Reuse and Standardization Principle

Overview

Principle: Maximize reuse of proven solutions and enforce standards across the organization.

Standardization reduces cognitive load, improves quality, and speeds development. Reuse avoids reinventing solutions.

Rationale

Every team shouldn’t solve same problems
Standards reduce decision paralysis
Reusable components accelerate delivery
Inconsistency creates confusion and bugs
Organizational knowledge should be shared

Core Implications

1. Reuse Levels

Code Reuse:

Level 1 (Copy-Paste)
❌ Worse: Duplicated code, bugs in multiple places

Level 2 (Shared Library)
✅ Better: One implementation, multiple consumers
   All teams use logging-library v1.2.3

Level 3 (Platform Service)
✅ Best: Service abstracts complexity
   All teams use Auth Service API
   Auth Service owns authentication logic and updates

Level 4 (Inner Source)
✅ Excellent: Internal open source
   Teams contribute improvements
   Shared ownership across organization

2. Standardization Categories

Technical Standards:

Code Style:
- Language-specific conventions
- Linting rules
- Formatting requirements

Example (TypeScript):

// GOOD - follows standard const getUserName = (userId: string): string => { return user.displayName; }

// BAD - doesn’t follow standard function get_user_name(userID){ return user.display_name }

Naming Conventions:
- Packages: lowercase with hyphens (my-package)
- Classes: PascalCase (UserService)
- Functions/variables: camelCase (getUserName)
- Constants: UPPER_SNAKE_CASE (MAX_RETRIES)

Database Naming:
- Tables: plural, lowercase (users, orders, order_items)
- Columns: lowercase with underscores (created_at, user_id)
- Primary keys: id (not user_id)
- Foreign keys: {entity}_id (user_id, order_id)

API Standards:

RESTful Convention:
- GET /api/v1/users - List users
- POST /api/v1/users - Create user
- GET /api/v1/users/{id} - Get specific user
- PUT /api/v1/users/{id} - Update user
- DELETE /api/v1/users/{id} - Delete user

Response Format:
{
  "status": "success|error",
  "data": {...},
  "error": {
    "code": "ERROR_CODE",
    "message": "Human readable message",
    "details": {...}
  }
}

Error Codes:
- 400: Bad Request (validation)
- 401: Unauthorized (auth needed)
- 403: Forbidden (permission denied)
- 404: Not Found
- 409: Conflict (duplicate)
- 500: Internal Server Error

Architectural Standards:

Deployment Pipeline:
Code → Build → Unit Test → Integration Test → Deploy to Staging → E2E Test → Deploy to Production

Configuration Management:
- Environment variables in .env
- Secrets in secure vault
- No secrets in code

Logging Format:
- JSON structured logging
- Standard fields: timestamp, level, message, context

Database Migration:
- Version control migrations
- Forward-only migrations (no rollbacks)
- Test migrations locally first

3. Reusable Component Patterns

Shared Library Example:

my-company/logging-library (npm package)
├─ Logger class
├─ Structured logging formatters
├─ Integration with ELK stack
└─ Metrics collection

Usage:
import { Logger } from "@my-company/logging-library"

const logger = new Logger("OrderService")
logger.info("Order created", {
  orderId: order.id,
  customerId: order.customerId,
  total: order.total
})

Shared Component Example:

my-company/auth-service (API service)
Provides:
- User authentication
- Token generation
- Permission checking
- OAuth integration

All services use:
GET /auth/v1/validate-token
POST /auth/v1/generate-token
GET /auth/v1/permissions/{userId}

Platform Template Example:

my-company/microservice-template (repository template)
New service creation: "Use this template"

Includes:
├─ Base service structure
├─ Logging configured
├─ Metrics collection
├─ CI/CD pipeline
├─ Docker configuration
├─ Documentation template
└─ Testing setup

Result: New service ready in 5 minutes instead of 2 hours

4. Architecture Decision Records (ADRs)

ADR Structure:

# ADR 0001: Use PostgreSQL for Primary Database

## Status
ACCEPTED

## Context
We need a database for user and order data. Options: PostgreSQL, MySQL, MongoDB

## Decision
Use PostgreSQL because:
- ACID compliance for financial transactions
- JSON support for flexible schema
- Strong community and tooling
- Proven at scale

## Consequences
- Must handle complex migrations
- Requires SQL expertise
- Licenses: Open source
- Infrastructure: Self-managed or RDS

## Alternatives Considered
- MySQL: Simpler but less flexible
- MongoDB: Too flexible, harder to maintain data integrity

## Related Decisions
- ADR 0002: Use Redis for caching
- ADR 0003: Event sourcing for audit trail

ADR Benefits:

Documents why decisions made
Prevents re-debating decisions
Onboards new team members
Guides similar decisions

Implementation Practices

1. Pattern Library

Organization Pattern Library:

Patterns: https://patterns.my-company.com

Patterns Documented:
├─ Authentication
│  ├─ OAuth2 integration
│  ├─ JWT token management
│  └─ Permission checking
│
├─ Data Access
│  ├─ Repository pattern
│  ├─ Query optimization
│  └─ Caching strategies
│
├─ API Design
│  ├─ Error handling
│  ├─ Pagination
│  └─ Versioning
│
├─ Resilience
│  ├─ Retry strategies
│  ├─ Circuit breaker
│  └─ Fallback handling
│
└─ Testing
   ├─ Unit test structure
   ├─ Mock strategies
   └─ Contract testing

Each pattern includes:
- Problem statement
- Solution
- Code example
- When to use
- Anti-patterns

2. Component Registry

Internal Component Registry: https://registry.my-company.com

Registered Components:
├─ @my-company/auth-service (v2.1.3)
│  Owner: Platform Team
│  Status: Stable
│  Usage: 14 services
│  Documentation: https://...
│
├─ @my-company/logging-library (v1.5.0)
│  Owner: Platform Team
│  Status: Stable
│  Usage: 42 services
│
├─ @my-company/message-queue (v3.0.0)
│  Owner: Platform Team
│  Status: Deprecated (use event-bus instead)
│
└─ @my-company/event-bus (v1.2.0)
   Owner: Platform Team
   Status: Recommended
   Usage: 8 services

3. Standardization Governance

Standards Committee:
├─ Meets monthly
├─ Proposes new standards
├─ Reviews deviations
├─ Makes exceptions

Standard Lifecycle:
Proposed → Draft (team feedback) → Approved (committee) → Enforced (tooling)

Exception Process:
1. Team documents why standard doesn't apply
2. Committee reviews and votes
3. If approved: document exception and monitor
4. Review exceptions quarterly

4. Tooling for Standards

Code Quality Enforcement:
- Linters (ESLint, Checkstyle): Enforce coding standards
- Formatters (Prettier, Black): Consistent formatting
- Static analysis (SonarQube): Code smell detection
- Dependency check: Vulnerable dependency detection

CI/CD Enforcement:
- Code review (GitHub): Changes reviewed by peers
- Status checks: Linting, tests, security scans pass
- Branch protection: Can't merge without approval

Documentation Requirements:
- All public APIs documented
- Examples provided
- Change logs maintained
- ADRs for major decisions

Common Scenarios

Scenario 1: Reducing Duplicate Work

Situation: Three teams implementing authentication differently.

Standardization Approach:

Assess implementations
Identify best approach
Create shared auth-service
Migrate teams incrementally
Retire duplicates

Result: -500 lines of duplicate code, easier security updates

Scenario 2: Onboarding New Team Member

Situation: Engineer joins, needs to understand standards.

With Reuse and Standardization:

Point to pattern library
Show examples in codebase
Review coding standards
Start contributing immediately

Without Standards:

Learn codebase conventions (scattered)
Ask seniors about patterns
Code reviews teach standards
Slow ramp-up

Scenario 3: Technology Upgrade

Situation: Need to upgrade PostgreSQL versions.

With Standardization:

Update shared library
All services get upgrade
One coordinated change

Without Standardization:

Each service upgrades independently
Different versions across company
Support nightmare
Duplicated testing effort

Risks of Ignoring This Principle

❌ Duplicate Effort: Every team reinvents solutions ❌ Inconsistency: Same problem solved differently each time ❌ Quality Issues: Some solutions better than others ❌ Onboarding Pain: New team members lost in inconsistency ❌ Maintenance Burden: Bugs fixed in multiple places ❌ Slow Development: Solving known problems over

Best Practices

✅ Start with shared libraries Extract common code early.

✅ Document patterns Make institutional knowledge explicit.

✅ Create templates Bootstrap new services quickly.

✅ Use ADRs Document why decisions made.

✅ Automate enforcement Don’t rely on humans to follow standards.

✅ Review and update standards Quarterly assessment of standard effectiveness.

✅ Make exceptions explicit Don’t hide deviations.

✅ Provide alternatives Standards should offer choices, not dictate.

✅ Celebrate reuse Recognize teams leveraging shared components.

Modularity and Loose Coupling - Standards enable modularity
API-First Design - Standardized contracts
Automation Over Manual - Automate standard enforcement

References

Building Microservices (Sam Newman) - Standardization patterns
Architecture Decision Records (Nygard) - https://adr.github.io/
The Phoenix Project (Gene Kim) - Cross-team coordination

Last Updated: November 2025 Principle Category: Organization Applies To: Development, architecture, operations Related Documents: Standards Guide, Pattern Library, ADR Template

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Reuse and Standardization Principle

Overview

Rationale

Core Implications

1. Reuse Levels

2. Standardization Categories

3. Reusable Component Patterns

4. Architecture Decision Records (ADRs)

Implementation Practices

1. Pattern Library

2. Component Registry

3. Standardization Governance

4. Tooling for Standards

Common Scenarios

Scenario 1: Reducing Duplicate Work

Scenario 2: Onboarding New Team Member

Scenario 3: Technology Upgrade

Risks of Ignoring This Principle

Best Practices

References

Reuse and Standardization Principle

Overview

Rationale

Core Implications

1. Reuse Levels

2. Standardization Categories

3. Reusable Component Patterns

4. Architecture Decision Records (ADRs)

Implementation Practices

1. Pattern Library

2. Component Registry

3. Standardization Governance

4. Tooling for Standards

Common Scenarios

Scenario 1: Reducing Duplicate Work

Scenario 2: Onboarding New Team Member

Scenario 3: Technology Upgrade

Risks of Ignoring This Principle

Best Practices

Related Principles

References