| name | moai-domain-database |
| description | Database specialist covering PostgreSQL, MongoDB, Redis, Oracle, and advanced data patterns for modern applications. Use when user asks about database schema design, query optimization, indexing strategies, data modeling, migrations, ORM configuration, or database performance tuning. Do NOT use for API design or server-side business logic (use moai-domain-backend instead).
|
| license | Apache-2.0 |
| compatibility | Designed for Claude Code |
| allowed-tools | Read Write Edit Bash(psql:*) Bash(mysql:*) Bash(sqlite3:*) Bash(mongosh:*) Bash(redis-cli:*) Bash(npm:*) Bash(npx:*) Bash(prisma:*) Grep Glob mcp__context7__resolve-library-id mcp__context7__get-library-docs |
| user-invocable | false |
| metadata | {"version":"1.0.0","category":"domain","status":"active","updated":"2026-01-11","modularized":"true","tags":"database, postgresql, mongodb, redis, oracle, data-patterns, performance","author":"MoAI-ADK Team"} |
| triggers | {"keywords":["database","PostgreSQL","MongoDB","Redis","Oracle","SQL","NoSQL","PL/SQL","query","schema","migration","indexing","ORM","ODM","SQLAlchemy","Mongoose","Prisma","Drizzle","python-oracledb","cx_Oracle","connection pool","transaction","data modeling","aggregation","partitioning","hierarchical query"]} |
Database Domain Specialist
Quick Reference
Enterprise Database Expertise - Comprehensive database patterns and implementations covering PostgreSQL, MongoDB, Redis, Oracle, and advanced data management for scalable modern applications.
Core Capabilities:
- PostgreSQL: Advanced relational patterns, optimization, and scaling
- MongoDB: Document modeling, aggregation, and NoSQL performance tuning
- Redis: In-memory caching, real-time analytics, and distributed systems
- Oracle: Enterprise patterns, PL/SQL, partitioning, and hierarchical queries
- Multi-Database: Hybrid architectures and data integration patterns
- Performance: Query optimization, indexing strategies, and scaling
- Operations: Connection management, migrations, and monitoring
When to Use:
- Designing database schemas and data models
- Implementing caching strategies and performance optimization
- Building scalable data architectures
- Working with multi-database systems
- Optimizing database queries and performance
Implementation Guide
Quick Start Workflow
Database Stack Initialization:
Create a DatabaseManager instance and configure multiple database connections. Set up PostgreSQL with connection string, pool size of 20, and query logging enabled. Configure MongoDB with connection string, database name, and sharding enabled. Configure Redis with connection string, max connections of 50, and clustering enabled. Use the unified interface to query user data with profile and analytics across all database types.
Single Database Operations:
Run PostgreSQL schema migrations using the migration command with the database type and migration file path. Execute MongoDB aggregation pipelines by specifying the collection name and pipeline JSON file. Warm Redis cache by specifying key patterns and TTL values.
Core Components
PostgreSQL Module:
- Advanced schema design and constraints
- Complex query optimization and indexing
- Window functions and CTEs
- Partitioning and materialized views
- Connection pooling and performance tuning
MongoDB Module:
- Document modeling and schema design
- Aggregation pipelines for analytics
- Indexing strategies and performance
- Sharding and scaling patterns
- Data consistency and validation
Redis Module:
- Multi-layer caching strategies
- Real-time analytics and counting
- Distributed locking and coordination
