using-document-databaseslisted

# 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