# Architecture Decision Records (ADR) Framework

## What is an ADR?

Architecture Decision Records capture important architectural decisions made along with their context and consequences. They help maintain institutional knowledge and explain why systems are built the way they are.

## ADR Template

### ADR-[NUMBER]: [TITLE]

**Date**: YYYY-MM-DD  
**Status**: [Proposed | Accepted | Deprecated | Superseded]  
**Deciders**: [List of people involved in decision]  
**Technical Story**: [Ticket/Issue reference]

#### Context and Problem Statement

[Describe the context and problem that needs to be solved. What are we trying to achieve?]

#### Decision Drivers

- [Driver 1: e.g., Performance requirements]
- [Driver 2: e.g., Time to market]
- [Driver 3: e.g., Team expertise]
- [Driver 4: e.g., Cost constraints]

#### Considered Options

1. **Option 1: [Name]**
2. **Option 2: [Name]**
3. **Option 3: [Name]**

#### Decision Outcome

**Chosen option**: "[Option Name]", because [justification]

##### Positive Consequences
- [Consequence 1]
- [Consequence 2]

##### Negative Consequences
- [Risk 1 and mitigation]
- [Risk 2 and mitigation]

#### Pros and Cons of Options

##### Option 1: [Name]
- **Pros**:
  - [Advantage 1]
  - [Advantage 2]
- **Cons**:
  - [Disadvantage 1]
  - [Disadvantage 2]

##### Option 2: [Name]
[Repeat structure]

#### Links
- [Related ADRs]
- [Documentation]
- [Research/PoCs]

---

## Example ADRs

### ADR-001: Microservices Architecture

**Date**: 2024-01-15  
**Status**: Accepted  
**Deciders**: CTO, VP Engineering, Tech Leads  
**Technical Story**: ARCH-001

#### Context and Problem Statement

Our monolithic application is becoming difficult to scale and deploy. Different teams are stepping on each other's toes, and deployment cycles are getting longer. We need to decide on our architectural approach for the next 3-5 years.

#### Decision Drivers

- Need for independent team deployment
- Requirement to scale different components independently
- Different components have different performance characteristics
- Team size growing from 25 to 75+ engineers
- Need to support multiple technology stacks

#### Considered Options

1. **Keep Monolith**: Continue with current architecture
2. **Modular Monolith**: Break into modules but single deployment
3. **Microservices**: Full service-oriented architecture
4. **Serverless**: Function-as-a-Service approach

#### Decision Outcome

**Chosen option**: "Microservices", because it best supports our team autonomy needs and scaling requirements, despite added complexity.

##### Positive Consequences
- Teams can deploy independently
- Services can scale based on individual needs
- Technology diversity is possible
- Fault isolation improved

##### Negative Consequences
- Increased operational complexity - Mitigated by investing in DevOps
- Network latency between services - Mitigated by careful service boundaries
- Data consistency challenges - Mitigated by event sourcing patterns

---

### ADR-002: Container Orchestration Platform

**Date**: 2024-02-01  
**Status**: Accepted  
**Deciders**: CTO, DevOps Lead, Platform Team  
**Technical Story**: INFRA-045

#### Context and Problem Statement

With the move to microservices (ADR-001), we need a container orchestration platform to manage deployment, scaling, and operations of application containers.

#### Decision Drivers

- Need for automated deployment and scaling
- High availability requirements (99.9% SLA)
- Multi-cloud strategy (avoid vendor lock-in)
- Team familiarity and ecosystem maturity
- Cost considerations

#### Considered Options

1. **Kubernetes**: Industry standard, self-managed
2. **Amazon ECS**: AWS-native solution
3. **Docker Swarm**: Simpler alternative
4. **Nomad**: HashiCorp solution

#### Decision Outcome

**Chosen option**: "Kubernetes", because of its maturity, ecosystem, and multi-cloud support.

##### Positive Consequences
- Industry standard with huge ecosystem
- Multi-cloud compatible
- Strong community support
- Extensive tooling available

##### Negative Consequences
- Steep learning curve - Mitigated by training and hiring
- Operational complexity - Mitigated by managed Kubernetes (EKS/GKE)

---

### ADR-003: API Gateway Strategy

**Date**: 2024-03-15  
**Status**: Accepted  
**Deciders**: CTO, Security Lead, API Team  
**Technical Story**: API-101

#### Context and Problem Statement

With multiple microservices, we need a unified entry point for external clients that handles cross-cutting concerns like authentication, rate limiting, and monitoring.

#### Decision Drivers

- Security requirements (OAuth2, API keys)
- Need for rate limiting and throttling
- Monitoring and analytics requirements
- Developer experience for API consumers
- Performance (sub-100ms overhead)

#### Considered Options

1. **Kong**: Open-source, plugin ecosystem
2. **AWS API Gateway**: Managed service
3. **Istio/Envoy**: Service mesh approach
4. **Build Custom**: In-house solution

#### Decision Outcome

**Chosen option**: "Kong", because of its flexibility and plugin ecosystem while avoiding vendor lock-in.

---

## Common Architecture Decisions

### 1. Frontend Architecture
- **Single Page Application (SPA)** vs **Server-Side Rendering (SSR)** vs **Static Site Generation (SSG)**
- **React** vs **Vue** vs **Angular** vs **Svelte**
- **Monorepo** vs **Polyrepo**
- **Micro-frontends** vs **Monolithic frontend**

### 2. Backend Architecture
- **Monolith** vs **Microservices** vs **Serverless**
- **REST** vs **GraphQL** vs **gRPC**
- **Synchronous** vs **Asynchronous** communication
- **Event-driven** vs **Request-response**

### 3. Data Architecture
- **SQL** vs **NoSQL** vs **NewSQL**
- **Single database** vs **Database per service**
- **CQRS** vs **Traditional CRUD**
- **Event Sourcing** vs **State-based storage**

### 4. Infrastructure Decisions
- **Cloud provider**: AWS vs Azure vs GCP vs Multi-cloud
- **Containers** vs **VMs** vs **Serverless**
- **Kubernetes** vs **ECS** vs **Cloud Run**
- **Self-hosted** vs **Managed services**

### 5. Development Practices
- **Continuous Deployment** vs **Continuous Delivery**
- **Feature flags** vs **Branch-based deployment**
- **Blue-green** vs **Canary** vs **Rolling deployment**
- **GitFlow** vs **GitHub Flow** vs **GitLab Flow**

## ADR Best Practices

### Writing Good ADRs

1. **Keep them short**: 1-2 pages maximum
2. **Be specific**: Include concrete examples
3. **Document why, not what**: Focus on reasoning
4. **Include all options**: Even obviously bad ones
5. **Be honest about drawbacks**: Every decision has trade-offs

### When to Write ADRs

Write an ADR when:
- The decision has significant impact
- Multiple options were seriously considered
- The decision is hard to reverse
- You find yourself explaining the same decision repeatedly
- There's disagreement about the approach

### ADR Lifecycle

1. **Proposed**: Under discussion
2. **Accepted**: Decision made and being implemented
3. **Deprecated**: No longer relevant but kept for history
4. **Superseded**: Replaced by another ADR

### Storage and Discovery

- Store ADRs in your main repository under `docs/architecture/decisions/`
- Use consistent numbering (ADR-001, ADR-002, etc.)
- Create an index file linking all ADRs
- Reference ADRs in code comments where relevant
- Review ADRs regularly (quarterly) for relevance

## Decision Evaluation Framework

### Technical Factors (40%)
- Performance impact
- Scalability potential
- Security implications
- Maintainability
- Technical debt

### Business Factors (30%)
- Time to market
- Cost (initial and ongoing)
- Revenue impact
- Competitive advantage
- Regulatory compliance

### Team Factors (30%)
- Current expertise
- Learning curve
- Hiring availability
- Team preference
- Training requirements

## Anti-patterns to Avoid

1. **Decision by Committee**: Too many stakeholders leading to compromise solutions
2. **Analysis Paralysis**: Over-analyzing instead of deciding
3. **Resume-Driven Development**: Choosing tech for personal goals
4. **Hype-Driven Development**: Choosing the newest/coolest tech
5. **Not-Invented-Here**: Rejecting external solutions by default
6. **Vendor Lock-in**: Over-dependence on proprietary solutions
7. **Premature Optimization**: Solving problems you don't have yet
8. **Under-documentation**: Not capturing the "why" behind decisions

## Review Checklist

Before finalizing an ADR, ensure:
- [ ] Problem is clearly stated
- [ ] All realistic options are considered
- [ ] Trade-offs are honestly evaluated
- [ ] Decision rationale is clear
- [ ] Consequences are identified
- [ ] Mitigation strategies are defined
- [ ] Success metrics are established
- [ ] Review date is set (if applicable)
