Python Code Does Not Have to Be Slow!
(or be limited by the Global Interpreter Lock)
With "Python Threading Jump-Start" you will learn how to develop concurrent Python programs in just 7 lessons.
How Much Faster Could Your Code Run
(if it used 10s, 100s or even 1,000s of threads)?
How many times has this happened to you...
- ...you write a program to download many files
- ...you write a program to execute many database queries
- ...you write a program to read many files
You use a loop, one iteration per file, per query, etc.
And it is SO SLOW.
You run the script and are frustrated at how long it takes.
Yet, you have 2, 4, 8 or more CPU cores sitting idle.
Waiting for work.
What a waste!
- What if you could develop Python programs that were concurrent from the start?
- What if your concurrent Programs performed each task simultaneously in separate threads?
This is possible right now with a rarely used Python module that offers thread-based concurrency (and is already installed on your system).
Python Concurrency is the Path to Faster Code
Python is a joy to use, but getting Python code to run fast is challenging.
Concurrency is a standard approach to running multiple functions simultaneously.
Python concurrency has a bad reputation. So bad, that many developers believe Python does not support true concurrency.
I'm happy to say that these misconceptions are dead wrong.
Python supports real concurrency with first-class native support for threads and processes.
It always has.
- ...on all recent Python versions, like Python v2.6+ and v3.0+.
- ...on all major platforms, like Windows, MacOS, and Linux.
- ...with all major hardware, like Intel, AMD, ARM, and Apple Silicon.
And most importantly, Python concurrency is easy and fun to use.
The trick is to use the
threading module and the right types of tasks.
But Threads Don't Work Because of the GIL...
Have you heard this before:
"Python doesn't support threads because of the Global Interpreter Lock (GIL)."
Yes, it's true. The
threading module is limited by the infamous Global Interpreter Lock (GIL).
Critically, the GIL is released allowing multiple threads to run in parallel.
When is the GIL released?
- ...the GIL is released when performing blocking I/O
- ...the GIL is released when calling some third-party libraries
- ...the GIL does not even exist in some third-party Python interpreters
The first point is key.
The GIL is released when performing blocking IO, such as:
- Reading or writing a file from the hard drive.
- Reading or writing to standard output, input, or error (stdin, stdout, stderr).
- Printing a document.
- Downloading or uploading a file.
- Querying a server.
- Querying a database.
- Taking a photo or recording a video.
- And so much more.
But Multiprocessing Offers Full Parallelism...
multiprocessing module offers full parallelism via process-based concurrency.
It's amazing at one thing:
There are three massive downsides of using processes as the unit of concurrency for IO-bound tasks:
- ...they are heavyweight and slow to start
(and we need to start many threads when doing IO)
- ...they are slow to share data
(and we need to read or write lots of data when doing IO)
- ...operating systems may limit the number that can be created
(and we need 100s or 1,000s for IO)
You need threads!
Threads are the tool to use for IO-bound tasks, and the threading module is the way to use them in Python.
Threading: Python Concurrency for IO-bound Tasks
threading module provides easy-to-use thread-based concurrency.
This is not some random third-party library that is hard to install, this is the Python standard library (already installed on your system).
This is the module you need to use to make your code run faster.
It is specifically designed for you to develop concurrent Python programs.
If you work with the Global Interpreter Lock, you can easily run 10s to 1,000s of threads simultaneously.
There's just one problem.
Few developers know about it (or how to use it well).
- ...the API documentation is thin at best, providing very little guidance on how to use its features
- ...the API docs are buried deep within the Python documentation website, making it impossible to find
- ...the few examples out there on the web are terse and 10 years out of date
And worst of all:
- ...most Python developers think Python does not support threads
This is madness!
The perfect module for faster Python code for IO-bound tasks is right there in the standard library, and very few Python developers know it exists or can locate it, let alone believe it will work.
One group of developers know about this module and how to use it well.
Python machine learning developers.
My Story: Who Is Jason Brownlee?
(from Python ML engineer to Python concurrency expert and evangelist)
Hi, I'm Jason Brownlee, Ph.D.
I'm a Python developer, husband, and father to two boys.
I want to share something with you.
I am obsessed with Python concurrency, but I wasn't always this way.
My background is in Artificial Intelligence and I have a few fancy degrees and past job titles to prove it.
You can see my LinkedIn profile here:
- Jason Brownlee LinkedIn Profile
(follow me if you like)
Well, AI and machine learning has been hot for the last decade and I have spent that time as a Python machine learning developer:
- ... working on a range of predictive modeling projects.
- ... writing more than 1,000+ tutorials.
- ... and authoring over 20+ books.
There's one thing about machine learning in Python, your code must be fast.
Modeling code is already generally fast, built on top of C and Fortran code libraries.
But you know how it is on real projects...
You always have to glue bits together, wrap the fast code and run it many times, and so on.
Making code run fast requires Python concurrency and I have spent most of the last decade using all the different types of Python concurrency available.
asyncio, and the suite of popular libraries.
I know my way around Python concurrency and I am deeply frustrated at the bad wrap it has.
This is why I started SuperFastPython.com where I share hundreds of free tutorials on Python concurrency.
And this is why I wrote this book...
"Python Threading Jump-Start"
...develop concurrent IO-bound programs and work with the GIL
You don't want to read a book or take a course.
You want faster Python code, and you want it yesterday.
I hear you.
This is not a textbook on Python Concurrency.
It's not a technical thesis on the internals of process-based concurrency in Python.
Instead, it's what you actually need.
It's a rapid-paced, 7-part ebook to get you started and get you good at using the
threading module, super fast.
Each of the 7 lessons was carefully designed to teach one critical aspect of the threading module, with explanations, code snippets and worked examples.
Each lesson ends with an exercise for you to complete to confirm you understood the topic, a summary of what was learned, and links for further reading if you want to go deeper.
Next, let's take a closer look at the 7-lessons in the book.
So What Are The Lessons?
...7 lessons to be completed over 7 days
This book is designed to bring you up-to-speed with how to use the
threading module as fast as possible.
As such, it is not exhaustive.
There are many topics that are interesting or helpful, that are not on the critical path to getting you productive.
This book is divided into 7 lessons, they are:
- Lesson 01: Thread-Based Concurrency
- Lesson 02: Create and Start New Threads
- Lesson 03: Configuring and Interacting with Threads
- Lesson 04: Synchronize and Coordinate Threads
- Lesson 05: Share Data Between Threads
- Lesson 06: Run Tasks with Reusable Workers in Thread Pools
- Lesson 07: Close, Stop, and Kill Threads
Next, let's look at the structure of each lesson.
So What is the Structure of Each Lesson?
...tutorial lessons followed by review and exercises
Each lesson has two main parts, they are:
- The body of the lesson.
- The lesson review.
The body of the lesson will introduce a topic with code examples, whereas the lesson review will review what was learned with exercises and links for further information.
Each lesson has a specific learning outcome and is designed to be completed in 10-to-20 minutes.
Each lesson is also designed to be self-contained so that you can read the lessons out of order if you choose, such as dipping into topics in the future to solve specific programming problems.
We Python developers learn best from real and working code examples. So each lesson has multiple large worked examples with sample output.
So, after completing the book, what will you know?
What You Will Know After Completing The Book
...from "Python Dev" to "Dev Who Can Write Multithreaded Programs"
This book will transform you from a Python developer into a Python developer that can confidently bring concurrency to your projects with the
After working through all of the lessons in this book, you will know:
- The difference between thread-based and process-based concurrency and the types of tasks that are well suited to the capabilities of the
- How to execute your own ad hoc functions concurrently using the
- How to identify the main thread and the life-cycle of new threads.
- How to configure a new thread and get access to all running threads and query their status.
- How to coordinate and synchronize threads using mutex locks, semaphores, condition variables and the full suite of concurrency primitives.
- How to share data between threads using shared global variables and how to return values from new threads using instance variables.
- How to send and receive data between threads using thread-safe queues.
- How to create and configure thread pools to execute ad hoc tasks using reusable worker threads.
- How to handle results and errors and query the status of asynchronous tasks executed in thread pools.
- How to close threads, and how to trigger a graceful shutdown or forcefully kill new threads.
How Long Will It Take To Finish?
...a few hours, or a week of one lesson per day
About 20-to-30 minutes per lesson, so a few hours total.
But that's too fast.
Instead, I recommend 1 lesson per day, over 7 days (1 week).
Work at your own pace.
There's no rush and I recommend that you take your time.
The book is designed to be read linearly from start to finish, guiding you from being a Python developer at the start of the book to being a Python developer that can confidently use the
threading module in your project by the end of the book.
In order to avoid overload, I recommend completing one or two lessons per day, such as in the evening or during your lunch break. This will allow you to complete the transformation in about one week.
I recommend you maintain a directory with all of the code you type from the lessons in the book. This will allow you to use the directory as your own private code library, allowing you to copy-paste code into your projects in the future.
I recommend trying to adapt and extend the examples in the lessons. Play with them. Break them. This will help you learn more about how the API works and why we follow specific usage patterns.
What Format Is the Ebook?
(pdf and epub)
The ebook is provide in 2 formats:
- PDF (.pdf): perfect for reading on the screen or tablet.
- EPub (.epub): perfect for reading on a tablet with a Kindle or iBooks app.
Who Is This Book For?
Before you pull the trigger, let's make sure it is a good fit for you.
This book is designed for Python developers who want to discover how to use and get the most out of the
threading module to write fast programs.
Specifically, this book is for:
- Developers that can write simple Python programs.
- Developers that need better performance from current or future Python programs.
- Developers that are working with IO-bound tasks.
This book does not require that you are an expert in the Python programming language or concurrency.
- You do not need to be an expert Python developer.
- You do not need to be an expert in concurrency.
What Version of Python is Used?
All code examples use Python 3.
Python 3.9+ to be exact.
Python 2.7 is not supported because it reached end of life in 2020.
Are There Code Examples?
There are 26 .py files.
Each lesson has one or more complete, standalone, and fully-working code examples.
The book is provided in a .zip file that includes a src/ directory containing all source code files used in the book.
How Many Pages Is The Book?
The PDF has 106 US letter size pages.
Can I Print The Book?
Although, I think it's better working through it on the screen.
- You can search, skip, and jump around really fast.
- You can copy-and-paste code examples.
- You can compare code output directly.
Is There Digital Rights Management (DRM)?
What if I Need Help?
The lessons were designed to be easy to read and follow.
Nevertheless, sometimes we need a little extra help.
A list of further reading resources is provided at the end of each lesson. These can be helpful if you are interested in learning more about the topic covered, such as fine grained details of the standard library and API functions used.
The conclusions at the end of the book provide a complete list of websites and books that can help if you want to learn more about Python concurrency and the relevant parts of the Python standard library. It also lists places where you can go online and ask questions about Python concurrency.
Finally, if you ever have questions about the lessons or code in this book, you can contact me any time and I will do my best to help. My contact details are provided at the end of the book.
Do I Get Free Updates to the Code and Book?
Each time I release an updated version, I will send you an email with a link so that you can download the latest version for free.
I want you to be happy, and I stand behind my materials.
If you decide that Python concurrency is not for you, or whatever reason, I'll understand.
I offer a 100% money back guarantee, no questions asked.
Get in touch at:
Can I Buy The Book Elsewhere?
You can get a kindle or paperback version from Amazon.
Is There a Paperback Version?
You can get a paperback version from Amazon.
Can I Download the Source Code Now?
The source code (.py) files are included in the .zip with the book.
Nevertheless, you can also download all of the code from the dedicated GitHub Project:
Does Python Concurrency Work on My OS?
Python parallelism is built into the Python programming language and works equally well on:
Does Python Concurrency Work on Hardware?
Python parallelism is agnostic to the underlying CPU hardware.
If you are running Python on a modern computer, then you will have support for parallelism, e.g. Intel, AMD, ARM, and Apple Silicon CPUs are supported.
Contact me directly, any time about this book or Python concurrency generally.
I'm here to help as best I can.
You can send an email directly to my inbox via:
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You'll get a .zip download containing both ebook formats (pdf and epub) and all .py code files.
FormatsPDF and EPUB
- You'll get a .zip download containing both ebook formats (pdf and epub) and all .py code files.
- Size2.09 MB
- FormatsPDF and EPUB