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Dynamic Duo of Inheritance vs. Delegation to Ignite Code Efficiency: 3 Main Factors

object-oriented programming concepts: inheritance vs. delegation

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When it comes to designing and implementing object-oriented programming, developers often face a crucial decision: whether to use inheritance vs. delegation.
This article aims to explore the concepts of inheritance vs. delegation, compare their benefits, and help you make an informed decision on choosing the right path for optimal code efficiency.

Understanding Inheritance

Inheritance is a fundamental concept in object-oriented programming (OOP) where a class inherits properties and behaviors from another class, forming an “is-a” relationship.

How Inheritance Works

Inheritance works by creating a hierarchy of classes. You have a base class that defines common properties and behaviors, and then you can create specialized classes that inherit from the base class.
When a class inherits from another, it gains access to the inherited members and can override or extend them to add their own unique features on top of what the base class provides


let’s say you have a “Robot” class with basic abilities like moving and talking. You can then create a “CleaningRobot” class that inherits from the “Robot” class.
The “CleaningRobot” can do everything a regular robot can, but it also has additional abilities specifically for cleaning tasks.
To do that, we make the “CleaningRobot” class inherit from the “Robot” class. That means the “CleaningRobot” gets all the abilities of a regular robot, like moving and talking. But it also has extra abilities just for cleaning.
Here’s an example in code:
					class Robot:
    def move(self):
        print("The robot is moving.")

    def talk(self):
        print("The robot is talking.")

class CleaningRobot(Robot):
    def clean(self):
        print("The cleaning robot is cleaning.")

# Create an instance of the CleaningRobot class
cleaning_robot = CleaningRobot()

# The cleaning robot can move, talk, and clean
In this example, the “CleaningRobot” class inherits from the “Robot” class. So, when we create a cleaning robot object, it can move, talk, and clean.
Inheritance helps us reuse code and build specialized classes on top of existing ones. It’s like having a family where each member inherits traits from their parents but can also have their own unique qualities.

Types of Inheritance

There are several types of inheritance, including single inheritance, multiple inheritance, and hierarchical inheritance.
Single inheritance involves a class inheriting from a single base class. Single inheritance is like having a child who inherits qualities from a single parent.
Imagine we have a class called “Animal” that represents generic animal characteristics. Now, we can create a specific animal class, like “Cat,” that inherits from the “Animal” class. The “Cat” class gets all the animal traits and can add its own unique features.
					# Single Inheritance
class Animal:
    def breathe(self):
        print("The animal is breathing.")

class Cat(Animal):
    def meow(self):
        print("The cat says meow.")

# Create a cat object and call its methods
cat = Cat()
Multiple inheritance allows a class to inherit from multiple base classes. It’s like a child having traits from both their mom and dad.
We could have a class called “Flying” that represents things that can fly, and another class called “Swimming” for things that can swim. Now, we can create a class called “FlyingFish” that inherits from both “Flying” and “Swimming” classes.
The “FlyingFish” class gets traits from both classes and can do both flying and swimming.
					# Multiple Inheritance
class Flying:
    def fly(self):
        print("Flying high in the sky.")

class Swimming:
    def swim(self):
        print("Swimming gracefully in the water.")

class FlyingFish(Flying, Swimming):

# Create a flying fish object and call its methods
flying_fish = FlyingFish()
Hierarchical inheritance is when multiple classes inherit from a single base class. It’s like having siblings who share common traits inherited from their parent.
Suppose we have a class called “Vehicle” that has common properties (wheels, speed) and behaviors (move) for any type of vehicle.
Now, let’s think about different types of vehicles, like cars, bicycles, and motorcycles. They all have something in common with each other since they are vehicles, right?
So, instead of rewriting the same characteristics in each class, we can make them inherit from the “Vehicle” class.
When we do this, it’s like saying, “Hey, cars, bicycles, and motorcycles, you all share these common traits from the ‘Vehicle’ class. So, you automatically get all these features without needing to define them again.”
Now, each of these classes can add their own special features on top of the ones they inherited.
For example, a car might have features like air conditioning and a radio, while a bicycle might have features like pedals and a bell.
Here’s a simple example in Python to demonstrate hierarchical inheritance:
					# Base class - Vehicle
class Vehicle:
    def __init__(self, wheels, speed):
        self.wheels = wheels
        self.speed = speed

    def move(self):
        print("The vehicle is moving.")

# Derived classes - Car, Bicycle, Motorcycle
class Car(Vehicle):
    def __init__(self, speed):
        super().__init__(4, speed)  # Cars have 4 wheels
        self.air_conditioning = True
        self.radio = True

    def honk(self):
        print("Beep beep!")

class Bicycle(Vehicle):
    def __init__(self, speed):
        super().__init__(2, speed)  # Bicycles have 2 wheels
        self.bell = True

    def ring_bell(self):
        print("Ring ring!")

class Motorcycle(Vehicle):
    def __init__(self, speed):
        super().__init__(2, speed)  # Motorcycles have 2 wheels
        self.helmet = True

    def rev_engine(self):
        print("Vroom vroom!")

# Let's create instances of these classes and see how they work
car = Car(100)
bicycle = Bicycle(20)
motorcycle = Motorcycle(80)

car.move()          # Output: The vehicle is moving.
bicycle.move()      # Output: The vehicle is moving.
motorcycle.move()   # Output: The vehicle is moving.

car.honk()          # Output: Beep beep!
bicycle.ring_bell() # Output: Ring ring!
motorcycle.rev_engine()  # Output: Vroom vroom!
This way, we keep our code organized and avoid repeating ourselves. Each type of vehicle can still have its unique features while sharing the essential characteristics from their parent, the “Vehicle” class.
In this example, the “Car,” “Bicycle,” and “Motorcycle” classes inherit the common properties and behaviors from the “Vehicle” class.

Use Cases and Best Practices

Inheritance is commonly used when creating class hierarchies, modeling relationships, and promoting code reuse.
It is useful when you want to create a family of classes that share common behavior. Think of it as creating different types of robots based on a common blueprint.
It’s great for situations where you have a base functionality that you want to extend and customize for specific needs.

However, be careful not to create deep hierarchies with too many levels of inheritance. It can make your code harder to understand and maintain. Stick to a reasonable level of hierarchy to keep things simple.

Benefits of Inheritance

Inheritance offers various benefits in software development.
It promotes code reuse, reducing redundancy and enhancing modularity, as derived classes can inherit and extend the functionality of their parent classes.
Inheritance also facilitates polymorphism, enabling objects of different classes to be treated uniformly through their common base class.
Moreover, it enhances the maintainability and extensibility of code by providing a clear and structured organization.

However, inheritance can lead to tight coupling, making the codebase less flexible and harder to maintain.

Imagine if you have a robot class that other classes inherit from. If you make changes to the robot class, it might affect all the classes that inherit from it. This can be a problem if you’re not expecting it.

Common Pitfalls

One common pitfall of inheritance is the “fragile base class” problem, where changes in the base class can break derived classes.
Imagine if you make changes to the base robot class, and those changes unintentionally break the specialized classes that inherit from it.
To avoid this, make sure to thoroughly test any changes you make to the base class and consider the impact on the derived classes.
Additionally, it can lead to deep hierarchies, making the code harder to understand and maintain.
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Careful! This pitfall is among the most occurrence that I also mentioned in the top 10 red flags of Object-oriented abusers.

Understanding Delegation

Delegation focuses on composition rather than inheritance. In delegation, objects delegate specific tasks or responsibilities to other objects.
Instead of inheriting behavior, an object holds a reference to another object and forwards method calls to it. This approach establishes a “has-a” relationship between objects

How Delegation Works

Delegation involves objects collaborating by passing responsibilities to one another. Each object is responsible for its assigned tasks, and they work together to accomplish a larger goal.


let’s say you have a “TaskManager” object that needs to handle various tasks. Instead of the “TaskManager” doing everything on its own, it delegates specific tasks to other objects, such as a “CleaningRobot” for cleaning tasks or a “SecurityRobot” for security-related tasks.
By delegating tasks, the “TaskManager” can focus on coordinating and managing the overall workflow, while the specialized objects handle their specific tasks efficiently. This approach establishes a “has-a” relationship, where the “TaskManager” has references to the other objects it delegates tasks to.
Here’s a simplified example of how delegation can be implemented in code:
					class CleaningRobot:
    def clean(self):
        # Code for cleaning tasks

class SecurityRobot:
    def ensure_security(self):
        # Code for security-related tasks

class TaskManager:
    def __init__(self):
        self.cleaning_robot = CleaningRobot()
        self.security_robot = SecurityRobot()

    def manage_tasks(self):
        # Delegating specific tasks to specialized objects
        # Additional task management code

# Usage
task_manager = TaskManager()
In this example, the “TaskManager” holds references to the “CleaningRobot” and “SecurityRobot” objects. When the “manage_tasks” method is called, the “TaskManager” delegates the cleaning task to the “CleaningRobot” and the security-related task to the “SecurityRobot
Delegation allows for flexible and modular design, as different objects can collaborate by sharing responsibilities and leveraging each other’s expertise. It promotes code reusability and simplifies maintenance since changes made to the delegated objects don’t affect the delegating object’s behavior.
Overall, delegation is a powerful technique that promotes loose coupling and enhances the flexibility and scalability of software systems.

Benefits of Delegation

Delegation offers its own set of benefits in software development.
  • It promotes loose coupling between objects, making the code more flexible and modular.
  • By encapsulating behavior within separate objects, delegation allows for easier maintenance and extensibility.
  • It also supports dynamic behavior modification at runtime, as objects can be swapped or replaced without affecting the overall system.

Use Cases and Best Practices

Delegation is often used when multiple objects need to share responsibilities, and dynamic behavior composition is required. It enables greater flexibility, as objects can change their collaborators at runtime.
It is great when you want to distribute responsibilities and create a modular code structure.
It’s like having a team of robots, each with its own expertise. You can delegate tasks to the robot best suited for the job, making your code more organized and flexible.

However, excessive delegation can lead to a complex web of interactions, increasing code complexity. When there're many objects keep passing tasks back and forth. This can make your code harder to follow and slower to execute. Keep the delegation chain simple and straightforward.

Common Pitfalls

Delegation pitfalls include the potential for a proliferation of small, specialized objects and an increased likelihood of coordination issues between objects.
If you have too many robots passing tasks back and forth, it can become confusing and slow down your code.
Care should be taken to strike the right balance between delegation and encapsulation.

Comparing Inheritance Vs. Delegation

1. Code Reusability

Inheritance excels in code reuse as derived classes inherit the properties and behaviors of their parent classes. However, it can lead to issues when the class hierarchy becomes complex or when multiple inheritance is involved.
Delegation, on the other hand, promotes code reuse through composition, allowing objects to collaborate and share functionality without being constrained by a rigid hierarchy.

2. Flexibility and Modularity

Delegation offers greater flexibility and modularity compared to inheritance. With delegation, objects can dynamically change their behavior by delegating responsibilities to different objects at runtime.
This flexibility enables the creation of highly modular systems, as objects can be composed and combined in various ways to achieve desired functionality.

3. Performance and Efficiency

When considering performance and efficiency, delegation often outperforms inheritance. Inheritance introduces a level of indirection and can lead to larger memory footprints and slower method dispatching.
Delegation, being more lightweight, avoids these overheads and can result in more efficient code execution.
But keep in mind that Inheritance can sometimes lead to unnecessary method calls and memory usage, while delegation can introduce some overhead when passing messages between robots.

Choosing the Right Approach : Inheritance Vs. Delegation

When deciding between inheritance and delegation, various factors should be considered to select the optimal solution.

Considerations for Inheritance

Inheritance is suitable when there is a clear “is-a” relationship between classes and when code reuse is a primary concern. It works well for creating class hierarchies and promoting a structured organization of code.
However, it can become complex and less maintainable in large projects or when multiple inheritance is involved.

Considerations for Delegation

Delegation is a suitable approach when loose coupling, flexibility, and dynamic behavior modification are essential. It shines in scenarios where a “has-a” relationship between objects makes more sense than an “is-a” relationship.
Delegation enables better separation of concerns and allows for easier testing, maintenance, and extensibility.
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In conclusion, choosing between inheritance and delegation requires careful consideration of the specific requirements and objectives of a project.
Inheritance excels in promoting code reuse and structured organization, while delegation offers greater flexibility and modularity.
Both approaches have their strengths and considerations. Ultimately, the right path depends on the specific context and trade-offs involved.

Go Further


Yes, inheritance and delegation can be used together in a project. Developers can leverage inheritance for creating class hierarchies and then use delegation to compose objects and delegate responsibilities dynamically.
Delegation is often more suitable for highly extensible systems as it allows for dynamic behavior modification at runtime without impacting the overall structure.
No, using inheritance does not always guarantee better code efficiency. In some cases, delegation can be more efficient, especially when there are concerns about method dispatching overhead and memory usage.
No, using inheritance does not always guarantee better code efficiency. In some cases, delegation can be more efficient, especially when there are concerns about method dispatching overhead and memory usage.
Many popular programming languages, such as Java, C++, Python, and Ruby, support both inheritance and delegation as fundamental concepts in their object-oriented programming paradigms.
Strike a balance between delegation and encapsulation, ensure clear object responsibilities, and minimize the delegation chain length.
Avoid deep inheritance hierarchies, favor composition over inheritance when appropriate, and design for future extensibility.
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