The Hidden Costs of Over-Engineering: When Simple Solutions Win

It started with a simple feature request: "We need a way to store user preferences." Three weeks, two design documents, five architectural discussions, and one microservice later, we had built a distributed preference management system with eventual consistency guarantees. What we actually needed was a JSON column in the users table.

Let's talk about over-engineering and its very real costs.

The Siren Song of Complexity

We've all been there. The excitement of implementing the latest architectural pattern, the allure of future-proofing our code, the satisfaction of building something "robust." But what if I told you that this pursuit of the perfect solution often leads us down a path of diminishing returns?

Real-World Case Study #1: The Authentication Service

The Over-Engineered Solution:

// A distributed authentication service with:
// - Multiple authentication providers
// - Custom OAuth implementation
// - Role-based access control with hierarchical permissions
// - Distributed session management
// - Custom token generation and validation

class AuthenticationService {
  constructor(
    private readonly tokenService: TokenService,
    private readonly userService: UserService,
    private readonly permissionService: PermissionService,
    private readonly sessionManager: SessionManager,
    private readonly cacheService: CacheService,
    // ... 5 more dependencies
  ) {}

  async authenticate(credentials: AuthCredentials): Promise<AuthResult> {
    // 200 lines of complex logic
  }
}

What They Actually Needed:

import { auth } from 'auth-provider';

const authenticate = async (email: string, password: string) => {
  return auth.signInWithEmailAndPassword(email, password);
};

The team spent three months building a custom authentication system when an existing service would have covered 95% of their needs in an afternoon of integration work.

Real-World Case Study #2: The Configuration System

The Over-Engineered Approach:

• Kubernetes ConfigMaps
• Multiple environment configurations
• Dynamic configuration updates
• Feature flags system
• Configuration validation layer
• Configuration inheritance system

What They Actually Needed:

const config = {
  apiUrl: process.env.API_URL,
  maxRetries: 3,
  timeout: 5000
};

The Hidden Costs

1. Maintenance Burden

   • Every line of custom code is a line you'll need to maintain
   • Complex systems require documentation
   • New team members need more time to onboard
   • Testing becomes exponentially more complex

2. Cognitive Load

   • Developers need to keep more context in their heads
   • Simple changes require understanding complex systems
   • Code reviews take longer
   • Bug fixing becomes archaeological work

3. Technical Debt Interest

   • Complex systems accumulate debt faster
   • Updating dependencies becomes a project
   • Security vulnerabilities have more surface area
   • Performance optimization becomes more challenging

Signs You're Over-Engineering

1. Your architecture diagram requires multiple pages
2. Simple feature requests lead to architectural discussions
3. You're solving problems you don't have yet
4. Your abstractions have abstractions
5. You've implemented your own version of existing solutions

The YAGNI Principle (You Ain't Gonna Need It)

Remember YAGNI? It's not just a catchy acronym – it's a lifeline. Here's how to apply it:

1. Start Simple

   // Instead of a complex caching system
   const cache = new Map();

   // Instead of a distributed event system
   const eventEmitter = new EventEmitter();

1. Add Complexity Only When Needed
   • Wait for actual requirements, not imagined ones
   • Let usage patterns guide your architecture
   • Keep refactoring cost in mind

Success Story: The Refactoring That Removed Code

One team reduced their codebase by 60% by:

1. Removing their custom ORM layer
2. Switching to SQL queries
3. Eliminating their homegrown caching system
4. Deleting their "future-proof" abstraction layers

Result: Faster performance, fewer bugs, happier developers.

How to Choose the Right Level of Engineering

Ask yourself:

1. What problem am I actually solving right now?
2. Could this be solved with existing tools?
3. What's the maintenance cost of this solution?
4. Will this make simple changes harder?
5. Am I optimizing for problems I don't have?

The Simple Solution Framework

1. Start With the Simplest Solution

   let users = [];

   const addUser = (user) => {
     users.push(user);
   };

1. Measure Real Problems

   • Use actual metrics
   • Listen to real user feedback
   • Monitor system performance

2. Increment Thoughtfully

   • Add complexity in small, measured steps
   • Document why each addition was necessary
   • Keep old solutions in mind

Conclusion

The next time you're tempted to build a distributed system for storing user preferences, remember: the simplest solution that solves the actual problem is often the best solution. Your future self (and your team) will thank you.

Remember: Every line of code you don't write is a line you don't have to debug, maintain, or explain to others.

Comments

[𒎏Wii 🏳️‍⚧️]

Simple feature requests lead to architectural discussions

This is usually a sign of bad engineering, rather than *over*engineering. What hides underneath this is the "cognitive load" problem: A simple system can likely be designed well by a moderately smart engineer, while a complex system can be screwed up even by the most experienced software architects.

Often the complexity of software systems is also premature: Someone predicts future use-cases and starts to account for them; but if the real requirements develop in a different direction, their system might be a worse match than the "naive" first implementation, and deciding to toss the entire thing out and re-implement is a painful one.

So projects end up with weird solutions that feel "taped on" to make logic fit requirements that it wasn't built for, while making no use of the things it can do best.

The problem there isn't necessarily that the system grew to be complex eventually, but that it did so before it was even clear what type of complexity was needed.

[Andrey Rusev]

Every line of code you don't write is a line you don't have to debug, maintain, or explain to others.

True! Some time ago I made it a rule - write less. Specifically because of the maintain part - less to live with. IMHO, quite important when you have some long-term plans for that code.

[david duymelinck]

I agree with the sentiment of the post. I also think that you shouldn't swing the pendulum too much in the other direction, under-engineering.

The simplest solution that solves the actual problem/feature can create problems in the long run. You can ask people about the current problem/feature, but also ask how they see the problem/feature in the future. The MoSCoW method is a good way to break down what they need right now, what they want in the future and things they don't want.
After a session like that, the boundaries of the problem/feature are going to be more clear than doing only the actual problem.

Instead of building it yourself, check out the packages that are available to come to a solution. Even if there are no packages that solve the problem/feature fully. Check how easy it is to come to the solution and compare it with the time it would cost to build it yourself.

The people that report the problem/feature are maybe not aware that the request they did can also be used for other parts of the application.
Lets take the case of storing user preferences. What if in the current code the site settings are done in code. Then the solution could be to create a single way to store similar data.

These are a few examples that show there goes more into creating solutions than what developers want to build. It should be a process that is understood by every stakeholder.