Architecture Decision Records (ADR) Framework

What are ADRs?

Architecture Decision Records (ADRs) are short documents that capture important architecture decisions along with their context and consequences. They create a historical record of why decisions were made.

Why Use ADRs?

Benefits

Knowledge Preservation: Decisions outlive the people who made them
Onboarding: New team members understand the “why” behind the architecture
Prevent Rework: Avoid revisiting already-settled decisions
Accountability: Clear record of who decided what and why
Learning: Reflect on past decisions and improve future ones

When to Create an ADR

Create an ADR for decisions that:

Are architecturally significant
Affect multiple teams or systems
Are difficult or expensive to reverse
Involve trade-offs between competing concerns
Set precedents for future decisions
Are contentious or require consensus

When NOT to Create an ADR

Don’t create ADRs for:

Trivial or easily reversible decisions
Implementation details within a single component
Obvious or universally accepted choices
Temporary workarounds

ADR Format

We use the Markdown Architectural Decision Records (MADR) format.

Template

# [Short title of solved problem and solution]

- **Status**: [proposed | accepted | rejected | deprecated | superseded by ADR-XXXX]
- **Date**: YYYY-MM-DD
- **Decision Makers**: [List of people involved]
- **Technical Story**: [Optional: Link to ticket/epic]

## Context and Problem Statement

[Describe the context and problem statement in free form. 
What is the issue we're trying to solve? What factors are influencing this decision?]

## Decision Drivers

- [Driver 1: e.g., performance requirements]
- [Driver 2: e.g., team expertise]
- [Driver 3: e.g., cost constraints]

## Considered Options

- [Option 1]
- [Option 2]
- [Option 3]

## Decision Outcome

**Chosen option**: "[Option X]"

### Rationale

[Explanation of why this option was chosen]

### Positive Consequences

- [Positive consequence 1]
- [Positive consequence 2]

### Negative Consequences

- [Negative consequence 1]
- [Negative consequence 2]

## Pros and Cons of the Options

### [Option 1]

[Short description]

**Pros**:
- [Pro 1]
- [Pro 2]

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

### [Option 2]

[Repeat for each option...]

## Links

- [Link to related ADRs]
- [Link to documentation]
- [Link to RFC, if applicable]

ADR Lifecycle

1. Proposed

Initial draft created
Under discussion and review
May be revised based on feedback

2. Accepted

Decision has been made
Approved by appropriate stakeholders
Ready to implement

3. Rejected

Option was considered but not chosen
Document kept for historical reference
Explains why it was rejected

4. Deprecated

Decision is no longer valid
Superseded by new context or requirements
Points to new ADR if applicable

5. Superseded

Replaced by a newer ADR
Links to the new ADR
Kept for historical reference

Numbering Convention

ADRs are numbered sequentially:

ADR-0001-use-kubernetes-for-orchestration.md
ADR-0002-adopt-event-driven-architecture.md
ADR-0003-select-postgresql-as-primary-database.md

Storage and Organization

Repository Structure

/docs/decisions/
  ├── README.md (index of all ADRs)
  ├── ADR-0001-example-decision.md
  ├── ADR-0002-another-decision.md
  └── templates/
      └── adr-template.md

Version Control

Store ADRs in the same repository as code (when project-specific)
Store in architecture repository (when enterprise-wide)
Use pull requests for review process
Never delete ADRs, only deprecate them

Review Process

1. Draft Creation

Author creates ADR from template
Fills in context, options, and initial recommendation
Marks status as “proposed”

2. Peer Review

Share with relevant architects and stakeholders
Gather feedback through PR comments
Refine based on input

3. Approval

Present to Architecture Review Board if required
Update with final decision
Mark as “accepted”
Merge to main branch

4. Communication

Announce decision to affected teams
Link from project documentation
Update architecture diagrams

Writing Good ADRs

Do’s ✅

Be Clear and Concise

Use simple language
Avoid jargon where possible
Be specific, not vague

Provide Context

Explain the problem thoroughly
List all constraints and requirements
Describe the current situation

Compare Options Objectively

Present all viable alternatives
Be fair in pros/cons
Use data when available

Document Consequences

Both positive and negative
Short-term and long-term impact
Dependencies and risks

Update as Needed

Mark status changes
Link to superseding ADRs
Add lessons learned

Don’ts ❌

Don’t Be Vague

❌ “We chose X because it’s better”
✅ “We chose X because it provides 50% better throughput based on our benchmarks”

Don’t Skip Options

❌ Only document the chosen option
✅ Document all options considered

Don’t Make It Too Long

❌ 10 pages of detailed analysis
✅ 1-2 pages hitting key points

Don’t Hide Trade-offs

❌ Only list positives
✅ Be honest about downsides

Don’t Forget to Update Status

❌ Leave as “proposed” forever
✅ Update to “accepted” or “rejected”

Example ADRs

Example 1: Technology Selection

# Use PostgreSQL for Primary Database

- **Status**: accepted
- **Date**: 2025-01-15
- **Decision Makers**: John Doe (Lead Architect), Jane Smith (DBA)
- **Technical Story**: ARCH-123

## Context and Problem Statement

Our new customer management system requires a robust, scalable database. 
We need ACID compliance, complex query support, and JSON capabilities.

## Decision Drivers

- ACID compliance required for financial data
- Need for complex relational queries
- JSON support for flexible schemas
- Team has PostgreSQL expertise
- Budget constraints favor open source
- Must support 100K+ transactions per second

## Considered Options

- PostgreSQL
- MySQL
- MongoDB
- Amazon Aurora PostgreSQL

## Decision Outcome

**Chosen option**: "PostgreSQL 15 on Amazon RDS"

### Rationale

PostgreSQL provides the best balance of features, performance, and cost. 
The team has deep expertise, and RDS provides managed infrastructure.

### Positive Consequences

- Strong ACID compliance
- Excellent JSON support via JSONB
- Mature ecosystem and tooling
- Team expertise reduces risk
- Open source with no licensing costs

### Negative Consequences

- Need to manage scaling strategy
- RDS costs can grow with data volume
- Migration from legacy Oracle DB required

## Pros and Cons of the Options

### PostgreSQL

**Pros**:
- ACID compliant
- Advanced indexing (B-tree, GIN, GIST)
- JSONB support
- Strong community
- Team expertise

**Cons**:
- Horizontal scaling requires sharding
- Write scaling limitations

### MySQL

**Pros**:
- Good performance
- Wide adoption
- Multiple storage engines

**Cons**:
- Limited JSON support
- Less feature-rich than PostgreSQL
- Team less familiar

### MongoDB

**Pros**:
- Flexible schema
- Easy horizontal scaling
- Native JSON

**Cons**:
- No ACID across documents (pre-4.0)
- Not ideal for complex joins
- Different mental model from SQL

### Amazon Aurora PostgreSQL

**Pros**:
- PostgreSQL compatible
- Better write scaling
- Automated backups

**Cons**:
- Vendor lock-in
- Higher cost
- Some feature lag behind PostgreSQL

## Links

- [Database Comparison Analysis](../analysis/db-comparison.md)
- [Performance Benchmarks](../analysis/db-benchmarks.md)
- [Migration Plan](../implementation/db-migration-plan.md)

Example 2: Architectural Pattern

# Adopt Event-Driven Architecture for Order Processing

- **Status**: accepted
- **Date**: 2025-02-01
- **Decision Makers**: Architecture Review Board
- **Technical Story**: ARCH-456

## Context and Problem Statement

Our order processing system is tightly coupled, making it difficult to scale 
individual components. We experience bottlenecks during peak loads, and 
adding new features requires changes across multiple services.

## Decision Drivers

- Need to scale order processing independently
- Reduce coupling between services
- Enable async processing for long-running tasks
- Support audit trail and event sourcing
- Improve system resilience
- Enable easier addition of new features

## Considered Options

- Event-Driven Architecture with message broker
- Synchronous REST API calls
- Batch processing
- Hybrid approach (events + synchronous)

## Decision Outcome

**Chosen option**: "Event-Driven Architecture with AWS EventBridge and SQS"

### Rationale

Event-driven architecture provides the loose coupling, scalability, and 
flexibility we need. EventBridge offers built-in routing and filtering, 
while SQS provides reliable queue processing.

### Positive Consequences

- Services can scale independently
- Loose coupling enables team autonomy
- Built-in retry and error handling
- Easy to add new event consumers
- Complete audit trail of all events
- Improved resilience (async processing)

### Negative Consequences

- Increased complexity in debugging
- Eventual consistency to manage
- Need for event schema management
- Monitoring and observability more complex
- Learning curve for team

## Pros and Cons of the Options

### Event-Driven Architecture

**Pros**:
- Loose coupling
- Independent scaling
- Resilience
- Flexibility
- Audit trail

**Cons**:
- Complexity
- Eventual consistency
- Debugging harder
- Schema management

### Synchronous REST

**Pros**:
- Simpler to understand
- Immediate consistency
- Easier debugging

**Cons**:
- Tight coupling
- Scaling bottlenecks
- Cascading failures
- Synchronous dependencies

### Batch Processing

**Pros**:
- Simple model
- Efficient for bulk operations

**Cons**:
- High latency
- Not real-time
- Doesn't solve coupling

## Links

- [Event-Driven Architecture Pattern](../patterns/event-driven.md)
- [EventBridge Architecture](../designs/eventbridge-setup.md)
- Supersedes: [ADR-0010-synchronous-microservices.md](ADR-0010-synchronous-microservices.md)

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