Database change management

Database change management ensures safe and reliable database modifications across development, testing, and production environments. GitScrum provides comprehensive tracking for database migrations, schema changes, and data transformations, enabling teams to maintain data integrity while supporting agile development practices.

Database change management fundamentals

Database change management is critical for maintaining data integrity and application stability during software evolution. Poor database practices can lead to data loss, application downtime, and difficult rollbacks.

Key challenges in database changes

• Schema evolution: Tables, columns, and relationships change over time
• Data migration: Existing data must be transformed or preserved
• Downtime minimization: Changes should not disrupt production systems
• Rollback capability: Ability to revert changes safely
• Multi-environment synchronization: Development, staging, and production consistency

Database change workflow

Plan Changes ──► Write Migrations ──► Test Migrations ──► Deploy to Staging ──► Deploy to Production
      │                │                    │                    │                    │
      ▼                ▼                    ▼                    ▼                    ▼
  Schema Design    Versioned Scripts     Unit & Integration   Data Validation     Rollback Plan
  & Requirements                        Testing            & Performance Test

Planning phase

Requirements analysis:

• Understand business requirements driving the change
• Assess impact on existing data and applications
• Identify dependencies and constraints
• Plan for backward compatibility

Risk assessment:

• Evaluate potential data loss scenarios
• Consider performance implications
• Plan for extended execution times
• Identify monitoring requirements

Development phase

Migration script development:

• Use declarative migration frameworks (Flyway, Liquibase)
• Write idempotent scripts that can run multiple times
• Include both up and down migrations
• Add proper error handling and logging

Data transformation logic:

• Handle complex data transformations
• Preserve referential integrity
• Plan for large dataset processing
• Implement batch processing for performance

Migration strategies

Forward-only migrations

Simple, linear progression of changes without rollback capability. Suitable for:

• New feature deployments
• Non-critical data changes
• Environments where rollback is not required

Up/down migrations

Full migration scripts with both forward and rollback capabilities:

-- Up migration
ALTER TABLE users ADD COLUMN email_verified BOOLEAN DEFAULT FALSE;

-- Down migration
ALTER TABLE users DROP COLUMN email_verified;

State-based migrations

Define desired database state, let tools handle transition logic:

• Declarative approach reduces manual scripting
• Tools like Terraform or Liquibase can manage state
• Automatic generation of migration scripts
• Better for complex schema changes

Branching migrations

Handle different deployment paths for feature branches:

• Separate migration paths for different features
• Merge conflict resolution for database changes
• Continuous integration testing across branches
• Safe feature flag implementations

Database change safety measures

Backup ──► Test Migration ──► Gradual Rollout ──► Monitor Performance ──► Verify Data Integrity
    │              │                │                    │                    │
    ▼              ▼                ▼                    ▼                    ▼
  Automated     Staging Environment  Feature Flags       Query Performance   Data Consistency
  Backups       & Data Copy         for Large Changes    & Resource Usage    Checks

Testing strategies

Unit testing for migrations:

• Test migration scripts in isolation
• Verify schema changes are correct
• Validate data transformation logic
• Ensure rollback scripts work properly

Integration testing:

• Test with application code changes
• Verify API contracts remain intact
• Performance testing with realistic data volumes
• End-to-end testing with complete workflows

Deployment patterns

Blue-green deployments:

• Create duplicate production environment
• Deploy and test changes in green environment
• Switch traffic when confident
• Keep blue environment as rollback option

Canary releases:

• Deploy to small percentage of users first
• Monitor metrics and error rates
• Gradually increase traffic if successful
• Quick rollback if issues detected

Feature flags:

• Deploy code changes behind feature flags
• Enable database changes gradually
• Test in production with real traffic
• Rollback by disabling flags if needed

Database versioning and branching

Version control for database changes

main branch ──► feature/database-refactor ──► staging ──► production
     │                    │                        │            │
     ▼                    ▼                        ▼            ▼
  Base Schema      Migration Scripts         Tested Changes   Live Database
  Definition       for New Feature           in Staging      with New Schema

Handling database branches

Development branches:

• Each feature branch can have its own migrations
• Test migrations in isolated databases
• Merge migrations when feature branches merge
• Resolve conflicts in migration scripts

Release branches:

• Tag database versions with releases
• Maintain migration history per release
• Support multiple versions in production
• Plan upgrade paths between versions

Monitoring and observability

Database change monitoring

Performance monitoring:

• Query execution times before and after changes
• Database connection pool utilization
• Memory and CPU usage patterns
• Slow query identification and optimization

Data integrity checks:

• Row count verification after migrations
• Referential integrity validation
• Data consistency across related tables
• Business rule compliance checking

Alerting and incident response

Automated alerts:

• Migration execution time thresholds
• Error rates during deployment windows
• Performance degradation detection
• Data inconsistency identification

Incident response plan:

• Pre-defined rollback procedures
• Communication protocols for stakeholders
• Backup restoration processes
• Post-mortem analysis requirements

Database change management tools

Migration frameworks

Flyway:

• Versioned SQL migrations
• Automatic execution order
• Clean and simple approach
• Good for SQL-centric teams

Liquibase:

• XML, YAML, or SQL migrations
• Change sets with preconditions
• Database-agnostic approach
• Enterprise features and support

Alembic (Python):

• Python-based migrations
• Integration with SQLAlchemy
• Good for Python-centric applications
• Flexible migration scripting

Database version control

Git for schema files:

• Version control for database schema
• Code review for schema changes
• Audit trail of all modifications
• Branch-based development support

Database CI/CD tools:

• Automated testing of schema changes
• Deployment pipeline integration
• Rollback automation
• Multi-environment support

Common database change patterns

Adding new columns

-- Safe column addition
ALTER TABLE users ADD COLUMN last_login_at TIMESTAMP NULL;
-- Add index separately to avoid table locks
CREATE INDEX idx_users_last_login ON users(last_login_at);

Data type changes

-- Safe data type migration
ALTER TABLE products ADD COLUMN price_decimal DECIMAL(10,2) NULL;
UPDATE products SET price_decimal = CAST(price AS DECIMAL(10,2));
ALTER TABLE products DROP COLUMN price;
ALTER TABLE products RENAME COLUMN price_decimal TO price;

Table restructuring

-- Create new table structure
CREATE TABLE users_new (
    id BIGINT PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP NOT NULL
);

-- Migrate data
INSERT INTO users_new (id, email, created_at)
SELECT id, email, created_at FROM users;

-- Rename tables
ALTER TABLE users RENAME TO users_old;
ALTER TABLE users_new RENAME TO users;

Database change management in agile teams

Sprint planning considerations

Database story sizing:

• Estimate complexity of schema changes
• Consider data migration effort
• Plan for testing and rollback time
• Include database experts in planning

Dependency management:

• Identify database changes needed for features
• Sequence changes to avoid conflicts
• Plan for cross-team coordination
• Consider shared database impacts

Continuous integration practices

Automated database testing:

• Schema validation in CI pipelines
• Migration testing on every commit
• Performance regression testing
• Data integrity validation

Database code reviews:

• Review migration scripts for correctness
• Validate rollback procedures
• Check for performance implications
• Ensure proper error handling

GitScrum integration for database changes

Task tracking for database work

Database task types:

• Schema change implementation
• Data migration scripting
• Performance optimization
• Backup and recovery testing
• Documentation updates

Progress tracking:

• Migration development status
• Testing completion
• Deployment readiness
• Rollback procedure validation

Risk management

Technical debt tracking:

• Document database complexity issues
• Track performance bottlenecks
• Monitor data quality problems
• Plan refactoring efforts

Risk mitigation:

• Create contingency plans for deployment failures
• Define rollback procedures for each change
• Establish monitoring thresholds
• Plan communication during deployments

Best practices summary

Do's

• ✅ Version control all database changes
• ✅ Test migrations thoroughly before production
• ✅ Have rollback scripts for every change
• ✅ Monitor performance during and after deployments
• ✅ Communicate changes to all stakeholders
• ✅ Use feature flags for risky changes
• ✅ Backup data before any schema changes

Don'ts

• ❌ Make direct changes to production databases
• ❌ Skip testing migrations on realistic data
• ❌ Deploy during peak business hours
• ❌ Forget to update application code for schema changes
• ❌ Ignore performance implications of changes
• ❌ Deploy without monitoring and alerting
• ❌ Lack proper documentation and runbooks

Related Solutions

• Database migration best practices
• Database migration management
• Database development project management
• Infrastructure as code practices
• Continuous delivery practices