architecture-decision-records

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

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

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.

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Proposed → Accepted → Deprecated → Superseded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

```

Template 1: Standard ADR (MADR Format)

```markdown

# ADR-0001: Use PostgreSQL as Primary Database

Accepted

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.

• 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

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

We will use PostgreSQL 15 as our primary database.

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

1. Team familiarity with SQL databases reduces learning curve
2. 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).

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

• 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

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

• [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

```markdown

# ADR-0012: Adopt TypeScript for Frontend Development

Status: Accepted

Date: 2024-01-15

Deciders: @alice, @bob, @charlie

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.

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

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

```markdown

# 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

```markdown

# ADR-0020: Deprecate MongoDB in Favor of PostgreSQL

Accepted (Supersedes ADR-0003)

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

Deprecate MongoDB and migrate user profiles to PostgreSQL.

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

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

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

```markdown

# RFC-0025: Adopt Event Sourcing for Order Management

Propose adopting event sourcing pattern for the order management domain to

improve auditability, enable temporal queries, and support business analytics.

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

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

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

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

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

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)

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

```

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)

```markdown

# Architecture Decision Records

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

| 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 |

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

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

```

Automation (adr-tools)

```bash

# 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"

```

```markdown

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

```

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

• [Documenting Architecture Decisions (Michael Nygard)](https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions)
• [MADR Template](https://adr.github.io/madr/)
• [ADR GitHub Organization](https://adr.github.io/)
• [adr-tools](https://github.com/npryce/adr-tools)