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Mastering Python

Mastering Python

By : Rick Hattem
4 (11)
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Mastering Python

Mastering Python

4 (11)
By: Rick Hattem

Overview of this book

Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.
Table of Contents (17 chapters)
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6
6. Generators and Coroutines – Infinity, One Step at a Time
16
Index

Performance monitoring


So far we have seen how to measure and improve both CPU and memory performance, but there is one part we have completely skipped over. Performance changes due to external factors such as growing amounts of data are very hard to predict. In real life applications, bottlenecks aren't constant. They change all the time and code that was once extremely fast might bog down as soon as more load is applied.

Because of that I recommend implementing a monitoring solution that tracks the performance of anything and everything over time. The big problem with performance monitoring is that you can't know what will slow down in the future and what the cause is going to be. I've even had websites slow down because of Memcached and Redis calls. These are memory only caching servers that respond well within a millisecond which makes slowdowns highly unlikely, until you do over a 100 cache calls and the latency towards the cache server increases from 0.1 milliseconds to 2 milliseconds...

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Mastering Python
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