AI Found 47 Memory Leaks in Our Node.js Backend (And How It Saved Us)
Introduction
Our Node.js backend was on its last legs. Every few hours, memory usage would skyrocket, the API response times would become snail-paced, and finally, the server would crash. We kept rebooting it, and the cycle repeated itself.
We spent 3 weeks trying to locate the bug. Our senior engineers studied the code, analyzed heap dumps, added logs everywhere, and ran profilers. We patched a couple of trivial memory leaks, but the memory kept rising.
Then we went for a different approach: we let AI analyze our codebase. Within less than half an hour, AI memory leak detection came up with 47 different memory leaks, problems we had completely overlooked during our several weeks of manual debugging.
This is how AI changed our production infrastructure story and showed us that memory leak detection is exactly the kind of problem AI can easily solve.
The Problem: Silent Memory Death
Memory leaks in Node.js are pretty stealthy. They don’t announce themselves as crashes or error messages do. You’re fooling yourself into thinking that your application is working when it’s actually dying.
Symptoms we observed:
- Memory usage increased from 200MB to 2GB over 4 to 6 hours.
- API response times worsened from 50ms to 3+ seconds.
- Garbage collection pauses caused the server to freeze for several seconds at a time.
- The final blow was a crash accompanied by a “JavaScript heap out of memory” error.
Some aspects that contributed to the difficulty of pinpointing the problem:
- The leaks were small and scattered throughout the codebase.
- Every single leak was negligible.
- Only the total effect was apparent.
- Leaks surfaced only in specific request patterns.
After three weeks of painstaking debugging, we located 5 memory leaks. AI located 42 more in 20 minutes.
Common Node.js Memory Leaks (That AI Found)
Here are some real memory leaks our AI memory leak detector spotted in our code:
1. Event Listener Accumulation (18 instances)
The pattern AI detected:
AI’s explanation: The problem is that every time a DataProcessor is created, it listens for events, but it never stops listening. So, even when you don’t need a DataProcessor anymore, it sticks around in memory because the event Emitter is still holding onto it.
The fix:
2. Closures Capturing Large Objects (12 instances)
The pattern AI detected:
AI’s explanation: Basically, with set Timeout, the callback function grabs the whole ‘req’ object (even that big file) because of how JavaScript handles closures. Even if you only want the filename, JavaScript keeps everything else hanging around, too.
The fix:
How AI Memory Leak Detection Works
So how did AI spot these problems that even skilled developers failed to? Here is the breakdown of the method
1. Pattern Recognition at Scale
AI took in our whole codebase at once and figured out the leaking memory patterns that got replicated over thousands of lines of code. People who review code one file at a time fail to catch such patterns.
What AI is on the lookout for:
- Event listeners that don’t have removeListener calls.
- Objects are being put into collections without removal logic.
- The use of closures unnecessarily captures large objects.
- Caches that have no size limits or expiration.
- Global variables are becoming infinitely large.
2. Context-Aware Analysis
Using AI-based memory leak detection, the system comprehends the surrounding context of code. It is aware of:
- Object lifecycles and knowing when the garbage collector should collect.
- Scope chains and what closures capture.
- Reference relationships between objects.
- Memory retention patterns of different code paths3.
3. Simulated Execution Paths
By simulating various execution paths, AI can find situations where objects stay in memory longer than they should.
AI noticed that, in our example, the new DataProcessor instances were created per request but never destroyed, even though the request had ended.
Using AI to Find Memory Leaks (Practical Guide)
Here’s how you can use AI memory leak detection for your own Node.js applications:
Step 1: Prepare Your Codebase
Step 2: Use AI Code Analysis
Step 3: Prioritize Fixes
AI ranks findings by severity. Focus on:
Critical:
- Leaks in hot code paths (executed frequently).
- Large object retention
- Unbounded growth.
High:
- Event listener accumulation.
- Cache without expiration.
- Closure capturing request objects.
Medium/Low:
- Small objects in uncommon code paths.
- Potential issues that need verification.
Step 4: Verify with Profiling
After AI identifies issues, verify with Node.js profiling tools:
Our Results After Fixing AI-Detected Leaks
After fixing the 47 memory leaks AI identified, our production metrics transformed:
Memory Usage:
- Before: 200MB → 2GB over 6 hours (crashed).
- After: 250MB stable indefinitely.
API Response Times:
- Before: 50ms → 3+ seconds over time.
- After: Consistent 45-60ms.
Garbage Collection:
- Before: 2-5 second GC pauses.
- After: < 50ms GC pauses.
Uptime:
- Before: Crashes every 4-6 hours.
- After: 30+ days without memory-related issues.
Cost Savings:
- Reduced server count from 12 to 6 instances.
- Saved $2,400/month in infrastructure costs.
Best Practices for Memory Leak Prevention
How to Avoid Memory Leaks: Lessons Learned. Here’s what we’ve picked up about stopping memory leaks, thanks to AI detection:
a. Always Clean Up Event Listeners
b. Use Weak References When Appropriate
c. Implement Proper Cache Expiration
d. Clear Timers and Intervals
Tools to Complement AI Analysis
While AI memory leak detection is powerful, combine it with traditional tools:
Node.js Built-in Tools
Clinic.js
Memory Monitoring in Production
Common Misconceptions About Memory Leaks
- Misconception 1: “Garbage collection will handle it”: Even if you don’t plan to use an object anymore, the GC in JavaScript won’t be able to collect it if the object still has references.
- Misconception 2: “Small leaks don’t matter”: 47 small leaks can have the same impact as one big leak. Our largest problem wasn’t a huge leak; it was a bunch of small ones.
- Misconception 3: “Memory leaks come from bad code only.”: You can have leaks in your good code. A lot of our leaks were hidden in innocent patterns that only leaked under very specific conditions.
- Misconception 4: “Profiling tools show everything.”: Profiling can only show the memory situation; however, it can’t tell why the objects have been kept. AI analysis gives a reason for the why.
Conclusion
Tracing memory leaks manually is a very tedious process prone to errors and takes a lot of time. We, as a team, spent three weeks and found 5 leaks. On the other hand, AI memory leak detection located 47 leaks in 20 minutes.
This is not about substituting developers but rather utilizing AI for the specific things it is good at, finding patterns in large codebases that humans can’t see. We were the ones who still had to comprehend the problems, decide on the adjustments, and carry out the fixes. But AI pointed us the way.
Should your Node.js app be having memory leak issues, do not waste several weeks trying to figure out the problem blindly. Let AI run an analysis of your codebase first. You will discover the issues you didn’t know existed and also save weeks of frustration.
The future of debugging is not manual heap analysis but AI-based pattern recognition that can detect problems even before they cause server crashes in your production environment.
Services and Support at Orbilon Technologies
Orbilon Technologies is a place where we specialize in helping teams that are developers of Node.js applications to get efficiencies through an analysis of the application, which is conducted by artificial intelligence, as well as through traditional performance tuning.
We have a track record of successful companies that we have helped in discovering and fixing their memory leaks, optimizing the database queries, and upgrading the application performance.
Our Services:
- AI-powered code analysis and memory leak detection.
- Node.js performance optimization.
- Production debugging and monitoring setup.
- Memory profiling and heap analysis.
- Developer training on performance best practices.
We leverage AI tools along with deep Node.js knowledge to deliver rapid solutions for issues such as memory leaks, performance bottlenecks, or scalability problems.
Is your memory leaking? Feel free to come and see us at orbilontech.com or email us at support@orbilontech.com, and we’ll be happy to discuss ways in which we can help you optimize your Node.js application.
Want to Hire Us?
Are you ready to turn your ideas into a reality? Hire Orbilon Technologies today and start working right away with qualified resources. We will take care of everything from design, development, security, quality assurance and deployment. We are just a click away.
