memory-leak-interviewer

Diagnose a production memory leak by guiding services through heap analysis, profiling, and retention-path tracing. The process applies to Java and Python services experiencing linear memory growth, unbounded caches, listener leaks, and closure-retained data in async workflows.

A heap dump captures the memory state of a service and is needed when diagnosing memory leaks to identify retained objects. It is used with profilers and GC logs to apply concrete diagnostic steps and trace retention paths.

Yes, this approach systematically investigates memory leaks in async workflows including closure-retained data. It validates retention logic and fixes for leaks in caches, listeners, and closures experienced by Java and Python services.

The best way to prevent memory leaks after root-cause verification is to implement tests, monitoring, and robust code patterns. These prevention strategies ensure future leaks in production services are avoided.

Yes, GC logs and profilers are needed to trace memory leak retention paths. Tooling guidance for heap dumps, profilers, and GC logs provides concrete diagnostic steps for systematic investigation and root-cause verification.