# Prometheus, bu uygulaman\u0131n \/metrics endpoint'inden metrikleri \u00e7ekecektir.\n<\/pre>\n<p><\/code><\/p>\n<p>Prometheus yap\u0131land\u0131rma dosyan\u0131za (<code>prometheus.yml<\/code>) a\u015fa\u011f\u0131daki gibi bir job ekleyerek Falcon uygulaman\u0131zdan metrikleri \u00e7ekmesini sa\u011flayabilirsiniz:<\/p>\n<pre><code>\n# prometheus.yml\nscrape_configs:\n  - job_name: 'falcon_api'\n    static_configs:\n      - targets: ['localhost:8000'] # Falcon uygulaman\u0131z\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 adres ve PrometheusMetricReader portu\n<\/pre>\n<p><\/code><\/p>\n<p>Prometheus metrikleri toplamaya ba\u015flad\u0131ktan sonra, Grafana'da Prometheus veri kayna\u011f\u0131n\u0131 yap\u0131land\u0131rabilir ve \u00f6zel panolar olu\u015fturabilirsiniz. \u00d6rne\u011fin, \"API \u0130stek Say\u0131s\u0131\", \"Ortalama Yan\u0131t S\u00fcresi\", \"Hata Oran\u0131\" gibi metrikleri zaman i\u00e7inde g\u00f6rselle\u015ftirerek uygulaman\u0131z\u0131n performans\u0131ndaki de\u011fi\u015fiklikleri kolayca g\u00f6zlemleyebilirsiniz. Ayr\u0131ca, belirli e\u015fik de\u011ferler a\u015f\u0131ld\u0131\u011f\u0131nda uyar\u0131lar (alertler) tetiklemek i\u00e7in Prometheus'un Alertmanager'\u0131n\u0131 veya Grafana'n\u0131n uyar\u0131 \u00f6zelliklerini kullanabilirsiniz. Bu, proaktif izleme ve operasyonel verimlilik i\u00e7in kritik bir ad\u0131md\u0131r.<\/p>\n<h3>Mikroservis Ortam\u0131nda Ba\u011flam Yay\u0131l\u0131m\u0131<\/h3>\n<p>Mikroservis ortamlar\u0131nda, bir iste\u011fin kimli\u011fi (trace ID) ve ebeveyn span'\u0131 gibi ba\u011flam bilgilerinin servisler aras\u0131nda d\u00fczg\u00fcn bir \u015fekilde yay\u0131lmas\u0131, tutarl\u0131 da\u011f\u0131t\u0131k izlemeler olu\u015fturmak i\u00e7in zorunludur. OpenTelemetry SDK'lar\u0131, bu ba\u011flam\u0131 HTTP ba\u015fl\u0131klar\u0131 (\u00f6rne\u011fin W3C Trace Context standard\u0131) arac\u0131l\u0131\u011f\u0131yla otomatik olarak yayar. Ancak, e\u011fer manuel HTTP \u00e7a\u011fr\u0131lar\u0131 yap\u0131yorsan\u0131z veya farkl\u0131 ileti\u015fim protokolleri kullan\u0131yorsan\u0131z, ba\u011flam\u0131 manuel olarak yayman\u0131z gerekebilir.<\/p>\n<p>\u00d6rne\u011fin, Falcon uygulaman\u0131zdan ba\u015fka bir servise istek atarken OpenTelemetry ba\u011flam\u0131n\u0131 manuel olarak nas\u0131l yayabilece\u011finizi g\u00f6steren bir \u00f6rnek:<\/p>\n<pre><code>\nimport requests\nfrom opentelemetry import trace\nfrom opentelemetry.propagate import inject\n\nclass ClientResource:\n    def on_get(self, req, resp):\n        with tracer.start_as_current_span(\"call-external-service\"):\n            headers = {}\n            # Mevcut OpenTelemetry ba\u011flam\u0131n\u0131 HTTP ba\u015fl\u0131klar\u0131na enjekte et\n            inject(headers) \n            \n            # Ba\u015fka bir servise HTTP \u00e7a\u011fr\u0131s\u0131 yap\n            try:\n                response = requests.get(\"http:\/\/another-service:8080\/data\", headers=headers)\n                response.raise_for_status()\n                resp.status = falcon.HTTP_200\n                resp.media = {\"data_from_external_service\": response.json()}\n            except requests.exceptions.RequestException as e:\n                resp.status = falcon.HTTP_500\n                resp.media = {\"error\": f\"Harici servis \u00e7a\u011fr\u0131s\u0131nda hata: {e}\"}\n                # Hata durumunda span'\u0131 i\u015faretleme\n                trace.get_current_span().set_attribute(\"error.message\", str(e))\n                trace.get_current_span().set_status(trace.StatusCode.ERROR, \"External service error\")\n\napp.add_route('\/client-data', ClientResource())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu \u00f6rnekte, <code>inject(headers)<\/code> fonksiyonu, mevcut izleme ba\u011flam\u0131n\u0131 <code>headers<\/code> s\u00f6zl\u00fc\u011f\u00fcne ekler. Daha sonra bu ba\u015fl\u0131klar, <code>requests.get()<\/code> \u00e7a\u011fr\u0131s\u0131 ile harici servise g\u00f6nderilir. Harici servis de OpenTelemetry ile enstr\u00fcmante edildi\u011fi takdirde, bu ba\u015fl\u0131klar\u0131 otomatik olarak alg\u0131lar ve kendi span'lar\u0131n\u0131 ayn\u0131 trace'e ekler. Bu sayede, uygulaman\u0131z\u0131n karma\u015f\u0131k bir mikroservis ortam\u0131nda bile u\u00e7tan uca izlenebilirli\u011fini sa\u011flars\u0131n\u0131z. Ba\u011flam yay\u0131l\u0131m\u0131, da\u011f\u0131t\u0131k izlemenin temelini olu\u015fturur ve olmadan bir iste\u011fin yolculu\u011funu takip etmek neredeyse imkans\u0131z hale gelir.<\/p>\n<h2>\u0130leri D\u00fczey \u0130zleme Teknikleri ve Optimizasyon \u0130pu\u00e7lar\u0131<\/h2>\n<p>Falcon ve OpenTelemetry ile temel entegrasyonu sa\u011flad\u0131ktan sonra, izleme altyap\u0131n\u0131z\u0131 daha verimli ve maliyet etkin hale getirmek i\u00e7in ileri d\u00fczey tekniklere ve optimizasyon ipu\u00e7lar\u0131na y\u00f6nelebilirsiniz. \u00d6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli uygulamalarda veya y\u00fcksek trafikli API'lerde, toplanan telemetri verisi hacmi \u00f6nemli hale gelebilir. Bu nedenle, do\u011fru stratejileri uygulamak hem performans hem de kaynak kullan\u0131m\u0131 a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir.<\/p>\n<h3>Sampling Stratejileri ve Maliyet Y\u00f6netimi<\/h3>\n<p>Y\u00fcksek trafikli uygulamalarda her iste\u011fi izlemek, hem performans \u00fczerinde ek y\u00fck olu\u015fturabilir hem de izleme backend'lerinde depolama ve i\u015fleme maliyetlerini art\u0131rabilir. \u0130\u015fte bu noktada \"sampling\" devreye girer. Sampling, t\u00fcm izlemelerin yerine sadece belirli bir y\u00fczdesini veya belirli kriterlere uyan izlemeleri toplama i\u015flemidir. OpenTelemetry, \u00e7e\u015fitli sampling stratejilerini destekler:<\/p>\n<ul>\n<li><strong>AlwaysOnSampler:<\/strong> T\u00fcm izlemeleri \u00f6rnekler (varsay\u0131lan).<\/li>\n<li><strong>AlwaysOffSampler:<\/strong> Hi\u00e7bir izlemeyi \u00f6rneklemez (sadece test\/geli\u015ftirme i\u00e7in).<\/li>\n<li><strong>ParentBased (Ebeveyn Tabanl\u0131) Sampler:<\/strong> Ebeveyn span'\u0131n sampling karar\u0131na g\u00f6re hareket eder.<\/li>\n<li><strong>TraceIdRatioBased (Trace ID Oran Tabanl\u0131) Sampler:<\/strong> Belirli bir orana g\u00f6re (\u00f6rne\u011fin, her 100 iste\u011fin 1'ini) izlemeyi \u00f6rnekler.<\/li>\n<\/ul>\n<p>Maliyetleri d\u00fc\u015f\u00fcrmek ve performans\u0131 optimize etmek i\u00e7in <code>TraceIdRatioBased<\/code> Sampler'\u0131 kullanabilirsiniz. \u00d6rne\u011fin, her 1000 iste\u011fin sadece 1'ini izlemek i\u00e7in:<\/p>\n<pre><code>\nfrom opentelemetry.sdk.trace.sampling import TraceIdRatioBased\n\n# ... \u00f6nceki OpenTelemetry trace_provider yap\u0131land\u0131rmas\u0131ndan \u00f6nce ...\n\n# Her 1000 iste\u011fin 1'ini \u00f6rnekle\nsampler = TraceIdRatioBased(1\/1000) \ntrace_provider = TracerProvider(resource=resource, sampler=sampler)\n\n# ... di\u011fer trace_provider yap\u0131land\u0131rmalar\u0131na devam ...\n<\/pre>\n<p><\/code><\/p>\n<p>Bu strateji, genel sistem davran\u0131\u015f\u0131 hakk\u0131nda istatistiksel olarak anlaml\u0131 veriler toplarken, toplama ve depolama y\u00fck\u00fcn\u00fc \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r. Ancak, bir hatay\u0131 ay\u0131klarken, hatal\u0131 isteklerin \u00f6rneklenmeme riski bulundu\u011funu unutmamak \u00f6nemlidir. Bu nedenle, hata oran\u0131 y\u00fcksek veya kritik \u00f6neme sahip endpoint'ler i\u00e7in farkl\u0131 sampling kurallar\u0131 uygulamay\u0131 d\u00fc\u015f\u00fcnebilirsiniz.<\/p>\n<div class=\"expert-tip\">\n    Uzman \u0130pucu: \u00dcretim ortamlar\u0131nda genellikle <code>TraceIdRatioBased<\/code> sampling kullan\u0131l\u0131r. Ancak, \u00f6nemli i\u015f ak\u0131\u015flar\u0131 veya hata durumlar\u0131 i\u00e7in daha y\u00fcksek bir sampling oran\u0131 uygulayan veya ko\u015fullu sampling yapan \u00f6zel sampler'lar geli\u015ftirmeyi d\u00fc\u015f\u00fcnebilirsiniz.\n<\/div>\n<h3>Otomatik Enstr\u00fcmantasyonun S\u0131n\u0131rlar\u0131 ve Manuel Yakla\u015f\u0131mlar<\/h3>\n<p>OpenTelemetry'nin Python i\u00e7in sa\u011flad\u0131\u011f\u0131 otomatik enstr\u00fcmantasyon (\u00f6rne\u011fin <code>opentelemetry-instrumentation-falcon<\/code>), \u00e7o\u011fu pop\u00fcler k\u00fct\u00fcphane ve \u00e7er\u00e7eve i\u00e7in hemen hemen \"s\u0131f\u0131r kod\" ile izleme ba\u015flatman\u0131z\u0131 sa\u011flar. Bu eklentiler, HTTP istekleri, veritaban\u0131 \u00e7a\u011fr\u0131lar\u0131, pop\u00fcler k\u00fct\u00fcphane fonksiyonlar\u0131 gibi yayg\u0131n i\u015flemleri otomatik olarak alg\u0131lar ve span'lar olu\u015fturur.<\/p>\n<p>Ancak, otomatik enstr\u00fcmantasyonun baz\u0131 s\u0131n\u0131rl\u0131l\u0131klar\u0131 vard\u0131r:<\/p>\n<ul>\n<li><strong>\u0130\u015f Mant\u0131\u011f\u0131 Detaylar\u0131:<\/strong> Uygulaman\u0131z\u0131n benzersiz i\u015f mant\u0131\u011f\u0131 i\u00e7indeki kritik ad\u0131mlar\u0131 veya alt i\u015flemleri otomatik olarak izleyemez. \u00d6rne\u011fin, bir veri d\u00f6n\u00fc\u015f\u00fcm ad\u0131m\u0131 veya karma\u015f\u0131k bir i\u015f kural\u0131 y\u00fcr\u00fctmesi gibi.<\/li>\n<li><strong>K\u00fct\u00fcphane Deste\u011fi:<\/strong> Kulland\u0131\u011f\u0131n\u0131z t\u00fcm k\u00fct\u00fcphaneler i\u00e7in otomatik enstr\u00fcmantasyon eklentisi bulunmayabilir.<\/li>\n<li><strong>\u00d6zel Nitelikler:<\/strong> Otomatik olarak olu\u015fturulan span'lara uygulaman\u0131za \u00f6zel ba\u011flamsal bilgileri (\u00f6rne\u011fin, kullan\u0131c\u0131 ID'si, sipari\u015f numaras\u0131) ekleyemezsiniz.<\/li>\n<\/ul>\n<p>Bu durumlarda, manuel enstr\u00fcmantasyon devreye girer. Yukar\u0131daki \u00f6rneklerde g\u00f6sterildi\u011fi gibi, <code>tracer.start_as_current_span()<\/code> veya <code>span.set_attribute()<\/code> kullanarak kodunuza \u00f6zel izleme noktalar\u0131 ve nitelikler ekleyebilirsiniz. Manuel enstr\u00fcmantasyon, uygulaman\u0131z\u0131n en kritik b\u00f6l\u00fcmleri i\u00e7in derinlemesine g\u00f6r\u00fcn\u00fcrl\u00fck sa\u011flar ve otomatik enstr\u00fcmantasyonun eksik kald\u0131\u011f\u0131 yerleri tamamlar. Ancak, \u00e7ok fazla manuel enstr\u00fcmantasyon eklemek kod karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 art\u0131rabilir; bu nedenle, stratejik ve hedefe y\u00f6nelik bir yakla\u015f\u0131m benimsemek \u00f6nemlidir.<\/p>\n<pre><code>\nimport falcon\nfrom opentelemetry import trace\n\n# ... OpenTelemetry yap\u0131land\u0131rmas\u0131 ...\ntracer = trace.get_tracer(__name__)\n\nclass PaymentResource:\n    def on_post(self, req, resp):\n        order_id = req.media.get(\"order_id\")\n        amount = req.media.get(\"amount\")\n\n        with tracer.start_as_current_span(\"process-payment\") as payment_span:\n            payment_span.set_attribute(\"order.id\", order_id)\n            payment_span.set_attribute(\"payment.amount\", amount)\n            payment_span.set_attribute(\"user.id\", req.context.get(\"user_id\", \"anonymous\")) # Varsay\u0131msal kullan\u0131c\u0131 bilgisi\n\n            # Ad\u0131m 1: \u00d6deme a\u011f ge\u00e7idiyle ileti\u015fim\n            with tracer.start_as_current_span(\"call-payment-gateway\") as gateway_span:\n                # ... \u00f6deme a\u011f ge\u00e7idi API \u00e7a\u011fr\u0131s\u0131 sim\u00fclasyonu ...\n                import time\n                time.sleep(0.08) \n                gateway_response = {\"status\": \"success\", \"transaction_id\": \"TXN12345\"}\n                gateway_span.set_attribute(\"gateway.response.status\", gateway_response[\"status\"])\n                gateway_span.set_attribute(\"gateway.transaction.id\", gateway_response[\"transaction_id\"])\n            \n            if gateway_response[\"status\"] == \"success\":\n                # Ad\u0131m 2: Veritaban\u0131na kaydetme\n                with tracer.start_as_current_span(\"save-payment-to-db\"):\n                    # ... veritaban\u0131 kayd\u0131 sim\u00fclasyonu ...\n                    time.sleep(0.03)\n                \n                resp.status = falcon.HTTP_200\n                resp.media = {\"message\": \"\u00d6deme ba\u015far\u0131yla i\u015flendi\", \"transaction_id\": gateway_response[\"transaction_id\"]}\n            else:\n                resp.status = falcon.HTTP_400\n                resp.media = {\"message\": \"\u00d6deme ba\u015far\u0131s\u0131z oldu\"}\n                payment_span.set_status(trace.StatusCode.ERROR, \"Payment gateway declined\")\n                payment_span.set_attribute(\"error.reason\", \"Gateway declined\")\n\napp = falcon.App()\napp.add_route('\/pay', PaymentResource())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu \u00f6rnek, bir \u00f6deme i\u015fleme ak\u0131\u015f\u0131ndaki kritik ad\u0131mlar\u0131 (\u00f6deme a\u011f ge\u00e7idi \u00e7a\u011fr\u0131s\u0131, veritaban\u0131 kayd\u0131) manuel olarak nas\u0131l izleyece\u011finizi g\u00f6steriyor. Her ad\u0131ma \u00f6zel span'lar olu\u015fturmak ve ilgili \u00f6znitelikleri eklemek, bir \u00f6demenin neden ba\u015far\u0131s\u0131z oldu\u011funu veya neden yava\u015flad\u0131\u011f\u0131n\u0131 anlamak i\u00e7in \u00e7ok de\u011ferli bilgiler sa\u011flar.<\/p>\n<h3>G\u00fcvenlik ve Gizlilik Hususlar\u0131<\/h3>\n<p>Telemetri verisi toplarken g\u00fcvenlik ve gizlilik daima \u00f6ncelikli olmal\u0131d\u0131r. \u00d6zellikle hassas verilerin (PII - Ki\u015fisel Tan\u0131mlay\u0131c\u0131 Bilgiler) izleme verilerine s\u0131zmas\u0131n\u0131 engellemek i\u00e7in dikkatli olmak gerekir. \u0130\u015fte dikkat etmeniz gereken baz\u0131 noktalar:<\/p>\n<ul>\n<li><strong>Hassas Veri Filtreleme\/Maskeleme:<\/strong> Kullan\u0131c\u0131 \u015fifreleri, kimlik numaralar\u0131, kredi kart\u0131 bilgileri gibi hassas verileri asla span \u00f6zniteliklerine veya loglara do\u011frudan eklemeyin. OpenTelemetry SDK'lar\u0131 genellikle span'lara eklenen \u00f6znitelikleri filtrelemek veya maskelemek i\u00e7in kancalar (hooks) sa\u011flar.<\/li>\n<li><strong>K\u0131s\u0131tl\u0131 Veri Eri\u015fimi:<\/strong> \u0130zleme sistemlerine eri\u015fimi k\u0131s\u0131tlay\u0131n ve sadece yetkili ki\u015filerin bu verilere ula\u015fabildi\u011finden emin olun.<\/li>\n<li><strong>TLS\/SSL Kullan\u0131m\u0131:<\/strong> Telemetri verilerini bir exporter arac\u0131l\u0131\u011f\u0131yla ba\u015fka bir sisteme (\u00f6rne\u011fin Jaeger, Prometheus) g\u00f6nderirken, verilerin \u015fifrelenmi\u015f bir kanal \u00fczerinden (TLS\/SSL) iletildi\u011finden emin olun.<\/li>\n<li><strong>Veri Saklama Politikalar\u0131:<\/strong> Telemetri verilerinin ne kadar s\u00fcreyle saklanaca\u011f\u0131n\u0131 belirleyen net politikalar olu\u015fturun ve bu politikalara uygun olarak eski verileri temizleyin.<\/li>\n<\/ul>\n<p>OpenTelemetry ile izleme, uygulaman\u0131z hakk\u0131nda derinlemesine bilgi edinmenizi sa\u011flarken, bu bilgiyi sorumlu ve g\u00fcvenli bir \u015fekilde y\u00f6netmek b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r. Telemetri pipeline'\u0131n\u0131z\u0131n her ad\u0131m\u0131nda gizlilik ve g\u00fcvenlik kontrollerini uygulamak, olas\u0131 veri ihlallerini \u00f6nlemeye yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>Bu makalede, Falcon tabanl\u0131 uygulamalar\u0131n\u0131z\u0131 OpenTelemetry ile nas\u0131l izleyece\u011finizi, da\u011f\u0131t\u0131k sistemlerde g\u00f6zlemlenebilirli\u011fi nas\u0131l art\u0131raca\u011f\u0131n\u0131z\u0131 ve performans metriklerini nas\u0131l g\u00f6rselle\u015ftirece\u011finizi detayl\u0131 bir \u015fekilde ele ald\u0131k. OpenTelemetry, modern bulut yerel uygulamalar\u0131 i\u00e7in standart, sat\u0131c\u0131dan ba\u011f\u0131ms\u0131z ve g\u00fc\u00e7l\u00fc bir telemetri toplama \u00e7er\u00e7evesi sunarak, geli\u015ftiricilerin ve operasyon ekiplerinin sistemlerinin davran\u0131\u015f\u0131n\u0131 derinlemesine anlamalar\u0131na olanak tan\u0131r.<\/p>\n<p>Falcon'un hafif yap\u0131s\u0131 ve OpenTelemetry'nin esnek enstr\u00fcmantasyon yetenekleri birle\u015fti\u011finde, API'lerinizin performans\u0131n\u0131 optimize etmek, hatalar\u0131 h\u0131zla tespit etmek ve genel sistem sa\u011fl\u0131\u011f\u0131n\u0131 proaktif olarak y\u00f6netmek i\u00e7in paha bi\u00e7ilmez bir ara\u00e7 setine sahip olursunuz. Otomatik enstr\u00fcmantasyon, \u00e7o\u011fu durumda h\u0131zl\u0131 bir ba\u015flang\u0131\u00e7 sa\u011flarken, manuel enstr\u00fcmantasyon ve ileri d\u00fczey sampling stratejileri, karma\u015f\u0131k senaryolarda derinlemesine g\u00f6r\u00fcn\u00fcrl\u00fck ve maliyet etkinli\u011fi sunar. G\u00fcvenlik ve gizlilik hususlar\u0131n\u0131 da g\u00f6z \u00f6n\u00fcnde bulundurarak, telemetri verilerinizi en verimli \u015fekilde kullanarak uygulamalar\u0131n\u0131z\u0131n gelecekteki ba\u015far\u0131s\u0131n\u0131 sa\u011flayabilirsiniz.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n<ol>\n<li>\n        <strong>OpenTelemetry, Falcon uygulamas\u0131n\u0131n performans\u0131n\u0131 olumsuz etkiler mi?<\/strong><br \/>\n        OpenTelemetry SDK'lar\u0131, performans \u00fczerindeki etkiyi minimumda tutmak \u00fczere tasarlanm\u0131\u015ft\u0131r. Genellikle ek y\u00fck olduk\u00e7a d\u00fc\u015f\u00fckt\u00fcr (tek haneli milisaniyeler veya daha az). Ancak, \u00e7ok fazla manuel enstr\u00fcmantasyon veya y\u00fcksek sampling oranlar\u0131 kullanmak bu etkiyi art\u0131rabilir. \u00dcretim ortam\u0131nda d\u00fc\u015f\u00fck sampling oranlar\u0131 ve asenkron exporter'lar kullanmak performans\u0131 optimize etmeye yard\u0131mc\u0131 olur.\n    <\/li>\n<li>\n        <strong>Hangi OpenTelemetry exporter'\u0131n\u0131 kullanmal\u0131y\u0131m?<\/strong><br \/>\n        Se\u00e7im, kulland\u0131\u011f\u0131n\u0131z backend sistemlerine ba\u011fl\u0131d\u0131r. Da\u011f\u0131t\u0131k izleme i\u00e7in JaegerExporter veya ZipkinExporter, metrikler i\u00e7in PrometheusMetricReader en yayg\u0131n se\u00e7eneklerdir. E\u011fer loglar\u0131 da OpenTelemetry ile topluyorsan\u0131z, OTLP (OpenTelemetry Protocol) exporter'\u0131n\u0131 kullanarak t\u00fcm verileri tek bir protokolle uyumlu bir backend'e (\u00f6rne\u011fin OpenTelemetry Collector) g\u00f6nderebilirsiniz.\n    <\/li>\n<li>\n        <strong>OpenTelemetry Collector nedir ve neden kullanmal\u0131y\u0131m?<\/strong><br \/>\n        OpenTelemetry Collector, uygulamalar\u0131n\u0131zdan telemetri verilerini al\u0131p, i\u015fleyip (filtreleme, d\u00f6n\u00fc\u015ft\u00fcrme, zenginle\u015ftirme) ve \u00e7e\u015fitli backend'lere (Jaeger, Prometheus, Loki vb.) d\u0131\u015fa aktarman\u0131z\u0131 sa\u011flayan bir arac\u0131d\u0131r. Do\u011frudan uygulamadan backend'e veri g\u00f6ndermek yerine Collector kullanmak, uygulamadaki ek y\u00fck\u00fc azalt\u0131r, merkezi bir veri i\u015fleme noktas\u0131 sa\u011flar ve veri format\u0131\/protokol\u00fc uyumlulu\u011fu konusunda esneklik sunar.\n    <\/li>\n<li>\n        <strong>Sadece izleme (tracing) mi yoksa metrikler mi daha \u00f6nemlidir?<\/strong><br \/>\n        Hem izleme hem de metrikler, uygulaman\u0131z\u0131n g\u00f6zlemlenebilirli\u011fi i\u00e7in hayati \u00f6neme sahiptir ve birbirini tamamlar. Metrikler, sistemin genel sa\u011fl\u0131\u011f\u0131 ve performans e\u011filimleri hakk\u0131nda geni\u015f bir bak\u0131\u015f a\u00e7\u0131s\u0131 sunarken, izlemeler belirli bir iste\u011fin neden yava\u015flad\u0131\u011f\u0131 veya hata verdi\u011fi gibi detayl\u0131 sorun giderme senaryolar\u0131nda derinlemesine bilgi sa\u011flar. Kapsaml\u0131 g\u00f6zlemlenebilirlik i\u00e7in her ikisini de kullanmak en iyisidir.\n    <\/li>\n<li>\n        <strong>OpenTelemetry ile Falcon uygulamamdaki loglar\u0131 nas\u0131l ili\u015fkilendirebilirim?<\/strong><br \/>\n        OpenTelemetry Python SDK's\u0131, standart Python logging mod\u00fcl\u00fcn\u00fc izleme ba\u011flam\u0131yla otomatik olarak zenginle\u015ftirebilen bir entegrasyon sunar (<code>opentelemetry-instrumentation-logging<\/code>). Bu enstr\u00fcmantasyonu etkinle\u015ftirdi\u011finizde, log kay\u0131tlar\u0131n\u0131za otomatik olarak <code>trace_id<\/code> ve <code>span_id<\/code> gibi \u00f6znitelikler eklenir. Bu sayede, loglar\u0131n\u0131z\u0131 bir izleme ile ili\u015fkilendirerek belirli bir iste\u011fin t\u00fcm loglar\u0131n\u0131 kolayca bulabilirsiniz.\n    <\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"Falcon Uygulamalar\u0131n\u0131 OpenTelemetry ile \u0130zleme: Derinlemesine Bir K\u0131lavuz Modern da\u011f\u0131t\u0131k sistemler giderek karma\u015f\u0131k hale gelirken, uygulamalar\u0131n\u0131z\u0131n ger\u00e7ek zamanl\u0131&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":[1],"tags":[],"class_list":{"0":"post-34125","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","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>OpenTelemetry ve Falcon \u0130zleme Neden \u00d6nemli?<\/title>\n<meta name=\"description\" content=\"Modern da\u011f\u0131t\u0131k sistemler giderek karma\u015f\u0131k hale gelirken, uygulamalar\u0131n\u0131z\u0131n ger\u00e7ek zamanl\u0131 performans\u0131n\u0131 ve davran\u0131\u015f\u0131n\u0131 anlamak hayati \u00f6nem ta\u015f\u0131yor. \u00d6zellikle Python ekosisteminde pop\u00fcler bir web \u00e7er\u00e7evesi olan Falcon ile geli\u015ftirilen API&#039;lerinizi izlemek, olas\u0131 darbo\u011fazlar\u0131 tespit etmek ve kullan\u0131c\u0131 deneyimini optimize etmek i\u00e7in kritik bir ihtiya\u00e7 haline gelmi\u015ftir. Bu makale, Falcon uygulamalar\u0131n\u0131z\u0131 OpenTelemetry ile entegre ederek kapsaml\u0131 bir g\u00f6zlemlenebilirlik (observability) kazanman\u0131n ad\u0131mlar\u0131n\u0131 ve ileri d\u00fczey tekniklerini derinlemesine inceliyor.\" \/>\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\/opentelemetry-ve-falcon-izleme-neden-onemli\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OpenTelemetry ve Falcon \u0130zleme Neden \u00d6nemli?\" \/>\n<meta property=\"og:description\" content=\"Modern da\u011f\u0131t\u0131k sistemler giderek karma\u015f\u0131k hale gelirken, uygulamalar\u0131n\u0131z\u0131n ger\u00e7ek zamanl\u0131 performans\u0131n\u0131 ve davran\u0131\u015f\u0131n\u0131 anlamak hayati \u00f6nem ta\u015f\u0131yor. \u00d6zellikle Python ekosisteminde pop\u00fcler bir web \u00e7er\u00e7evesi olan Falcon ile geli\u015ftirilen API&#039;lerinizi izlemek, olas\u0131 darbo\u011fazlar\u0131 tespit etmek ve kullan\u0131c\u0131 deneyimini optimize etmek i\u00e7in kritik bir ihtiya\u00e7 haline gelmi\u015ftir. Bu makale, Falcon uygulamalar\u0131n\u0131z\u0131 OpenTelemetry ile entegre ederek kapsaml\u0131 bir g\u00f6zlemlenebilirlik (observability) kazanman\u0131n ad\u0131mlar\u0131n\u0131 ve ileri d\u00fczey tekniklerini derinlemesine inceliyor.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-11T10:31:10+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"24 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"OpenTelemetry ve Falcon \u0130zleme Neden \u00d6nemli?\",\"datePublished\":\"2025-11-11T10:31:10+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/\"},\"wordCount\":3834,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/opentelemetry-ve-falcon-izleme-neden-onemli\/\",\"name\":\"OpenTelemetry ve Falcon \u0130zleme Neden \u00d6nemli?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-11-11T10:31:10+00:00\",\"description\":\"Modern da\u011f\u0131t\u0131k sistemler giderek karma\u015f\u0131k hale gelirken, uygulamalar\u0131n\u0131z\u0131n ger\u00e7ek zamanl\u0131 performans\u0131n\u0131 ve davran\u0131\u015f\u0131n\u0131 anlamak hayati \u00f6nem ta\u015f\u0131yor. \u00d6zellikle Python ekosisteminde pop\u00fcler bir web \u00e7er\u00e7evesi olan Falcon ile geli\u015ftirilen API'lerinizi izlemek, olas\u0131 darbo\u011fazlar\u0131 tespit etmek ve kullan\u0131c\u0131 deneyimini optimize etmek i\u00e7in kritik bir ihtiya\u00e7 haline gelmi\u015ftir. 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