Observabilidad en Python para principiantes
summary
Explains how observability maps to actionable signals by showing Python code for logging, metrics and distributed tracing, using the Prometheus client plus exporters and dashboards with Prometheus and Grafana and error capture with Sentry / Datadog. Demonstrates concrete thresholds: throughput jumping from 500 to 700–800 req/s, error rate rising from ~10% and latency spiking from ~2ms to ~20ms, and shows the instrumented app emitting logs, metrics and traces that trigger alerts and let operators find the faulty component.
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chapters (12)
- 0:00Introduction to observability
- 2:02Monitoring vs observability
- 5:24Ejemplo práctico de caja negra y transparencia
- 8:24Observability definition and importance
- 12:59Three pillars of observability
- 14:06Metrics definition and use
- 17:47Logs function and structure
- 19:44Distributed tracing explained
- 20:47Python observability tools
- 22:46Prometheus and Grafana integration
- 24:00Python error and log tools
- 25:29Conclusion and resources
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