Monitoring & Observability

The foundational data types - Metrics, Logs, and Traces - required to understand the internal state of a complex distributed system.

Understanding the cost and performance impact of attaching highly unique labels, like user IDs, to time-series metrics data.

Tracking a single request as it travels across multiple network boundaries and microservices using injected context headers.

The shift from human-readable text logs to machine-parsable JSON logs to enable complex querying and automated alerting.

Centralizing logs from distributed ephemeral containers into a searchable, indexed database.

Designing alerts that require human intervention and eliminating noisy warnings that train engineers to ignore paging systems.

Defining numerical targets for system reliability that balance the need for feature velocity against the risk of user-facing outages.

The essential metrics - Latency, Traffic, Errors, and Saturation - that provide a complete picture of a service's health.

A vendor-neutral standard for generating, collecting, and exporting telemetry data across modern distributed systems.

The open, text-based standard for exposing dimensional metric data over HTTP so that monitoring systems can scrape and index it.

The IETF standard (RFC 9474) defining a consistent, machine-readable JSON schema for APIs and microservices to report their health status to orchestrators.