# Database Selection

## Decision Tree: Database Selection

```
Which database should we use for this project?
  ├─ Relational (SQL) [weight: 8] [priority: high] [risk: low] [recommended]
  │   ├─ PostgreSQL [weight: 9] [recommended]
  │   │   ├─ Use cases
  │   │   │   ├─ Complex queries and relationships → Best SQL features
  │   │   │   ├─ JSON/JSONB support → Flexible schema when needed
  │   │   │   └─ Full-text search → Built-in search capabilities
  │   │   ├─ Hosting
  │   │   │   ├─ Managed (RDS, Heroku, Supabase) → Easy setup, auto backups
  │   │   │   └─ Self-hosted → Full control, lower cost at scale
  │   │   └─ Choose if: Complex data, ACID compliance needed
  │   │
  │   ├─ MySQL [weight: 7]
  │   │   ├─ Use cases
  │   │   │   ├─ Read-heavy workloads → Excellent read performance
  │   │   │   ├─ Web applications → Battle-tested for web
  │   │   │   └─ WordPress/common CMSs → Wide compatibility
  │   │   ├─ Hosting
  │   │   │   ├─ Managed (RDS, PlanetScale) → Scalable, managed
  │   │   │   └─ Self-hosted → Traditional choice
  │   │   └─ Choose if: Proven stability, read performance priority
  │   │
  │   └─ SQLite [weight: 6]
  │       ├─ Use cases
  │       │   ├─ Development/testing → Zero setup required
  │       │   ├─ Small applications → Embedded, serverless
  │       │   └─ Edge deployments → Single file database
  │       └─ Choose if: Simple needs, low traffic, or prototyping
  │
  ├─ Document (NoSQL) [weight: 7] [priority: high] [risk: medium]
  │   ├─ MongoDB [weight: 8]
  │   │   ├─ Use cases
  │   │   │   ├─ Flexible schema → Rapid iteration
  │   │   │   ├─ Nested documents → Natural data modeling
  │   │   │   └─ Real-time applications → Change streams
  │   │   ├─ Hosting
  │   │   │   ├─ MongoDB Atlas [recommended] → Fully managed
  │   │   │   └─ Self-hosted → Cost-effective at scale
  │   │   └─ Choose if: Schema flexibility, rapid development
  │   │
  │   ├─ DynamoDB [weight: 7]
  │   │   ├─ Use cases
  │   │   │   ├─ AWS ecosystem → Native AWS integration
  │   │   │   ├─ Serverless apps → Auto-scaling
  │   │   │   └─ High throughput → Predictable performance
  │   │   └─ Choose if: AWS-based, need infinite scale
  │   │
  │   └─ Firestore [weight: 6]
  │       ├─ Use cases
  │       │   ├─ Mobile apps → Real-time sync
  │       │   ├─ Firebase ecosystem → All-in-one solution
  │       │   └─ Rapid prototyping → Quick setup
  │       └─ Choose if: Mobile-first, Google Cloud Platform
  │
  ├─ Key-Value Store [weight: 6] [priority: medium] [risk: low]
  │   ├─ Redis [weight: 8] [recommended]
  │   │   ├─ Use cases
  │   │   │   ├─ Caching → Blazing fast reads
  │   │   │   ├─ Session storage → Low latency needed
  │   │   │   ├─ Real-time features → Pub/sub messaging
  │   │   │   └─ Rate limiting → Atomic operations
  │   │   └─ Choose if: Speed critical, caching layer needed
  │   │
  │   └─ Memcached [weight: 5]
  │       ├─ Use cases
  │       │   ├─ Simple caching → Lightweight
  │       │   └─ Multi-threaded → Good for multi-core
  │       └─ Choose if: Simple caching needs only
  │
  ├─ Graph Database [weight: 5] [priority: low] [risk: medium]
  │   ├─ Neo4j [weight: 7]
  │   │   ├─ Use cases
  │   │   │   ├─ Social networks → Friend relationships
  │   │   │   ├─ Recommendation engines → Connection patterns
  │   │   │   └─ Knowledge graphs → Complex relationships
  │   │   └─ Choose if: Relationships are primary data
  │   │
  │   └─ Amazon Neptune [weight: 6]
  │       ├─ Use cases
  │       │   ├─ AWS ecosystem → Managed service
  │       │   └─ Gremlin/SPARQL support → Standard graph queries
  │       └─ Choose if: AWS-based, need managed graph DB
  │
  └─ Time-Series Database [weight: 5] [priority: low] [risk: low]
      ├─ InfluxDB [weight: 7]
      │   ├─ Use cases
      │   │   ├─ Metrics and monitoring → Purpose-built
      │   │   ├─ IoT data → High write throughput
      │   │   └─ Analytics → Time-based queries optimized
      │   └─ Choose if: Time-series data is primary workload
      │
      └─ TimescaleDB [weight: 6]
          ├─ Use cases
          │   ├─ PostgreSQL + time-series → SQL familiarity
          │   └─ Hybrid workload → Mix of relational and time-series
          └─ Choose if: Want SQL with time-series features
```

## Context

Choose your database based on:
- Data structure (relational vs. document vs. graph)
- Query patterns (read-heavy vs. write-heavy)
- Scalability requirements
- Team expertise
- Budget constraints
- Hosting preferences

## Recommendations

**PostgreSQL is recommended** for most applications because:
- Excellent balance of features and reliability
- ACID compliance with great performance
- JSON support for flexibility when needed
- Rich ecosystem and tooling
- Free and open source

**Use MongoDB** if:
- Schema changes frequently
- You work with nested, hierarchical data
- You want rapid development iteration

**Use Redis** as a complement (not primary database) for:
- Caching frequently accessed data
- Session management
- Real-time features (pub/sub)

## Multi-Database Strategy

Many applications benefit from using multiple databases:

- **Primary Database**: PostgreSQL or MongoDB for core data
- **Cache Layer**: Redis for performance
- **Search**: Elasticsearch for full-text search
- **Analytics**: Separate data warehouse (BigQuery, Redshift)

This is perfectly normal and often the right choice!

## Implementation Checklist

After choosing:

- [ ] Set up local development database
- [ ] Configure connection pooling
- [ ] Implement database migrations
- [ ] Set up automated backups
- [ ] Configure monitoring and alerts
- [ ] Plan scaling strategy
- [ ] Document schema and relationships
- [ ] Set up read replicas (if needed)
- [ ] Configure SSL/TLS for connections
- [ ] Plan disaster recovery procedure