Pydantic to Django Storage Approaches

This document compares two different approaches for storing Pydantic models in Django: Dynamic Field Conversion vs Direct Serialization.

Overview of Approaches

The library provides two distinct mechanisms for storing Pydantic models in Django:

1. Dynamic Field Conversion (Pydantic2DjangoBaseClass)
2. Direct Serialization (Pydantic2DjangoStorePydanticObject)

Core Approach Differences

Dynamic Field Conversion

• Field Mapping: Creates individual Django model fields for each Pydantic field
• Database Schema: Maintains a structured database schema matching the Pydantic model
• Relationship Handling: Proper foreign key relationships in the database
• Query Support: Full Django ORM query capabilities on individual fields

Direct Serialization

• JSON Storage: Stores entire Pydantic object as JSON in a single field
• Schema Flexibility: No need to modify database schema for model changes
• Simplicity: Simpler codebase with less conversion logic
• Document Style: Better suited for document-style data storage

Technical Implementation Differences

Storage Structure

• Dynamic Field Conversion:
• Each Pydantic field becomes a database column
• Proper database types for each field
• Support for indexes and constraints

• Native database relationships

• Direct Serialization:

• Single JSON field contains all data
• No individual field columns
• No direct database relationships
• Flexible schema evolution

Query Capabilities

• Dynamic Field Conversion:

  # Can query on specific fields
  MyModel.objects.filter(specific_field="value")
  MyModel.objects.exclude(number_field__gt=100)
  MyModel.objects.values('specific_field')
  

• Direct Serialization:

  # Must query on JSON field
  MyModel.objects.filter(data__contains={"field": "value"})
  # Must load entire object to access specific fields
  instance = MyModel.objects.get(id=1)
  pydantic_obj = instance.to_pydantic()
  

Advantages and Disadvantages

Dynamic Field Conversion

Advantages

• Better query performance on specific fields
• Proper database-level relationships
• Full Django ORM capabilities
• Database-level constraints and validation
• Better support for complex queries and joins
• Field-level indexing
• Efficient partial data retrieval

Disadvantages

• More complex implementation
• Requires database migrations for model changes
• More code to maintain
• Higher initial development overhead
• More complex relationship handling

Direct Serialization

Advantages

• Simpler implementation
• No database migrations needed for model changes
• Easier to maintain
• Better for rapidly evolving schemas
• Simpler relationship handling
• More flexible data structure
• Faster development iteration

Disadvantages

• Limited query capabilities
• Must load entire object to access fields
• No database-level relationships
• Less efficient for partial data access
• Limited database-level validation
• No field-level indexing

Use Case Recommendations

Use Dynamic Field Conversion When:

• You need efficient querying on specific fields
• You have complex relationships that benefit from proper foreign keys
• You need database-level constraints
• Performance of field-specific queries is important
• You're integrating heavily with other Django models
• You need aggregation and complex filtering
• Your schema is relatively stable

Use Direct Serialization When:

• Your models change frequently
• You don't need to query specific fields often
• You're dealing with document-style data
• You want simpler code maintenance
• You don't need database-level relationships
• You prioritize development speed over query performance
• You need maximum schema flexibility

Code Examples

Dynamic Field Conversion

from pydantic import BaseModel
from pydantic2django import Pydantic2DjangoBaseClass

class MyPydanticModel(BaseModel):
    name: str
    age: int
    email: str

class MyDjangoModel(Pydantic2DjangoBaseClass[MyPydanticModel]):
    class Meta:
        app_label = 'myapp'

# Efficient field-specific queries
young_users = MyDjangoModel.objects.filter(age__lt=25)
email_list = MyDjangoModel.objects.values_list('email', flat=True)

Direct Serialization

from pydantic2django import Pydantic2DjangoStorePydanticObject

class MyDjangoModel(Pydantic2DjangoStorePydanticObject):
    class Meta:
        app_label = 'myapp'

# Store entire object
instance = MyDjangoModel.from_pydantic(pydantic_obj)
instance.save()

# Must load entire object to access fields
loaded = MyDjangoModel.objects.get(id=1)
pydantic_obj = loaded.to_pydantic()

Performance Implications

Query Performance

• Dynamic Field Conversion: Better performance for field-specific queries
• Direct Serialization: Better performance for whole-object operations

Storage Efficiency

• Dynamic Field Conversion: More efficient for partial data access
• Direct Serialization: More efficient for whole-object storage

Memory Usage

• Dynamic Field Conversion: More efficient when accessing specific fields
• Direct Serialization: Must load entire object into memory

Conclusion

The choice between these approaches depends on your specific requirements:

• Choose Dynamic Field Conversion if you need:
• Efficient field-specific queries
• Database-level relationships
• Complex filtering and aggregation

• Integration with Django's ORM

• Choose Direct Serialization if you need:

• Schema flexibility
• Simpler maintenance
• Faster development iteration
• Document-style storage

In practice, you might use both approaches in different parts of your application based on specific needs. The library's design allows for this flexibility by providing both implementations.