# 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**:
  ```python
  # 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**:
  ```python
  # 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
```python
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
```python
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.
