OpenTelemetry Adoption — Vendor-Neutral Observability

"Datadog SDK + Prometheus client + Loki driver hep aynı bilgiyi tekrar yazıyor. Datadog'u bırakmak istesem, kodun her yerinde değişiklik."

OpenTelemetry (OTel) çözümü: tek SDK, tek protokol (OTLP), vendor-neutral.

---

🎯 Niye OTel?

Eski model	OTel ile
Datadog SDK + Prometheus + Loki client	Tek SDK
Vendor değiştirmek = kod değişikliği	Collector config değişir, kod aynı
Trace ID metric/log'da yok	Auto-correlation built-in
Standart yok, herkes farklı tag	Semantic conventions standardı
Polyglot stack (Go/Python/Node) ayrı tools	Aynı SDK her dilde

---

🏛️ Mimari

┌─────────────────────────────────────────┐
│  Application                             │
│  ┌──────────────────┐                    │
│  │ OTel SDK         │  metrics/logs/     │
│  │ (auto-instrument)│  traces            │
│  └────────┬─────────┘                    │
└───────────┼──────────────────────────────┘
            │ OTLP (gRPC veya HTTP)
            ▼
┌─────────────────────────────────────────┐
│  OpenTelemetry Collector                 │
│                                          │
│  Receivers → Processors → Exporters     │
│                                          │
│  - OTLP receiver (apps'ten)              │
│  - Prometheus receiver (scrape)          │
│  - Filelog receiver (file logs)          │
│                                          │
│  Processors:                             │
│  - Batch (efficient send)                │
│  - Memory limiter                        │
│  - Tail sampling (smart trace selection) │
│  - Resource detection (cloud metadata)   │
│  - Attributes (PII redact, transform)    │
│                                          │
│  Exporters:                              │
│  - Prometheus / Mimir / VictoriaMetrics  │
│  - Loki / Datadog / Splunk               │
│  - Tempo / Jaeger / Honeycomb            │
└─────────────────────────────────────────┘

---

🚀 1. SDK kurulum (örnekler)

Python (FastAPI)

# pip install opentelemetry-distro opentelemetry-exporter-otlp \
#             opentelemetry-instrumentation-fastapi
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.psycopg2 import Psycopg2Instrumentor

from fastapi import FastAPI
app = FastAPI()

FastAPIInstrumentor.instrument_app(app)
RequestsInstrumentor().instrument()
Psycopg2Instrumentor().instrument()

opentelemetry-instrument ile sıfır-kod auto-instrumentation:

opentelemetry-instrument \
  --traces_exporter otlp \
  --metrics_exporter otlp \
  --logs_exporter otlp \
  --service_name my-api \
  --exporter_otlp_endpoint http://otel-collector:4317 \
  uvicorn main:app

Node.js

// npm i @opentelemetry/sdk-node @opentelemetry/auto-instrumentations-node
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-grpc');

const sdk = new NodeSDK({
  serviceName: 'my-app',
  traceExporter: new OTLPTraceExporter({ url: 'http://otel-collector:4317' }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

Go

Go'da auto-instrument zayıf, manuel:

import (
  "go.opentelemetry.io/otel"
  "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
  "go.opentelemetry.io/otel/sdk/trace"
)

exporter, _ := otlptracegrpc.New(ctx)
tp := trace.NewTracerProvider(
  trace.WithBatcher(exporter),
  trace.WithResource(resource.NewWithAttributes(
    semconv.ServiceName("my-api"),
  )),
)
otel.SetTracerProvider(tp)

// HTTP middleware:
import "go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp"
handler := otelhttp.NewHandler(myHandler, "operation")

Manuel span eklemek

from opentelemetry import trace
tracer = trace.get_tracer(__name__)

@tracer.start_as_current_span("compute_tax")
def compute_tax(amount, region):
    span = trace.get_current_span()
    span.set_attribute("amount", float(amount))
    span.set_attribute("region", region)
    # ...

---

⚙️ 2. Collector kurulum (Kubernetes)

Helm install

helm install otel-collector \
  open-telemetry/opentelemetry-collector \
  -n observability --create-namespace \
  -f values.yaml

values.yaml

mode: deployment            # veya: daemonset, statefulset

config:
  receivers:
    otlp:
      protocols:
        grpc: { endpoint: 0.0.0.0:4317 }
        http: { endpoint: 0.0.0.0:4318 }

    prometheus:
      config:
        scrape_configs:
          - job_name: 'kubernetes-pods'
            kubernetes_sd_configs:
              - role: pod

  processors:
    batch:
      timeout: 10s
      send_batch_size: 1024

    memory_limiter:
      check_interval: 1s
      limit_mib: 1500
      spike_limit_mib: 512

    resource:
      attributes:
        - key: deployment.environment
          value: "<ENV>"
          action: upsert

    # Tail sampling: errors veya slow span'ları %100 al, geri kalan %5
    tail_sampling:
      decision_wait: 10s
      policies:
        - name: errors
          type: status_code
          status_code: { status_codes: [ERROR] }
        - name: slow
          type: latency
          latency: { threshold_ms: 1000 }
        - name: random
          type: probabilistic
          probabilistic: { sampling_percentage: 5 }

    # PII redaction
    attributes:
      actions:
        - key: http.user_agent
          action: hash
        - key: user.email
          action: delete

  exporters:
    otlphttp/mimir:
      endpoint: http://mimir:9009/otlp

    loki:
      endpoint: http://loki:3100/loki/api/v1/push

    otlp/tempo:
      endpoint: tempo:4317
      tls: { insecure: true }

  service:
    pipelines:
      metrics:
        receivers: [otlp, prometheus]
        processors: [memory_limiter, batch, resource]
        exporters: [otlphttp/mimir]
      logs:
        receivers: [otlp]
        processors: [memory_limiter, batch, resource, attributes]
        exporters: [loki]
      traces:
        receivers: [otlp]
        processors: [memory_limiter, batch, resource, tail_sampling, attributes]
        exporters: [otlp/tempo]

---

🏷️ 3. Semantic Conventions

OTel'in vendor-neutral'lığının temeli: standart attribute isimleri.

Doğru	Yanlış
http.method	httpMethod, method
http.status_code	statusCode
http.route	endpoint
http.target	path
db.system	database
db.statement	sql
service.name	app
service.version	version
deployment.environment	env

Tam liste: https://opentelemetry.io/docs/specs/semconv/

---

🎯 4. Trace ID propagation

İki servis konuşuyor → trace ID otomatik forward:

Service A (handler) ─── traceparent: 00-abc-... ─── Service B (handler)
                                                      │
Service B'nin span'ı Service A'nın trace'ine bağlı   ▼
                                          DB query span'ı eklenir

W3C traceparent header standardı. Çoğu HTTP client SDK auto-inject eder.

Logger correlation

# logger her log'a trace_id ekle
import logging
from opentelemetry.trace import get_current_span

class TraceIDFilter(logging.Filter):
    def filter(self, record):
        span = get_current_span()
        ctx = span.get_span_context() if span else None
        record.trace_id = format(ctx.trace_id, "032x") if ctx and ctx.is_valid else None
        return True

# JSON log: { "trace_id": "abc123...", "msg": "DB error" }
# Loki'de: {service="api"} | json | trace_id="abc123..." → ilgili tüm log'lar

Grafana'da trace'ten log'a, log'dan trace'e tek tıklamayla geçilir.

---

📊 5. Metrics

OTel SDK Prometheus formatı destekler ama yeni metric'leri OTel API ile yaz:

from opentelemetry import metrics

meter = metrics.get_meter(__name__)

# Counter
order_counter = meter.create_counter(
    "orders.total",
    unit="1",
    description="Number of orders created",
)
order_counter.add(1, {"status": "success", "tier": user.tier})

# Histogram (latency için)
processing_time = meter.create_histogram(
    "order.processing_time",
    unit="ms",
    description="Order processing latency",
)
processing_time.record(elapsed_ms, {"endpoint": "create"})

# Gauge / observable
def cb(observer):
    observer.observe(get_queue_depth(), {"queue": "orders"})
queue_gauge = meter.create_observable_gauge("queue.depth", callbacks=[cb])

---

🔄 6. "Migration": Mevcut stack'ten OTel'e geçiş

Faz 1: Trace (en az invaziv)

• OTel SDK + auto-instrument PR
• Collector deploy
• Tempo backend (veya mevcut Datadog'a OTLP gönder)
• Mevcut metric/log stack'inde değişiklik yok

Faz 2: Metric

• OTel SDK metric API
• Prometheus → OTLP exporter
• Eski Prometheus client kodunu yavaş yavaş OTel API'ye çevir

Faz 3: Log

• OTel logging bridge
• Eski log forwarder (fluent-bit) → OTel Collector
• Loki/ELK aynı kalsın, ortada Collector

Faz 4: Vendor-neutral

• Datadog/NewRelic gibi paid SaaS'ten çıkış kararı verirsen, sadece Collector exporter'ı değiştir. Kod aynı.

---

⚠️ Yaygın tuzaklar

Cardinality patlaması

• user_id attribute'u her span'a eklersen → milyonlarca unique trace dimension
• Çözüm: high-cardinality attribute'ları metrics'e ekleme; trace'te zaten var

Sampling stratejisi

• Head-based: %5 her şey (basit ama error miss eder)
• Tail-based: error + slow %100, geri kalan %5 (daha akıllı, collector'da)
• Önerilen: tail-based prod için

Auto-instrument noise

• Auto-instrument bazen istemediğin span'ları yaratır (her HTTP redirect, DB ping)
• Span filter / sampler ile sustur

Performance overhead

• SDK + collector overhead < %1 (tipik), ama batch processor doğru ayarlanmalı
• Memory limiter zorunlu (OOM önle)

---

📚 Devamı

• opentelemetry.io
• OTel Demo App — eksiksiz örnek mikroservis stack
• OTel Collector Receivers/Processors/Exporters list
• 07-Observability/Prometheus-Best-Practices.md (yakında)