moai-domain-database

name	moai-domain-database
version	4.0.0
status	production
description	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.
allowed-tools	["Read","Write","Edit","Bash","Glob","WebFetch","WebSearch"]
tags	["database","postgresql","mysql","mongodb","redis","sql","nosql","optimization"]

Enterprise Database Architecture

Level 1: Quick Reference

Core Capabilities

• Relational Databases: PostgreSQL 17, MySQL 8.4 LTS, MariaDB 11.4
• NoSQL Solutions: MongoDB 8.0, Redis 7.4, Cassandra 5.0
• ORM & Query Builders: SQLAlchemy 2.0, Django ORM 5.1, Prisma 5
• Connection Management: PgBouncer 1.23, ProxySQL 2.6
• Performance: Query optimization, indexing strategies, caching

Quick Setup Examples

# PostgreSQL 17 with optimized configuration
docker run -d --name postgres17 \
  -e POSTGRES_DB=myapp -e POSTGRES_USER=admin -e POSTGRES_PASSWORD=secure_pass \
  -p 5432:5432 postgres:17-alpine \
  -c shared_preload_libraries=pg_stat_statements \
  -c max_connections=200 -c shared_buffers=256MB

# Redis 7.4 with persistence
docker run -d --name redis74 \
  -p 6379:6379 redis:7.4-alpine \
  redis-server --appendonly yes --maxmemory 512mb

# SQLAlchemy 2.0 async connection pooling
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

engine = create_async_engine(
    "postgresql+asyncpg://user:pass@localhost/db",
    pool_size=20,
    max_overflow=30,
    pool_pre_ping=True,
    pool_recycle=3600
)

AsyncSessionLocal = sessionmaker(
    engine, class_=AsyncSession, expire_on_commit=False
)

Level 2: Practical Implementation

Database Architecture Patterns

1. Connection Pool Management

# PgBouncer configuration for transaction pooling
[databases]
myapp = host=localhost port=5432 dbname=myapp

[pgbouncer]
listen_port = 6432
listen_addr = 127.0.0.1
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt
logfile = /var/log/pgbouncer/pgbouncer.log
pidfile = /var/run/pgbouncer/pgbouncer.pid
admin_users = postgres
stats_users = stats, postgres

pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20
min_pool_size = 5
reserve_pool_size = 5
reserve_pool_timeout = 5
max_db_connections = 50
max_user_connections = 50
server_reset_query = DISCARD ALL
server_check_delay = 30
server_check_query = select 1
server_lifetime = 3600
server_idle_timeout = 600

2. Query Optimization Patterns

# PostgreSQL 17 advanced query optimization
class QueryOptimizer:
    def __init__(self, db_session):
        self.db = db_session
    
    async def analyze_query_performance(self, query: str):
        """Analyze query execution plan and performance"""
        explain_query = f"EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) {query}"
        result = await self.db.execute(explain_query)
        plan = result.scalar()[0]
        
        return {
            'execution_time': plan['Execution Time'],
            'planning_time': plan['Planning Time'],
            'total_cost': plan['Total Cost'],
            'actual_rows': plan['Actual Rows'],
            'buffers': plan['Buffers'],
            'recommendations': self._generate_optimization_tips(plan)
        }
    
    def _generate_optimization_tips(self, plan):
        """Generate optimization recommendations based on execution plan"""
        tips = []
        
        # Check for sequential scans on large tables
        for node in self._extract_plan_nodes(plan):
            if node.get('Node Type') == 'Seq Scan' and node.get(, ) > :
                tips.append()
            
             node.get() ==   node.get(, ) > :
                tips.append()
        
         tips
    
      ():
        
        index_name = 
        
        index_sql = 
        
         .db.execute(text(index_sql))
         .db.commit()

3. Caching Strategy Implementation

# Redis 7.4 advanced caching patterns
class DatabaseCache:
    def __init__(self, redis_client, db_session):
        self.redis = redis_client
        self.db = db_session
    
    async def cached_query(self, key: str, query: str, ttl: int = 3600):
        """Execute query with Redis caching"""
        # Try cache first
        cached_result = await self.redis.get(key)
        if cached_result:
            return json.loads(cached_result)
        
        # Execute query and cache result
        result = await self.db.execute(text(query))
        rows = result.fetchall()
        
        # Convert to JSON-serializable format
        data = [dict(row._mapping) for row in rows]
        
        # Cache with TTL
        await self.redis.setex(key, ttl, json.dumps(data))
        
        return data
    
    async def invalidate_cache_pattern(self, pattern: str):
        """Invalidate cache keys matching pattern"""
        keys = await self.redis.keys(pattern)
        if keys:
            await .redis.delete(*keys)
    
      ():
        
         key, query, ttl  queries:
             .cached_query(key, query, ttl)

Database Monitoring & Observability

# Comprehensive database monitoring setup
import psycopg2
from prometheus_client import Counter, Histogram, Gauge
import time

# Prometheus metrics
DB_QUERY_DURATION = Histogram('db_query_duration_seconds', 'Database query duration')
DB_CONNECTION_POOL = Gauge('db_connection_pool_active', 'Active database connections')
DB_ERROR_COUNT = Counter('db_error_count', 'Database error count', ['error_type'])

class DatabaseMonitor:
    def __init__(self, connection_string):
        self.connection_string = connection_string
    
    @DB_QUERY_DURATION.time()
    async def execute_with_monitoring(self, query: str, params: dict = None):
        """Execute query with comprehensive monitoring"""
        start_time = time.time()
        
        try:
            async with self.pool.acquire() as conn:
                result = await conn.execute(query, params or {})
                
                # Log slow queries
                duration = time.time() - start_time
                if duration > 1.0:  # Slow query threshold
                    await self._log_slow_query(query, duration, params)
                
                return result
                
         Exception  e:
            DB_ERROR_COUNT.labels(error_type=(e).__name__).inc()
             ._log_database_error(query, e, params)
            
    
      ():
        
        metrics = {}
        
        
        pool_stats =  ._get_pool_stats()
        metrics.update(pool_stats)
        
        
        perf_stats =  ._get_performance_stats()
        metrics.update(perf_stats)
        
        
        lock_stats =  ._get_lock_stats()
        metrics.update(lock_stats)
        
         metrics

Level 3: Advanced Integration

Database DevOps & Automation

1. Database Migration as Code

# Database migration management with Alembic
from alembic import command
from alembic.config import Config
import asyncio

class DatabaseMigrator:
    def __init__(self, database_url: str):
        self.database_url = database_url
        self.alembic_cfg = Config("alembic.ini")
        self.alembic_cfg.set_main_option("sqlalchemy.url", database_url)
    
    async def create_migration(self, message: str):
        """Create new database migration"""
        command.revision(self.alembic_cfg, autogenerate=True, message=message)
    
    async def run_migrations(self):
        """Run pending migrations"""
        command.upgrade(self.alembic_cfg, "head")
    
    async def rollback_migration(self, revision: str):
        """Rollback to specific revision"""
        command.downgrade(self.alembic_cfg, revision)
    
    async def get_migration_history(self):
        """Get migration history"""
        from alembic.runtime.migration  MigrationContext
         sqlalchemy  create_engine
        
        engine = create_engine(.database_url)
         engine.connect()  connection:
            context = MigrationContext.configure(connection)
             context.get_current_revision()

2. Multi-Database Replication Setup

# PostgreSQL logical replication setup
class DatabaseReplication:
    def __init__(self, primary_config, replica_configs):
        self.primary = primary_config
        self.replicas = replica_configs
    
    async def setup_logical_replication(self):
        """Setup logical replication from primary to replicas"""
        
        # Setup replication user on primary
        await self._create_replication_user()
        
        # Create publication on primary
        await self._create_publication()
        
        # Setup subscriptions on replicas
        for replica in self.replicas:
            await self._create_subscription(replica)
    
    async def _create_replication_user(self):
        """Create replication user on primary database"""
        sql = """
        CREATE USER IF NOT EXISTS replicator WITH REPLICATION ENCRYPTED PASSWORD 'replicator_password';
        GRANT CONNECT ON DATABASE {database} TO replicator;
        GRANT USAGE ON SCHEMA public TO replicator;
        GRANT SELECT ON ALL TABLES IN SCHEMA public TO replicator;
        ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO replicator;
        """.format(database=self.primary['database'])
        
        await self._execute_on_primary(sql)
    
    async  ():
        
        sql = 
        
         ._execute_on_primary(sql)
    
      ():
        
        sql = 
        
         ._execute_on_replica(replica_config, sql)

Database Security & Compliance

1. Row-Level Security Implementation

-- PostgreSQL Row-Level Security (RLS)
-- Enable RLS on sensitive tables
ALTER TABLE sensitive_data ENABLE ROW LEVEL SECURITY;

-- Create policy for user access based on department
CREATE POLICY user_department_access ON sensitive_data
    FOR ALL
    TO application_user
    USING (department = current_setting('app.current_department'));

-- Create policy for admin access
CREATE POLICY admin_full_access ON sensitive_data
    FOR ALL
    TO admin_user
    USING (true);

-- Audit logging for data access
CREATE OR REPLACE FUNCTION audit_sensitive_access()
RETURNS TRIGGER AS $$
BEGIN
    INSERT INTO audit_log (table_name, operation, user_id, timestamp, row_data)
    VALUES (
        TG_TABLE_NAME,
        TG_OP,
        current_setting('app.current_user_id'),
        NOW(),
        row_to_json(NEW)
    );
    RETURN COALESCE(NEW, OLD);
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;

-- Create audit trigger
CREATE TRIGGER sensitive_data_audit
    AFTER INSERT OR UPDATE    sensitive_data
         audit_sensitive_access();

Related Skills

• moai-domain-backend: Microservices database patterns
• moai-domain-testing: Database testing strategies
• moai-domain-ml-ops: ML model database integration
• moai-domain-devops: Database infrastructure as code

Quick Start Checklist

• Select appropriate database engine (PostgreSQL 17/MySQL 8.4/MongoDB 8.0)
• Configure connection pooling (PgBouncer/ProxySQL)
• Implement query monitoring and optimization
• Setup caching strategy with Redis 7.4
• Configure database backups and replication
• Implement security measures and RLS
• Setup monitoring and alerting
• Create disaster recovery procedures

Performance Optimization Tips

1. Connection Pooling: Always use connection pools with appropriate sizing
2. Query Optimization: Use EXPLAIN ANALYZE for slow query analysis
3. Index Strategy: Create indexes based on actual query patterns
4. Caching Layers: Implement Redis caching for frequently accessed data
5. Monitoring: Set up comprehensive monitoring for all database metrics
6. Security: Use row-level security and audit logging for sensitive data
7. Backups: Implement automated backup with point-in-time recovery
8. Replication: Use logical replication for high availability

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Enterprise Database Architecture - Build scalable, secure, and high-performance database systems with modern best practices and comprehensive automation.