Database Migration Testing: A Comprehensive Guide

class MigrationTestingFramework:
    def __init__(self):
        self.testing_levels = {
            'unit_tests': {
                'scope': 'Individual data elements and transformation rules',
                'automation_level': 'high',
                'execution_frequency': 'continuous',
                'coverage_target': 90
            },
            'integration_tests': {
                'scope': 'Cross-table relationships and referential integrity',
                'automation_level': 'high',
                'execution_frequency': 'daily',
                'coverage_target': 80
            },
            'system_tests': {
                'scope': 'End-to-end application functionality',
                'automation_level': 'medium',
                'execution_frequency': 'weekly',
                'coverage_target': 70
            },
            'acceptance_tests': {
                'scope': 'Business process validation',
                'automation_level': 'low',
                'execution_frequency': 'milestone',
                'coverage_target': 60
            }
        }
        
        self.test_categories = {
            'data_validation': [
                'row_count_validation',
                'data_type_validation',
                'constraint_validation',
                'referential_integrity_validation',
                'data_accuracy_validation'
            ],
            'schema_validation': [
                'table_structure_validation',
                'index_validation',
                'constraint_validation',
                'trigger_validation',
                'stored_procedure_validation'
            ],
            'performance_validation': [
                'query_performance_validation',
                'throughput_validation',
                'concurrency_validation',
                'resource_utilization_validation'
            ],
            'functional_validation': [
                'application_functionality_validation',
                'business_logic_validation',
                'user_interface_validation',
                'integration_validation'
            ]
        }
    
    def create_test_plan(self, migration_scope, business_requirements):
        """Create comprehensive test plan for database migration"""
        test_plan = {
            'test_strategy': self.define_test_strategy(migration_scope),
            'test_scenarios': self.generate_test_scenarios(migration_scope),
            'test_environment_requirements': self.define_environment_requirements(),
            'test_schedule': self.create_test_schedule(migration_scope),
            'success_criteria': self.define_success_criteria(business_requirements),
            'risk_mitigation': self.identify_test_risks(migration_scope)
        }
        return test_plan

    def define_test_strategy(self, migration_scope):
        """Define testing strategy based on migration characteristics"""
        # Determine testing approach based on migration complexity
        complexity_score = self.calculate_migration_complexity(migration_scope)
        
        if complexity_score > 80:
            strategy_type = 'comprehensive'
        elif complexity_score > 60:
            strategy_type = 'standard'
        else:
            strategy_type = 'simplified'
        
        strategies = {
            'comprehensive': {
                'test_phases': ['unit', 'integration', 'system', 'performance', 'acceptance'],
                'automation_target': 85,
                'test_data_volume': 'production_scale',
                'environment_count': 4,
                'validation_depth': 'deep'
            },
            'standard': {
                'test_phases': ['integration', 'system', 'performance', 'acceptance'],
                'automation_target': 70,
                'test_data_volume': 'representative_sample',
                'environment_count': 3,
                'validation_depth': 'standard'
            },
            'simplified': {
                'test_phases': ['system', 'acceptance'],
                'automation_target': 50,
                'test_data_volume': 'minimal_sample',
                'environment_count': 2,
                'validation_depth': 'basic'
            }
        }
        
        return strategies[strategy_type]

class TestDataManager:
    def __init__(self):
        self.data_generation_strategies = {
            'production_copy': {
                'description': 'Copy of production data with sensitive data masked',
                'pros': ['Realistic data distribution', 'Complex relationships preserved'],
                'cons': ['Privacy concerns', 'Large storage requirements'],
                'use_cases': ['Performance testing', 'Complex business logic validation']
            },
            'synthetic_data': {
                'description': 'Artificially generated data matching production patterns',
                'pros': ['No privacy issues', 'Scalable generation', 'Controlled scenarios'],
                'cons': ['May miss edge cases', 'Complex to generate realistic data'],
                'use_cases': ['Development testing', 'Automated regression testing']
            },
            'hybrid_approach': {
                'description': 'Combination of production and synthetic data',
                'pros': ['Balanced approach', 'Covers most scenarios'],
                'cons': ['More complex to manage'],
                'use_cases': ['Comprehensive migration testing']
            }
        }
    
    def mask_sensitive_data(self, source_data, masking_rules):
        """Apply data masking rules to sensitive information"""
        import hashlib
        import random
        import string
        
        masking_functions = {
            'hash': lambda x: hashlib.sha256(str(x).encode()).hexdigest()[:16],
            'randomize_email': lambda x: f"user{random.randint(1000, 9999)}@example.com",
            'randomize_phone': lambda x: f"+1-555-{random.randint(100, 999)}-{random.randint(1000, 9999)}",
            'anonymize_name': lambda x: ''.join(random.choices(string.ascii_uppercase, k=8)),
            'preserve_format': lambda x: self.preserve_format_randomize(x)
        }
        
        masked_data = source_data.copy()
        for table_name, table_rules in masking_rules.items():
            if table_name in masked_data:
                for column_name, masking_rule in table_rules.items():
                    if column_name in masked_data[table_name]:
                        masking_function = masking_functions.get(masking_rule['type'])
                        if masking_function:
                            masked_data[table_name][column_name] = [
                                masking_function(value) 
                                for value in masked_data[table_name][column_name]
                            ]
        
        return masked_data

WITH source_counts AS (
    SELECT 'customers' as table_name, COUNT(*) as row_count
    FROM source_db.customers
    UNION ALL
    SELECT 'orders' as table_name, COUNT(*) as row_count
    FROM source_db.orders
    UNION ALL
    SELECT 'order_items' as table_name, COUNT(*) as row_count
    FROM source_db.order_items
),
target_counts AS (
    SELECT 'customers' as table_name, COUNT(*) as row_count
    FROM target_db.customers
    UNION ALL
    SELECT 'orders' as table_name, COUNT(*) as row_count
    FROM target_db.orders
    UNION ALL
    SELECT 'order_items' as table_name, COUNT(*) as row_count
    FROM target_db.order_items
)
SELECT 
    s.table_name,
    s.row_count as source_count,
    t.row_count as target_count,
    s.row_count - t.row_count as difference,
    CASE 
        WHEN s.row_count = t.row_count THEN 'PASS' 
        ELSE 'FAIL' 
    END as validation_result
FROM source_counts s
JOIN target_counts t ON s.table_name = t.table_name
ORDER BY s.table_name;

-- Check for orphaned records in order_items
SELECT 
    'order_items_orphaned_orders' as validation_check,
    COUNT(*) as orphaned_count
FROM target_db.order_items oi
LEFT JOIN target_db.orders o ON oi.order_id = o.order_id
WHERE o.order_id IS NULL

UNION ALL

-- Check for orphaned records in orders
SELECT 
    'orders_orphaned_customers' as validation_check,
    COUNT(*) as orphaned_count
FROM target_db.orders o
LEFT JOIN target_db.customers c ON o.customer_id = c.customer_id
WHERE c.customer_id IS NULL;

-- Sample-based data comparison
WITH sample_data AS (
    SELECT customer_id, first_name, last_name, email, phone, created_at
    FROM target_db.customers
    WHERE customer_id IN (
        SELECT customer_id FROM target_db.customers
        ORDER BY RANDOM()
        LIMIT 100
    )
)
SELECT 
    sd.customer_id,
    sd.first_name as target_first_name,
    sc.first_name as source_first_name,
    CASE WHEN sd.first_name = sc.first_name THEN 'MATCH' ELSE 'MISMATCH' END as name_status,
    sd.email as target_email,
    sc.email as source_email,
    CASE WHEN sd.email = sc.email THEN 'MATCH' ELSE 'MISMATCH' END as email_status
FROM sample_data sd
JOIN source_db.customers sc ON sd.customer_id = sc.customer_id;

class DataValidationEngine:
    def __init__(self, source_connection, target_connection):
        self.source_connection = source_connection
        self.target_connection = target_connection
        self.validation_results = []
    
    def run_comprehensive_validation(self, validation_config):
        """Run comprehensive data validation suite"""
        validation_suite = [
            self.validate_row_counts,
            self.validate_data_types,
            self.validate_constraints,
            self.validate_referential_integrity,
            self.validate_data_accuracy,
            self.validate_aggregates,
            self.validate_null_patterns,
            self.validate_duplicates,
            self.validate_character_encoding
        ]
        
        for validation_function in validation_suite:
            try:
                result = validation_function(validation_config)
                self.validation_results.append(result)
            except Exception as e:
                error_result = {
                    'validation_type': validation_function.__name__,
                    'status': 'ERROR',
                    'error_message': str(e),
                    'timestamp': datetime.utcnow().isoformat()
                }
                self.validation_results.append(error_result)
        
        return self.generate_validation_report()
    
    def validate_data_accuracy(self, config):
        """Validate data accuracy using sample-based comparison"""
        tables_to_validate = config.get('tables', [])
        sample_size = config.get('sample_size', 1000)
        results = []
        
        for table in tables_to_validate:
            table_config = config.get('table_configs', {}).get(table, {})
            columns_to_check = table_config.get('columns', [])
            primary_key = table_config.get('primary_key', 'id')
            
            # Get random sample of records
            sample_ids = self.get_random_sample_ids(
                self.target_connection, table, primary_key, sample_size
            )
            
            accuracy_results = []
            for record_id in sample_ids:
                source_record = self.get_record_by_id(
                    self.source_connection, table, primary_key, record_id
                )
                target_record = self.get_record_by_id(
                    self.target_connection, table, primary_key, record_id
                )
                
                record_comparison = self.compare_records(
                    source_record, target_record, columns_to_check
                )
                accuracy_results.append(record_comparison)
            
            # Calculate accuracy statistics
            total_comparisons = sum(len(r['column_comparisons']) for r in accuracy_results)
            matching_comparisons = sum(
                len([c for c in r['column_comparisons'] if c['match']])
                for r in accuracy_results
            )
            
            accuracy_percentage = (matching_comparisons / total_comparisons) * 100 if total_comparisons > 0 else 0
            
            results.append({
                'table': table,
                'sample_size': len(sample_ids),
                'accuracy_percentage': accuracy_percentage,
                'mismatches': [r for r in accuracy_results if not r['exact_match']],
                'status': 'PASS' if accuracy_percentage >= config.get('accuracy_threshold', 99.9) else 'FAIL'
            })
        
        return {
            'validation_type': 'data_accuracy_validation',
            'results': results
        }