Table of Contents
The best way to implement a singleton in Python
Implementation method
Method 1: Decorator
Method 2: Base class
Method 3: Metaclass
Method 4: Return the decorator of the class with the same name
Method 5: Module
Recommended Method
The role of metaclass
Of course
Python 2 and 3 compatible versions
Correction
Home Backend Development Python Tutorial What is the best way to implement a singleton in Python?

What is the best way to implement a singleton in Python?

Dec 17, 2024 pm 04:07 PM

What is the best way to implement a singleton in Python?

The best way to implement a singleton in Python

Although the advantages and disadvantages of the singleton design pattern are not the focus of this article, this article will explore how to implement the singleton in Python in the best possible way. Implement this pattern in a Pythonic way. Here, "most Pythonic" means following the "principle of least surprise".

Implementation method

Method 1: Decorator

def singleton(class_):
    instances = {}

    def getinstance(*args, **kwargs):
        if class_ not in instances:
            instances[class_] = class_(*args, **kwargs)
        return instances[class_]

    return getinstance

@singleton
class MyClass(BaseClass):
    pass
Copy after login
Copy after login
Copy after login

Advantages:

  • The decorator has addition sex, more intuitive than multiple inheritance.

Disadvantages:

  • The object created using MyClass() is a real singleton object, but MyClass itself is a function, not a class, so class methods cannot be called.

Method 2: Base class

class Singleton(object):
    _instance = None

    def __new__(class_, *args, **kwargs):
        if not isinstance(class_._instance, class_):
            class_._instance = object.__new__(class_, *args, **kwargs)
        return class_._instance

class MyClass(Singleton, BaseClass):
    pass
Copy after login
Copy after login

Advantages:

  • It is a real class.

Disadvantages:

  • Multiple inheritance, unpleasant. When inheriting from a second base class, __new__ may be overridden.

Method 3: Metaclass

class Singleton(type):
    _instances = {}

    def __call__(cls, *args, **kwargs):
        if cls not in cls._instances:
            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
        return cls._instances[cls]

# Python2
class MyClass(BaseClass):
    __metaclass__ = Singleton

# Python3
class MyClass(BaseClass, metaclass=Singleton):
    pass
Copy after login

Advantages:

  • It is a real class.
  • Inheritance is automatically covered.
  • Use __metaclass__ correctly (and make me understand it).

Disadvantages:

  • No disadvantages.

Method 4: Return the decorator of the class with the same name

def singleton(class_):
    class class_w(class_):
        _instance = None

        def __new__(class_, *args, **kwargs):
            if class_w._instance is None:
                class_w._instance = super(class_w, class_).__new__(class_, *args, **kwargs)
                class_w._instance._sealed = False
            return class_w._instance

        def __init__(self, *args, **kwargs):
            if self._sealed:
                return
            super(class_w, self).__init__(*args, **kwargs)
            self._sealed = True

    class_w.__name__ = class_.__name__
    return class_w

@singleton
class MyClass(BaseClass):
    pass
Copy after login

Advantages:

  • It is a real class.
  • Inheritance is automatically covered.

Cons:

  • Is there an overhead in creating two classes for each class that you want to become a singleton? While this works fine in my case, I'm worried it might not scale. What is the purpose of the
  • _sealed attribute?
  • You cannot use super() to call methods with the same name in a base class because they would be recursive. This means that __new__ cannot be customized, nor can a class that requires calling __init__ be subclassed.

Method 5: Module

Singleton module singleton.py.

Pros:

  • Simple is better than complex.

Disadvantages:

  • Not deferred instantiation.

I recommend using Method 2, but it is better to use metaclasses instead of base classes. Here is an example implementation:

class Singleton(type):
    _instances = {}

    def __call__(cls, *args, **kwargs):
        if cls not in cls._instances:
            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
        return cls._instances[cls]

class Logger(object):
    __metaclass__ = Singleton
Copy after login

Or in Python3:

class Logger(metaclass=Singleton):
    pass
Copy after login

If you want __init__ to run every time a class is called, add the following code to Singleton.__call__ In the if statement:

def singleton(class_):
    instances = {}

    def getinstance(*args, **kwargs):
        if class_ not in instances:
            instances[class_] = class_(*args, **kwargs)
        return instances[class_]

    return getinstance

@singleton
class MyClass(BaseClass):
    pass
Copy after login
Copy after login
Copy after login

The role of metaclass

A metaclass is a class of classes, that is, a class is an instance of its metaclass. The metaclass of an object in Python can be found via type(obj). Normal new classes are of type type. The above Logger will be of type class 'your_module.Singleton' just like the (only) instance of Logger will be of type class 'your_module.Logger' . When a logger is called using Logger(), Python first asks the Logger's metaclass Singleton what it should do, allowing preemptive instance creation. The process is similar to how Python asks a class what it should do with its attributes by calling __getattr__, and you reference its attributes by doing myclass.attribute.

Metaclasses essentially determine what the calling class means and how to implement that meaning. See e.g. http://code.activestate.com/recipes/498149/ which uses metaclasses to essentially recreate C-style structures in Python. Discussion Thread [What are the specific use cases for metaclasses? ](https://codereview.stackexchange.com/questions/82786/what-are-some-concrete-use-cases-for-metaclasses) also provides some examples, which are generally related to declarative programming, especially in ORM used in.

In this case, if you use your Method 2 and a subclass defines a __new__ method, it will be executed every time SubClassOfSingleton() is called, because It is responsible for calling methods that return stored instances. With metaclasses, it is only executed once, when the unique instance is created. You need to customize the definition of the calling class, which is determined by its type.

In general, it makes sense to use metaclasses to implement singletons. A singleton is special because its instance is created only once, while a metaclass is a custom implementation of a created class that makes it behave differently than a normal class. Using metaclasses gives you more control when you would otherwise need to customize your singleton class definition.

Of course

Your singleton does not need multiple inheritance (because the metaclass is not a base class), but for inheritance to create a subclass of a class, you need to make sure the singleton class is the first/ The leftmost metaclass redefines __call__. This is unlikely to be a problem. The instance dictionary is not in the instance's namespace, so it cannot be accidentally overwritten.

You will also hear that the singleton pattern violates the "single responsibility principle", which means that each class should only do one thing. This way, you don't have to worry about breaking one thing the code does when you need to change another code because they are independent and encapsulated. The metaclass implementation passes this test. Metaclasses are responsible for enforcing the pattern, creating classes and subclasses that don't need to be aware that they are singletons. Method 1 fails this test, as you pointed out with "MyClass itself is a function, not a class, so class methods cannot be called".

Python 2 and 3 compatible versions

Writing code in Python 2 and 3 requires a slightly more complicated scheme. Since metaclasses are usually subclasses of the type class, you can use a metaclass to dynamically create an intermediary base class with it as a metaclass at runtime, and then use that base class as the base class for a public singleton base class. This is easier said than done, as follows:

def singleton(class_):
    instances = {}

    def getinstance(*args, **kwargs):
        if class_ not in instances:
            instances[class_] = class_(*args, **kwargs)
        return instances[class_]

    return getinstance

@singleton
class MyClass(BaseClass):
    pass
Copy after login
Copy after login
Copy after login

One irony of this approach is that it uses subclassing to implement metaclasses. One possible advantage is that, unlike a pure metaclass, isinstance(inst, Singleton) will return True.

Correction

Regarding another topic, you may have noticed, but the base class implementation in your original post was wrong. To reference _instances within a class, you need to use super(), or a static method of the class method, since the actual class has not yet been created at the time of the call. All of this holds true for metaclass implementations as well.

class Singleton(object):
    _instance = None

    def __new__(class_, *args, **kwargs):
        if not isinstance(class_._instance, class_):
            class_._instance = object.__new__(class_, *args, **kwargs)
        return class_._instance

class MyClass(Singleton, BaseClass):
    pass
Copy after login
Copy after login

The above is the detailed content of What is the best way to implement a singleton in Python?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1268
29
C# Tutorial
1243
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

See all articles