ranbot-ai/architecture-decision-records

Architecture Decision Records

Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.

Use this skill when

• Making significant architectural decisions
• Documenting technology choices
• Recording design trade-offs
• Onboarding new team members
• Reviewing historical decisions
• Establishing decision-making processes

Do not use this skill when

• You only need to document small implementation details
• The change is a minor patch or routine maintenance
• There is no architectural decision to capture

Instructions

1. Capture the decision context, constraints, and drivers.
2. Document considered options with tradeoffs.
3. Record the decision, rationale, and consequences.
4. Link related ADRs and update status over time.

Core Concepts

1. What is an ADR?

An Architecture Decision Record captures:

• Context: Why we needed to make a decision
• Decision: What we decided
• Consequences: What happens as a result

2. When to Write an ADR

| Write ADR | Skip ADR | |-----------|----------| | New framework adoption | Minor version upgrades | | Database technology choice | Bug fixes | | API design patterns | Implementation details | | Security architecture | Routine maintenance | | Integration patterns | Configuration changes |

3. ADR Lifecycle

Proposed → Accepted → Deprecated → Superseded
              ↓
           Rejected

Templates

Template 1: Standard ADR (MADR Format)

# ADR-0001: Use PostgreSQL as Primary Database

## Status

Accepted

## Context

We need to select a primary database for our new e-commerce platform. The system
will handle:
- ~10,000 concurrent users
- Complex product catalog with hierarchical categories
- Transaction processing for orders and payments
- Full-text search for products
- Geospatial queries for store locator

The team has experience with MySQL, PostgreSQL, and MongoDB. We need ACID
compliance for financial transactions.

## Decision Drivers

* **Must have ACID compliance** for payment processing
* **Must support complex queries** for reporting
* **Should support full-text search** to reduce infrastructure complexity
* **Should have good JSON support** for flexible product attributes
* **Team familiarity** reduces onboarding time

## Considered Options

### Option 1: PostgreSQL
- **Pros**: ACID compliant, excellent JSON support (JSONB), built-in full-text
  search, PostGIS for geospatial, team has experience
- **Cons**: Slightly more complex replication setup than MySQL

### Option 2: MySQL
- **Pros**: Very familiar to team, simple replication, large community
- **Cons**: Weaker JSON support, no built-in full-text search (need
  Elasticsearch), no geospatial without extensions

### Option 3: MongoDB
- **Pros**: Flexible schema, native JSON, horizontal scaling
- **Cons**: No ACID for multi-document transactions (at decision time),
  team has limited experience, requires schema design discipline

## Decision

We will use **PostgreSQL 15** as our primary database.

## Rationale

PostgreSQL provides the best balance of:
1. **ACID compliance** essential for e-commerce transactions
2. **Built-in capabilities** (full-text search, JSONB, PostGIS) reduce
   infrastructure complexity
3. **Team familiarity** with SQL databases reduces learning curve
4. **Mature ecosystem** with excellent tooling and community support

The slight complexity in replication is outweighed by the reduction in
additional services (no separate Elasticsearch needed).

## Consequences

### Positive
- Single database handles transactions, search, and geospatial queries
- Reduced operational complexity (fewer services to manage)
- Strong consistency guarantees for financial data
- Team can leverage existing SQL expertise

### Negative
- Need to learn PostgreSQL-specific features (JSONB, full-text search syntax)
- Vertical scaling limits may require read replicas sooner
- Some team members need PostgreSQL-specific training

### Risks
- Full-text search may not scale as well as dedicated search engines
- Mitigation: Design for potential Elasticsearch addition if needed

## Implementation Notes

- Use JSONB for flexible product attributes
- Implement connection pooling with PgBouncer
- Set up streaming replication for read replicas
- Use pg_trgm extension for fuzzy search

## Related Decisions

- ADR-0002: Caching Strategy (Redis) - complements database choice
- ADR-0005: Search Architecture - may supersede if Elasticsearch needed

## References

- [PostgreSQL JSON Documentation](https://www.postgresql.org/docs/current/datatype-json.html)
- [PostgreSQL Full Text Search](https://www.postgresql.org/docs/current/textsearch.html)
- Internal: Performance benchmarks in `/docs/benchmarks/database-comparison.md`

Template 2: Lightweight ADR

# ADR-0012: Adopt TypeScript for Frontend Development

**Status**: Accepted
**Da