Gain insights into Python's scalability, concurrency, and distribution. Learn about CPU scaling, event loops, queue-based distribution, and building REST APIs to optimize and deploy high-performance applications.

python.tar.gz

Chapter2

Chapter7

Chapter5

Chapter6

Chapter3

Chapter7-without-etcd

Chapter8-with-etcd

Chapter9

Chapter9-gabbi

Chapter10-heroku

Chapter10-aws_eb

Chapter11_Tox_integration

Chapter11_Tox_integration-pifpaf

Chapter11_pifpaf_fixtures

Chapter12_caching

Chapter13_Performance

Chapter13_Performace_KCacheGrind

Chapter5-rq-dashboard

Chapter5-celery-flower

Chapter10-gcloud

Chapter2-zsh

Chapter13_Performace_KCacheGrind-copy

Chapter11_Tox_integration-pifpaf-postgresql

Celery-Project1_Sol

Celery-Project1_Sol2

Python, in all of its greatness, is often dismissed when needing to write performant and distributed applications. It’s considered to be slow and not suited for the task. In this course, you will find that with the right implementation of Python, you can write applications that scale horizontally, perform well, and are distributed. 

To kick things off, you’ll learn about CPU scaling, concurrency, and event loops, all of which are crucial for implementing a distributed system. After that, you’ll move on to queue-based distribution, lock management, and group memberships, which are fundamental concepts for maintaining availability and consistency in distributed applications.

In the last half, you’ll learn how to build a REST API, how to deploy your app to a PaaS, and how to optimize your application's performance.

By the end of this course, you’ll be more productive with Python, you’ll be able to write faster applications that are distributed, and you’ll have a great new skill that is highly coveted.

The Hacker's Guide to Scaling Python

There’s a common pattern that can be seen across all sorts of programs that can be
summarized with the word "retrying". It is directly tied to the idea of trying something again that returned an error or raised an exception when it was not expected.

# Basic retrying pattern

The basic pattern that most developers implement looks like what is shown in the following example.

while True:
  try:
    do_something()
  except:
    pass
  else:
    break

The above example does not provide any down time for the called function. Usually, smarter strategies are implemented. That is especially important if the code is going to do something like connecting to an external system. The last thing you want is to repeatedly hammer the remote system.

# Retry and sleep pattern
The next example illustrates the most common implementation of this retry and sleep pattern.

> To run the application below, click**Run** and run the command `python retrying-naive.py`.

> If you want to update the source code in coding playgrounds, you need to press the **Run** button again after changing the code and repeat the instructions to run the application.

Naive Retrying

Scaling

CPU Scaling

Event Loops

Functional Programming

Queue-Based Distribution

Designing for Failure

Create a Web Crawler

Project Walkthrough

Lock Management

Group Membership

REST Interfaces

Deploying on PaaS

Testing Distributed Systems

Caching

Performance

Conclusion

Naive Retrying

Basic retrying pattern