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Architecture Decisions

Purpose

This document maintains a repository of significant architecture decisions made across projects using the Architecture Decision Record (ADR) format. ADRs document the context, decision, and consequences of architectural choices for future reference.

What is an Architecture Decision Record?

An ADR is a document that captures a significant architectural decision with:

  • Context: What forces motivated the decision?
  • Decision: What was decided?
  • Rationale: Why was it chosen?
  • Consequences: What are the trade-offs?

Benefits:

  • Preserves decision rationale (not lost when people leave)
  • Prevents re-litigating decisions
  • Guides future similar decisions
  • Helps onboard new team members
  • Serves as design documentation

Decision Log

By Status

Active (Accepted):
- ADR-2024-001: Use Kubernetes for container orchestration
- ADR-2024-002: PostgreSQL as primary relational database
- ADR-2024-003: Event-driven architecture with Kafka
- ADR-2024-004: AWS as primary cloud provider

Proposed:
- ADR-2024-XXX: Use GraphQL for mobile API

Deprecated:
- ADR-2023-005: Use RabbitMQ for messaging (Superseded by Kafka)
- ADR-2023-010: On-premises infrastructure (Superseded by Cloud)

Superseded:
- ADR-2023-005: Use RabbitMQ → ADR-2024-003: Kafka
- ADR-2022-015: Monolithic architecture → ADR-2024-001: Microservices

Active Decisions (Accepted)

ADR-2024-001: Adopt Kubernetes for Container Orchestration

Status: Accepted
Date: January 2024
Decision Makers: Architecture Board

Context:

We deploy microservices across multiple environments (dev, staging, production). Current approaches:

  • Manual EC2 instance management → difficult to scale
  • Docker Compose in dev → doesn’t match production
  • Inconsistent deployment processes → operational risk
  • Difficulty with auto-scaling and service discovery

Decision:

We will use Kubernetes (AWS EKS) for container orchestration across all environments.

Rationale:

  1. Industry Standard: Kubernetes is the de-facto standard (CNCF data shows 96% adoption in large enterprises)
  2. Ecosystem: Mature tooling (Helm, Prometheus, ArgoCD, etc.)
  3. Skill Portability: Team skills transferable across employers
  4. Multi-cloud: Not locked to AWS (can migrate to GCP/Azure)
  5. Auto-scaling: Built-in horizontal and vertical scaling
  6. Service Discovery: Automatic service discovery and load balancing

Consequences:

Positive:

  • Consistent deployment across environments
  • Automatic scaling and recovery
  • Rich ecosystem of tools and integrations
  • Team develops portable skills
  • Easy integration with CI/CD (GitHub Actions → Kubernetes)

Negative:

  • Operational complexity (learning curve: 2-3 weeks)
  • Overhead for small projects (vs serverless)
  • Requires monitoring/observability setup
  • More resource usage than minimal setups (control plane)

Alternatives Considered:

  1. AWS ECS - Easier but AWS-only, smaller ecosystem
  2. AWS Lambda/Fargate - Simpler but limited to specific workload types
  3. Docker Swarm - Simpler but declining adoption, smaller ecosystem
  4. Cloud Run (GCP) - Good but cloud-provider specific

Trade-offs:

  • Simplicity vs Flexibility: Kubernetes is more complex but more flexible
  • Cost vs Control: EKS managed service costs $73/month but eliminates control plane management
  • Learning Curve vs Long-term Value: Initial overhead but significant long-term efficiency gains

Mitigation:

  • Team training on Kubernetes fundamentals (2-week program)
  • Use managed EKS (AWS handles control plane)
  • Helm charts for common deployments
  • Internal runbooks and documentation

Related Decisions:

  • ADR-2024-003: Event-driven architecture (works well with K8s microservices)
  • ADR-2024-002: PostgreSQL database (stateless workloads in K8s)

ADR-2024-002: PostgreSQL as Primary Relational Database

Status: Accepted
Date: January 2024
Decision Makers: Architecture Board, Database Team

Context:

Current databases:

  • MySQL 5.7 (approaching EOL October 2025)
  • Multiple PostgreSQL 13 instances (good experience)
  • Need for modern JSONB support and advanced features
  • Desire to consolidate database platforms

Decision:

We will standardize on PostgreSQL 15+ as the primary relational database for all new projects and migrate from MySQL.

Rationale:

  1. Maturity: Battle-tested in enterprise environments
  2. Features: JSONB, arrays, full-text search, JSON operators
  3. Performance: Excellent for OLTP workloads (competitive with MySQL/MariaDB)
  4. Consistency: ACID guarantees, no eventual consistency issues
  5. Community: Large active community, regular updates
  6. Cost: Open source, no licensing costs

Consequences:

Positive:

  • Better feature set for modern applications
  • Improved query performance on complex queries
  • Better JSON support (JSONB type)
  • Full-text search built-in (no separate Elasticsearch needed)
  • Team expertise already developed
  • Open source (no licensing concerns)

Negative:

  • Migration effort from MySQL (1-2 months for large systems)
  • Different query syntax in some areas
  • Replication setup more complex than MySQL (initially)
  • Some team members more comfortable with MySQL

Alternatives Considered:

  1. MySQL 8.0+ - Acceptable but less advanced features
  2. MariaDB - MySQL compatible but less community support
  3. CockroachDB - Distributed SQL, but more complex operations
  4. MongoDB - Document database, but not good for relational data

Migration Strategy:

  • Phase 1: New projects use PostgreSQL 15+
  • Phase 2: Non-critical projects migrate (2024-2025)
  • Phase 3: Critical projects migrate (2025) before MySQL EOL
  • Parallel running: Keep MySQL during migration for rollback

ADR-2024-003: Event-Driven Architecture with Apache Kafka

Status: Accepted
Date: February 2024
Decision Makers: Architecture Board

Context:

Current integration approach:

  • Synchronous REST API calls between services
  • Cascading failures (if one service down, others fail)
  • Difficult to add new consumers
  • Real-time data propagation needed for reporting
  • Auditability of all changes needed

Decision:

We will use Apache Kafka (3.6+) as the primary event broker for service-to-service communication and event streaming.

Rationale:

  1. Decoupling: Services don’t need to know about each other
  2. Resilience: Services can fail independently
  3. Scalability: High throughput (1M+ events/sec)
  4. Ordering: Guaranteed message ordering per partition
  5. Auditability: Event log for compliance and debugging
  6. Stream Processing: Foundation for analytics (Spark, Flink)
  7. Maturity: Proven at companies like LinkedIn, Netflix, Uber

Consequences:

Positive:

  • Improved system resilience (services fail independently)
  • Easier to add new data consumers
  • Audit trail of all events
  • Foundation for analytics and reporting
  • Decoupled architecture enables independent scaling

Negative:

  • Operational complexity (Kafka cluster management)
  • Learning curve for event-driven patterns
  • Eventual consistency (not immediate)
  • Additional infrastructure cost (~$500-2000/month)
  • Requires monitoring (broker health, consumer lag)

Alternatives Considered:

  1. AWS SQS/SNS - Simpler, managed service but less powerful
  2. RabbitMQ - Good but less high-performance than Kafka
  3. AWS Kinesis - Good but AWS-only

Topics Strategy:

Topic Naming Convention: [domain].[entity].[event-type]

Examples:
- orders.order.created (when order is placed)
- orders.order.shipped (when order ships)
- payments.payment.charged (when payment succeeds)
- inventory.item.reserved (when inventory reserved)
- inventory.item.released (when reservation cancelled)

Retention: 7 days default (2 weeks for critical topics)
Replication Factor: 3 (across availability zones)

Related Decisions:

  • ADR-2024-001: Kubernetes (event-driven services in K8s)
  • ADR-2024-004: AWS (AWS MSK for managed Kafka)

ADR-2024-004: AWS as Primary Cloud Provider

Status: Accepted
Date: January 2024
Decision Makers: Architecture Board, Infrastructure Team

Context:

Cloud infrastructure currently:

  • 70% workloads in AWS
  • Some pilot projects in Azure (enterprise requirements)
  • Some data in Google Cloud (ML pipeline)
  • Operational overhead managing multi-cloud
  • Need to consolidate to reduce complexity

Decision:

AWS will be our primary cloud provider for all new infrastructure. Other clouds will be used only for specific strategic reasons (approved by architecture board).

Rationale:

  1. Maturity: Largest cloud (11 years ahead of competitors)
  2. Services: 200+ AWS services vs competitors’ 100+
  3. Ecosystem: Largest partner ecosystem
  4. Cost: Most competitive pricing at scale
  5. Talent: Largest AWS expertise in market
  6. Organizational Knowledge: Team expertise already developed

Consequences:

Positive:

  • Reduced operational complexity
  • Standardized tooling and practices
  • Better volume discounts
  • Easier team training (consolidated skills)
  • Simpler automation and governance

Negative:

  • Potential vendor lock-in (mitigated by using managed services, not proprietary)
  • Reduced leverage for negotiation
  • Single-cloud risk (mitigated by multi-AZ deployment)
  • May not be optimal for all workloads (accepted trade-off)

Alternatives Considered:

  1. Multi-cloud - Flexibility but complexity overhead exceeds benefits
  2. Azure First - Good for enterprise customers but less mature ecosystem
  3. Google Cloud - Excellent for ML but smaller overall ecosystem

Exceptions Policy:

Azure will be used only if:

  • Customer requirement (enterprise agreement)
  • Better cost at huge scale (>100 instances)
  • Architectural board approval

Google Cloud will be used for:

  • Machine learning pipelines (BigQuery, Vertex AI)
  • With architectural board approval

Related Decisions:

  • ADR-2024-001: Kubernetes (AWS EKS)
  • ADR-2024-003: Kafka (AWS MSK)
  • ADR-2024-002: PostgreSQL (AWS RDS)

Decision Templates

For Technology Selection

When deciding on a new technology, document:

  1. Current pain points / forces
  2. Options evaluated
  3. Selected option and rationale
  4. Trade-offs accepted
  5. Migration/implementation plan
  6. Monitoring success metrics

For Architectural Patterns

When selecting an architectural pattern:

  1. Problem statement
  2. Pattern explanation
  3. How it solves the problem
  4. Consequences (benefits & drawbacks)
  5. Alternative patterns considered
  6. When to reconsider (review triggers)

For Infrastructure Decisions

When deciding on infrastructure:

  1. Current architecture / limitation
  2. Proposed architecture
  3. Cost impact
  4. Operational impact
  5. Risk assessment
  6. Fallback plan

How to Create a New ADR

Step 1: Get Approval for Topic

Open GitHub issue with [ADR PROPOSAL] label:
- Title of decision
- Estimated impact (low/medium/high)
- Timeline (when needed)
- Stakeholders to involve

Step 2: Gather Context

Research:
- What is the current situation?
- What forces are driving this decision?
- What are all available options?
- What does the team think?
- What do we learn from similar decisions?

Step 3: Facilitate Decision Discussion

- Architecture review board meeting
- Present options with pros/cons
- Discuss trade-offs
- Reach consensus (or escalate if not)

Step 4: Document Decision

Create file: docs/knowledge-base/decisions/ADR-YYYY-NNN.md

Template:
# ADR-YYYY-NNN: [Title]

## Status: [Proposed | Accepted | ...]

## Context
[Problem description]

## Decision
[What was decided]

## Rationale
[Why this choice]

## Consequences
[Benefits and drawbacks]

## Alternatives Considered
1. [Option A]
2. [Option B]

## Approval
- [Decision maker]: [Signature/Approval]
- Date: [Date]

Step 5: Share & Archive

- Add to decisions.md index
- Update related documentation
- Archive old decisions if superseded
- Announce to team

Decision Review Triggers

Periodically review decisions if:

□ Technology reaches end-of-life
□ Better alternative becomes available
□ Business requirements change
□ Operational costs exceed expectations
□ Architecture becomes problematic
□ Team feedback indicates concern
□ Regulatory requirements change

Review Frequency:
- Critical decisions: Annually
- Major decisions: Every 2 years
- Minor decisions: Every 3 years

Superseded Decisions

When a decision becomes obsolete, mark it as “Superseded”:

# ADR-2023-005: Use RabbitMQ for Messaging

## Status: Superseded by ADR-2024-003

## Supersession Notice
This decision was superseded by ADR-2024-003 (Kafka adoption) in February 2024.

**Reason:** Kafka provides superior performance, auditability, and stream processing capabilities needed for our event-driven architecture.

**Migration Plan:** RabbitMQ will be deprecated by December 2024.

[Original ADR content below...]

Decision Metrics & Tracking

Track decision outcomes:

For Technology Decisions:
- Adoption rate (% teams using it)
- Incident rate (problems introduced)
- Team satisfaction (survey)
- Cost tracking (actual vs estimated)
- Performance metrics (latency, throughput)

Example: Kubernetes Adoption
- Jan 2024: Decision made
- Feb 2024: 20% services on K8s
- May 2024: 60% services on K8s
- Aug 2024: 95% services on K8s
- Incidents: 3 K8s-related incidents (0.1% of total)
- Team satisfaction: 8.2/10 (survey)

Status: Complete
Last Updated: November 2025
Version: 1.0
Contributors: Architecture Practice Team
Maintained By: Architecture Board