Unlock concurrency with Python threads (and run 100s or 1,000s of tasks simultaneously) The threading module provides easy-to-use thread-based concurrency in Python. Unlike Python multiprocessing, the threading module is limited by the infamous Global Interpreter Lock (GIL). Critically, the GIL is released when performing blocking I/O. Additionally, threads can share memory making them perfectly suited to I/O-bound tasks such as reading and writing from files and socket connections. This is the API you need to use to make your code run faster. Introducing: "Python Threading Jump-Start". A new book designed to teach you the threading module in Python, super fast! You will get a rapid-paced, 7-part course to get you started and make you awesome at using the threading API. Each of the 7 lessons was carefully designed to teach one critical aspect of the threading module, with explanations, code snippets and worked examples. You will discover: * How to choose tasks that are well suited to threads. * How to create and run new threads. * How to locate and query running threads. * How to use locks, semaphores, barriers and more. * How to share data between threads using queues. * How to execute ad hoc tasks with reusable worker threads. * How to gracefully stop and forcefully kill threads. Each lesson ends with an exercise for you to complete to confirm you understand the topic, a summary of what was learned, and links for further reading if you want to go deeper. Stop copy-pasting code from StackOverflow answers. Learn Python concurrency correctly, step-by-step.

How much faster could your Python code run (if you used 100s of threads)? The ThreadPool 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). This is the class you need to make your code run faster. There's just one problem. No one knows about it (or how to use it well). Introducing: "Python ThreadPool Jump-Start". A new book designed to teach you thread pools in Python, super fast! You will get a rapid-paced, 7-part course to get you started and make you awesome at using the ThreadPool. Including: * How to create thread pools and when to use them. * How to configure thread pools including the number of threads. * How to execute tasks with worker threads and wait for results. * How to execute tasks in the thread pool asynchronously. * How to execute tasks lazily and respond to results as tasks complete. * How to handle results with callbacks and check the status of tasks. * How to develop a port scanner that is 70x faster than the sequential version. Each of the 7 lessons was carefully designed to teach one critical aspect of the ThreadPool, 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. Stop copy-pasting code from StackOverflow answers. Learn Python concurrency correctly, step-by-step.

How much faster could your Python code run (if you used 100s of thread workers)? The ThreadPoolExecutor class provides modern thread pools 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). This is the class you need to make your code run faster. There's just one problem. No one knows about it (or how to use it well). Introducing: "Python ThreadPoolExecutor Jump-Start". A new book designed to teach you thread pools in Python, super fast! You will get a rapid-paced, 7-part course to get you started and make you awesome at using the ThreadPoolExecutor. Including: * How to create thread pools and when to use them. * How to configure thread pools including the number of threads. * How to execute tasks with worker threads and handle for results. * How to execute tasks in the thread pool asynchronously. * How to query and get results from handles on asynchronous tasks called futures. * How to wait on and manage diverse collections of asynchronous tasks. * How to develop a concurrent website status checker that is 5x faster than the sequential version. Each of the 7 lessons was carefully designed to teach one critical aspect of the ThreadPoolExecutor, 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. Stop copy-pasting code from StackOverflow answers. Learn Python concurrency correctly, step-by-step.

Unlock parallel programming in Python (and run your code on all CPUs). The multiprocessing module provides easy-to-use process-based concurrency in Python. Unlike Python threading, multiprocessing side-steps the infamous Global Interpreter Lock (GIL), allowing full parallelism in Python. This is not some random third-party library, this is an API provided in the Python standard library (already installed on your system). This is the API you need to use to make your code run faster. There's just one problem. Few developers know about it (or how to use it well). Introducing: "Python Multiprocessing Jump-Start". A new book designed to teach you the multiprocessing module in Python, super fast! You will get a fast-paced, 7-part course to get you started and make you awesome at using the multiprocessing API. Each of the 7 lessons was carefully designed to teach one critical aspect of the multiprocessing module, with explanations, code snippets and worked examples. Each lesson ends with an exercise for you to complete to confirm you understand the topic, a summary of what was learned, and links for further reading if you want to go deeper. Stop copy-pasting code from StackOverflow answers. Learn Python concurrency correctly, step-by-step.

Asyncio is an exciting new addition to Python. It allows regular Python programs to be developed using the asynchronous programming paradigm. It includes changes to the language to support coroutines as first-class objects, such as the async def and await expressions, and the lesser discussed async for and async with expressions for asynchronous iterators and context managers respectively. Asyncio is the way to rapidly develop scalable Python programs capable of tens or hundreds of thousands of concurrent tasks. Developing concurrent programs using coroutines and the asyncio module API can be very challenging for beginners, especially those new to asynchronous programming. Introducing: "Python Asyncio Jump-Start". A new book designed to teach you asyncio in Python, super fast! You will get a rapid-paced, 7-part course focused on getting you started and make you awesome at using asyncio. Including: * How to define, schedule, and execute asynchronous tasks as coroutines. * How to manage groups of asynchronous tasks, including waiting for all tasks, the first that, or the first task to fail. * How to define, create, and use asynchronous iterators, generators, and context manages * How to share data between coroutines with queues and how to synchronize coroutines to make code coroutine-safe. * How to run commands as subprocesses and how to implement asynchronous socket programming with streams. * How to develop a port scanner that is nearly 1,000 times faster than the sequential version. Each of the 7 lessons was carefully designed to teach one critical aspect of asyncio, with explanations, code snippets, and complete 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. Stop copy-pasting code from StackOverflow answers. Learn Python concurrency correctly, step-by-step.