Advanced Schema Evolution Recipe

This recipe demonstrates advanced schema evolution patterns for complex data structures in Delta Lake and Apache Iceberg tables.

Overview

Schema evolution is crucial for maintaining data lakes as business requirements change. This example shows how to:

  • Add/remove columns with data migration
  • Rename columns while maintaining compatibility
  • Change data types safely
  • Restructure nested data
  • Maintain backward compatibility for queries

Key Features Demonstrated

1. Complex Schema Management

  • Nested structures with arrays and maps
  • Multi-level nesting
  • Complex data type handling

2. Evolution Operations

  • Add Columns: With default values and constraints
  • Drop Columns: With safety checks and dependency analysis
  • Rename Columns: With data migration and aliasing
  • Type Changes: Safe type conversions with validation
  • Nested Restructuring: Flatten or create nested structures

3. Safety & Validation

  • Pre-evolution validation
  • Impact assessment
  • Backward compatibility checks
  • Data integrity validation

Running the Example

# Install dependencies
pip install -r requirements.txt

# Run the evolution demo
python solution.py

Expected Output

The script will:

  1. Create a complex initial schema with nested customer data
  2. Apply multiple evolution operations
  3. Show schema changes at each step
  4. Demonstrate backward compatibility
  5. Display evolution history

Key Classes

SchemaEvolutionManager

Main class handling all evolution operations:

  • evolve_schema(): Execute evolution based on specification
  • validate_evolution(): Pre-validate operations
  • get_evolution_history(): Track all changes

Evolution Operations

Add Columns

{
    "operation": "add_columns",
    "columns": [
        {"name": "new_field", "type": "STRING", "default": "'default_value'"}
    ]
}

Rename Columns

{
    "operation": "rename_columns",
    "renames": {"old_name": "new_name"}
}

Type Changes

{
    "operation": "change_data_types",
    "type_changes": [
        {
            "column": "field_name",
            "new_type": "DOUBLE",
            "conversion_func": "CAST(field_name AS DOUBLE)"
        }
    ]
}

Production Considerations

Safety Measures

  • Always backup before evolution
  • Test on sample data first
  • Validate downstream impacts
  • Use transactions for atomic changes

Performance Impact

  • Some operations require full table rewrites
  • Plan for downtime or use blue-green deployments
  • Consider partitioning strategies

Compatibility

  • Maintain query compatibility across versions
  • Use views for complex migrations
  • Document breaking changes

Troubleshooting

Common Issues

  1. Column Not Found: Check column name spelling and case
  2. Type Conversion Errors: Validate data before conversion
  3. Nested Structure Issues: Ensure proper field paths
  4. Permission Errors: Verify write access to table location

Recovery

  • Use time travel to revert changes
  • Restore from backups if needed
  • Check evolution history for debugging

Next Steps

  • Explore conditional evolution based on data patterns
  • Implement automated evolution pipelines
  • Add support for custom transformation functions
  • Integrate with schema registries