Run Concurrent and Asynchronous Tasks, Once You Learn The ThreadPoolExecutor
Imagine developing Python programs that effortlessly scaled to as many as 1,000+ concurrent tasks (and much more).
Your python code does not have to be slow! (or limited by the GIL)
If you can invest as little as one hour per day, then in one week you will know how to confidently bring concurrent programming to your current and future projects.
Convinced? Jump to the book, otherwise read on…
How Much Faster Could Your Code Run
(if it used 100s or 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!
- You could change a slow sequential for-loop into a blazingly-fast concurrent for-loop.
- You could change run-and-wait tasks into fire-and-forget asynchronous tasks.
This is possible right now with a lesser-known Python class that offers super-easy-to-use thread-based concurrency (and is already installed on your system).
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 ThreadPoolExecutor class and the right types of tasks.
But What About The GIL…?
Have you heard this before:
“Python doesn’t support threads because of the Global Interpreter Lock (GIL).“
Yes, it’s true. Python threads are 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.
- 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.
And the easiest way to use threads is with a ThreadPoolExecutor class.
ThreadPoolExecutor: The Modern Thread Pool Class
The ThreadPoolExecutor class provides easy-to-use thread-based concurrency for IO-bound tasks.
This is not some random third-party library, this is a class provided in the Python standard library (already installed on your system).
The ThreadPoolExecutor class has been in the standard library since Python 2 and has been improved further since Python 3.
This is the class you need to use to make your code run faster.
It is specifically designed for you to run for loops concurrently.
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 API docs, making it impossible to find.
- The few examples out there on the web are terse and limited.
This is madness!
The perfect class for faster concurrent Python code is right there in the standard library, and very few people know it exists or can locate it, let alone decipher the API documentation.
“Python ThreadPoolExecutor Jump-Start” is my new book that will teach you how to develop concurrent loops in Python, from scratch.
This book distills only what you need to know to get started and be effective with the ThreadPoolExecutor class, super fast.
It’s exactly how I would teach you how to use the ThreadPoolExecutor if we were sitting together, pair programming.
- 7 lessons taught by example with full working code listings.
- 28 .py code files included that you can use as templates
- 99 pages for on-screen reading open next to your editor.
- 2 formats (PDF and EPUB) for screen, tablet, and kindle reading.
- 2 megabyte .zip download that contains the ebook and all source code.
“Python ThreadPoolExecutor Jump-Start” will lead you on the path from a Python developer interested in concurrency to a developer that can confidently develop concurrent for-loops using threads.
- No fluff.
- Just concepts and code.
- With full working examples.
The book is divided into 7 lessons.
The idea is you can read and work through one lesson per day, and become capable in one week.
It is the shortest and most effective path that I know of transforming you into a Python Concurrency Engineer.
Choose your package:
All prices are in USD.
You Get 7 Laser-Focused Lessons
The structure of the book is a course with 7 lessons.
- Lesson 01: Threads, Executors, and Thread Pools
- What is Thread-Based Concurrency
- Thread Pools Provide Reusable Workers
- ThreadPoolExecutor for Python Thread Pools
- Welcome to the ThreadPoolExecutor Class
- When to Use the ThreadPoolExecutor
- Lesson 02: Configure the ThreadPoolExecutor
- How to Configure the ThreadPoolExecutor
- How to Inspect the Default Configuration
- How to Configure the Number of Worker Threads
- How to Configure the Worker Thread Names
- How to Configure Worker Thread Initialization
- Lesson 03: Execute Multiple Tasks Concurrently
- What is the map() Method
- How to Execute Tasks with One Argument
- How to Execute Tasks with Multiple Arguments
- How to Execute Tasks with No Return Values
- How to Execute Tasks with a Timeout
- Lesson 04: Execute One-Off Tasks Asynchronously
- What is the submit() Method
- How to Execute a Task with One Argument
- How to Execute a Task with Multiple Arguments
- How to Execute a Task with No Arguments
- How to Execute a Task with No Return Value
- How to Execute Many One-Off Tasks
- Lesson 05: Query Asynchronous Tasks
- What are Future Objects
- How to Check the Status of Future Objects
- How to Get Results From Future Objects
- How to Cancel Future Objects
- How to Add Callbacks to Future Objects
- How to Get Exceptions From Future Objects
- Lesson 06: Manage Collections of Asynchronous Tasks
- What are the Module Functions
- How to Handle Results as Tasks Finish
- How to Wait for All Tasks
- How to Wait for the First Task
- How to Wait for First Task Failure
- Lesson 07: Case Study Checking the Status of Websites
- How to Check Website Status One-by-One (slow)
- How to Check Website Status Concurrently (fast)
Next, let’s look at the structure of each lesson.
Highly-structured lessons on how you can get results
Each lesson has two main parts, they are:
- 1) The lesson body that teaches the concepts and working code examples.
- 2) The lesson review that summarizes what was learned with exercises.
The body of the lesson will introduce a topic with code examples, whereas the lesson overview 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 about one hour.
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.
Next, let’s look at what you will know after finishing the book.
Your Learning Outcomes
Transform from “python developer” into “Python developer that can create scalable and concurrent programs using multithreaded programming“
After working through all of the lessons in this book, you will know:
- How to create a ThreadPoolExecutor to execute IO-bound tasks concurrently.
- How to configure the ThreadPoolExecutor including inspecting the default configuration, how to set the number of worker threads, thread names, and how to initialize worker threads.
- How to execute multiple tasks concurrently using the map() method.
- How to issue asynchronous tasks to the ThreadPoolExecutor using the submit() method.
- How to query, get results, handle exceptions and use callback functions with Future objects for asynchronous tasks.
- How to manage collections of asynchronous tasks issued to the ThreadPoolExecutor including how to handle results in task completion order and wait for all tasks or for the first task to complete.
- How to build upon what you have learned to speed-up the IO-bound task of checking website statuses one-by-one so that the websites are checked concurrently more than 5x faster.
You will learn from code examples, not pages and pages of fluff.
Get your copy now:
100% Money-Back Guarantee
(no questions asked)
I want you to actually learn the ThreadPoolExecutor class so well that you can confidently use it on current and future projects.
I designed my book to read just like I’m sitting beside you, showing you how.
I want you to be happy. I want you to win!
I stand behind all of my materials. I know they get results and I’m proud of them.
Nevertheless, if you decide that my books are not a good fit for you, I’ll understand.
I offer a 100% money-back guarantee, no questions asked.
To get a refund, contact me with your purchase name and email address.
Frequently Asked Questions
This section covers some frequently asked questions.
If you have any questions. Contact me directly. Any time about anything. I will do my best to help.
What are your prerequisites?
This book is designed for Python developers who want to discover how to use and get the most out of the ThreadPoolExecutor class.
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 want to learn concurrent programming in Python.
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 do you need?
All code examples use Python 3.
Python 3.9+ to be exact.
Python 2.7 is not supported because it reached its “end of life” in 2020.
Are there code examples?
There are 28 .py code 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 long will the book take you to finish?
About 1 hour per lesson.
Work at your own pace.
I recommend 1 lesson per day, over 7 days (1 week).
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 ThreadPoolExecutor class in your projects by the end of the book.
In order to avoid overload, I recommend completing one lesson 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 book?
You can read the book on your screen, next to your editor.
You can also read the book on your tablet, away from your workstation.
The ebook is provided 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.
Many developers like to read technical books on their tablet or iPad.
How can you get more help?
The lessons in this book 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.
How many pages is the book?
The PDF is 99 US letter-sized pages.
Can you print the book?
Although, I think it’s better to work 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?
The ebooks have no DRM.
Do you get free updates?
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.
You can also email me any time and I will send you the latest version.
Can you buy the book elsewhere?
You can get a kindle or paperback version from Amazon.
Many developers prefer to buy from the kindle store on amazon directly.
Can you get a paperback version of the book?
You can get a paperback version from Amazon.
Can you read a sample of the book?
You can read a book sample via google books “preview” or via the amazon “look inside” feature:
Generally, if you like my writing style on SuperFastPython, then you will like the books.
Can you 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 the ThreadPoolExecutor class work on your operating system?
Python concurrency is built into the Python programming language and works equally well on:
Does the ThreadPoolExecutor class work on your hardware?
Python concurrency is agnostic to the underlying CPU hardware.
If you are running Python on a modern computer, then you will have support for concurrency, e.g. Intel, AMD, ARM, and Apple Silicon CPUs are supported.
How does the “Jump-Start Book” compare to the “Complete Guides“?
The SuperFastPython Jump-Start books are laser-focused on making you productive with a Python concurrency module or class as fast as possible.
This means that many broader topics are not covered because they are not on the critical path.
The guides on SuperFastPython.com are broader in scope and compare the class or module to related modules, describing best practices, common errors, common usage questions, and common objects.
This material may be interesting but is a distraction when you are focused on getting productive as fast as possible.
Another important difference is that the jump-start books are provided in book form, whereas the guides are very long web pages.
This makes the books easy to read on a kindle, tablet, or paperback, as well as the screen, whereas the guides must be read in the browser.
About the Author
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. 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.
- 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.
Including threading, multiprocessing, 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 you can find hundreds of free tutorials on Python concurrency.
And this is why I wrote this book.
Praise for Super Fast Python
Python developers write to me all the time and let me know how helpful my tutorials and books have been.
Below are some select examples posted to LinkedIn.
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