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Explains optimistic concurrency with EF Core rowversion tokens, API conflict responses, and safe retry boundaries.
Defines what happens when several operations fail, cancellation arrives, or only part of a parallel batch completes, without orphaning work or hiding errors.
Keeps long-running queue work safe when message ownership expires, renewal fails, settlement is uncertain, or another worker receives the same job.
Explains Redis memory pressure, eviction policies, key sizing, TTL discipline, and what happens when the cache is full.
Explains how an API behaves when Redis is unavailable and which cache usages can safely fall back.
Explains initial incident triage: confirm impact, mitigate, assign roles, preserve evidence, and communicate.
Explains why application-level duplicate checks are not enough and how EF Core APIs map database constraints to friendly errors.
Separates process start from readiness, coordinates hosted-service startup, and drains work within a bounded shutdown window.
Connects EF Core change tracking to object retention, query shape, projections, and read-only API performance.
Shows how publisher-to-subscriber references can retain objects and how to clean up event-based lifetimes.
Explains how App Service can resolve Key Vault references through managed identity and what to watch during rotation and environment setup.
Explains when to capture dumps, what to compare, and how to use roots without turning dumps into guesswork.
Explains safe EF Core migrations with practical .NET data-access examples and production trade-offs.
Explains transient database errors, retry strategy, EF Core execution strategy, idempotency, timeouts, and avoiding retry storms.
Covers allocation rate, collection frequency, latency symptoms, and safe production steps for GC-heavy services.
Uses file upload processing to practice bounded memory, stream ownership, temporary files, and retained buffers.
Covers chunking, bounded concurrency, scoped dependencies, progress, retries, and avoiding full-dataset memory spikes.
Covers queue-backed work, idempotency, retries, visibility timeout, poison messages, progress tracking, and choosing Storage Queues or Service Bus.
Covers deployment logs, startup failures, runtime stack, app settings, missing files, Key Vault references, and post-deployment diagnosis.
Turns LOH theory into an API investigation around large payloads, buffers, serialization, and concurrency.
Gives a practical incident flow for memory growth using metrics, traffic correlation, heap evidence, and safe mitigation.
Frames allocation reduction as measurement-driven API work rather than premature micro-optimization.
Explains correlation IDs, distributed tracing, dependency telemetry, message boundaries, and logs across App Service, queues, and downstream APIs.
Builds a bounded readiness check for one required dependency while keeping liveness independent and responses non-sensitive.