observability-integrator

Instrument Kotlin and Spring services for end-to-end observability by defining core service journeys, then adding correlated metrics, traces, and health endpoints to understand service behavior and dependencies during normal and failure conditions.

Maintaining tracing across async boundaries in Kotlin coroutines requires specific instrumentation patterns that propagate context, ensuring async flows remain observable in production environments for accurate incident diagnosis.

Design SLO-driven metrics and alerting for Kotlin microservices by specifying instrumentation patterns and cardinality controls that align telemetry with operational questions and downstream dependencies to meet service level objectives.

Control metric cardinality when adding observability to Spring services by specifying instrumentation patterns that limit high-dimensional labels, optimizing telemetry budgets while ensuring metrics remain actionable for alerting readiness.

Yes, this observability approach works for diagnosing production incidents in microservices by providing structured metrics, logs, and traces that answer operational questions and make service behavior transparent under failure conditions.