Structured Logging: JSON, Context, and Trace Correlation in Production

Turn your logs into queryable data: JSON output with structlog (Python) and pino (Node.js), per-request context with request_id, sensitive-data redaction, OpenTelemetry trace correlation, and the mistakes that make your logs useless right when you need them most. Expanded edition: ProcessorFormatter to unify third-party libraries, exceptions with context, canonical log lines, logging in workers and queues, OTLP export, sampling and cost control, targeted debug, log storms, a shared schema (OTel/ECS), Kubernetes internals, Fluent Bit and Vector, buffering and backpressure, serverless, multi-tenant, security and compliance, log testing, LogQL and CloudWatch queries, legacy-service migration, a complete hands-on incident case study, and an FAQ. With production-ready code. New expansion: logging for LLM applications (tokens, cost, and privacy), browser-to-backend correlation, log-based alerts with the Loki Ruler, pipeline cost control, and evolving the event schema without breaking dashboards.

45 minutes read

Josue Puig

2026-07-21

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