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
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

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.

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 including SQLAlchemy, Mongoose, and TypeORM
Connection pooling with PgBouncer and connection pools
Migration tools including Alembic, Flyway, and Data Pump
Monitoring with pg_stat_statements, MongoDB Atlas, and Oracle AWR
python-oracledb for Oracle connectivity and PL/SQL execution
Cache invalidation and synchronization

Technology Stack

Relational Database:

PostgreSQL 14+ as primary database
MySQL 8.0+ as alternative
Connection pooling with PgBouncer and SQLAlchemy

NoSQL Database:

MongoDB 6.0+ as primary document store
Document modeling and validation
Aggregation framework
Sharding and replication

In-Memory Database:

Redis 7.0+ as primary cache
Redis Stack for advanced features
Clustering and high availability
Advanced data structures

Enterprise Database:

Oracle 19c+ / 21c+ for enterprise workloads
python-oracledb (successor to cx_Oracle)
PL/SQL procedures and packages
Partitioning and advanced analytics

Supporting Tools:

Migration tools including Alembic and Flyway
Monitoring with Prometheus and Grafana
ORMs and ODMs including SQLAlchemy and Mongoose
Connection management utilities

Performance Features:

Query optimization and analysis
Index management and strategies
Caching layers and invalidation
Load balancing and failover

Resources

For working code examples, see examples.md.

For detailed implementation patterns and database-specific optimizations, see the modules directory.

Status: Production Ready Last Updated: 2026-01-11 Maintained by: MoAI-ADK Database Team

Common Rationalizations

Rationalization	Reality
"I do not need an index, the table is small"	Tables grow. The missing index that is invisible at 1K rows becomes a production incident at 1M rows.
"I will add the migration later"	Schema changes without migrations are unreproducible. Every change must have a reversible migration script.
"This query works fine in development"	Development databases have tiny datasets. Production query plans differ dramatically at scale. Explain analyze first.
"NoSQL does not need schema design"	Schemaless does not mean designless. Document structure decisions affect every query and index.
"I will just add a column, it is non-breaking"	Adding a NOT NULL column without a default breaks existing inserts. Column additions need default values or migration backfills.
"Connection pooling is handled by the framework"	Framework defaults are generic. Pool size, timeout, and idle limits must be tuned to the workload.

Red Flags

Schema change committed without a corresponding migration file
Query uses SELECT * in production code instead of explicit column list
No index exists for columns used in WHERE, JOIN, or ORDER BY clauses
Connection string hardcoded in source instead of environment variable
Transaction scope spans user-facing HTTP request duration (long-held locks)
No EXPLAIN ANALYZE output for new queries touching large tables

Verification

Migration file exists for every schema change (show migration file list)
Indexes exist for frequently queried columns (show index definitions)
EXPLAIN ANALYZE run for new queries on representative data (show output)
Connection credentials sourced from environment variables
Transaction scopes are minimal and do not span I/O waits
Backup and restore procedure documented and tested
Connection pool settings configured with explicit size and timeout

Cloud Vendor Guide (absorbed from moai-platform-database-cloud)

Cloud database platform selection and configuration for Neon, Supabase, and Firebase Firestore.

Quick Decision Guide

Need	Platform
Serverless PostgreSQL with auto-scaling	Neon
Database branching for CI/CD previews	Neon branching
Edge-compatible connection pooling	Neon + Neon Proxy
Vector search (pgvector) for AI/ML	Supabase
Row-Level Security for multi-tenant apps	Supabase RLS
Real-time subscriptions + full-stack	Supabase
Mobile-first with offline sync	Firebase Firestore
Cross-platform (iOS/Android/Web)	Firebase Firestore

Neon (Serverless PostgreSQL)

Key features: Auto-scaling compute, scale-to-zero, database branching, pg_bouncer pooling.

Setup:

npm install @neondatabase/serverless
# Connection string: postgresql://user:pass@ep-xxx.neon.tech/dbname?sslmode=require

Branch workflow: Create a branch per PR (neonctl branches create --name pr-123), run migrations, test, delete on merge. Zero cost during idle periods.

Supabase (PostgreSQL 16)

Key features: pgvector, Row-Level Security, real-time subscriptions, built-in auth/storage.

RLS policy pattern:

CREATE POLICY "users_own_data" ON items
  FOR ALL USING (auth.uid() = user_id);

pgvector search: SELECT * FROM embeddings ORDER BY embedding <-> $1 LIMIT 10;

Firebase Firestore (NoSQL)

Key features: Real-time sync, offline caching, Security Rules, mobile SDKs.

Security Rules pattern:

match /users/{userId} {
  allow read, write: if request.auth.uid == userId;
}

Offline persistence: Enable via enableIndexedDbPersistence(db) (web) or SDK default (mobile).

Full platform reference: modules/cloud-database.md

Refactor Notes

R4 audit verdict (2026-04-23): REFACTOR — MERGE target (absorbs moai-platform-database-cloud) with additional restructuring needed SPEC: SPEC-V3R2-WF-001 §6.2 line 263 Refactor scope (deferred to future sub-SPEC):

Separate moai-domain-db-docs workflow skill from this query/schema design skill
Extract cloud vendor deep-dives (Neon, Supabase, Firestore) into dedicated Level-3 modules
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.

moai-domain-database