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Design patterns are proven solutions to common problems in software development. If you've ever found yourself writing repetitive object creation code or struggling to manage different types of objects, the factory pattern might be exactly what you need.
In this tutorial, you'll learn what the factory pattern is, why it's useful, and how to implement it in Python. We'll build practical examples that show you when and how to use this pattern in real-world applications.
You can find the code on GitHub.
Prerequisites
Before we start, make sure you have:
Python 3.10 or higher installed
Understanding of Python classes and methods
Familiarity with object-oriented programming (OOP) concepts
Let’s get started!
Table of Contents
What Is the Factory Pattern?
The factory pattern is a creational design pattern that provides an interface for creating objects without specifying their exact classes. Instead of calling a constructor directly, you call a factory method that decides which class to instantiate.
Think of it like ordering food at a restaurant. You don't go into the kitchen and make the food yourself. You tell the waiter what you want, and the kitchen (the factory) creates it for you. You get your meal without worrying about the recipe or cooking process.
The factory pattern is useful when:
You have multiple related classes and need to decide which one to instantiate at runtime
Object creation logic is complex and you want to encapsulate it
You want to make your code more maintainable and testable
A Simple Factory Example
Let's start with a basic example. Say you're building a notification system that can send messages via email, SMS, or push notifications.
Without a factory, you might write code like this everywhere in your application:
# Bad approach - tight coupling
if notification_type == "email":
notifier = EmailNotifier()
elif notification_type == "sms":
notifier = SMSNotifier()
elif notification_type == "push":
notifier = PushNotifier()
This gets messy quickly. Let's use a factory instead:
class EmailNotifier:
def send(self, message):
return f"Sending email: {message}"
class SMSNotifier:
def send(self, message):
return f"Sending SMS: {message}"
class PushNotifier:
def send(self, message):
return f"Sending push notification: {message}"
class NotificationFactory:
@staticmethod
def create_notifier(notifier_type):
if notifier_type == "email":
return EmailNotifier()
elif notifier_type == "sms":
return SMSNotifier()
elif notifier_type == "push":
return PushNotifier()
else:
raise ValueError(f"Unknown notifier type: {notifier_type}")
In this code, we define three notifier classes, each with a send method.
Note: In a real application, these would have different implementations for sending notifications.
The NotificationFactory class has a static method called create_notifier. This is our factory method. It takes a string parameter and returns the appropriate notifier object.
The @staticmethod decorator means we can call this method without creating an instance of the factory. We just use NotificationFactory.create_notifier().
# Using the factory
notifier = NotificationFactory.create_notifier("email")
result = notifier.send("Hello, World!")
Now, whenever we need a notifier, we call the factory instead of instantiating classes directly. This centralizes our object creation logic in one place.
Using a Dictionary for Cleaner Code
The if-elif chain in our factory can get unwieldy as we add more notifier types. Let's refactor using a dictionary:
class NotificationFactory:
notifier_types = {
"email": EmailNotifier,
"sms": SMSNotifier,
"push": PushNotifier
}
@staticmethod
def create_notifier(notifier_type):
notifier_class = NotificationFactory.notifier_types.get(notifier_type)
if notifier_class:
return notifier_class()
else:
raise ValueError(f"Unknown notifier type: {notifier_type}")
This approach is much cleaner. We store a dictionary that maps strings to class objects and not instances. The keys are notifier type names, and the values are the actual class references.
The get method retrieves the class from the dictionary. If the key doesn't exist, it returns None. We then instantiate the class by calling it with parentheses: notifier_class().
# Test with different types
email_notifier = NotificationFactory.create_notifier("email")
sms_notifier = NotificationFactory.create_notifier("sms")
push_notifier = NotificationFactory.create_notifier("push")
This makes adding new notifier types easier. You just add another entry to the dictionary.
Factory Pattern with Parameters
Real-world objects often need configuration. Let's extend our factory to handle notifiers that require initialization parameters.
We'll create a document generator that produces different file formats with custom settings:
class PDFDocument:
def __init__(self, title, author):
self.title = title
self.author = author
self.format = "PDF"
def generate(self):
return f"Generating {self.format}: '{self.title}' by {self.author}"
class WordDocument:
def __init__(self, title, author):
self.title = title
self.author = author
self.format = "DOCX"
def generate(self):
return f"Generating {self.format}: '{self.title}' by {self.author}"
class MarkdownDocument:
def __init__(self, title, author):
self.title = title
self.author = author
self.format = "MD"
def generate(self):
return f"Generating {self.format}: '{self.title}' by {self.author}"
class DocumentFactory:
document_types = {
"pdf": PDFDocument,
"word": WordDocument,
"markdown": MarkdownDocument
}
@staticmethod
def create_document(doc_type, title, author):
document_class = DocumentFactory.document_types.get(doc_type)
if document_class:
return document_class(title, author)
else:
raise ValueError(f"Unknown document type: {doc_type}")
The key difference here is that our factory method now accepts additional parameters.
The create_document method takes doc_type, title, and author as arguments. When we instantiate the class, we pass the title and author to the create_document constructor: document_class(title, author).
# Create different documents with parameters
pdf = DocumentFactory.create_document("pdf", "Python Guide", "Tutorial Team")
word = DocumentFactory.create_document("word", "Meeting Notes", "Grace Dev")
markdown = DocumentFactory.create_document("markdown", "README", "DevTeam")
This lets us create fully configured objects through the factory while keeping the creation logic centralized.
Using Abstract Base Classes
To make our factory more robust, we can use Python's Abstract Base Classes (ABC) to enforce a common interface.
Let's create a super simple payment processing system:
from abc import ABC, abstractmethod
class PaymentProcessor(ABC):
@abstractmethod
def process_payment(self, amount):
pass
@abstractmethod
def refund(self, transaction_id):
pass
class CreditCardProcessor(PaymentProcessor):
def process_payment(self, amount):
return f"Processing ${amount} via Credit Card"
def refund(self, transaction_id):
return f"Refunding credit card transaction {transaction_id}"
class PayPalProcessor(PaymentProcessor):
def process_payment(self, amount):
return f"Processing ${amount} via PayPal"
def refund(self, transaction_id):
return f"Refunding PayPal transaction {transaction_id}"
class PaymentFactory:
processors = {
"credit_card": CreditCardProcessor,
"paypal": PayPalProcessor
}
@staticmethod
def create_processor(processor_type):
processor_class = PaymentFactory.processors.get(processor_type)
if processor_class:
return processor_class()
else:
raise ValueError(f"Unknown processor type: {processor_type}")
Here, the PaymentProcessor class defines an interface that all payment processors must implement. The @abstractmethod decorator marks methods that subclasses must override.
You cannot instantiate PaymentProcessor directly. It only serves as a blueprint. All concrete processors (CreditCardProcessor, PayPalProcessor) must implement both process_payment and refund methods. If they don't, Python will raise an error. This guarantees that any object created by our factory will have the expected methods, making our code more predictable and safer.
You can use the factory like so:
processor = PaymentFactory.create_processor("paypal")
A More Helpful Example: Database Connection Factory
Let's build something practical: a factory that creates different database connection objects based on configuration.
class MySQLConnection:
def __init__(self, host, database):
self.host = host
self.database = database
self.connection_type = "MySQL"
def connect(self):
return f"Connected to {self.connection_type} at {self.host}/{self.database}"
def execute_query(self, query):
return f"Executing on MySQL: {query}"
class PostgreSQLConnection:
def __init__(self, host, database):
self.host = host
self.database = database
self.connection_type = "PostgreSQL"
def connect(self):
return f"Connected to {self.connection_type} at {self.host}/{self.database}"
def execute_query(self, query):
return f"Executing on PostgreSQL: {query}"
class SQLiteConnection:
def __init__(self, host, database):
self.host = host
self.database = database
self.connection_type = "SQLite"
def connect(self):
return f"Connected to {self.connection_type} at {self.host}/{self.database}"
def execute_query(self, query):
return f"Executing on SQLite: {query}"
class DatabaseFactory:
db_types = {
"mysql": MySQLConnection,
"postgresql": PostgreSQLConnection,
"sqlite": SQLiteConnection
}
@staticmethod
def create_connection(db_type, host, database):
db_class = DatabaseFactory.db_types.get(db_type)
if db_class:
return db_class(host, database)
else:
raise ValueError(f"Unknown database type: {db_type}")
@staticmethod
def create_from_config(config):
"""Create a database connection from a configuration dictionary"""
return DatabaseFactory.create_connection(
config["type"],
config["host"],
config["database"]
)
This example shows a more realistic use case. We have multiple database connection classes, each with the same interface but different implementations.
The factory has two creation methods: create_connection for direct parameters and create_from_config for configuration dictionaries.
The create_from_config method is particularly useful because it lets you load database settings from a config file or environment variables and create the appropriate connection object.
This pattern makes it easy to switch between different databases without changing your application code. You just change the configuration as shown:
# Use with direct parameters
db1 = DatabaseFactory.create_connection("mysql", "localhost", "myapp_db")
print(db1.connect())
print(db1.execute_query("SELECT * FROM users"))
# Use with configuration dictionary
config = {
"type": "postgresql",
"host": "db.example.com",
"database": "production_db"
}
db2 = DatabaseFactory.create_from_config(config)
When to Use the Factory Pattern
The factory pattern is useful when you have the following:
Multiple related classes: When you have several classes that share a common interface but have different implementations (like the payment processors or database connections we had in the examples).
Runtime decisions: When you need to decide which class to instantiate based on user input, configuration, or other runtime conditions.
Complex object creation: When creating an object involves multiple steps or requires specific logic that you want to encapsulate.
However, don't use the factory pattern when:
You only have one or two simple classes
Object creation is straightforward with no special logic
The added abstraction makes your code harder to understand
Wrapping Up
The factory pattern is a useful tool for managing object creation in Python. It helps you write cleaner, more maintainable code by centralizing creation logic and decoupling your code from specific class implementations. We've covered:
Basic factory implementation with simple examples
Using dictionaries for cleaner factory code
Passing parameters to factory-created objects
Using abstract base classes for cleaner interfaces
The key takeaway is this: whenever you find yourself writing repetitive object creation code or need to decide which class to instantiate at runtime, consider using the factory pattern. Start simple and add complexity only when needed. The basic dictionary-based factory is often all you need for most applications.
Happy coding!
