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Diagnose thread-pool starvation caused by blocking work, unbounded concurrency, or sync-over-async in .NET APIs.
Diagnose an ASP.NET Core API that becomes slow because request threads are blocked or exhausted.
Uses runtime-supported initialization rather than timing-dependent manual checks and defines failure, retry, and disposal behavior explicitly.
Distinguish timeouts from cancellation and explain how to combine them around external calls and request handling.
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.
Attaches a key-only stub and marks one property modified for a narrow update while naming the rules this pattern bypasses.
Explains closed, open, and half-open behavior, failure classification, recovery probes, and the limits of circuit breakers.
Explains the boundary between OAuth/OIDC identity, application users, claims mapping, roles, and app-specific permissions.
Explains when a Next.js route layer helps with cookies, tokens, aggregation, and UI-specific API adaptation.
Explains when a reducer clarifies related state transitions and when independent state remains simpler.
Explains policy-based authorization, requirements, handlers, claims, capabilities, and resource-aware checks.
Explains the cache-aside pattern for read-heavy endpoints and how misses, TTLs, and source-of-truth reads fit together.
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.
Uses EF Core context pooling only when reusable context state, tenant isolation, and underlying connection state are controlled and tested.
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 when set-based EF Core updates are better than loading entities and where lifecycle behavior can differ.