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

Task executions might fail, and in this case, it is crucial to handle that properly. It is common for tasks to depend on external services, such as a remote database or a REST API. Connection failure might be transient; it is therefore better to deal with defeat and retry later.

> **Note:** The example in this lesson assumes that you have a **Redis server**. We already run a Redis server as a daemon when **Run** is clicked on a widget so you do not need to worry about starting the server yourself.

# Retry backoff
In the following application, after running `python celery-task-retry.py`, open another terminal and enter command `cd examples/queue-based-distribution/` to move to the directory where `celery-task-retry.py` is located. Then, enter the command: `celery -A celery-task-retry worker`. The `celery-task-retry.py` will keep waiting until the worker is started. Once it does, the result is calculated by the `celery-task-retry.py`.

Celery: Handling Failures

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

Celery: Handling Failures