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Separates application composition in AppHost from reusable in-process defaults for telemetry, health, resilience, and service discovery.
Explains dependency duration, error rate, timeout rate, query tagging, Redis latency, HTTP downstream calls, and endpoint correlation.
Compares rollback, roll-forward, feature kill switches, scaling, and degraded mode as incident mitigations.
Compares vendor-neutral OpenTelemetry concepts with Application Insights usage for .NET production services.
Explains sampling, high-cardinality labels, retention, cost, and keeping enough signal for incident diagnosis.
Explains deployment markers, version telemetry, feature-flag state, commit traceability, and incident diagnosis after releases.
Explains what belongs in runbooks, how on-call handoff preserves context, and how operational docs stay useful.
Uses resource state, structured logs, traces, metrics, health, and configuration evidence to isolate a cross-service failure.
Explains how to troubleshoot absent logs or telemetry by checking configuration, sampling, sinks, levels, deployment, and correlation.
Explains alert design based on user-impact symptoms, SLOs, burn rate, thresholds, and avoiding noisy cause-only alerts.
Explains dashboard panels that help an API owner see user impact, dependency health, releases, and current incidents.
Explains health checks that prove an API can serve traffic without becoming fragile dependency probes.
Explains a telemetry-driven path from slow endpoint symptoms to database, cache, dependency, CPU, or payload root cause.
Explains initial incident triage: confirm impact, mitigate, assign roles, preserve evidence, and communicate.
Explains how to triage post-release 500 errors using deployment correlation, logs, traces, feature flags, and rollback safety.
Explains how to diagnose 401/403 spikes across token validation, identity provider issues, clock skew, deployment changes, and abuse.
Explains correlation IDs across request logs, dependency calls, queues, workers, and support investigations.
Explains how to diagnose and mitigate a downstream dependency slowing the API through timeouts, retries, and degraded behavior.
Explains how to investigate queue age, backlog, worker health, poison messages, dependency failures, and safe catch-up.
Explains post-incident review, root cause, contributing factors, action items, tests, alerts, runbooks, and ownership.
Explains saturation signals and how systems shed, queue, throttle, or degrade before cascading failure.
Explains the different diagnostic roles of logs, metrics, and traces during backend production issues.
Explains structured logging fields, safe context, redaction, event naming, and avoiding secret or personal-data leaks.
Captures test activities to prove one incoming request and its outgoing HTTP dependency share trace context.