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Use correlation IDs, structured logs, and logging scopes to make API failures traceable in production.
Explains delayed delivery, scheduled work, reminders, retry timing, cancellation, and clock-related trade-offs.
Explains when EF Core compiled queries help, what they do not fix, and how to measure their value.
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 Redis-backed rate-limit counters, atomic increments, windows, identity keys, and fallback behavior.
Explains the risks of Redis-based distributed locks and when database constraints, queues, or idempotency are safer.
Explains HTTPS, HSTS, secure cookies, HttpOnly, SameSite, no-store, and practical API/browser security hardening.
Explains structured logging fields, safe context, redaction, event naming, and avoiding secret or personal-data leaks.
Makes consumer-side deduplication atomic with the business change while supporting concurrent delivery, retention, replay, and failed processing.
Captures test activities to prove one incoming request and its outgoing HTTP dependency share trace context.
Explains event schema evolution, backward compatibility, additive changes, consumer rollout, and contract ownership.
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.
Combines Spring Boot signals into an operator-focused baseline for service health, dependencies, capacity, releases, and actionable alerts.
Identifies useful memory and GC signals for alerting without overwhelming the team with noisy telemetry.
Explains how to choose an E2E flow, control test data, avoid brittleness, and cover frontend/API integration risk.