Understanding Parallel Hierarchies
In the realm of software development, one common issue that can arise is the presence of parallel hierarchies. Parallel hierarchies occur when different class hierarchies in an application mirror each other, often leading to redundant code and increased maintenance complexity. This article delves into the concept of parallel hierarchies, explores the problems they pose, and presents strategies to address them, complete with coding examples.
Parallel hierarchies typically arise when different parts of an application evolve independently but end up having similar structures. For instance, consider an application with a domain model hierarchy and a corresponding data transfer object (DTO) hierarchy. If these hierarchies mirror each other too closely, any change in one hierarchy often necessitates a corresponding change in the other, leading to a maintenance burden.
Example of Parallel Hierarchies
Consider the following domain model for a simple e-commerce application:
python
class Product:
def __init__(self, id, name, price):
self.id = id
self.name = name
self.price = pricedef __init__(self, id, product, quantity):
self.id = id
self.product = product
self.quantity = quantity
Now, imagine we have a corresponding DTO hierarchy for transferring data:
python
class ProductDTO:
def __init__(self, id, name, price):
self.id = id
self.name = name
self.price = pricedef __init__(self, id, product_dto, quantity):
self.id = id
self.product_dto = product_dto
self.quantity = quantity
Here, the Product and ProductDTO classes are almost identical, as are the Order and OrderDTO classes. This duplication indicates a parallel hierarchy.
Problems with Parallel Hierarchies
Parallel hierarchies introduce several issues:
- Redundant Code: The same logic may be duplicated across different hierarchies, increasing the codebase size and complexity.
- Maintenance Overhead: Changes in one hierarchy necessitate changes in the other, leading to increased maintenance efforts.
- Inconsistent Behavior: Ensuring consistent behavior across parallel hierarchies can be challenging and error-prone.
Strategies to Address Parallel Hierarchies
To address parallel hierarchies, developers can employ various strategies, such as using design patterns, introducing abstraction layers, or utilizing data mapping libraries.
Strategy 1: Using Design Patterns
Design patterns like the Adapter, Decorator, or Facade can help bridge the gap between parallel hierarchies.
Example: Adapter Pattern
The Adapter pattern allows an interface to be used as another interface. Here’s how it can be applied to our example:
python
class ProductAdapter:
def __init__(self, product):
self.id = product.id
self.name = product.name
self.price = product.pricedef __init__(self, order):
self.id = order.id
self.product_dto = ProductAdapter(order.product)
self.quantity = order.quantity
By using the Adapter pattern, we create a bridge between the domain model and the DTO, reducing redundancy and easing maintenance.
Strategy 2: Introducing Abstraction Layers
Introducing an abstraction layer can help separate concerns and reduce duplication.
Example: Abstract Base Class
We can create abstract base classes for common properties and behaviors:
python
class AbstractProduct:
def __init__(self, id, name, price):
self.id = id
self.name = name
self.price = pricepass
pass
def __init__(self, id, product, quantity):
self.id = id
self.product = product
self.quantity = quantity
pass
pass
By introducing abstract base classes, we can centralize common properties and behaviors, reducing duplication and improving maintainability.
Strategy 3: Utilizing Data Mapping Libraries
Data mapping libraries like AutoMapper (in .NET) or similar solutions in other languages can automate the process of converting between different object types.
Example: Using Marshmallow for Data Mapping in Python
In Python, the Marshmallow library can be used to serialize and deserialize data between objects:
python
from marshmallow import Schema, fields, post_load
class ProductSchema(Schema):
id = fields.Int()
name = fields.Str()
price = fields.Float()
def make_product(self, data, **kwargs):
return Product(**data)
class OrderSchema(Schema):
id = fields.Int()
product = fields.Nested(ProductSchema)
quantity = fields.Int()
def make_order(self, data, **kwargs):
return Order(**data)
# Example Usage
product_schema = ProductSchema()
order_schema = OrderSchema()
product_data = {“id”: 1, “name”: “Laptop”, “price”: 1200.00}
order_data = {“id”: 1, “product”: product_data, “quantity”: 2}
product = product_schema.load(product_data)
order = order_schema.load(order_data)
print(product.__dict__)
print(order.__dict__)
Using Marshmallow, we can automate the conversion between domain models and DTOs, reducing the need for parallel hierarchies.
Conclusion
Addressing parallel hierarchies in code is crucial for maintaining a clean, maintainable, and scalable codebase. Parallel hierarchies introduce redundancy, increase maintenance efforts, and can lead to inconsistent behavior across different parts of an application. By leveraging design patterns, introducing abstraction layers, and utilizing data mapping libraries, developers can effectively manage and mitigate the issues associated with parallel hierarchies.
The Adapter pattern provides a way to bridge different interfaces, reducing duplication and easing maintenance. Abstract base classes centralize common properties and behaviors, promoting code reuse and reducing redundancy. Data mapping libraries automate the conversion between different object types, streamlining the process of data transfer and reducing the need for parallel hierarchies.
By adopting these strategies, developers can ensure that their code remains clean, maintainable, and adaptable to changing requirements. Addressing parallel hierarchies is not just about reducing code duplication; it is about fostering a more organized and efficient development process, ultimately leading to higher quality software.