Architecture Decision Record Template

A standardized template for documenting architectural decisions, their context, and consequences.

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What is an ADR?

An Architecture Decision Record (ADR) is a document that captures an important architectural decision along with its context and consequences. ADRs help teams:

• Remember why decisions were made
• Onboard new team members faster
• Avoid relitigating settled decisions
• Understand trade-offs that were considered

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The Template

Copy and use this template for each significant architectural decision:

# ADR-[NUMBER]: [TITLE]

## Status

[Proposed | Accepted | Deprecated | Superseded by ADR-XXX]

## Date

[YYYY-MM-DD]

## Context

[Describe the issue motivating this decision. What is the problem or opportunity?
Include relevant background, constraints, and forces at play.]

## Decision Drivers

* [Driver 1: e.g., "Need to support 10x current traffic"]
* [Driver 2: e.g., "Team has limited experience with technology X"]
* [Driver 3: e.g., "Must integrate with existing system Y"]
* [Driver 4: e.g., "Budget constraints limit options"]

## Considered Options

### Option 1: [Name]
[Brief description]

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

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

### Option 2: [Name]
[Brief description]

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

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

### Option 3: [Name]
[Brief description]

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

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

## Decision

[State the decision that was made. Be clear and specific.
Example: "We will use PostgreSQL as our primary database."]

## Rationale

[Explain why this option was chosen over the alternatives.
Reference the decision drivers and how this option addresses them.]

## Consequences

### Positive
* [Positive consequence 1]
* [Positive consequence 2]

### Negative
* [Negative consequence 1]
* [Negative consequence 2]

### Neutral
* [Neutral consequence or follow-up action required]

## Related Decisions

* [ADR-XXX: Related decision]
* [ADR-YYY: Another related decision]

## References

* [Link to relevant documentation]
* [Link to relevant discussion]
* [Link to relevant RFC or proposal]

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Example ADR

# ADR-001: Use Event Sourcing for Order Management

## Status

Accepted

## Date

2024-03-15

## Context

Our current order management system stores only the current state of orders
in a relational database. This creates several problems:

- We cannot audit the history of order changes
- Investigating customer complaints requires searching through logs
- Calculating historical reports requires complex queries
- Data inconsistencies between services are difficult to debug

The business requires complete audit trails for compliance and has requested
the ability to "replay" order history for analysis.

## Decision Drivers

* Must maintain complete audit trail of all order changes
* Need to support temporal queries ("What was the order state at time T?")
* System must handle 50,000 orders per day
* Team has moderate experience with event-driven architectures
* Must integrate with existing inventory and shipping systems

## Considered Options

### Option 1: Event Sourcing with Apache Kafka

Use event sourcing pattern with Kafka as the event store.

**Pros:**
* Complete audit trail built in
* Natural fit for temporal queries
* Kafka provides high throughput and durability
* Enables CQRS pattern for read optimization

**Cons:**
* Significant learning curve for team
* Requires new infrastructure (Kafka cluster)
* Event schema evolution requires careful management
* Eventually consistent reads

### Option 2: Database Audit Tables

Add audit tables that log all changes to order records.

**Pros:**
* Simpler to implement
* Uses existing database infrastructure
* Team already familiar with pattern
* Immediate consistency

**Cons:**
* Audit tables can grow very large
* Historical queries still complex
* Not designed for replay scenarios
* Audit logic scattered across codebase

### Option 3: Change Data Capture (CDC)

Use CDC tools to capture database changes to a separate store.

**Pros:**
* No application code changes for capture
* Works with existing database
* Can stream to various destinations

**Cons:**
* Physical vs logical changes (internal DB changes captured)
* Limited semantic meaning of events
* Additional infrastructure (Debezium, etc.)
* Doesn't enable true event sourcing benefits

## Decision

We will implement event sourcing with Apache Kafka for the order
management domain.

## Rationale

Event sourcing with Kafka best addresses our core requirements:

1. **Audit trail**: Events are the source of truth, providing complete history
2. **Temporal queries**: Can rebuild state at any point in time
3. **Scale**: Kafka can easily handle our throughput requirements
4. **Integration**: Event-driven architecture simplifies integration with
   inventory and shipping systems

While the learning curve is higher, the long-term benefits outweigh the
short-term investment. We will mitigate risks through:
- Training for the team before implementation
- Starting with order management only (bounded scope)
- Maintaining a read model for common queries

## Consequences

### Positive
* Complete audit trail for all order changes
* Can answer "what was the state at time X" queries
* Simplified integration with downstream systems via events
* Foundation for future event-driven capabilities

### Negative
* Team needs training on event sourcing patterns
* Need to manage Kafka infrastructure
* Must design and evolve event schemas carefully
* Read-after-write consistency requires careful handling

### Neutral
* Will need to build projections for common query patterns
* Existing systems will continue as-is; new pattern for orders only

## Related Decisions

* ADR-002: Event Schema Versioning Strategy
* ADR-003: CQRS Read Model Implementation

## References

* [Event Sourcing Pattern - Microsoft](https://docs.microsoft.com/en-us/azure/architecture/patterns/event-sourcing)
* [Kafka Architecture](https://kafka.apache.org/documentation/#design)
* Internal RFC: Order Management Modernization

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Best Practices

When to Write an ADR

Write an ADR when:

• The decision affects system structure significantly
• The decision is difficult to reverse
• Multiple valid options exist
• Team members have differing opinions
• Future team members would ask “why did we do this?”

Keeping ADRs Useful

Do:

• Keep them short and focused
• Write them at decision time (not after)
• Include the options you rejected
• Update status when decisions change
• Store them with the code they describe

Don’t:

• Write ADRs for trivial decisions
• Use them as detailed design documents
• Let them become stale
• Skip the consequences section

Organizing ADRs

File Naming:

docs/decisions/
  0001-use-postgresql-for-primary-database.md
  0002-implement-event-sourcing-for-orders.md
  0003-adopt-kubernetes-for-deployment.md

Index Document:

# Architecture Decision Records

| ADR | Title | Status | Date |
|-----|-------|--------|------|
| 001 | Use PostgreSQL | Accepted | 2024-01-15 |
| 002 | Event Sourcing for Orders | Accepted | 2024-03-15 |
| 003 | Kubernetes Deployment | Proposed | 2024-04-01 |

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Tools

• adr-tools: Command-line tools for managing ADRs
• Log4brains: ADR management with web UI
• Markdown in Git: Simple, version-controlled ADRs

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For architecture review or decision-making support, contact our team.