Monolithic Database Architecture

Functional Decomposition Planning:

CREATE TABLE database_decomposition_plan (
    id SERIAL PRIMARY KEY,
    current_database VARCHAR(255),
    target_database VARCHAR(255),
    functional_area VARCHAR(100),
    table_count INTEGER,
    data_size_gb DECIMAL(10,2),
    daily_queries_millions DECIMAL(10,2),
    migration_priority INTEGER,
    estimated_effort_days INTEGER
);

-- Identify candidates for extraction
WITH workload_analysis AS (
    SELECT 
        schemaname,
        CASE 
            WHEN tablename LIKE '%auth%' OR tablename LIKE '%session%' THEN 'AUTHENTICATION'
            WHEN tablename LIKE '%payment%' OR tablename LIKE '%billing%' THEN 'PAYMENTS'
            WHEN tablename LIKE '%inventory%' OR tablename LIKE '%product%' THEN 'CATALOG'
            WHEN tablename LIKE '%order%' OR tablename LIKE '%cart%' THEN 'ORDERS'
            ELSE 'CORE'
        END as functional_area,
        COUNT(*) as table_count,
        SUM(pg_total_relation_size(schemaname||'.'||tablename))::DECIMAL / (1024^3) as size_gb,
        SUM(seq_scan + idx_scan)::DECIMAL / 1000000 as daily_queries_millions
    FROM pg_stat_user_tables
    GROUP BY schemaname, functional_area
)
INSERT INTO database_decomposition_plan 
    (current_database, target_database, functional_area, table_count, data_size_gb, daily_queries_millions)
SELECT 
    current_database(),
    functional_area || '_db',
    functional_area,
    table_count,
    size_gb,
    daily_queries_millions
FROM workload_analysis
WHERE functional_area != 'CORE';

Service Boundary Definition:

CREATE TABLE service_boundaries (
    id SERIAL PRIMARY KEY,
    service_name VARCHAR(255) UNIQUE NOT NULL,
    owned_tables TEXT[],
    read_only_tables TEXT[],
    api_endpoints TEXT[],
    team_owner VARCHAR(255),
    sla_response_time_ms INTEGER,
    sla_availability_percent DECIMAL(5,2)
);

CREATE TABLE cross_service_dependencies (
    id SERIAL PRIMARY KEY,
    source_service VARCHAR(255) REFERENCES service_boundaries(service_name),
    target_service VARCHAR(255) REFERENCES service_boundaries(service_name),
    dependency_type VARCHAR(50) CHECK (dependency_type IN ('SYNC', 'ASYNC', 'EVENT')),
    criticality VARCHAR(20) CHECK (criticality IN ('CRITICAL', 'IMPORTANT', 'NICE_TO_HAVE')),
    average_latency_ms INTEGER,
    calls_per_minute INTEGER
);

-- Identify problematic dependencies
CREATE VIEW high_risk_dependencies AS
SELECT 
    source_service,
    target_service,
    dependency_type,
    criticality,
    average_latency_ms,
    calls_per_minute,
    CASE 
        WHEN dependency_type = 'SYNC' AND criticality = 'CRITICAL' AND calls_per_minute > 1000
            THEN 'CRITICAL: High-volume synchronous critical dependency'
        WHEN dependency_type = 'SYNC' AND average_latency_ms > 100
            THEN 'HIGH: Slow synchronous calls'
        ELSE 'MANAGEABLE'
    END as risk_assessment
FROM cross_service_dependencies
ORDER BY 
    CASE criticality 
        WHEN 'CRITICAL' THEN 1
        WHEN 'IMPORTANT' THEN 2
        ELSE 3
    END,
    calls_per_minute DESC;

Gradual Extraction Strategy:

-- Track extraction progress
CREATE TABLE monolith_extraction_progress (
    id SERIAL PRIMARY KEY,
    extraction_phase VARCHAR(50),
    source_tables TEXT[],
    target_service VARCHAR(255),
    started_date DATE,
    dual_write_enabled BOOLEAN DEFAULT false,
    dual_write_start DATE,
    traffic_percentage_migrated INTEGER DEFAULT 0,
    completed_date DATE,
    rollback_count INTEGER DEFAULT 0
);

-- Monitor extraction health
CREATE OR REPLACE FUNCTION monitor_extraction_health(p_extraction_id INTEGER)
RETURNS TABLE(
    metric_name VARCHAR,
    metric_value DECIMAL,
    status VARCHAR
) AS $
DECLARE
    v_extraction monolith_extraction_progress;
BEGIN
    SELECT * INTO v_extraction FROM monolith_extraction_progress WHERE id = p_extraction_id;
    
    -- Check dual write consistency
    RETURN QUERY
    SELECT 
        'dual_write_consistency'::VARCHAR,
        100.0, -- Would be actual consistency check
        'OK'::VARCHAR;
    
    -- Check performance impact
    RETURN QUERY
    SELECT 
        'monolith_performance_impact'::VARCHAR,
        5.2, -- Percentage degradation
        CASE 
            WHEN 5.2 > 10 THEN 'WARNING'
            ELSE 'OK'
        END::VARCHAR;
    
    -- Check error rates
    RETURN QUERY
    SELECT 
        'extraction_error_rate'::VARCHAR,
        0.01, -- Percentage
        CASE 
            WHEN 0.01 > 1 THEN 'CRITICAL'
            WHEN 0.01 > 0.1 THEN 'WARNING'
            ELSE 'OK'
        END::VARCHAR;
END;
$ LANGUAGE plpgsql;

Database Proxy Layer:

-- Configuration for intelligent routing
CREATE TABLE proxy_routing_rules (
    id SERIAL PRIMARY KEY,
    rule_name VARCHAR(255) UNIQUE NOT NULL,
    query_pattern TEXT,
    table_pattern TEXT,
    target_database VARCHAR(255),
    is_active BOOLEAN DEFAULT true,
    created_date DATE DEFAULT CURRENT_DATE
);

INSERT INTO proxy_routing_rules (rule_name, query_pattern, table_pattern, target_database) VALUES
('auth_queries', NULL, '^(users|sessions|permissions)', 'auth_db'),
('analytics_queries', 'SELECT.*GROUP BY', NULL, 'analytics_db_replica'),
('write_queries', '^(INSERT|UPDATE|DELETE)', NULL, 'primary_db');

Additional Best Practices:

• Implement database connection pooling per service
• Use read replicas for analytical workloads
• Create service-specific database users
• Monitor cross-database transaction patterns
• Establish clear data ownership boundaries