using-document-databases

# Document Database Implementation

Guide NoSQL document database selection and implementation for flexible schema applications across Python, TypeScript, Rust, and Go.

## When to Use This Skill

Use document databases when applications need:
- **Flexible schemas** - Data models evolve rapidly without migrations
- **Nested structures** - JSON-like hierarchical data
- **Horizontal scaling** - Built-in sharding and replication
- **Developer velocity** - Object-to-database mapping without ORM complexity

## Database Selection

### Quick Decision Framework

```
DEPLOYMENT ENVIRONMENT?
├── AWS-Native Application → DynamoDB
│   ✓ Serverless, auto-scaling, single-digit ms latency
│   ✗ Limited query flexibility
│
├── Firebase/GCP Ecosystem → Firestore
│   ✓ Real-time sync, offline support, mobile-first
│   ✗ More expensive for heavy reads
│
└── General-Purpose/Complex Queries → MongoDB
    ✓ Rich aggregation, full-text search, vector search
    ✓ ACID transactions, self-hosted or managed
```

### Database Comparison

| Database | Best For | Latency | Max Item | Query Language |
|----------|----------|---------|----------|----------------|
| **MongoDB** | General-purpose, complex queries | 1-5ms | 16MB | MQL (rich) |
| **DynamoDB** | AWS serverless, predictable performance | <10ms | 400KB | PartiQL (limited) |
| **Firestore** | Real-time apps, mobile-first | 50-200ms | 1MB | Firebase queries |

See `references/mongodb.md` for MongoDB details
See `references/dynamodb.md` for DynamoDB single-table design
See `references/firestore.md` for Firestore real-time patterns

## Schema Design Patterns

### Embedding vs Referencing

**Use the decision matrix in `references/schema-design-patterns.md`**

Quick guide:

| Relationship | Pattern | Example |
|--------------|---------|---------|
| One-to-Few | Embed | User addresses (2-3 max) |
| One-to-Many | Hybrid | Blog posts → comments |
| One-to-Millions | Reference | User → events (logging) |
| Many-to-Many | Reference | Products ↔ Ca