# Database Design and Development Guidelines

Comprehensive guidelines for designing, implementing, and maintaining databases across all major database types.

## Overview

This module provides detailed guidelines for database design and development, covering flat databases, relational databases, NoSQL databases, and vector databases. It includes universal best practices, security standards, performance optimization, migration strategies, and testing approaches.

## Key Benefits

- **Universal Best Practices**: Security, backup, monitoring, and compliance guidelines applicable to all database types
- **Security Standards**: SQL injection prevention, encryption, access controls, and audit logging
- **Performance Optimization**: Indexing strategies, query optimization, caching, and profiling
- **Migration Management**: Schema versioning, zero-downtime migrations, and rollback strategies
- **Testing Strategies**: Unit testing, integration testing, performance testing, and data validation
- **Multi-Database Coverage**: Relational (SQL), NoSQL (document, key-value, graph), vector, and flat databases

## Installation

```bash
augx link domain-rules/database
```

## Database Types Covered

### Flat Databases
- CSV/JSON file management
- Schema validation and enforcement
- File locking and concurrency
- When to use and when to migrate

### Relational Databases
- Schema design and normalization
- Indexing and query optimization
- Transactions and ACID properties
- SQL best practices

### NoSQL Databases
- Document databases (MongoDB, CouchDB)
- Key-value stores (Redis, DynamoDB)
- Graph databases (Neo4j, ArangoDB)
- Data modeling patterns

### Vector Databases
- Embedding generation and storage
- Similarity search and indexing
- Hybrid search strategies
- Vector database selection

## Directory Structure

```
augment-extensions/domain-rules/database/
├── module.json                          # Module metadata
├── README.md                            # This file
├── rules/                               # Detailed guidelines
│   ├── universal-best-practices.md      # Security, backup, monitoring
│   ├── security-standards.md            # SQL injection, encryption, access control
│   ├── performance-optimization.md      # Indexing, caching, profiling
│   ├── data-migration.md                # Migration planning and execution
│   ├── testing-strategies.md            # Database testing approaches
│   ├── relational-databases.md          # SQL database design
│   ├── schema-design.md                 # Schema design principles
│   ├── normalization.md                 # Normalization and denormalization
│   ├── indexing-strategies.md           # Index types and usage
│   ├── query-optimization.md            # Query performance tuning
│   ├── transactions.md                  # Transaction management
│   ├── nosql-databases.md               # NoSQL database patterns
│   ├── document-databases.md            # Document database design
│   ├── key-value-stores.md              # Key-value database patterns
│   ├── graph-databases.md               # Graph database modeling
│   ├── vector-databases.md              # Vector database design
│   ├── vector-embeddings.md             # Embedding strategies
│   ├── vector-indexing.md               # Vector index optimization
│   └── flat-databases.md                # Flat file database management
└── examples/                            # Code examples
    ├── relational-database-example.md   # E-commerce schema example
    ├── nosql-database-example.md        # Blog platform example
    ├── vector-database-example.md       # Semantic search example
    ├── flat-database-example.md         # Configuration management example
    └── hybrid-database-example.md       # Multi-database architecture
```

## Core Workflows

### Schema Design
1. Requirements gathering
2. Entity identification
3. Relationship mapping
4. Normalization decisions
5. Constraint definition
6. Index planning

### Migration Management
1. Migration planning
2. Schema versioning
3. Migration script creation
4. Testing strategy
5. Rollback planning
6. Deployment and validation

### Performance Optimization
1. Performance profiling
2. Query analysis
3. Index optimization
4. Schema optimization
5. Caching strategies
6. Monitoring and benchmarking

## Usage Examples

### Schema Design
```sql
-- Example: E-commerce database schema
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    user_id INTEGER REFERENCES users(id),
    total DECIMAL(10, 2) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_orders_created_at ON orders(created_at);
```

### Vector Database
```python
# Example: Semantic search with Pinecone
import pinecone
from openai import OpenAI

# Initialize
pinecone.init(api_key="your-api-key")
index = pinecone.Index("semantic-search")

# Generate embedding
client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-ada-002",
    input="search query"
)
embedding = response.data[0].embedding

# Search
results = index.query(vector=embedding, top_k=10)
```

## Character Count

~0 characters (to be calculated after all rules and examples are created)

## Version

1.0.0

