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

Memoization is a technique used to speed up function calls by caching their results.
The results can be cached only if the function is pure, meaning that it has no side effects or outputs and that it does not depend on any global state.

# Memoizing `sin` function
A trivial function that can be memoized is the sine function `sin`.

import math

_SIN_MEMOIZED_VALUES = {}

def memoized_sin(x):
  if x not in _SIN_MEMOIZED_VALUES:
    _SIN_MEMOIZED_VALUES[x] = math.sin(x)
  return _SIN_MEMOIZED_VALUES[x]

print('memoized_sin(1): ', memoized_sin(1))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)

print('memoized_sin(2):', memoized_sin(2))
print('memoized_sin(2):', memoized_sin(2))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)

print('memoized_sin(1):', memoized_sin(1))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)

The first time that `memoized_sin` is called with an argument that is not stored in `_SIN_MEMOIZED_VALUES`, the value will be computed and stored in this dictionary.
Later on, if we call the function with the same value again, the result will be retrieved from the dictionary rather than being computed another time. While sin is a function that computes very quickly, this may not be true for some advanced functions
involving more complicated computations.

Usage of memoization can be simplified in Python by using a decorator. [**PyPI**](https://pypi.org/) lists a few implementations of memoization through decorators, from very simple cases to the most complex and comprehensive.

Memoization

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

Memoization

Memoizing `sin` function

Create a Web Crawler

Memoization

Memoizing sin function

Memoizing `sin` function