Legacy System Brittleness

Legacy System Inventory:

CREATE TABLE legacy_system_catalog (
    id SERIAL PRIMARY KEY,
    system_name VARCHAR(255) UNIQUE NOT NULL,
    technology_stack TEXT[],
    deployment_date DATE,
    last_major_update DATE,
    criticality_score INTEGER CHECK (criticality_score BETWEEN 1 AND 10),
    user_count INTEGER,
    peak_load_capacity INTEGER,
    elastic_scaling BOOLEAN DEFAULT false,
    maintenance_cost_annual DECIMAL(12,2),
    expert_count INTEGER,
    modernization_status VARCHAR(50)
);

CREATE TABLE legacy_system_risks (
    id SERIAL PRIMARY KEY,
    system_id INTEGER REFERENCES legacy_system_catalog(id),
    risk_type VARCHAR(100),
    risk_description TEXT,
    likelihood VARCHAR(20) CHECK (likelihood IN ('LOW', 'MEDIUM', 'HIGH', 'CERTAIN')),
    impact VARCHAR(20) CHECK (impact IN ('LOW', 'MEDIUM', 'HIGH', 'CATASTROPHIC')),
    mitigation_plan TEXT,
    mitigation_cost DECIMAL(12,2),
    target_completion DATE
);

Gradual Modernization Strategy:

-- Strangler pattern implementation tracking
CREATE TABLE modernization_progress (
    id SERIAL PRIMARY KEY,
    legacy_system_id INTEGER REFERENCES legacy_system_catalog(id),
    functionality_name VARCHAR(255),
    total_endpoints INTEGER,
    migrated_endpoints INTEGER,
    migration_started DATE,
    expected_completion DATE,
    actual_completion DATE,
    rollback_count INTEGER DEFAULT 0
);

-- Track gradual migration success
CREATE VIEW modernization_dashboard AS
SELECT 
    ls.system_name,
    COUNT(mp.id) as total_functions,
    SUM(mp.migrated_endpoints) as migrated_endpoints,
    SUM(mp.total_endpoints) as total_endpoints,
    ROUND(100.0 * SUM(mp.migrated_endpoints) / NULLIF(SUM(mp.total_endpoints), 0), 2) as percent_complete,
    MAX(mp.expected_completion) as full_migration_date
FROM legacy_system_catalog ls
LEFT JOIN modernization_progress mp ON ls.id = mp.legacy_system_id
GROUP BY ls.system_name
ORDER BY percent_complete DESC;

Load Testing and Capacity Planning:

CREATE TABLE load_test_results (
    id SERIAL PRIMARY KEY,
    system_id INTEGER REFERENCES legacy_system_catalog(id),
    test_date DATE,
    normal_load INTEGER,
    test_load_multiplier DECIMAL(5,2),
    response_time_ms INTEGER,
    error_rate DECIMAL(5,2),
    system_crashed BOOLEAN,
    bottleneck_identified TEXT
);

-- Identify breaking points
CREATE OR REPLACE FUNCTION find_system_breaking_point(p_system_id INTEGER)
RETURNS TABLE(
    load_multiplier DECIMAL,
    status TEXT,
    response_time_ms INTEGER
) AS $
BEGIN
    RETURN QUERY
    SELECT 
        test_load_multiplier,
        CASE 
            WHEN system_crashed THEN 'SYSTEM CRASH'
            WHEN error_rate > 50 THEN 'SEVERE DEGRADATION'
            WHEN error_rate > 10 THEN 'DEGRADED'
            WHEN response_time_ms > 5000 THEN 'SLOW'
            ELSE 'ACCEPTABLE'
        END as status,
        response_time_ms
    FROM load_test_results
    WHERE system_id = p_system_id
    ORDER BY test_date DESC, test_load_multiplier;
END;
$ LANGUAGE plpgsql;

Knowledge Preservation:

CREATE TABLE legacy_knowledge_base (
    id SERIAL PRIMARY KEY,
    system_id INTEGER REFERENCES legacy_system_catalog(id),
    knowledge_type VARCHAR(50) CHECK (knowledge_type IN ('ARCHITECTURE', 'OPERATION', 'TROUBLESHOOTING', 'BUSINESS_RULES')),
    title VARCHAR(255),
    content TEXT,
    created_by VARCHAR(255),
    created_date DATE,
    last_verified DATE,
    verification_status VARCHAR(50)
);

-- Track knowledge gaps
CREATE VIEW knowledge_coverage AS
SELECT 
    ls.system_name,
    COUNT(DISTINCT lkb.knowledge_type) as documented_areas,
    4 as total_areas, -- Four knowledge types
    ARRAY_AGG(DISTINCT lkb.knowledge_type) as documented_types,
    CASE 
        WHEN COUNT(DISTINCT lkb.knowledge_type) < 2 THEN 'CRITICAL: Major gaps'
        WHEN COUNT(DISTINCT lkb.knowledge_type) < 4 THEN 'WARNING: Some gaps'
        ELSE 'GOOD: Well documented'
    END as documentation_status
FROM legacy_system_catalog ls
LEFT JOIN legacy_knowledge_base lkb ON ls.id = lkb.system_id
GROUP BY ls.system_name;

Additional Best Practices:

• Implement API facades over legacy systems
• Create elastic scaling layers in front of fixed-capacity systems
• Regular "chaos day" exercises to test failure modes
• Cross-training programs for legacy technologies
• Automated documentation generation from legacy code