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

# Problem

Suppose you are managing a multi-user file system, and in that system, files can be accessed by multiple users at the same time. All nodes can read and write to the files and doing that concurrently can corrupt the files.

In this challenge, we assume for simplicity that there is only one file `demofile.txt` which contains a simple counter (an integer). Suppose all the nodes count something (i.e., words in the documents) and add the total count to the file counter.

The nodes are represented by the processes that read the file and add 1 to the counter and store the updated value again to the file. We deploy 50 processes and 100 jobs (each job adds 1 to the counter), so the total count after the process is done should be 100. However, since many processes are running concurrently, the count gets corrupted, and the result is less than 100 because of the race condition.

This unsafe code is provided below. Your task is to change this code to make it process-safe and guarantee the final count to be 100 on every run.

> Run the below code by clicking **Run** and enter command `python lock-test.py`.

Test your lock management in python skill by solving the coding challenge presented below.

Coding Challenge: Lock Management

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

Coding Challenge: Lock Management

Problem