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Page 7 of 8
Explains how to inspect, fix, replay, skip, or archive dead-letter messages without repeating bad side effects.
Replaces a parent query plus one status query per row with one server-side projection.
Explains compatible full-stack deployment, backend enforcement, staged rollout, telemetry, safe disablement, rollback decisions, and flag cleanup.
Explains safe error responses for auth failures, validation failures, server errors, and sensitive resource access.
Explains why Redis connections are reused, how timeouts protect API threads, and how to diagnose client-side Redis pressure.
Explains how multiple worker instances share queue work and what limits safe scaling.
Chooses event retention and compaction from consumer outage, replay, rebuild, audit, and disaster-recovery requirements rather than storage cost alone.
Selects synchronous calls, asynchronous messaging, or a deliberate combination based on user feedback, coupling, latency, durability, and failure behavior.
Adds a stable query tag and inspects generated SQL without embedding user data or enabling unsafe global logging.
Tests that an unexpected failure returns a safe correlation identifier and records the same value in structured logs.
Explains tracing one user action across browser, frontend server, API, dependencies, logs, metrics, and safe diagnostics.
Uses live runtime and application counters to classify CPU, memory, GC, exception, request, and ThreadPool symptoms before deeper collection.
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 policy-based authorization, requirements, handlers, claims, capabilities, and resource-aware checks.
Explains saturation signals and how systems shed, queue, throttle, or degrade before cascading failure.
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.