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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.
Designs scheduled work for overlap, multiple replicas, restart, failure, and durable ownership instead of assuming one execution.
Separates transaction-phase callbacks inside one process from durable database-backed publication across process failure.
Focuses unit tests on stable behavior, meaningful boundaries, deterministic outcomes, and useful failure messages.
Treats @Async as an in-process executor boundary with proxy, failure, context, capacity, and durability consequences.
Practices incident response after an OOM: restore service, collect evidence, identify trigger, and prevent recurrence.
Compares Azure hosting choices for a .NET API using operational complexity, scaling model, deployment ownership, and workload shape.
Covers large exports, uploads, and API responses where bounded memory matters more than convenience.
Separates in-process decoupling from durable cross-process delivery and makes timing, failure, and transaction expectations explicit.
Adds dependency health only when it supports a specific operational decision and can be checked cheaply and safely.
Explains cache memory growth, eviction, size limits, stale data, and why cache design needs explicit bounds.
Explains local filesystem limits, multiple instances, in-memory state, distributed cache, durable storage, and scale-out-safe API design.
Uses browser and direct HTTP checks to verify nested route entry, reload, base paths, assets, genuine 404s, and API boundaries after deployment.