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
Pub/sub messaging and streams
Advanced data structures including HyperLogLog and Geo
Oracle Module:
Hierarchical and recursive query patterns (CONNECT BY)
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.
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
Consolidate overlapping ORM pattern content across database types
This skill is retained in v3.0 but its body will be restructured in a follow-up SPEC. Cloud vendor content absorbed from moai-platform-database-cloud in Wave 1.2.
