Book Image

Hands-On Enterprise Application Development with Python

By : Saurabh Badhwar
Book Image

Hands-On Enterprise Application Development with Python

By: Saurabh Badhwar

Overview of this book

Dynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you’ll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You’ll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Summary


In this chapter, we took a look at how the performance of an application is an important aspect of the software's development and what kind of issues usually cause performance bottlenecks to appear in the application. Moving forward, we took a look at the different ways in which we can profile an application for performance issues. This involved, first the writing of benchmark tests for individual components as well as the individual APIs and then moving to more specific, component-level analysis, where we took a look at different ways of profiling the components. These profiling techniques included the use of simple timing profiles of methods using the Python timeit module, then we moved on to using more sophisticated techniques with Python cProfile and covered memory profiling. Another topic we took a look at during our journey is the use of logging techniques to help us evaluate slow requests whenever we want. Finally, we took a look at some of the general principles that can...