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Learning Python Design Patterns - Second Edition
Learning Python Design Patterns - Second Edition

Learning Python Design Patterns - Second Edition: , Second Edition

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Learning Python Design Patterns - Second Edition

Chapter 2. The Singleton Design Pattern

In the previous chapter, we explored design patterns and their classifications. As we are aware, design patterns can be classified under three main categories: structural, behavioral, and creational patterns.

In this chapter, we will go through the Singleton design pattern—one of the simplest and well-known Creational design patterns used in application development. This chapter will give you a brief introduction to the Singleton pattern, take you through a real-world example where this pattern can be used, and explain it in detail with the help of Python implementations. You will learn about the Monostate (or Borg) design pattern that is a variant of the Singleton design pattern.

In this chapter, we will cover the following topics in brief:

  • An understanding of the Singleton design pattern
  • A real-world example of the Singleton pattern
  • The Singleton pattern implementation in Python
  • The Monostate (Borg) pattern

At the end of the chapter, we...

Understanding the Singleton design pattern

Singleton provides you with a mechanism to have one, and only one, object of a given type and provides a global point of access. Hence, Singletons are typically used in cases such as logging or database operations, printer spoolers, and many others, where there is a need to have only one instance that is available across the application to avoid conflicting requests on the same resource. For example, we may want to use one database object to perform operations on the DB to maintain data consistency or one object of the logging class across multiple services to dump log messages in a particular log file sequentially.

In brief, the intentions of the Singleton design pattern are as follows:

  • Ensuring that one and only one object of the class gets created
  • Providing an access point for an object that is global to the program
  • Controlling concurrent access to resources that are shared

The following is the UML diagram for Singleton:

Understanding the Singleton design pattern

A simple way of implementing...

Lazy instantiation in the Singleton pattern

One of the use cases for the Singleton pattern is lazy instantiation. For example, in the case of module imports, we may accidently create an object even when it's not needed. Lazy instantiation makes sure that the object gets created when it's actually needed. Consider lazy instantiation as the way to work with reduced resources and create them only when needed.

In the following code example, when we say s=Singleton(), it calls the __init__ method but no new object gets created. However, actual object creation happens when we call Singleton.getInstance(). This is how lazy instantiation is achieved.

class Singleton:
    __instance = None
    def __init__(self):
        if not Singleton.__instance:
            print(" __init__ method called..")
        else:
            print("Instance already created:", self.getInstance())
    @classmethod
    def getInstance(cls):
        if not cls.__instance:
            cls.__instance...

Module-level Singletons

All modules are Singletons by default because of Python's importing behavior. Python works in the following way:

  1. Checks whether a Python module has been imported.
  2. If imported, returns the object for the module. If not imported, imports and instantiates it.
  3. So when a module gets imported, it is initialized. However, when the same module is imported again, it's not initialized again, which relates to the Singleton behavior of having only one object and returning the same object.

The Monostate Singleton pattern

We discussed the Gang of Four and their book in Chapter 1, Introduction to Design Patterns. GoF's Singleton design pattern says that there should be one and only one object of a class. However, as per Alex Martelli, typically what a programmer needs is to have instances sharing the same state. He suggests that developers should be bothered about the state and behavior rather than the identity. As the concept is based on all objects sharing the same state, it is also known as the Monostate pattern.

The Monostate pattern can be achieved in a very simple way in Python. In the following code, we assign the __dict__ variable (a special variable of Python) with the __shared_state class variable. Python uses __dict__ to store the state of every object of a class. In the following code, we intentionally assign __shared_state to all the created instances. So when we create two instances, 'b' and 'b1', we get two different objects unlike Singleton...

Singletons and metaclasses

Let's start with a brief introduction to metaclasses. A metaclass is a class of a class, which means that the class is an instance of its metaclass. With metaclasses, programmers get an opportunity to create classes of their own type from the predefined Python classes. For instance, if you have an object, MyClass, you can create a metaclass, MyKls, that redefines the behavior of MyClass to the way that you need. Let's understand them in detail.

In Python, everything is an object. If we say a=5, then type(a) returns <type 'int'>, which means a is of the int type. However, type(int) returns <type 'type'>, which suggests the presence of a metaclass as int is a class of the type type.

The definition of class is decided by its metaclass, so when we create a class with class A, Python creates it by A = type(name, bases, dict):

  • name: This is the name of the class
  • base: This is the base class
  • dict: This is the attribute variable

Now...

Understanding the Singleton design pattern


Singleton provides you with a mechanism to have one, and only one, object of a given type and provides a global point of access. Hence, Singletons are typically used in cases such as logging or database operations, printer spoolers, and many others, where there is a need to have only one instance that is available across the application to avoid conflicting requests on the same resource. For example, we may want to use one database object to perform operations on the DB to maintain data consistency or one object of the logging class across multiple services to dump log messages in a particular log file sequentially.

In brief, the intentions of the Singleton design pattern are as follows:

  • Ensuring that one and only one object of the class gets created

  • Providing an access point for an object that is global to the program

  • Controlling concurrent access to resources that are shared

The following is the UML diagram for Singleton:

A simple way of implementing...

Lazy instantiation in the Singleton pattern


One of the use cases for the Singleton pattern is lazy instantiation. For example, in the case of module imports, we may accidently create an object even when it's not needed. Lazy instantiation makes sure that the object gets created when it's actually needed. Consider lazy instantiation as the way to work with reduced resources and create them only when needed.

In the following code example, when we say s=Singleton(), it calls the __init__ method but no new object gets created. However, actual object creation happens when we call Singleton.getInstance(). This is how lazy instantiation is achieved.

class Singleton:
    __instance = None
    def __init__(self):
        if not Singleton.__instance:
            print(" __init__ method called..")
        else:
            print("Instance already created:", self.getInstance())
    @classmethod
    def getInstance(cls):
        if not cls.__instance:
            cls.__instance = Singleton()
        return...

Module-level Singletons


All modules are Singletons by default because of Python's importing behavior. Python works in the following way:

  1. Checks whether a Python module has been imported.

  2. If imported, returns the object for the module. If not imported, imports and instantiates it.

  3. So when a module gets imported, it is initialized. However, when the same module is imported again, it's not initialized again, which relates to the Singleton behavior of having only one object and returning the same object.

The Monostate Singleton pattern


We discussed the Gang of Four and their book in Chapter 1, Introduction to Design Patterns. GoF's Singleton design pattern says that there should be one and only one object of a class. However, as per Alex Martelli, typically what a programmer needs is to have instances sharing the same state. He suggests that developers should be bothered about the state and behavior rather than the identity. As the concept is based on all objects sharing the same state, it is also known as the Monostate pattern.

The Monostate pattern can be achieved in a very simple way in Python. In the following code, we assign the __dict__ variable (a special variable of Python) with the __shared_state class variable. Python uses __dict__ to store the state of every object of a class. In the following code, we intentionally assign __shared_state to all the created instances. So when we create two instances, 'b' and 'b1', we get two different objects unlike Singleton where we have just...

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Key benefits

  • Understand the structural, creational, and behavioral Python design patterns
  • Get to know the context and application of design patterns to solve real-world problems in software architecture, design, and application development
  • Get practical exposure through sample implementations in Python v3.5 for the design patterns featured

Description

With the increasing focus on optimized software architecture and design it is important that software architects think about optimizations in object creation, code structure, and interaction between objects at the architecture or design level. This makes sure that the cost of software maintenance is low and code can be easily reused or is adaptable to change. The key to this is reusability and low maintenance in design patterns. Building on the success of the previous edition, Learning Python Design Patterns, Second Edition will help you implement real-world scenarios with Python’s latest release, Python v3.5. We start by introducing design patterns from the Python perspective. As you progress through the book, you will learn about Singleton patterns, Factory patterns, and Façade patterns in detail. After this, we’ll look at how to control object access with proxy patterns. It also covers observer patterns, command patterns, and compound patterns. By the end of the book, you will have enhanced your professional abilities in software architecture, design, and development.

Who is this book for?

This book is for Software architects and Python application developers who are passionate about software design. It will be very useful to engineers with beginner level proficiency in Python and who love to work with Python 3.5

What you will learn

  • Enhance your skills to create better software architecture
  • Understand proven solutions to commonly occurring design issues
  • Explore the design principles that form the basis of software design, such as loose coupling, the Hollywood principle and the Open Close principle among others
  • Delve into the object-oriented programming concepts and find out how they are used in software applications
  • Develop an understanding of Creational Design Patterns and the different object creation methods that help you solve issues in software development
  • Use Structural Design Patterns and find out how objects and classes interact to build larger applications
  • Focus on the interaction between objects with the command and observer patterns
  • Improve the productivity and code base of your application using Python design patterns
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Publication date : Feb 15, 2016
Length: 164 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785888038
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Table of Contents

12 Chapters
1. Introduction to Design Patterns Chevron down icon Chevron up icon
2. The Singleton Design Pattern Chevron down icon Chevron up icon
3. The Factory Pattern – Building Factories to Create Objects Chevron down icon Chevron up icon
4. The Façade Pattern – Being Adaptive with Façade Chevron down icon Chevron up icon
5. The Proxy Pattern – Controlling Object Access Chevron down icon Chevron up icon
6. The Observer Pattern – Keeping Objects in the Know Chevron down icon Chevron up icon
7. The Command Pattern – Encapsulating Invocation Chevron down icon Chevron up icon
8. The Template Method Pattern – Encapsulating Algorithm Chevron down icon Chevron up icon
9. Model-View-Controller – Compound Patterns Chevron down icon Chevron up icon
10. The State Design Pattern Chevron down icon Chevron up icon
11. AntiPatterns Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.8
(13 Ratings)
5 star 30.8%
4 star 0%
3 star 23.1%
2 star 15.4%
1 star 30.8%
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maurice ling Mar 22, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: I am a technical reviewer for this book.Through the entire period serving as technical reviewer for this book, I am impressed with the easy-to-follow style of writing. I had to review the book in chapters across a few months and there is no point in time where I need to wonder if I really understood what the author had to say. As such, I can safely say that this book is well-written and each chapter is contained (there is almost no need to flip back and forth the book to understand the material within any chapter on hand) - quite a remarkable feat for a book.Each chapter is focused on a specific design pattern. The why and how of each pattern is explained in a clear fashion. This makes it easy to read for anyone, both beginners and experienced Python programmers. The materials are even suitable for pre-bedtime leisure reading. Personally, I recommend this to the developers in my own company.
Amazon Verified review Amazon
Govind Karmakar Nov 11, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A good blend of theory with code snippets. It gives a good start to the folks want to delve into the concepts of design patterns using python.
Amazon Verified review Amazon
Sudhir Chawla May 02, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
With the growing popularity of Python as preferred programming language, the book serves the need to educate readers on the importance of design patterns in python. The author does a very good job by first delving into the basics of OOPs and then gradually introducing the subject of design patterns, all in python 3.5.However reference to python docs could have been included in the book while using some of the more esoteric functions in python. The UML Diagrams mentioned at the beginning of each pattern helps the reader to visualize the hierarchy of objects. The real world examples of all patterns certainly does drive the point home.In short, the book provides concise but well illustrated information about the design pattern and its implementation in python.
Amazon Verified review Amazon
Christian Scheunert Oct 21, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Short and conclusive pattern description with very nice examples. The layout is not the best (looks like a copy of a web page), however, the content is definitely worth it.
Amazon Verified review Amazon
B Jul 29, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
The narrative style the book, at least up to where I've read so far, feels more like a brain dump from a friend to another than a carefully thought out and written book. The author clearly understands the concepts but struggles to explain them.However, my main gripe is with the code examples. They seem to go off at a tangent trying to do something clever (or asinine) instead of focusing on what they're meant to be illustrating.All in all I'm happy with this book, but at the price purchased.
Amazon Verified review Amazon
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