# Data Migration Strategies

## Introduction

Data migration is the process of transferring data between storage systems, formats, or applications. In the context of rhosocial ActiveRecord, data migrations often accompany schema changes or occur when transitioning between different database systems. This document outlines strategies for effectively planning and executing data migrations.

## Types of Data Migrations

### 1. Schema-related Data Migrations

These migrations occur when schema changes require data transformation:

- **Column Renaming**: Moving data from an old column to a new one
- **Data Restructuring**: Changing how data is organized (e.g., normalizing or denormalizing tables)
- **Data Type Conversions**: Converting data from one type to another
- **Default Value Population**: Filling new columns with default or calculated values

### 2. System Migrations

These migrations involve moving data between different systems:

- **Database Platform Migration**: Moving from one database system to another
- **Application Migration**: Transitioning data from one application to another
- **Version Upgrades**: Moving data during major version upgrades

## Migration Planning

### 1. Assessment and Planning

- **Data Inventory**: Catalog all data that needs to be migrated
- **Dependency Mapping**: Identify relationships between data entities
- **Volume Analysis**: Estimate data volumes to plan for performance considerations
- **Validation Strategy**: Define how data will be validated before, during, and after migration

### 2. Risk Management

- **Backup Strategy**: Ensure comprehensive backups before migration
- **Rollback Plan**: Define clear procedures for reverting changes if needed
- **Testing Approach**: Create a testing strategy for the migration process
- **Downtime Planning**: Estimate and communicate any required downtime

## Implementation Techniques

### Using Migration Scripts

rhosocial ActiveRecord's migration framework can handle data migrations along with schema changes:

```python
from rhosocial.activerecord.migration import Migration

class MigrateUserNames(Migration):
    """Split full_name into first_name and last_name."""
    
    def up(self):
        # Add new columns
        self.add_column('user', 'first_name', 'string', length=100, null=True)
        self.add_column('user', 'last_name', 'string', length=100, null=True)
        
        # Migrate data
        self.execute("""
            UPDATE user 
            SET first_name = SUBSTRING_INDEX(full_name, ' ', 1),
                last_name = SUBSTRING_INDEX(full_name, ' ', -1)
            WHERE full_name IS NOT NULL
        """)
        
        # Make columns non-nullable if appropriate
        self.change_column('user', 'first_name', 'string', length=100, null=False)
        self.change_column('user', 'last_name', 'string', length=100, null=False)
        
        # Optionally remove the old column
        self.remove_column('user', 'full_name')
    
    def down(self):
        # Add back the original column
        self.add_column('user', 'full_name', 'string', length=200, null=True)
        
        # Restore data
        self.execute("""
            UPDATE user
            SET full_name = CONCAT(first_name, ' ', last_name)
        """)
        
        # Remove new columns
        self.remove_column('user', 'first_name')
        self.remove_column('user', 'last_name')
    }
```

### Using ActiveRecord Models

For more complex migrations, you can use ActiveRecord models directly:

```python
from rhosocial.activerecord.migration import Migration
from app.models import OldUser, NewUser

class MigrateUserData(Migration):
    """Migrate user data to new structure."""
    
    def up(self):
        # Create schema for new table
        self.create_table('new_user', [
            self.column('id', 'integer', primary_key=True, auto_increment=True),
            self.column('username', 'string', length=64, null=False),
            self.column('email', 'string', length=255, null=False),
            self.column('profile_data', 'json', null=True),
            self.column('created_at', 'datetime'),
            self.column('updated_at', 'datetime')
        ])
        
        # Use models for complex data transformation
        batch_size = 1000
        offset = 0
        
        while True:
            old_users = OldUser.find().limit(batch_size).offset(offset).all()
            if not old_users:
                break
                
            for old_user in old_users:
                new_user = NewUser()
                new_user.username = old_user.username
                new_user.email = old_user.email
                
                # Complex transformation - consolidating profile fields into JSON
                profile_data = {
                    'address': old_user.address,
                    'phone': old_user.phone,
                    'preferences': {
                        'theme': old_user.theme,
                        'notifications': old_user.notifications_enabled
                    }
                }
                new_user.profile_data = profile_data
                
                new_user.created_at = old_user.created_at
                new_user.updated_at = old_user.updated_at
                new_user.save()
                
            offset += batch_size
    
    def down(self):
        self.drop_table('new_user')
```

### Batch Processing

For large datasets, batch processing is essential:

```python
def migrate_large_table(self):
    # Get total count for progress tracking
    total = self.execute("SELECT COUNT(*) FROM large_table")[0][0]
    
    batch_size = 5000
    processed = 0
    
    while processed < total:
        # Process one batch
        self.execute(f"""
            INSERT INTO new_large_table (id, name, transformed_data)
            SELECT id, name, UPPER(data) AS transformed_data
            FROM large_table
            ORDER BY id
            LIMIT {batch_size} OFFSET {processed}
        """)
        
        processed += batch_size
        print(f"Processed {processed}/{total} records")
```

## Performance Optimization

### 1. Indexing Strategies

- **Temporarily Drop Indexes**: Remove non-primary key indexes during bulk data loading
- **Create Indexes After Loading**: Add indexes after data is loaded
- **Optimize Query Indexes**: Ensure queries used in migration have appropriate indexes

### 2. Transaction Management

- **Batch Transactions**: Use transactions around batches rather than individual records
- **Savepoints**: For very large transactions, use savepoints to avoid rollback overhead

### 3. Resource Management

- **Connection Pooling**: Configure appropriate connection pool settings
- **Memory Management**: Monitor and optimize memory usage during migration
- **Parallel Processing**: Consider parallel processing for independent data sets

## Validation and Testing

### 1. Data Validation

- **Pre-migration Validation**: Validate source data before migration
- **Post-migration Validation**: Verify data integrity after migration
- **Reconciliation Reports**: Generate reports comparing source and target data

### 2. Testing Approaches

- **Dry Runs**: Perform migration in a test environment first
- **Subset Testing**: Test with a representative subset of data
- **Performance Testing**: Measure migration performance with production-like volumes

## Handling Special Cases

### 1. Dealing with Legacy Data

- **Data Cleansing**: Clean and normalize data before migration
- **Handling NULL Values**: Define strategies for NULL or missing values
- **Data Type Incompatibilities**: Plan for type conversion edge cases

### 2. Continuous Operation Requirements

- **Zero-Downtime Migration**: Strategies for migrating without service interruption
- **Dual-Write Patterns**: Writing to both old and new systems during transition
- **Incremental Migration**: Migrating data in smaller, manageable increments

## Conclusion

Effective data migration requires careful planning, appropriate techniques, and thorough validation. By following the strategies outlined in this document, you can minimize risks and ensure successful data transitions as your application evolves.

Remember that each migration scenario is unique, and you should adapt these strategies to your specific requirements, data volumes, and system constraints.