intermediate⏱50 minutesLección 6 de 8

Introduction to Design Patterns

Understand what design patterns are, the Gang of Four classification, and when and how to use patterns effectively

Introduction to Design Patterns

Design patterns are reusable solutions to common problems in software design. They are not finished designs that you copy-paste, but templates for solving problems in context. Patterns represent battle-tested approaches that have evolved over decades.

ℹ️Note

The concept of design patterns was popularized by the "Gang of Four" (GoF) — Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides — in their 1994 book Design Patterns: Elements of Reusable Object-Oriented Software.

What Are Design Patterns?

A design pattern describes a problem that occurs repeatedly, the core of the solution, and the consequences of applying it. Each pattern has four essential elements:

Pattern name — a handle for description
Problem — when to apply the pattern
Solution — the elements and their relationships
Consequences — trade-offs and results

python

# Without a pattern: ad-hoc approach to creating objects
def create_database_connection():
    # Every caller creates their own connection
    return Database("localhost", 5432)

# With Singleton pattern: controlled instance management
class DatabaseConnection:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._connection = Database("localhost", 5432)
        return cls._instance

The Gang of Four Classification

The GoF cataloged 23 patterns into three categories based on their purpose:

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Creational Patterns

Creational patterns abstract the instantiation process. They make a system independent of how its objects are created, composed, and represented.

Pattern	Purpose	Python Example
Singleton	Ensure a class has only one instance	Database connection pool
Factory	Create objects without specifying exact class	Payment processor creation
Builder	Construct complex objects step by step	HTML report builder
Prototype	Clone existing objects	Configuration templates
Abstract Factory	Create families of related objects	UI theme factory

Structural Patterns

Structural patterns compose classes and objects into larger structures while keeping them flexible and efficient.

Pattern	Purpose	Python Example
Adapter	Match interfaces of different classes	Legacy API wrapper
Decorator	Add responsibilities to objects dynamically	Logging middleware
Facade	Provide a unified interface to a subsystem	API gateway
Proxy	Control access to another object	Lazy loading
Composite	Treat individual and composite objects uniformly	File system tree

Behavioral Patterns

Behavioral patterns distribute algorithms and responsibilities among objects.

Pattern	Purpose	Python Example
Observer	Notify multiple objects about state changes	Event system
Strategy	Swap algorithms at runtime	Sorting strategy
Command	Encapsulate requests as objects	Undo/redo
Iterator	Traverse collections without exposing internals	Custom iterable
Template	Define skeleton of algorithm with hook methods	Data pipeline

When to Use Design Patterns

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⚠️Warning

Do not overuse patterns. A pattern should solve a problem, not create one. If a simple function suffices, do not add a Strategy pattern. Patterns add complexity — use them only when the complexity pays off.

Pattern Selection Guide

python

# Problem: I need to create different types of objects based on input
# Solution: Factory pattern

def create_payment_processor(method: str) -> PaymentProcessor:
    processors = {
        "credit_card": CreditCardProcessor,
        "paypal": PayPalProcessor,
        "stripe": StripeProcessor,
    }
    processor_class = processors.get(method)
    if not processor_class:
        raise ValueError(f"Unknown payment method: {method}")
    return processor_class()

python

# Problem: I need to add behavior to objects without modifying them
# Solution: Decorator pattern

def log_execution(func):
    def wrapper(*args, **kwargs):
        logger.info(f"Calling {func.__name__}")
        result = func(*args, **kwargs)
        logger.info(f"{func.__name__} returned {result}")
        return result
    return wrapper

@log_execution
def process_payment(amount: float) -> str:
    # Payment logic
    return "success"

Patterns Are Not Recipes

A common misconception is that design patterns are plug-and-play solutions. They are not. Each pattern must be adapted to your specific context.

python

# Anti-pattern: copying GoF example blindly (Java-style Singleton)
class Singleton:
    _instance = None

    @staticmethod
    def get_instance():
        if Singleton._instance is None:
            Singleton._instance = Singleton()
        return Singleton._instance

# Pythonic Singleton: use module-level constant
# database.py
connection = DatabaseConnection("localhost", 5432)

# Use: from database import connection

Benefits of Design Patterns

Benefit	Description
Reusability	Proven solutions you do not have to reinvent
Communication	Shared vocabulary for design discussions
Maintainability	Well-structured code is easier to change
Flexibility	Loosely coupled systems adapt to new requirements
Documentation	Pattern names convey design intent
Best practices	Incorporate decades of industry wisdom

Common Misconceptions About Design Patterns

"Patterns are a silver bullet"

No single pattern solves all problems. Each pattern addresses a specific concern and introduces trade-offs. The Factory pattern adds complexity in exchange for flexibility. The Singleton solves instance control but creates global state.

"You must use patterns everywhere"

Patterns are solutions, not goals. If a simple dictionary lookup replaces a Strategy pattern, use the dictionary. Over-engineering with patterns is a common anti-pattern itself.

"Patterns are only for OOP languages"

While GoF patterns emphasize OOP concepts like inheritance and polymorphism, many patterns translate to functional or multi-paradigm languages. Python, for instance, implements Strategy with first-class functions and Decorator with function wrappers.

"Patterns are outdated"

Some developers claim patterns are no longer relevant due to modern frameworks. In reality, frameworks are built using patterns. Understanding patterns helps you understand frameworks at a deeper level.

Pattern Documentation Template

When documenting a pattern for your team, use this template:

markdown

## Pattern: [Name]

**Intent**: One-sentence description.

**Problem**: When should you use this pattern?

**Solution**: Core structure and participants.

**Python Example**: Minimal working code.

**Trade-offs**: What do you gain and lose?

**Related Patterns**: How does this connect to other patterns?

Example:

python

"""
Pattern: Factory Method

Intent: Define an interface for creating an object, but let subclasses
        decide which class to instantiate.

Problem: A class cannot anticipate the class of objects it must create.
         A class wants its subclasses to specify the objects it creates.

Solution: Define a factory method that returns a product. Subclasses
          override to return different product implementations.

Trade-offs:
  + Eliminates tight coupling to concrete classes
  + Follows Open/Closed Principle
  - Introduces many subclasses
  - Complexity may be unnecessary for simple cases
"""

Real-World Pattern Usage

Web Frameworks

Django and FastAPI use patterns extensively:

Singleton: Django's settings object is effectively a Singleton
Factory: Django REST Framework's serializer classes use Factory
Observer: Django signals (pre_save, post_save) implement Observer
Strategy: Authentication backends in Django use Strategy
Command: Management commands in Django use Command pattern
Decorator: @login_required, @permission_required are Decorators

Standard Library Examples

Python's standard library itself contains pattern implementations:

python

# Iterator pattern: built into every collection
for item in my_list:
    print(item)

# Strategy pattern: key functions for sorting
sorted(data, key=str.lower)

# Decorator pattern: property, classmethod, staticmethod
@property
def full_name(self):
    return f"{self.first} {self.last}"

# Observer pattern: asyncio events
import asyncio
event = asyncio.Event()
# Multiple coroutines can wait for the same event

Benefits of Design Patterns Flow

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Common Anti-Patterns to Avoid

1. Pattern Overuse

python

# Over-engineered: Strategy pattern for a simple if-else
class GreetingStrategy:
    def greet(self, name: str) -> str:
        raise NotImplementedError

class EnglishGreeting(GreetingStrategy):
    def greet(self, name: str) -> str:
        return f"Hello, {name}!"

class SpanishGreeting(GreetingStrategy):
    def greet(self, name: str) -> str:
        return f"¡Hola, {name}!"

def greet(name: str, language: str) -> str:
    strategies = {"en": EnglishGreeting(), "es": SpanishGreeting()}
    return strategies[language].greet(name)

# Simple alternative: just use a dictionary of functions
def greet(name: str, language: str) -> str:
    greetings = {"en": f"Hello, {name}!", "es": f"¡Hola, {name}!"}
    return greetings[language]

2. Singleton Abuse

Using Singleton for everything that "should have only one instance" creates hidden global dependencies.

3. Pattern for Pattern's Sake

Applying patterns just to have patterns in your resume code.

Patterns in Python vs Other Languages

Python's dynamic nature means some patterns are simpler or even built-in:

Pattern	Java Approach	Python Approach
Singleton	Class with static instance	Module-level variable
Iterator	Implement Iterable interface	`__iter__` / `__next__` or generator
Decorator	Annotation framework	First-class decorator functions
Strategy	Interface + implementation classes	First-class functions / dict
Observer	Event listener interfaces	Callbacks / signals

python

# Java-style Iterator
class Range:
    def __init__(self, start, end):
        self.start = start
        self.end = end

    def __iter__(self):
        self.current = self.start
        return self

    def __next__(self):
        if self.current >= self.end:
            raise StopIteration
        value = self.current
        self.current += 1
        return value

# Pythonic generator
def range_generator(start: int, end: int):
    for i in range(start, end):
        yield i

Pattern Relationships

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✅Success

Design patterns are tools, not rules. Learn them to build your vocabulary and recognize common problems, but always choose the simplest solution that works.

Practice Exercises

Pattern identification: Look at your current project and identify at least 2 places where you are already using a design pattern (even unknowingly).
GoF catalog: Research one pattern from each category (Creational, Structural, Behavioral) and write a one-paragraph summary of each.
Pattern vs simple: Take a piece of code that uses a simple if-else chain. Implement it with a Strategy pattern. Compare the two approaches — which is better?
Anti-pattern hunt: Find a place in your codebase where a pattern is overused or misapplied. Simplify it.
Pythonic rewrite: Take a Java-style pattern implementation and rewrite it in a more Pythonic way.
Pattern decision: You need to create a system that can send notifications via email, SMS, and push notifications. Which pattern(s) would you use? Justify.
Singleton audit: Find all Singleton-like patterns in your codebase. Determine which are appropriate and which should be replaced with module-level variables.
Pattern communication: Describe a complex part of your architecture to a colleague using only pattern names. Does this improve understanding?

Progreso75%

Code Formatting & Style

Creational & Structural Patterns

Contenido del Curso

1. What is Clean Code?2. Naming Conventions 3. Functions Done Right 4. Comments & Documentation 5. Code Formatting & Style 6. Introduction to Design Patterns 7. Creational & Structural Patterns 8. Behavioral Patterns & Best Practices