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Explains correlation IDs across request logs, dependency calls, queues, workers, and support investigations.
Explains how to choose rollback, swap-back, hotfix, or roll-forward after a failed Azure App Service deployment.
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 how query tags, request correlation, and telemetry help connect slow SQL back to code paths.
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.
Covers useful request, dependency, exception, log, metric, and availability telemetry for a production .NET API.
Covers symptom-based alerting for latency, error rate, availability, dependency failures, queue length, and resource pressure.
Explains scale-out bugs from in-memory sessions, local caches, background queues, uploaded files, and per-instance behavior.