Architecture Decision Record Example

Date:	2024-06-15
Authors:	Architecture Team (John Doe, Jane Smith)
Status:	Final

Date:

2024-06-15

Authors:

Architecture Team (John Doe, Jane Smith)

Status:

Final

Problem Description and Context

Our e-commerce platform currently uses a monolithic database architecture where all services (user management, product catalog, orders, inventory) share a single PostgreSQL database. This has led to:

Scalability bottlenecks: Different services have varying load patterns but cannot be scaled independently
Performance issues: Complex queries from the product catalog service impact order processing performance
Deployment coupling: Database schema changes require coordination across all teams
Single point of failure: Any database issue affects the entire platform

The system needs to handle: * 10M+ active users * 1M+ products in catalog * Peak loads of 50,000 concurrent users during sales events * 99.9% availability SLA requirement

Alternative Evaluation (Pugh Matrix)

Criterion	Shared Database (Baseline)	Database per Service	Shared + Read Replicas	Event Sourcing + CQRS
Development Complexity	0	-2	-1	-3
Operational Complexity	0	-1	-1	-2
Scalability	0	+3	+2	+3
Performance	0	+2	+2	+3
Data Consistency	0	-2	0	-1
Team Independence	0	+3	+1	+2
Cost	0	-1	-1	-2
Migration Effort	0	-2	-1	-3
Total Score	0	0	+1	-3

Rejected Alternatives:

Database per Service: While providing excellent isolation and scalability, the migration effort and operational complexity were deemed too high for our current team size
Event Sourcing + CQRS: Offers superior performance and scalability but introduces significant complexity that exceeds our team’s current expertise

Decision

Selected Solution: Shared Database + Read Replicas

We will implement a hybrid approach: 1. Maintain the current shared database for write operations 2. Add dedicated read replicas for each major service domain 3. Implement service-specific data access layers 4. Plan for future migration to database per service

This provides immediate performance improvements with manageable complexity while creating a migration path for future architectural evolution.

Consequences

Positive Effects

Immediate performance improvement: Read replicas will reduce query contention
Service isolation: Each service gets dedicated read capacity
Manageable migration: Low-risk incremental approach
Cost effective: Minimal infrastructure changes required
Team readiness: Builds expertise for future database per service migration

Risks

Data lag: Read replicas may have slight delay (< 100ms acceptable)
Complexity increase: Need to manage multiple database connections
Partial solution: Still maintains some coupling through shared write database
Future migration debt: Eventually need to migrate to full service isolation

Technical Debt

Schema coupling: Write database still requires cross-team coordination
Query optimization: Need to optimize queries for replica performance
Monitoring complexity: Additional database instances to monitor
Connection pooling: May need to adjust connection pool configurations

Additional Information

Reference: Database per Service pattern - https://microservices.io/patterns/data/database-per-service.html
Timeline: Implementation planned for Q3 2024, 8 weeks duration
Success Metrics:
- Reduce average query response time by 40%
- Eliminate read query interference with write operations
- Maintain 99.9% availability during implementation

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