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

Distributed applications all show the same set of well-known problems that they need to solve. One of them is distributed locking of resources so that no resource inadvertently gets shared access, as discussed in the previous chapter ([Lock Management](https://www.educative.io/courses/hackers-guide-scaling-python/introduction-to-lock-management)). The second one involves cluster membership awareness and liveliness.

A typical use case for distributed workers is the need to know how many workers are alive in the cluster. This data allows for implementing different scenarios, such as high availability (one node doing the work and the other one standing-by) and load
balancing (several nodes spreading some work across each other).


In all cases, an external service is needed to achieve this functionality. Rather
than picking one and implementing all those low-level features in your application,
this chapter demonstrates how to use the abstraction layer and algorithms that the
[Tooz](https://pypi.python.org/pypi/tooz) library provides to solve that efficiently.

Get an introduction to the group membership in Python.

Introduction to Group Membership

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

Introduction to Group Membership