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

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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

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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

**Caching** is an essential component when scaling applications to large proportions.

It can solve various problems:

- **High cost of computing data:** caching allows reusing already-computed results, so
rather than computing the same result over and over again, a database of results can be queried rather than computing the whole result again. This is the basic principle behind _memoization_.
- **High-latency access to data:** some data might be accessible but the latency to access it is too high. If the data is retrieved enough times, storing it in a closer (in terms of latency) data store will improve performance. This introduces the well-known problem of _cache invalidity_.

Different techniques described regarding caching in the upcoming chapters can be combined to bring about very high performances for your applications.

Get an introduction to caching in Python.

Introduction to Caching

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

Build a REST API in Flask

Deploying on PaaS

Testing Distributed Systems

Caching

Performance

Conclusion

Introduction to Caching