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skills/adr-architecture/resources/examples/database-selection.md

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+# ADR-042: Use PostgreSQL for Primary Application Database
+
+**Status:** Accepted
+**Date:** 2024-01-15
+**Deciders:** Backend team (Sarah, James, Alex), CTO (Michael), DevOps lead (Christine)
+**Related ADRs:** ADR-015 (Data Model Design), ADR-051 (Read Replica Strategy - pending)
+
+## Context
+
+### Background
+Our new SaaS platform for project management is scheduled to launch Q2 2024. We need to select a primary database that will store user data, projects, tasks, and collaboration information for the next 3-5 years.
+
+Current situation:
+- Prototype uses SQLite (clearly insufficient for production)
+- Expected launch: 500 organizations, ~5,000 users
+- Growth projection: 10,000 organizations, ~100,000 users within 18 months
+- Data model is relational with complex queries (projects → tasks → subtasks → comments → attachments)
+
+### Requirements
+
+**Functional:**
+- Support for complex relational queries with JOINs across 4-6 tables
+- ACID transactions (critical for billing and permissions)
+- Full-text search across project content
+- JSON support for flexible metadata fields
+- Row-level security for multi-tenant isolation
+
+**Non-Functional:**
+- Handle 10,000 QPS at launch (mostly reads)
+- < 100ms p95 latency for queries
+- 99.9% uptime SLA
+- Support for read replicas (anticipated need at 50k+ QPS)
+- Point-in-time recovery for disaster recovery
+
+### Constraints
+- Budget: $5,000/month maximum for database infrastructure
+- Team expertise: Strong SQL experience, limited NoSQL experience
+- Timeline: Must finalize in 2 weeks to stay on schedule
+- Compliance: SOC 2 Type II required (data encryption at rest/transit)
+- Existing stack: Node.js backend, React frontend, deploying on AWS
+
+## Decision
+
+We will use **PostgreSQL 15+** as our primary application database, hosted on AWS RDS with the following configuration:
+
+**Infrastructure:**
+- AWS RDS PostgreSQL 15.x
+- Initially: db.r6g.xlarge instance (4 vCPU, 32GB RAM)
+- Multi-AZ deployment for high availability
+- Automated daily backups with 7-day retention
+- Point-in-time recovery enabled
+
+**Architecture:**
+- Single primary database initially
+- Prepared for read replicas when QPS exceeds 40k (anticipated 12-18 months)
+- Connection pooling via PgBouncer (deployed on application servers)
+- Row-Level Security (RLS) policies for multi-tenancy
+
+**Scope:**
+- All application data (users, organizations, projects, tasks)
+- Session storage (using pgSession)
+- Background job queue (using pg-boss)
+- Excludes: Analytics data (separate data warehouse), file metadata (DynamoDB)
+
+## Alternatives Considered
+
+### MySQL 8.0
+**Description:** Popular open-source relational database, strong AWS RDS support
+
+**Pros:**
+- Team has some MySQL experience
+- Excellent AWS RDS integration
+- Strong replication support
+- Lower cost than commercial databases
+
+**Cons:**
+- Weaker JSON support compared to PostgreSQL (JSON functions less mature)
+- Less robust constraint enforcement (e.g., CHECK constraints)
+- Full-text search less powerful than PostgreSQL's
+- InnoDB row-level locking can be problematic under high concurrency
+
+**Why not chosen:** PostgreSQL's superior JSON support is critical for our flexible metadata requirements. Our data model has complex constraints that PostgreSQL handles more elegantly.
+
+### MongoDB Atlas
+**Description:** Managed NoSQL document database with flexible schema
+
+**Pros:**
+- Excellent horizontal scalability
+- Flexible schema for evolving data model
+- Strong JSON/document support
+- Good full-text search
+
+**Cons:**
+- No multi-document ACID transactions (critical for our billing logic)
+- Team has limited NoSQL experience (learning curve risk)
+- Eventual consistency model incompatible with our requirements
+- JOIN-like operations ($lookup) are slow and cumbersome
+- More expensive at our scale (~$7k/month vs $3k for PostgreSQL)
+
+**Why not chosen:** Lack of ACID transactions across documents is a dealbreaker for billing and permission changes. Our relational data model doesn't fit document paradigm well.
+
+### Amazon Aurora PostgreSQL
+**Description:** AWS's PostgreSQL-compatible database with performance enhancements
+
+**Pros:**
+- PostgreSQL compatibility with AWS optimizations
+- Better read scaling (15 read replicas vs 5)
+- Faster failover (< 30s vs 60-120s)
+- Continuous backup to S3
+
+**Cons:**
+- 20-30% more expensive than RDS PostgreSQL
+- Some PostgreSQL extensions not supported
+- Vendor lock-in to AWS (harder to migrate to other clouds)
+- Adds complexity we don't need yet
+
+**Why not chosen:** Premium cost not justified at our current scale. Standard RDS PostgreSQL meets our needs. We can migrate to Aurora later if needed (minimal code changes).
+
+### CockroachDB
+**Description:** Distributed SQL database with PostgreSQL compatibility
+
+**Pros:**
+- Horizontal scalability built-in
+- Multi-region support for global deployment
+- PostgreSQL wire protocol compatibility
+- Strong consistency guarantees
+
+**Cons:**
+- Significantly more complex to operate (distributed systems expertise needed)
+- Higher latency for single-region workloads (consensus overhead)
+- Limited ecosystem compared to PostgreSQL
+- Team has zero distributed database experience
+- More expensive (~2-3x cost of RDS PostgreSQL)
+
+**Why not chosen:** Operational complexity far exceeds our current needs. We're a single-region deployment for the foreseeable future. Can revisit if we expand globally.
+
+## Consequences
+
+### Benefits
+
+**Strong Data Integrity:**
+- ACID transactions ensure billing accuracy and permission consistency
+- Robust constraint enforcement catches data errors at write-time
+- Foreign keys prevent orphaned records
+
+**Excellent Query Capabilities:**
+- Complex JOINs perform well with proper indexing
+- Window functions enable sophisticated analytics
+- CTEs (Common Table Expressions) simplify complex query logic
+- Full-text search with GIN indexes for project content search
+
+**JSON Flexibility:**
+- JSONB type allows flexible metadata without schema migrations
+- JSON operators enable querying nested structures efficiently
+- Balances schema enforcement (relations) with flexibility (JSON)
+
+**Team Productivity:**
+- Team's SQL expertise means fast development velocity
+- Mature ORM support (Sequelize, TypeORM) accelerates development
+- Extensive community resources and documentation
+- Familiar debugging and optimization tools
+
+**Operational Maturity:**
+- AWS RDS handles backups, patching, monitoring automatically
+- Point-in-time recovery provides disaster recovery
+- Multi-AZ deployment ensures high availability
+- Well-understood scaling path (read replicas, connection pooling)
+
+**Cost Efficiency:**
+- Estimated $3,000/month at launch scale (db.r6g.xlarge + storage)
+- Scales to ~$8,000/month with read replicas (at 100k users)
+- Well within $5k/month budget initially
+
+### Drawbacks
+
+**Vertical Scaling Limits:**
+- Single primary database limits write throughput to one instance
+- At ~50-60k QPS, will need read replicas (adds operational complexity)
+- Ultimate write limit around 100k QPS even with largest instance
+- Mitigation: Implement caching (Redis) for read-heavy workloads
+
+**Sharding Complexity:**
+- Horizontal partitioning (sharding) is manual and complex
+- If we exceed single-instance limits, migration to sharded setup is expensive
+- Not as straightforward as DynamoDB or Cassandra for horizontal scaling
+- Mitigation: Monitor growth carefully; consider Aurora or CockroachDB if needed
+
+**Replication Lag:**
+- Read replicas have eventual consistency (typically 10-100ms lag)
+- Application must handle stale reads if using replicas
+- Some queries must route to primary for consistency
+- Mitigation: Use replicas only for analytics and non-critical reads
+
+**Backup Window:**
+- Automated backups cause brief I/O pause (usually < 5s)
+- Scheduled during low-traffic window (3-4 AM PST)
+- Multi-AZ deployment minimizes impact
+- Mitigation: Accept brief latency spike during backup window
+
+### Risks
+
+**Performance Bottleneck:**
+- **Risk:** Single database becomes bottleneck before we implement read replicas
+- **Likelihood:** Medium (depends on growth rate)
+- **Mitigation:** Implement aggressive caching (Redis) for frequently accessed data; monitor QPS weekly; prepare read replica configuration in advance
+
+**Data Migration Challenges:**
+- **Risk:** If we need to migrate to different database, data size makes migration slow
+- **Likelihood:** Low (PostgreSQL should serve us for 3-5 years)
+- **Mitigation:** Regularly test backup/restore procedures; maintain clear data export processes
+
+**Team Scaling:**
+- **Risk:** As team grows, need to train new hires on PostgreSQL specifics (RLS, JSONB)
+- **Likelihood:** High (we plan to grow team)
+- **Mitigation:** Document database patterns; create onboarding materials; conduct code reviews
+
+### Trade-offs Accepted
+
+**Trading horizontal scalability for operational simplicity:** We're choosing a database that's simple to operate now but harder to scale horizontally later, accepting that we may need to re-architect in 3-5 years if we grow beyond single-instance limits.
+
+**Trading NoSQL flexibility for data integrity:** We're prioritizing ACID guarantees and relational integrity over schema flexibility, accepting that schema migrations will be required for data model changes.
+
+**Trading vendor portability for convenience:** AWS RDS lock-in is acceptable given the operational benefits. We could migrate to other managed PostgreSQL services (Google Cloud SQL, Azure) if needed, though with effort.
+
+## Implementation
+
+### Rollout Plan
+
+**Phase 1: Setup (Week 1-2)**
+- Provision AWS RDS PostgreSQL instance
+- Configure VPC security groups and IAM roles
+- Set up automated backups and monitoring
+- Configure PgBouncer connection pooling
+
+**Phase 2: Migration (Week 3-4)**
+- Migrate schema from SQLite prototype
+- Load seed data and test data
+- Performance test with simulated load
+- Configure monitoring alerts (CloudWatch, Datadog)
+
+**Phase 3: Launch (Q2 2024)**
+- Deploy to production
+- Monitor query performance and optimize slow queries
+- Weekly capacity review for first 3 months
+
+### Success Criteria
+
+**Technical:**
+- p95 query latency < 100ms (measured via APM)
+- Zero data integrity issues in first 6 months
+- 99.9% uptime achieved
+
+**Operational:**
+- Team can confidently make schema changes
+- Backup/restore tested and verified monthly
+- On-call incidents < 2 per month related to database
+
+**Business:**
+- Database costs remain under $5k/month through 10k users
+- Support 100k users without re-architecture
+
+### Future Considerations
+
+**Short-term (3-6 months):**
+- Implement Redis caching for hot data paths
+- Tune connection pool settings based on actual load
+- Create read-only database user for analytics
+
+**Medium-term (6-18 months):**
+- Add read replicas when QPS exceeds 40k
+- Implement query result caching
+- Consider Aurora migration if cost-benefit justifies
+
+**Long-term (18+ months):**
+- Evaluate sharding strategy if approaching single-instance limits
+- Consider multi-region deployment for global users
+- Explore specialized databases for specific workloads (e.g., time-series data)
+
+## References
+
+- [PostgreSQL 15 Release Notes](https://www.postgresql.org/docs/15/release-15.html)
+- [AWS RDS PostgreSQL Best Practices](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_BestPractices.html)
+- [Internal: Database Performance Requirements Doc](https://docs.internal/db-requirements)
+- [Internal: Load Testing Results](https://docs.internal/load-test-2024-01)
+- [Benchmark: PostgreSQL vs MySQL JSON Performance](https://www.enterprisedb.com/postgres-tutorials/postgresql-vs-mysql-json-performance)