# ADR-0001: Adopt Microservices Architecture

**Status**: Accepted

**Date**: 2025-10-15

**Authors**: Jane Smith (Backend Architect), John Doe (Tech Lead)

**Supersedes**: N/A

**Superseded by**: N/A

---

## Context

Our e-commerce platform has grown from 10,000 to 500,000 daily active users over the past 18 months. The current monolithic architecture is experiencing significant scalability and operational challenges.

**Problem Statement:**
The monolithic application architecture is preventing us from scaling effectively to meet growth projections of 10x traffic over the next 12 months.

**Current Situation:**
- Single Node.js application (250,000 lines of code)
- Shared PostgreSQL database
- Deployment requires full application restart (15-minute downtime)
- 45-minute build times
- Database connection pool exhausted during peak hours
- Teams blocked waiting for shared resources

**Requirements:**
- **Business**: Support 5M daily active users by Q4 2026
- **Technical**: Enable independent team deployments without downtime
- **Operational**: Reduce build times to under 5 minutes
- **Product**: Decrease time-to-market for new features by 40%

**Constraints:**
- Team expertise: Node.js, Python, PostgreSQL
- Infrastructure: AWS (existing investment)
- Budget: $75k for migration, 2 senior DevOps engineers allocated
- Timeline: Complete migration within 6 months (Q1-Q2 2026)

**Forces:**
- **Scale vs Complexity**: Need to scale but don't want operational burden
- **Speed vs Stability**: Fast feature development vs system reliability
- **Autonomy vs Coordination**: Team independence vs system coherence
- **Cost vs Performance**: Infrastructure costs vs user experience

---

## Decision

**We will migrate from our monolithic architecture to a microservices architecture using a strangler fig pattern.**

**Technology Stack:**
- **Services**: Node.js 20+ with Express framework
- **Databases**: PostgreSQL 15+ (one per service)
- **Caching**: Redis 7+ for session management and caching
- **Messaging**: RabbitMQ 3.12+ for async inter-service communication
- **API Gateway**: Kong for routing and rate limiting
- **Orchestration**: Kubernetes (EKS on AWS)
- **Observability**: Jaeger for distributed tracing, Prometheus for metrics

**Service Boundaries:**
1. **User Service**: Authentication, user profiles, preferences
2. **Order Service**: Order processing, payment integration, order history
3. **Inventory Service**: Product catalog, stock management, pricing
4. **Notification Service**: Email, SMS, push notifications
5. **Analytics Service**: User behavior tracking, reporting

**Implementation Strategy:**
- **Pattern**: Strangler Fig - gradually extract services from monolith
- **Phase 1** (Month 1-2): Notification Service (lowest risk, clear boundaries)
- **Phase 2** (Month 2-3): Analytics Service (read-only, non-critical)
- **Phase 3** (Month 3-4): User Service (core functionality, highest risk)
- **Phase 4** (Month 4-5): Inventory Service (moderate complexity)
- **Phase 5** (Month 5-6): Order Service (most critical, saved for last)

**Timeline:**
- **Q1 2026**: Infrastructure setup + Notification & Analytics services
- **Q2 2026**: User, Inventory, and Order services
- **Q3 2026**: Monolith decommissioned

**Responsibility:**
- **Backend Architect** (Jane Smith): Service design, API contracts
- **DevOps Team** (Led by Sarah Johnson): Kubernetes setup, CI/CD pipelines
- **Team Leads**: Service migration execution and team coordination
- **QA Lead**: Testing strategy and service contract validation

---

## Consequences

### Positive
- **Independent Scalability**: Each service scales based on its specific load patterns
  - Notification Service: 10x scale during campaigns
  - Order Service: 3x scale during Black Friday

- **Deployment Independence**: Teams deploy services without coordination
  - 10+ deployments per day vs 1-2 per week currently
  - Zero-downtime deployments

- **Technology Flexibility**: Services can adopt optimal tech stacks
  - Analytics Service may use Python for ML libraries
  - Real-time services optimized with Node.js

- **Fault Isolation**: Service failures don't cascade system-wide
  - Notification Service failure doesn't affect orders
  - Graceful degradation possible

- **Faster Build Times**: 2-5 minutes per service vs 45 minutes for monolith
  - Improved developer experience
  - Faster feedback loops

- **Team Autonomy**: Teams own services end-to-end
  - Reduced coordination overhead
  - Faster feature delivery

### Negative
- **Operational Complexity**: Managing 5+ services vs 1 application
  - Need service mesh for traffic management
  - More monitoring and alerting required
  - On-call rotation complexity increases

- **Network Latency**: Inter-service calls add overhead
  - 10-50ms per service hop
  - Requires request optimization and caching

- **Distributed Debugging**: Tracing requests across services harder
  - Need distributed tracing (Jaeger)
  - Correlation IDs required for all requests

- **Data Consistency**: Eventual consistency vs immediate
  - Inventory updates may lag order placement
  - Need compensation logic for failures

- **Learning Curve**: Team needs new skills
  - Kubernetes: 2-3 month ramp-up
  - Service mesh concepts
  - Distributed systems patterns

- **Initial Slowdown**: Infrastructure setup before productivity gains
  - Q1 focused on foundation, not features
  - 2-3 months before velocity improvements visible

- **Testing Complexity**: Contract tests, integration tests across services
  - New testing strategies required
  - Requires investment in test infrastructure

- **Cost Increase**: Higher infrastructure costs initially
  - 5 databases instead of 1
  - Kubernetes overhead
  - Additional monitoring tools
  - Offset by improved productivity (net positive after 12 months)

### Neutral
- **Monitoring**: Shift from centralized logging to distributed tracing
  - Different tools (Jaeger vs simple logs)
  - More powerful but requires learning

- **Database Strategy**: Per-service databases instead of shared schema
  - More isolation but harder for reporting
  - Requires data aggregation service for analytics

- **API Contracts**: Need formal API versioning and contracts
  - OpenAPI specifications required
  - Contract testing between services

---

## Alternatives Considered

### Alternative 1: Optimize Existing Monolith

**Description:**
Keep monolithic architecture but add:
- PostgreSQL read replicas (3 replicas)
- Redis caching layer
- Horizontal scaling with load balancer (4 instances)
- Database connection pooling improvements
- Code optimization and query tuning

**Pros:**
- **Lower Complexity**: Team already familiar with architecture
- **Faster Implementation**: 4-6 weeks vs 6 months
- **Lower Risk**: No fundamental architecture change
- **Cost Effective**: $10k vs $75k for microservices
- **No Learning Curve**: Existing team skills sufficient

**Cons:**
- **Limited Scalability**: Eventually hit ceiling again
- **Deployment Coupling**: Still requires full restarts
- **Build Times**: Remains 45 minutes (can't improve significantly)
- **Team Bottlenecks**: Shared codebase still blocks teams
- **Technical Debt**: Doesn't address root architectural issues
- **Short-Term Fix**: Same problems resurface in 12-18 months

**Why not chosen:**
This addresses symptoms but not root causes. Based on our growth trajectory, we'd face the same scalability crisis again within 18 months. The deployment coupling continues to slow feature velocity, and the monolith's complexity makes onboarding difficult. While cheaper short-term, the total cost over 2 years exceeds microservices due to repeated optimization cycles and slower feature delivery.

**Cost-Benefit Analysis:**
- Year 1: $10k (optimization) + $50k (opportunity cost from slow velocity)
- Year 2: $15k (more optimization) + $75k (opportunity cost)
- **Total**: $150k over 2 years vs $75k one-time for microservices

### Alternative 2: Serverless Architecture (AWS Lambda)

**Description:**
Decompose application into AWS Lambda functions:
- API Gateway for routing
- Lambda functions for business logic (Node.js)
- DynamoDB for data storage
- S3 for static assets
- EventBridge for async communication

**Pros:**
- **Extreme Scalability**: Auto-scales to any load
- **Pay-Per-Use**: No cost when idle, pay only for executions
- **No Server Management**: AWS handles all infrastructure
- **Built-in High Availability**: Multi-AZ by default
- **Fast Deployment**: Deploy functions independently in seconds

**Cons:**
- **Vendor Lock-In**: Heavily tied to AWS services
- **Cold Start Latency**: 500ms - 2s for cold starts
  - Unacceptable for our real-time order processing requirements
- **Execution Time Limit**: 15-minute maximum
  - Problematic for batch processing and reports
- **Local Development**: Difficult to replicate environment locally
  - SAM/LocalStack not perfect
- **Team Inexperience**: Zero serverless experience on team
  - 6-12 month learning curve
- **Debugging Complexity**: CloudWatch logs harder than standard logging
- **State Management**: Stateless-only, requires external state store
- **Cost Unpredictability**: Hard to forecast costs at scale

**Why not chosen:**
Risk assessment showed this approach has too many unknowns:
1. **Cold Starts**: Real-time requirements mean 500ms delays unacceptable
   - Critical for checkout flow (our highest revenue path)
2. **Team Readiness**: Zero serverless experience = high learning curve
   - Would extend timeline to 9-12 months vs 6 months
3. **Vendor Lock-In**: Concern about being tied to AWS ecosystem
   - Makes future multi-cloud strategy difficult
4. **Debugging**: Production incidents harder to resolve
   - Distributed logs across Lambda, API Gateway, DynamoDB

**Alternative Consideration:**
We may revisit serverless for specific use cases later (e.g., image processing, scheduled jobs) once team has microservices experience. Hybrid approach possible in future.

### Alternative 3: Modular Monolith

**Description:**
Restructure monolith into well-defined modules with clear boundaries:
- Module per domain (User, Order, Inventory, etc.)
- Enforce module boundaries with linting rules
- Separate databases per module within monolith
- Keep deployment as single unit but enable parallel development

**Pros:**
- **Low Operational Complexity**: Still one deployment unit
- **Module Independence**: Teams can work in parallel
- **Shared Infrastructure**: Database connections, caching shared
- **Gradual Path**: Can extract modules to services later
- **Familiar Tooling**: Same dev/deploy tools

**Cons:**
- **Build Time**: Still 30-40 minutes (only marginal improvement)
- **Deployment Coupling**: Any change requires full restart
- **Scaling Limitations**: Can't scale modules independently
- **Database Contention**: Modules still share connection pool
- **Enforcement Challenges**: Module boundaries violated over time

**Why not chosen:**
This is a good intermediate step but doesn't solve our core problems:
- Still can't scale Order Service independently during Black Friday
- Deployment coupling remains (15-minute downtime window)
- Doesn't reduce build times enough for velocity improvements

**Note**: We considered this as Phase 0 but decided the investment would delay microservices benefits. Team consensus: do it right once vs incremental half-measures.

---

## References

### Research & Best Practices
- Martin Fowler: [Microservices Guide](https://martinfowler.com/microservices/)
- Sam Newman: *Building Microservices* (O'Reilly, 2021)
- Chris Richardson: [Microservices Patterns](https://microservices.io/patterns/)

### Internal Discussions
- Architecture Review Meeting: 2025-09-20 ([Confluence Link](https://wiki.company.com/arch-review-sep2025))
- Slack #architecture channel: Discussion thread from 2025-10-01
- Tech Talk: "Our Journey to Microservices" by Jane Smith (internal recording)

### Proof of Concept
- Notification Service PoC: [GitHub PR #1234](https://github.com/company/platform/pull/1234)
  - Demonstrated 10x throughput improvement
  - Validated Kubernetes setup on EKS
  - Confirmed 3-minute build times

### Related ADRs
- ADR-0002: Choose PostgreSQL over MongoDB (coming soon)
- ADR-0003: API Versioning Strategy (coming soon)
- ADR-0004: Service Mesh Evaluation (coming soon)

### Cost Analysis
- Infrastructure Cost Projection: [Spreadsheet](https://docs.google.com/spreadsheets/d/xxx)
- ROI Analysis: [Presentation](https://drive.google.com/file/d/yyy)

---

## Review Notes

**Reviewers**: Architecture Team, Engineering Leads, DevOps, Product, Security

**Questions Raised:**
- Q: Can we afford 2-3 months of reduced velocity during migration?
  - A: Yes, roadmap adjusted. Q1 has fewer features planned to accommodate.

- Q: What's our rollback plan if microservices fails?
  - A: Strangler fig keeps monolith running. Can pause migration at any point.

- Q: How do we handle distributed transactions?
  - A: Saga pattern with compensation logic. Details in future ADR.

**Concerns Addressed:**
- **Cost**: CFO approved $75k budget after ROI analysis
- **Timeline**: Product accepted 6-month migration window
- **Risk**: PoC de-risked Kubernetes and deployment approach
- **Skills**: DevOps hiring approved, team training scheduled

**Approval:**
- ✅ Architecture Team (2025-10-12)
- ✅ Engineering VP (2025-10-13)
- ✅ Product VP (2025-10-14)
- ✅ DevOps Lead (2025-10-15)
- ✅ Security Team (2025-10-15)

**Status Change**: Proposed → **Accepted** (2025-10-15)

---

**ADR Version**: 1.0
**Created**: 2025-10-15
**Accepted**: 2025-10-15
**Implemented**: TBD (Target: Q2 2026)