Production Troubleshooting: Scenario-Based Questions

Memory leaks in production degrade performance over time, leading to crashes or restarts. Detecting and remediating them quickly is essential for reliability and customer trust.

🧠 What Is a Memory Leak?

• Application fails to release unused memory.
• Leak accumulates over time, triggering OOM kills or degraded performance.

🔍 Detection Techniques

• Monitor memory usage over time via dashboards (Prometheus, Datadog, etc.).
• Set alerts on unusual memory growth patterns (linear growth per hour).
• Heap dumps and analysis tools (MAT, VisualVM, LeakCanary, etc.).
• Use tracing/profiling tools in staging before full production.

🧰 Common Tools by Language

• Java: jmap, jstat, Eclipse MAT, JProfiler
• Node.js: heapdump, clinic.js, Chrome DevTools
• Python: objgraph, memory-profiler, tracemalloc
• Go: pprof, runtime/pprof, memory debug endpoints

✅ Best Practices

• Enable resource limits and restarts in Kubernetes (liveness probes, memory limits).
• Run periodic soak tests to catch leaks under sustained load.
• Instrument long-lived objects (e.g., caches, queues) for size monitoring.
• Automate heap dump collection on OOM or memory threshold breach.

🚫 Common Pitfalls

• Assuming GC handles all memory issues — leaks can be reference-based.
• No rollback plan if leak was introduced in recent release.
• Analyzing only CPU or request logs — missing memory signals.

📌 Final Insight

Memory leaks are silent killers in production. Proactive monitoring, heap analysis, and automated safeguards ensure resilience and rapid recovery before users notice.