Every developer has faced that frustrating moment: the code works perfectly on their local machine, tests pass, everything looks fine… and then - boom - the production server crashes. The infamous line echoes:
“But it worked on my machine!”
This gap between development and production isn’t just about luck or bad coding. It happens because software systems are far more complex in the real world than what we simulate locally. Understanding why code breaks in production is the first step toward preventing late-night firefighting sessions and keeping users happy.
In this blog, we’ll explore the most common reasons production bugs occur and how to prevent them with better practices. Think of it as a developer’s survival guide for real-world deployments.
1. Common Reasons Code Breaks in Production
A. Environment Differences
One of the biggest culprits is the difference between local, staging, and production environments.
- Locally, you might run Node.js v20, but production uses Node.js v18.
- Your local
.envfile might have test credentials, but in production, a single missing environment variable can stop your entire app from booting. - A dependency works on MacOS but fails on Linux-based servers.
💡 Example: Imagine you use an image-processing library that depends on libvips. On your machine, everything works fine because you already had the right version installed. But when deployed to production, the library fails because the server doesn’t have that dependency.
This is why Docker, containerization, and Infrastructure-as-Code tools are so popular - they ensure the environment is consistent across dev, staging, and production.
B. Uncaught Edge Cases
Developers usually test the happy path - the expected way users interact with a feature. But in reality, users are unpredictable, and APIs can respond in unexpected ways.
- A form where you expected a string, but a user uploads an emoji or leaves it blank.
- An API you depend on suddenly returns an empty object instead of the usual JSON payload.
- A database query returns
nullbecause the data didn’t exist in production.
💡 Example: A payment system that assumes every transaction has a valid email may suddenly crash when a user signs up with a missing or malformed email.
The lesson? Production is full of “what ifs.” Writing defensive code, using input validation, and thorough testing of edge cases is critical.
C. Third-Party Dependencies
Modern applications are built on a mountain of third-party libraries, frameworks, and APIs. While this speeds up development, it also introduces risks - because your app is only as stable as the services it depends on.
- Library updates: A new version of a library may contain breaking changes. If you deploy without pinning versions, production can suddenly break.
- API downtime: Relying on external APIs like Stripe, Google Maps, or AWS services means that if they’re down, your app suffers too.
- Rate limits: APIs often have usage limits. Everything may work fine in testing, but production traffic can hit those limits and cause failures.
💡 Example: Imagine your app depends on a currency conversion API. In development, it works perfectly with your limited testing. But in production, thousands of requests per hour exceed the API’s free-tier limit, and suddenly, your app starts returning errors to all users.
That’s why developers often use caching, retries, fallbacks, and monitoring to handle dependency issues gracefully.
D. Race Conditions & Concurrency Issues
Another big reason for production failures is concurrency. Code that runs perfectly with a single user may behave unpredictably when multiple users interact simultaneously.
- Race conditions: Two processes trying to update the same record at the same time can lead to inconsistent data.
- Deadlocks: Multiple processes waiting on each other can freeze the system.
- Thread safety: Some operations aren’t safe when executed concurrently, leading to strange, hard-to-reproduce bugs.
💡 Example: Let’s say you’re running an e-commerce store. Two users buy the last item at the same time. Without proper concurrency control, both orders might succeed - and suddenly, you’ve sold one product to two different people.
These issues rarely show up locally because the traffic is low. But in production, under real-world load, they can cause chaos. This is why techniques like transactions, locks, queues, and stress testing are essential for production-grade systems.
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