/moai-domain-database

name: moai-domain-database
description: >
  Database specialist covering PostgreSQL, MongoDB, Redis, Oracle, and cloud database
  platforms (Neon, Supabase, Firestore). Use for schema design, query optimization,
  indexing strategies, data modeling, or cloud database selection.
  Cloud vendor guide absorbed from moai-platform-database-cloud.

when_to_use: >
  Use for schema design, query optimization, indexing strategies, and
  ORMs/ODMs (Prisma, Mongoose, SQLAlchemy, Drizzle). Covers PostgreSQL,
  MongoDB, Redis, Oracle, and cloud databases (Neon, Supabase, Firestore).

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:*), Bash(neonctl:*), Bash(firebase:*), Bash(supabase:*), Grep, Glob
user-invocable: false
metadata:
  version: "2.0.0"
  category: "domain"
  status: "active"
  updated: "2026-04-25"
  tags: "database, postgresql, mongodb, redis, oracle, data-patterns, performance, neon, supabase, firestore, cloud-database, serverless"
  author: "MoAI-ADK Team"
  related-skills: "moai-platform-database-cloud"

# MoAI Extension: Progressive Disclosure
progressive_disclosure:
  enabled: true
  level1_tokens: 100
  level2_tokens: 5000

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

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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
• Pub/sub messaging and streams
• Advanced data structures including HyperLogLog and Geo

Oracle Module:

• Hierarchical and recursive query patterns (CONNECT BY)
• PL/SQL procedures, packages, and batch operations
• Partitioning strategies (range, list, hash, composite)
• Enterprise features and statement caching
• LOB handling and large data processing

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Advanced Patterns

Multi-Database Architecture

Polyglot Persistence Pattern:

Create a DataRouter class that initializes connections to PostgreSQL, MongoDB, Redis, and Oracle. Implement get_user_profile method that retrieves structured user data from PostgreSQL or Oracle, flexible profile data from MongoDB, and real-time status from Redis, then merges all data sources. Implement update_user_data method that routes structured data updates to PostgreSQL/Oracle, profile data updates to MongoDB, and real-time data updates to Redis, followed by cache invalidation.

Data Synchronization:

Create a DataSyncManager class that synchronizes user data across databases. Implement sync_user_data method that retrieves user from PostgreSQL, creates a search document for MongoDB, upserts to the MongoDB search collection, creates cache data, and updates Redis cache with TTL.

Performance Optimization

Query Performance Analysis:

For PostgreSQL, execute EXPLAIN ANALYZE BUFFERS on queries and use a QueryAnalyzer to generate optimization suggestions. For MongoDB, create an AggregationOptimizer to analyze and optimize aggregation pipelines. For Redis, retrieve info metrics and use a PerformanceAnalyzer to generate recommendations.

Scaling Strategies:

Configure PostgreSQL read replicas by providing replica connection URLs. Set up MongoDB sharding with shard key and number of shards. Configure Redis clustering by providing node URLs for the cluster.

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Works Well With

Complementary Skills:

• moai-domain-backend - API integration and business logic
• moai-foundation-core - Database migration and schema management
• moai-workflow-project - Database project setup and configuration
• moai-platform-supabase - Supabase database integration patterns
• moai-platform-neon - Neon database integration patterns
• moai-platform-firestore - Firestore database integration patterns

Technology Integration:

• ORMs and ODMs i