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Chooses heap-focused GC evidence or complete process state based on memory growth, roots, hangs, crashes, locks, and collection risk.
Explains when to capture dumps, what to compare, and how to use roots without turning dumps into guesswork.
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.
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.
Explains why memory diagnostics need access control, retention limits, and careful handling of secrets and user data.
Practices incident response after an OOM: restore service, collect evidence, identify trigger, and prevent recurrence.
Identifies useful memory and GC signals for alerting without overwhelming the team with noisy telemetry.