Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

The proxy pattern


The proxy design pattern gets its name from the proxy (also known as surrogate) object used to perform an important action before accessing the actual object. There are four different well-known proxy types (j.mp/proxypat). They are as follows:

  • A remote proxy, which acts as the local representation of an object that really exists in a different address space (for example, a network server).
  • A virtual proxy, which uses lazy initialization to defer the creation of a computationally expensive object until the moment it is actually needed.
  • A protection/protective proxy, which controls access to a sensitive object.
  • A smart (reference) proxy, which performs extra actions when an object is accessed. Examples of such actions are reference counting and thread-safety checks.

I find virtual proxies very useful so let's see an example of how we can implement them in Python right now. In the Implementation section, you will learn how to create protective proxies.

 

There are many ways to create...