Enterprise database architecture specialist with PostgreSQL 17, MySQL 8.4 LTS, MongoDB 8.0, Redis 7.4 expertise. Master connection pooling, query optimization, caching strategies, and database DevOps automation. Build scalable, resilient database systems with comprehensive monitoring and disaster recovery.
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name: "moai-domain-database" description: "Database specialist covering PostgreSQL, MongoDB, Redis, and advanced data patterns for modern applications" version: 1.0.0 category: "domain" modularized: true tags: ['database', 'postgresql', 'mongodb', 'redis', 'data-patterns', 'performance'] allowed-tools: "Read, Write, Edit, Bash, Grep, Glob, mcp__context7__resolve-library-id, mcp__context7__get-library-docs" updated: 2025-12-06 status: "active" author: "MoAI-ADK Team"
Database Domain Specialist
Quick Reference (30 seconds)
Enterprise Database Expertise - Comprehensive database patterns and implementations covering PostgreSQL, MongoDB, Redis, 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
- 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 (5 minutes)
Quick Start Workflow
Database Stack Initialization:
from moai_domain_database import DatabaseManager
# Initialize multi-database stack
db_manager = DatabaseManager()
# Configure PostgreSQL for relational data
postgresql = db_manager.setup_postgresql(
connection_string="postgresql://...",
connection_pool_size=20,
enable_query_logging=True
)
# Configure MongoDB for document storage
mongodb = db_manager.setup_mongodb(
connection_string="mongodb://...",
database_name="app_data",
enable_sharding=True
)
# Configure Redis for caching and real-time features
redis = db_manager.setup_redis(
connection_string="redis://...",
max_connections=50,
enable_clustering=True
)
# Use unified database interface
user_data = db_manager.get_user_with_profile(user_id)
analytics = db_manager.get_user_analytics(user_id, time_range="30d")
Single Database Operations:
# PostgreSQL schema migration
moai db:migrate --database postgresql --migration-file schema_v2.sql
# MongoDB aggregation pipeline
moai db:aggregate --collection users --pipeline analytics_pipeline.json
# Redis cache warming
moai db:cache:warm --pattern "user:*" --ttl 3600
Core Components
- PostgreSQL (
modules/postgresql.md)
- Advanced schema design and constraints
- Complex query optimization and indexing
- Window functions and CTEs
- Partitioning and materialized views
- Connection pooling and performance tuning
- MongoDB (
modules/mongodb.md)
- Document modeling and schema design
- Aggregation pipelines for analytics
- Indexing strategies and performance
- Sharding and scaling patterns
- Data consistency and validation
- Redis (
modules/redis.md)
- Multi-layer caching strategies
- Real-time analytics and counting
- Distributed locking and coordination
- Pub/sub messaging and streams
- Advanced data structures (HyperLogLog, Geo)
Advanced Patterns (10+ minutes)
Multi-Database Architecture
Polyglot Persistence Pattern:
class DataRouter:
def __init__(self):
self.postgresql = PostgreSQLConnection()
self.mongodb = MongoDBConnection()
self.redis = RedisConnection()
def get_user_profile(self, user_id):
# Get structured user data from PostgreSQL
user = self.postgresql.get_user(user_id)
# Get flexible profile data from MongoDB
profile = self.mongodb.get_user_profile(user_id)
# Get real-time status from Redis
status = self.redis.get_user_status(user_id)
return self.merge_user_data(user, profile, status)
def update_user_data(self, user_id, data):
# Route different data types to appropriate databases
if 'structured_data' in data:
self.postgresql.update_user(user_id, data['structured_data'])
if 'profile_data' in data:
self.mongodb.update_user_profile(user_id, data['profile_data'])
if 'real_time_data' in data:
self.redis.set_user_status(user_id, data['real_time_data'])
# Invalidate cache across databases
self.invalidate_user_cache(user_id)
Data Synchronization:
class DataSyncManager:
def sync_user_data(self, user_id):
# Sync from PostgreSQL to MongoDB for search
pg_user = self.postgresql.get_user(user_id)
search_document = self.create_search_document(pg_user)
self.mongodb.upsert_user_search(user_id, search_document)
# Update cache in Redis
cache_data = self.create_cache_document(pg_user)
self.redis.set_user_cache(user_id, cache_data, ttl=3600)
Performance Optimization
Query Performance Analysis:
# PostgreSQL query optimization
def analyze_query_performance(query):
explain_result = postgresql.execute(f"EXPLAIN (ANALYZE, BUFFERS) {query}")
return QueryAnalyzer(explain_result).get_optimization_suggestions()
# MongoDB aggregation optimization
def optimize_aggregation_pipeline(pipeline):
optimizer = AggregationOptimizer()
return optimizer.optimize_pipeline(pipeline)
# Redis performance monitoring
def monitor_redis_performance():
metrics = redis.info()
return PerformanceAnalyzer(metrics).get_recommendations()
Scaling Strategies:
# Read replicas for PostgreSQL
read_replicas = postgresql.setup_read_replicas([
"postgresql://replica1...",
"postgresql://replica2..."
])
# Sharding for MongoDB
mongodb.setup_sharding(
shard_key="user_id",
num_shards=4
)
# Redis clustering
redis.setup_cluster([
"redis://node1:7000",
"redis://node2:7000",
"redis://node3:7000"
])
Works Well With
Complementary Skills:
moai-domain-backend- API integration and business logicmoai-foundation-core- Database migration and schema managementmoai-workflow-project- Database project setup and configurationmoai-platform-supabase- Supabase database integration patternsmoai-platform-neon- Neon database integration patternsmoai-platform-firestore- Firestore database integration patterns
Technology Integration:
- ORMs and ODMs (SQLAlchemy, Mongoose, TypeORM)
- Connection pooling (PgBouncer, connection pools)
- Migration tools (Alembic, Flyway)
- Monitoring (pg_stat_statements, MongoDB Atlas)
- Cache invalidation and synchronization
Usage Examples
Database Operations
# PostgreSQL advanced queries
users = postgresql.query(
"SELECT * FROM users WHERE created_at > %s ORDER BY activity_score DESC LIMIT 100",
[datetime.now() - timedelta(days=30)]
)
# MongoDB analytics
analytics = mongodb.aggregate('events', [
{"$match": {"timestamp": {"$gte": start_date}}},
{"$group": {"_id": "$type", "count": {"$sum": 1}}},
{"$sort": {"count": -1}}
])
# Redis caching operations
async def get_user_data(user_id):
cache_key = f"user:{user_id}"
data = await redis.get(cache_key)
if not data:
data = fetch_from_database(user_id)
await redis.setex(cache_key, 3600, json.dumps(data))
return json.loads(data)
Multi-Database Transactions
async def create_user_with_profile(user_data, profile_data):
try:
# Start transaction across databases
async with transaction_manager():
# Create user in PostgreSQL
user_id = await postgresql.insert_user(user_data)
# Create profile in MongoDB
await mongodb.insert_user_profile(user_id, profile_data)
# Set initial cache in Redis
await redis.set_user_cache(user_id, {
"id": user_id,
"status": "active",
"created_at": datetime.now().isoformat()
})
return user_id
except Exception as e:
# Automatic rollback across databases
logger.error(f"User creation failed: {e}")
raise
Technology Stack
Relational Database:
- PostgreSQL 14+ (primary)
- MySQL 8.0+ (alternative)
- Connection pooling (PgBouncer, SQLAlchemy)
NoSQL Database:
- MongoDB 6.0+ (primary)
- Document modeling and validation
- Aggregation framework
- Sharding and replication
In-Memory Database:
- Redis 7.0+ (primary)
- Redis Stack for advanced features
- Clustering and high availability
- Advanced data structures
Supporting Tools:
- Migration tools (Alembic, Flyway)
- Monitoring (Prometheus, Grafana)
- ORMs/ODMs (SQLAlchemy, Mongoose)
- Connection management
Performance Features:
- Query optimization and analysis
- Index management and strategies
- Caching layers and invalidation
- Load balancing and failover
For detailed implementation patterns and database-specific optimizations, see the modules/ directory.
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