Distributed Tracing with OpenTelemetry: Follow Every Request Across Your Services

Learn to instrument your services with OpenTelemetry and see each request's full journey: auto-instrumentation in Python and Node.js, custom spans with useful attributes, W3C context propagation (including manual propagation across queues), sampling strategies, and Collector configuration. Covers the cardinality and propagation mistakes that ruin traces in production. Expanded with span kinds, events and links, semantic conventions, correlating traces with logs and metrics (exemplars and span metrics), baggage, the Collector in production (agent + gateway and scaled tail sampling), sensitive-data redaction with OTTL, instrumentation testing, overhead control, systematic debugging of broken traces, gRPC and streaming, the OTel Operator on Kubernetes, burn-rate SLOs and a complete hands-on investigation case study.

40 minutes read

2026-07-12

0 views

Verificando acceso...

Loading comments...

Related Resources

Guía

PREMIUM

asyncio in Production: Never Block the Event Loop — TaskGroups, Cancellation and Bounded Concurrency

A practical asyncio guide for Python services in production: why blocking the event loop degrades the whole process without raising a single exception, how to catch it by measuring loop lag and with Python 3.14 introspection, structured concurrency with TaskGroup and handling ExceptionGroup via except*, the task the garbage collector makes vanish, timeouts with a deadline budget propagated across services, correct cancellation with cleanup and shield, bounded concurrency with semaphores and backpressured queues, synchronization primitives, and what changes with eager tasks, python -m asyncio pstree and free-threading. With production-ready code and a deployment checklist.

Guía

PREMIUM

Cache-Aside in Production: TTLs, Invalidation, and How to Prevent Cache Stampedes

The complete guide to the cache-aside pattern with Redis: jittered TTLs, correct invalidation, and the three defenses against cache stampedes (distributed lock, single-flight, and XFetch). Expanded with stale-while-revalidate, fail-open and circuit breakers, two-tier caching with RESP3 invalidation, delayed double delete and CDC, hot keys, eviction and memory management, observability with Prometheus, testing, choosing an engine (Redis, Valkey, Memcached), and a complete TypeScript implementation. With production-ready code in Python and TypeScript.

Guía

PREMIUM

Circuit Breakers: How to Prevent Cascading Failures in Distributed Systems

Learn to implement the circuit breaker pattern so a failing dependency never drags down your whole system: the three states (closed, open, half-open), sliding failure windows, limited probes to avoid thundering herds, robust fallbacks, and how to combine it with timeouts, retries, and bulkheads. Includes distributed state in Redis, observability with Prometheus, pytest testing, circuit breaking in Envoy/Istio, a full case study, and production-ready code in Python and TypeScript.