Stop Writing Code Twice: DRY Principle Rules That Actually Work

Your codebase has become a maintenance nightmare. Functions repeated across services, constants scattered everywhere, business logic duplicated between frontend and backend. Every bug fix requires hunting down multiple copies of the same code. It's time to stop the madness.

The Duplication Problem That's Costing You Time

Here's what happens when DRY principles break down in real projects:

Constants Everywhere: User roles defined in 6 different files. Change "admin" to "administrator" and break half your application because you missed one.

Business Logic Drift: Price calculation logic exists in React components, Express routes, Django views, and dbt models. They all implement it slightly differently. Which one is correct?

Infrastructure Copy-Paste: Every new service gets its own Terraform configuration with 90% identical code. Security updates require touching 12 different files.

SQL Repetition: The same complex joins appear in every analytics query. Performance optimization means updating dozens of scripts.

This isn't just technical debt—it's developer productivity suicide.

The Solution: Comprehensive DRY Rules That Scale

These Cursor Rules transform how you handle duplication across your entire stack. Instead of fighting repeated code after it appears, you'll prevent it systematically through smart abstractions, centralized definitions, and automated enforcement.

Key Principle: One authoritative source for every piece of knowledge in your system—whether it's code, logic, data definitions, or infrastructure configurations.

Real Impact on Your Development Workflow

TypeScript/JavaScript Transformation

Before: Validation logic scattered across components

// In UserForm.tsx
const validateEmail = (email: string) => {
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return emailRegex.test(email);
};

// In api/users.ts  
const isValidEmail = (email: string) => {
  const regex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return regex.test(email);
};

After: Single source of truth with Zod schemas

// lib/validation.ts
export const UserSchema = z.object({
  email: z.string().email(),
  name: z.string().min(2)
});

// Used everywhere
export type User = z.infer<typeof UserSchema>;

Result: 70% fewer validation bugs, instant API/frontend sync, single place to update rules.

Python/Django Efficiency

Before: Repeated model patterns

# In multiple models
class Article(models.Model):
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)
    is_active = models.BooleanField(default=True)

class Product(models.Model):
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)  
    is_active = models.BooleanField(default=True)

After: Mixin-based composition

class TimestampMixin(models.Model):
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)
    is_active = models.BooleanField(default=True)
    
    class Meta:
        abstract = True

class Article(TimestampMixin):
    title = models.CharField(max_length=200)

Result: 50% less model code, consistent field behavior, easier schema migrations.

SQL/dbt Analytics Revolution

Before: Metric calculations duplicated everywhere

-- In 15 different queries
SELECT 
  user_id,
  SUM(CASE WHEN status = 'completed' THEN amount ELSE 0 END) as revenue
FROM orders 
GROUP BY user_id

After: Centralized metrics via dbt

-- models/metrics/revenue_by_user.sql
{{ config(materialized='view') }}
SELECT 
  user_id,
  {{ calculate_revenue('orders') }} as revenue
FROM {{ ref('orders') }}
GROUP BY user_id

Result: Consistent metrics across all reports, single place to fix calculation bugs, 90% faster new dashboard creation.

Infrastructure as Code Streamlining

Before: Copy-paste Terraform configurations

# Repeated in 8 service directories
resource "aws_s3_bucket" "logs" {
  bucket = "service-a-logs"
  
  versioning {
    enabled = true
  }
  
  server_side_encryption_configuration {
    rule {
      apply_server_side_encryption_by_default {
        sse_algorithm = "AES256"
      }
    }
  }
}

After: Reusable modules

# modules/s3_bucket/main.tf
module "service_bucket" {
  source = "./modules/s3_bucket"
  name   = "${var.service_name}-logs"
  tags   = local.common_tags
}

Result: 80% reduction in infrastructure code, consistent security policies, 5-minute service setup instead of 30.

Implementation Guide

1. Set Up Detection and Prevention

Add duplication detection to your CI/CD pipeline:

# .github/workflows/code-quality.yml
- name: Check for duplicated code
  run: |
    npx jscpd --threshold 3 --reporters console,json
    pylint --duplicate-code src/
    terraform fmt -check=true

2. Establish Shared Libraries

Create centralized locations for reusable code:

/lib/           # TypeScript utilities
/services/      # Business logic
/modules/       # Terraform modules  
/macros/        # dbt macros
/mixins/        # Django mixins

3. Implement the Three-Strike Rule

Abstract on the third duplication, not the first. This prevents premature optimization while catching real patterns.

4. Centralize Configuration

Move all constants, feature flags, and environment variables to single configuration files:

// config/constants.ts
export const USER_ROLES = {
  ADMIN: 'admin',
  USER: 'user',
  MODERATOR: 'moderator'
} as const;

export type UserRole = typeof USER_ROLES[keyof typeof USER_ROLES];

Measurable Results You'll See

Week 1: Automated detection catches 80% of new duplication before it hits production

Week 2: Shared utility libraries reduce new feature development time by 30%

Month 1: Bug fixes that previously required 5+ file changes now touch single source of truth

Month 3: New team members productive 50% faster due to consistent patterns and centralized logic

Ongoing: Maintenance overhead drops dramatically as your codebase becomes self-documenting through consistent abstractions

Advanced Patterns for Experienced Teams

Microservices Boundaries

Allow deliberate duplication across service boundaries to maintain autonomy, but never within services:

// ✅ OK: Simple DTOs duplicated across services
// user-service/types.ts
interface User { id: string; email: string; }

// order-service/types.ts  
interface User { id: string; email: string; }

// ❌ Not OK: Complex business logic duplicated

Performance-Conscious Abstractions

Use tree-shaking and bundle analysis to prevent utility library bloat:

// lib/index.ts - Barrel exports for tree-shaking
export { formatCurrency } from './formatters';
export { validateEmail } from './validators';

// Import only what you need
import { formatCurrency } from '@/lib';

Documentation Generation

Generate API documentation from your single source of truth:

/**
 * Formats monetary values consistently across the application
 * @param cents - Amount in cents to avoid floating point issues
 * @param currency - Currency code (USD, EUR, GBP)
 */
export function formatPrice(cents: number, currency: Currency): string {
  // Implementation...
}

Your codebase transformation starts with implementing these rules. Stop accepting duplication as inevitable—make it impossible instead.

The time you save debugging inconsistent logic and hunting down scattered constants will immediately pay for the setup effort. Your future self will thank you when that critical bug fix touches one file instead of ten.