Architect

/architect [arguments]

## Requirements

### Functional
- Users can search products by name/category
- Users can add items to cart
- Users can checkout and pay

### Non-Functional
- Search response time < 200ms (p95)
- Support 10,000 concurrent users
- 99.9% uptime
- PCI DSS compliant for payments
- Team of 5 developers can maintain

┌─────────────────────────────────────────────┐
│             Client Apps                      │
│  (Web, iOS, Android)                         │
└────────────────┬────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────┐
│          API Gateway / Load Balancer         │
└────────────────┬────────────────────────────┘
                 │
        ┌────────┴────────┐
        ▼                 ▼
┌───────────────┐  ┌───────────────┐
│   Auth        │  │   Core API     │
│   Service     │  │   Service      │
└───────┬───────┘  └───────┬───────┘
        │                  │
        │         ┌────────┴────────┐
        │         ▼                 ▼
        │  ┌──────────────┐  ┌──────────────┐
        │  │  PostgreSQL  │  │   Redis      │
        │  │  (Primary)   │  │   (Cache)    │
        │  └──────────────┘  └──────────────┘
        │
        ▼
┌───────────────┐
│   User DB     │
└───────────────┘

## Components

### API Gateway
**Responsibility:** Route requests, rate limiting, authentication
**Technology:** Nginx
**Dependencies:** Auth Service, Core API Service
**Scale:** 2-3 instances behind load balancer

### Auth Service
**Responsibility:** User authentication, session management, JWT issuing
**Technology:** Python (Flask), PostgreSQL
**API:** REST
**Scale:** Stateless, 2-N instances

### Core API Service
**Responsibility:** Business logic, data access, external integrations
**Technology:** Python (FastAPI), PostgreSQL, Redis
**API:** REST
**Scale:** Stateless, 2-N instances

### PostgreSQL
**Responsibility:** Primary data store
**Scale:** Primary with read replica

### Redis
**Responsibility:** Session storage, caching, rate limiting
**Scale:** Cluster mode (3 nodes)

## API Design

### POST /api/auth/login
**Purpose:** Authenticate user, issue JWT

**Request:**
```json
{
  "email": "user@example.com",
  "password": "secure_password"
}

{
  "token": "eyJ...",
  "user": {
    "id": "123",
    "email": "user@example.com",
    "name": "John Doe"
  }
}

### 7. Plan for Failure

**What can go wrong?**
- Database unavailable
- External API down
- Network partition
- High load
- Data corruption

**Mitigation strategies:**
- Retry with exponential backoff
- Circuit breakers for external services
- Graceful degradation
- Health checks and monitoring
- Database backups

**Example:**
```markdown
## Failure Scenarios

### Database Unavailable
**Impact:** Cannot read/write data
**Mitigation:**
- Read replica failover (automated)
- Circuit breaker after 3 failures
- Cache serves stale data for 5 minutes
- User sees degraded experience message
**Recovery:** Manual failover to replica, fix primary

### External Payment API Down
**Impact:** Cannot process payments
**Mitigation:**
- Retry 3 times with exponential backoff
- Queue payments for later processing
- User notified of delay
- Alert on-call engineer
**Recovery:** Process queued payments once API recovers

# ADR-001: Use PostgreSQL for Primary Database

**Status:** Accepted
**Date:** 2024-01-15
**Deciders:** Tech Lead, Backend Team

## Context

We need to choose a primary database for user data, products, and orders.

Requirements:
- Strong consistency (ACID)
- Complex queries (joins, aggregations)
- < 200ms query time for 90% of queries
- Support 100k users initially

## Decision

Use PostgreSQL as primary database.

## Alternatives Considered

### MongoDB
- **Pros:** Flexible schema, horizontal scaling
- **Cons:** Team unfamiliar, eventual consistency issues
- **Why not:** Team expertise more valuable than flexibility

### DynamoDB
- **Pros:** Managed service, auto-scaling
- **Cons:** Vendor lock-in, limited query capability, cost
- **Why not:** Query limitations would hurt development velocity

### MySQL
- **Pros:** Similar to PostgreSQL, team knows it
- **Cons:** Less feature-rich than PostgreSQL
- **Why not:** PostgreSQL offers JSON support, better full-text search

## Consequences

**Positive:**
- Team can be productive immediately
- Strong consistency guarantees
- Rich query capabilities
- JSON support for flexible data

**Negative:**
- Vertical scaling limits (mitigated: can add read replicas)
- More complex than managed services (mitigated: use RDS)
- Higher operational overhead

**Trade-offs:**
- Chose familiarity over horizontal scaling
- Chose rich queries over eventual consistency
- Can re-evaluate if scale requirements change

## Validation

- Team confirmed expertise in PostgreSQL
- Load testing shows meets performance requirements
- Cost analysis shows acceptable for first year

# Architecture Design: [System/Feature Name]

## Context

### Problem Statement
[What business problem are we solving?]

### Goals
[What are we trying to achieve?]

### Non-Goals
[What are we explicitly NOT trying to achieve?]

### Requirements

**Functional:**
- [Requirement 1]
- [Requirement 2]

**Non-Functional:**
- Performance: [e.g., < 200ms response time]
- Scalability: [e.g., handle 10k concurrent users]
- Security: [e.g., PCI compliance]
- Maintainability: [e.g., easy to modify]

### Constraints
[Technical, time, resource, or business constraints]

## Current Architecture
[What exists today? What needs to change?]

## Proposed Architecture

### High-Level Design

[Diagram or description of system components]

### Components

#### Component A
**Responsibility:** [What it does]
**Interface:** [How others interact with it]
**Dependencies:** [What it depends on]
**Technology:** [Implementation stack]

#### Component B
[Similar structure...]

### Data Flow
[How data moves through the system]

### API Design
[Key endpoints, schemas, contracts]

### Data Model
[Database schema, key entities]

### Security Model
[Authentication, authorization, data protection]

## Alternative Approaches Considered

### Alternative 1: [Approach name]
**Pros:**
- [Advantage 1]
- [Advantage 2]

**Cons:**
- [Disadvantage 1]
- [Disadvantage 2]

**Why not chosen:** [Reasoning]

### Alternative 2: [Another approach]
[Similar structure...]

## Decision Rationale

### Why This Architecture?
[Explain the key decisions and trade-offs]

### Trade-offs Accepted
[What we gave up for what benefits]

### Assumptions
[What we're assuming to be true]

## Implementation Strategy

### Phase 1: [Foundation]
[What to build first]

### Phase 2: [Core Features]
[Next phase]

### Phase 3: [Enhancement]
[Final phase]

### Migration Strategy
[If replacing existing system, how to transition?]

## Risks & Mitigation

| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| [Risk 1] | High | Medium | [How to mitigate] |
| [Risk 2] | Medium | Low | [How to mitigate] |

## Testing Strategy
[How will we validate this architecture?]

## Monitoring & Observability
[How will we know if it's working?]

## Success Metrics
[How will we measure success?]

## Open Questions
[What still needs to be resolved?]

## References
[Links to research, related docs, RFCs]

BAD: Microservices from day 1 with 20 services
GOOD: Start with monolith, split when needed

GOOD:
- Auth Service: Authentication only
- User Service: User profile management
- Order Service: Order processing

BAD:
- God Service: Does everything

// BAD: Tight coupling
class OrderService {
  constructor(private db: PostgresDatabase) {}
}

// GOOD: Loose coupling
class OrderService {
  constructor(private db: Database) {}  // Interface
}

GOOD:
user/
  - create_user.ts
  - update_user.ts
  - delete_user.ts
  - user_repository.ts

BAD:
create/
  - create_user.ts
  - create_order.ts
update/
  - update_user.ts
  - update_order.ts

// BAD: Implicit dependency
function processOrder(orderId: string) {
  const db = global.database  // Where does this come from?
  // ...
}

// GOOD: Explicit dependency
function processOrder(
  orderId: string,
  db: Database,
  logger: Logger
) {
  // Dependencies are clear
}

// BAD: Silent failure
function divide(a: number, b: number) {
  if (b === 0) return 0  // Wrong!
  return a / b
}

// GOOD: Fail fast
function divide(a: number, b: number) {
  if (b === 0) {
    throw new Error('Division by zero')
  }
  return a / b
}

// BAD: Hard to test
class OrderService {
  processOrder(orderId: string) {
    const db = new PostgresDatabase()  // Can't mock
    const api = new PaymentAPI()       // Can't mock
    // ...
  }
}

// GOOD: Easy to test
class OrderService {
  constructor(
    private db: Database,      // Can inject mock
    private api: PaymentAPI    // Can inject mock
  ) {}

  processOrder(orderId: string) {
    // ...
  }
}