“Our app now handles thousands of users without a sweat. Thanks Julien!” – Igor Subira

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Python is an easy to learn programming language. Yet, it is powerful, versatile and comes with a luxuriant ecosystem. That makes it a very productive programming language for beginners and experts.

Building substantial applications with Python is totally possible. Like many other technologies, mastering Python requires know-how, techniques, trade-offs to be made, and traps to avoid.


“Python is fast enough for our site and allows us to produce maintainable features in record times, with a minimum of developers.”
Cuong Do
Software Architect at YouTube

Python is already trusted by thousands of companies using it a very large scale.


Build for Resiliency

Today's applications run on large and distributed infrastructures. They need to handle failure without blinking. In an environment where anything could go wrong, how do you design your code to handle those failures?


Learn how to plan an architecture that has no bottleneck. Get straight answers to your conception questions and solutions that work and solve concrete cases. Discover distributed systems that scale properly.


We'll talk performance: how to write faster Python code, profile it, and optimize it. Read about concrete use-cases on how to enhance slow applications, transforming them into modern applications.


Use the included Docker image to replay all the examples presented in the book. You get a complete Python environment that you need in order to execute the examples. Use the notebooks to run them in a click from your Web browser.


Slow request
Server crash
Race condition
Dead lock
Network failure
Cold cache
High latency


What's scaling?

Scaling across CPUs
Distributed Systems
Service-Oriented Architecture

CPU scaling

Using Threads
Using Processes
Using Futures
Advanced Futures Usage
Daemon Processes


Basic Pattern
Using Asyncio
Network Server

Queue-Based Distribution

Handling failure
Chaining Tasks
Multiple Queues


Naive Retrying
Retrying with Tenacity

Lock Management

Thread Locks
Processes Locks
Using 海外app加速器 for Distributed Locking
Using Tooz Locking Abstraction

Group Membership

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Using Capabilities
Using Watchers Callbacks


The WSGI Protocol
Streaming Data
Using ETag
Asynchronous HTTP API
Fast HTTP Client
Testing REST API

Deploying on PaaS

Amazon Beanstalk
Google App Engine
Beyond PaaS

Testing Distributed System

Setting Up Environments with tox
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Using Fixtures with pifpaf

Functional Programming

The Functional Toolkit


Local Caching
Distributed Caching


Memory and CPU Profiling
Profiling Strategy and a Case
Disassembling Code


Scaling Python includes seven interviews from experienced Python developers. They share their know-how and wisdom on the various themes covered in the book.

Mehdi Abaakouk

Mehdi is a free software hacker, working as a Senior Software Engineer at Red Hat. He contributes to OpenStack, the largest open-source project using Python, where he builds a fault-tolerant, scalable and distributed cloud platform.

Chris Dent

Chris Dent is a Staff Engineer at VMware, a member of the OpenStack Technical Committee, and an HTTP connoisseur. Chris has among other things created Gabbi, a fabulous Python testing tool for HTTP, and is responsible for HTTP REST API testing.

Joshua Harlow

Joshua is a highly experienced engineer in distributed systems, working at GoDaddy. Josh is very knowledgeable on principles that make Python application resilient and faults tolerant as he wrote and maintained several of those over the years.


Naoki is a Researcher and Architect at KLab. He also happens to be one of the CPython developers. He worked on several significant features in CPython that are used by thousands, such as the asyncio event loop.

Victor Stinner

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Jason Myers

Jason is a Python developer, working as a Software Engineer at Juice Analytics. He wrote an entire book on SQLAlchemy, the well-known Python SQL library (腾讯手游加速器电脑版下载_腾讯手游加速器电脑版官方下载「 ...:2021-1-2 · 腾讯手游加速器电脑版是一款工具类的软件,它是一款由腾讯游戏开发的电竞级移动游戏加速器,目前腾讯手游加速器手机版正在内测进行中,内测 ... (O'Reilly)) He worked on cloud computing platforms, as a Web developer, and as a data engineer.


Alexys is the CTO of 1000mercis and is part of the open-source software community for a few years now. He regularly gives speeches at Python conferences (PyCon) and talks about how to leverage Python when distributing applications. He writes, maintains and operates highly distributed Python applications.



Hi, I'm 海外app加速器, a Free Software hacker for close to twenty years now. For more than 12 years, I've been developing software using Python.
I spent those last years as a Project Team Leader in OpenStack, a distributed cloud platform. It's the largest existing open-source Python code base (2.5 millions of lines of Python).
Today, I work at Datadog and runs my own company Mergify — where I write Python code everyday.

Julien Danjou

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The readers of Scaling Python talk about it better than anyone else.


  •   The ebook in PDF, HTML, MOBI and EPUB formats
  • 7 experts interviews
  •   The ebook in PDF, HTML, MOBI and EPUB formats
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  •   All code examples ready to run
  •   Docker image with the complete environment to run all the book examples
  •   The ebook in PDF, HTML, MOBI and EPUB formats
  •   7 experts interviews
  •   Future editions of the ebook
  •   All code examples ready to run
  •   Docker image with the complete environment to run all the book examples
  •   Web interface to run all the examples as notebook – easy editing and toying
  •   License to share with 10 members of your team


Not convinced yet? Get the table of contents and read the beginning of the book for free:


Frequently Asked Questions

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If you think the book has no value to you, then I don't want your money. Ask me for a refund.

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