Architecture Decision Records

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

When to Use This Skill

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

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Proposed → Accepted → Deprecated → Superseded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
**Date**: 2024-01-15
**Deciders**: @alice, @bob, @charlie

## Context

Our React codebase has grown to 50+ components with increasing bug reports
related to prop type mismatches and undefined errors. PropTypes provide
runtime-only checking.

## Decision

Adopt TypeScript for all new frontend code. Migrate existing code incrementally.

## Consequences

**Good**: Catch type errors at compile time, better IDE support, self-documenting
code.

**Bad**: Learning curve for team, initial slowdown, build complexity increase.

**Mitigations**: TypeScript training sessions, allow gradual adoption with
`allowJs: true`.

Template 3: Y-Statement Format

# ADR-0015: API Gateway Selection

In the context of **building a microservices architecture**,
facing **the need for centralized API management, authentication, and rate limiting**,
we decided for **Kong Gateway**
and against **AWS API Gateway and custom Nginx solution**,
to achieve **vendor independence, plugin extensibility, and team familiarity with Lua**,
accepting that **we need to manage Kong infrastructure ourselves**.

Template 4: ADR for Deprecation

# ADR-0020: Deprecate MongoDB in Favor of PostgreSQL

## Status

Accepted (Supersedes ADR-0003)

## Context

ADR-0003 (2021) chose MongoDB for user profile storage due to schema flexibility
needs. Since then:

- MongoDB's multi-document transactions remain problematic for our use case
- Our schema has stabilized and rarely changes
- We now have PostgreSQL expertise from other services
- Maintaining two databases increases operational burden

## Decision

Deprecate MongoDB and migrate user profiles to PostgreSQL.

## Migration Plan

1. **Phase 1** (Week 1-2): Create PostgreSQL schema, dual-write enabled
2. **Phase 2** (Week 3-4): Backfill historical data, validate consistency
3. **Phase 3** (Week 5): Switch reads to PostgreSQL, monitor
4. **Phase 4** (Week 6): Remove MongoDB writes, decommission

## Consequences

### Positive

- Single database technology reduces operational complexity
- ACID transactions for user data
- Team can focus PostgreSQL expertise

### Negative

- Migration effort (~4 weeks)
- Risk of data issues during migration
- Lose some schema flexibility

## Lessons Learned

Document from ADR-0003 experience:

- Schema flexibility benefits were overestimated
- Operational cost of multiple databases was underestimated
- Consider long-term maintenance in technology decisions

Template 5: Request for Comments (RFC) Style

# RFC-0025: Adopt Event Sourcing for Order Management

## Summary

Propose adopting event sourcing pattern for the order management domain to
improve auditability, enable temporal queries, and support business analytics.

## Motivation

Current challenges:

1. Audit requirements need complete order history
2. "What was the order state at time X?" queries are impossible
3. Analytics team needs event stream for real-time dashboards
4. Order state reconstruction for customer support is manual

## Detailed Design

### Event Store

OrderCreated { orderId, customerId, items[], timestamp } OrderItemAdded { orderId, item, timestamp } OrderItemRemoved { orderId, itemId, timestamp } PaymentReceived { orderId, amount, paymentId, timestamp } OrderShipped { orderId, trackingNumber, timestamp }

### Projections

- **CurrentOrderState**: Materialized view for queries
- **OrderHistory**: Complete timeline for audit
- **DailyOrderMetrics**: Analytics aggregation

### Technology

- Event Store: EventStoreDB (purpose-built, handles projections)
- Alternative considered: Kafka + custom projection service

## Drawbacks

- Learning curve for team
- Increased complexity vs. CRUD
- Need to design events carefully (immutable once stored)
- Storage growth (events never deleted)

## Alternatives

1. **Audit tables**: Simpler but doesn't enable temporal queries
2. **CDC from existing DB**: Complex, doesn't change data model
3. **Hybrid**: Event source only for order state changes

## Unresolved Questions

- [ ] Event schema versioning strategy
- [ ] Retention policy for events
- [ ] Snapshot frequency for performance

## Implementation Plan

1. Prototype with single order type (2 weeks)
2. Team training on event sourcing (1 week)
3. Full implementation and migration (4 weeks)
4. Monitoring and optimization (ongoing)

## References

- [Event Sourcing by Martin Fowler](https://martinfowler.com/eaaDev/EventSourcing.html)
- [EventStoreDB Documentation](https://www.eventstore.com/docs)

ADR Management

Directory Structure

docs/
├── adr/
│   ├── README.md           # Index and guidelines
│   ├── template.md         # Team's ADR template
│   ├── 0001-use-postgresql.md
│   ├── 0002-caching-strategy.md
│   ├── 0003-mongodb-user-profiles.md  # [DEPRECATED]
│   └── 0020-deprecate-mongodb.md      # Supersedes 0003

ADR Index (README.md)

# Architecture Decision Records

This directory contains Architecture Decision Records (ADRs) for [Project Name].

## Index

| ADR                                   | Title                              | Status     | Date       |
| ------------------------------------- | ---------------------------------- | ---------- | ---------- |
| [0001](0001-use-postgresql.md)        | Use PostgreSQL as Primary Database | Accepted   | 2024-01-10 |
| [0002](0002-caching-strategy.md)      | Caching Strategy with Redis        | Accepted   | 2024-01-12 |
| [0003](0003-mongodb-user-profiles.md) | MongoDB for User Profiles          | Deprecated | 2023-06-15 |
| [0020](0020-deprecate-mongodb.md)     | Deprecate MongoDB                  | Accepted   | 2024-01-15 |

## Creating a New ADR

1. Copy `template.md` to `NNNN-title-with-dashes.md`
2. Fill in the template
3. Submit PR for review
4. Update this index after approval

## ADR Status

- **Proposed**: Under discussion
- **Accepted**: Decision made, implementing
- **Deprecated**: No longer relevant
- **Superseded**: Replaced by another ADR
- **Rejected**: Considered but not adopted

Automation (adr-tools)

# Install adr-tools
brew install adr-tools

# Initialize ADR directory
adr init docs/adr

# Create new ADR
adr new "Use PostgreSQL as Primary Database"

# Supersede an ADR
adr new -s 3 "Deprecate MongoDB in Favor of PostgreSQL"

# Generate table of contents
adr generate toc > docs/adr/README.md

# Link related ADRs
adr link 2 "Complements" 1 "Is complemented by"

Review Process

## ADR Review Checklist

### Before Submission

- [ ] Context clearly explains the problem
- [ ] All viable options considered
- [ ] Pros/cons balanced and honest
- [ ] Consequences (positive and negative) documented
- [ ] Related ADRs linked

### During Review

- [ ] At least 2 senior engineers reviewed
- [ ] Affected teams consulted
- [ ] Security implications considered
- [ ] Cost implications documented
- [ ] Reversibility assessed

### After Acceptance

- [ ] ADR index updated
- [ ] Team notified
- [ ] Implementation tickets created
- [ ] Related documentation updated

Best Practices

Do's

• Write ADRs early - Before implementation starts
• Keep them short - 1-2 pages maximum
• Be honest about trade-offs - Include real cons
• Link related decisions - Build decision graph
• Update status - Deprecate when superseded

Don'ts

• Don't change accepted ADRs - Write new ones to supersede
• Don't skip context - Future readers need background
• Don't hide failures - Rejected decisions are valuable
• Don't be vague - Specific decisions, specific consequences
• Don't forget implementation - ADR without action is waste