How I Debugged a Nightmare Java Memory Leak

Every engineer has that one bug that makes you question your career choices.
For me, it was a Java memory leak that slowly choked our service in production.

At first, I thought it was just a spike in traffic… but the graphs told a different story. Memory usage kept climbing like a stubborn mountain goat, never dropping back. Then came the dreaded OutOfMemoryError.

I’ll be honest—those first few hours were pure panic. But once I calmed down, I went into detective mode. Here’s how I hunted it down step by step.

Step 1: Spotting the Symptoms

• After every deployment, the service ran fine for a few hours.

• Then heap usage would climb and never fall, even after GC.

• Eventually, the JVM keeled over with Java heap space errors.

• Restarting the service worked… until it didn’t.

Classic sign: something in memory wasn’t being released.

Step 2: Confirming It’s a Leak

Instead of randomly guessing, I gathered evidence.

• Enabled GC logs with:

• Hooked up VisualVM to watch the heap in real time.
  → The graph was a staircase climbing upward with no drops.

• Captured a heap dump when memory was about to blow up:

At this point, I knew: yep, we’ve got a leak.

Step 3: Hunting the Culprit

I loaded the dump into Eclipse MAT (Memory Analyzer Tool).

💡 Pro tip: always start with the Leak Suspect Report.

And there it was—an oversized ConcurrentHashMap. Thousands of entries piling up, none being removed.

Digging deeper, I realized… guilty as charged 😅.
I had added a caching mechanism earlier, but forgot an eviction policy. Every request kept adding new data to the map, and nothing ever left.

Step 4: The Fix

Once I knew the “who,” the “how” was easy:

• Replaced my DIY ConcurrentHashMap with Caffeine Cache.

• Configured max size + time-based eviction.

• Added metrics for cache size, hit/miss, and GC monitoring.

This time, heap usage looked healthy. No more runaway leaks.

Lessons Learned (the hard way)

• Don’t reinvent the wheel → use proven libraries instead of quick hacks.

• Heap dumps are gold → stop guessing, start analyzing.

• Add observability early → metrics and GC logs are cheap insurance.

• Panicking doesn’t help → systematic debugging does.

Final Thoughts

Debugging this leak was painful, but it made me a sharper engineer. I learned that memory never lies, and tools like MAT can turn a nightmare into a solvable puzzle.

If you ever face a memory leak, remember: measure, don’t guess. And for heaven’s sake—use a proper cache library.