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분산 트레이싱의 모든 것! Trace/Span/Context Propagation, OpenTelemetry SDK + Collector, Jaeger vs Tempo vs Zipkin, W3C Trace Context, 샘플링 전략(Head/Tail/Probabilistic), Auto-instrumentation, 비용 최적화.
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- Published on
분산 시스템에서 가장 중요한 역량은 "모르는 것을 알게 되는 능력"이다. 2024-2025년 관측 가능성은 혁명의 한복판에 있다. OpenTelemetry 1.0 GA, eBPF 기반 agentless 프로파일링, Continuous Profiling 주류화, Datadog vs Grafana vs Honeycomb 경쟁 재편. Metrics/Logs/Traces/Profiles 4-pillar부터 SLO 실전까지 추적한다.- Published on
Observability의 모든 것 — Monitoring과 Observability의 차이, 네 가지 기둥(Metrics, Logs, Traces, Profiling), Prometheus의 시계열 DB와 PromQL, OpenTelemetry의 통합 데이터 모델, Jaeger의 span/trace 모델, Loki의 label 인덱싱, Grafana 대시보드, SRE의 SLI/SLO/SLA, alerting 베스트 프랙티스, eBPF 기반 zero-instrumentation observability, cardinality 폭증과 비용 통제까지 1,400줄로 정리한 클라우드 네이티브 관찰 가능성 딥다이브.- Published on
마이크로서비스 환경에서 분산 트레이싱을 구현하는 방법을 OpenTelemetry SDK 계측부터 Jaeger, Grafana Tempo 백엔드 구축까지 실전 코드로 다룹니다.- Published on
OpenTelemetry 아키텍처(SDK, API, Collector, Exporters), 트레이스 모델(Span, SpanContext, TraceID, SpanID, Baggage), Python·Node.js·Go 수동 계측, 자동 계측, Collector 파이프라인(receivers/processors/exporters), 샘플링 전략(head-based, tail-based), 백엔드 비교(Jaeger vs Tempo vs Zipkin vs Datadog), 컨텍스트 전파, eBPF 기반 제로코드 계측, 실패 사례와 프로덕션 체크리스트를 다룹니다.- Published on
OpenTelemetry를 활용한 분산 트레이싱의 핵심 개념부터 Python/Java 자동 계측, OTel Collector 구성, Jaeger 시각화, 프로덕션 운영 팁까지 코드와 함께 다룹니다.- Published on
W3C Baggage를 활용한 비즈니스 컨텍스트 전파, 자동·수동 계측 하이브리드 전략, 테일 기반 샘플링으로 비용 최적화, Semantic Conventions 표준화, OTLP gRPC vs HTTP 선택 전략까지 — OpenTelemetry로 분산 시스템의 가시성을 확보하는 5가지 핵심 전략을 아키텍트 관점에서 심층 분석한다.