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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.
Chooses counters, traces, stack snapshots, GC dumps, full dumps, or a profiler from the production symptom and investigation cost.
Designs controlled diagnostic access, collection triggers, container permissions, artifact egress, privacy, retention, and post-capture verification.
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.
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.
Collects a bounded EventPipe CPU trace and reads stack evidence without confusing hot methods, blocked time, native work, or sampling artifacts.
Builds a bounded readiness check for one required dependency while keeping liveness independent and responses non-sensitive.
Explains correlation IDs across request logs, dependency calls, queues, workers, and support investigations.
Tests that an unexpected failure returns a safe correlation identifier and records the same value in structured logs.
Uses live runtime and application counters to classify CPU, memory, GC, exception, request, and ThreadPool symptoms before deeper collection.
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.