Concurrent and Parallel Programming in Python: Speed up your programs with concurrency and parallelism in Python

In a big data project, a plethora of information is retrieved, big numbers are crunched on our machine, or both. If the coding is sequential or synchronous, our application will struggle to execute. Two mechanisms to alleviate such bottlenecks are concurrency and parallelism. In Python, concurrency is represented by threading, whereas multiprocessing achieves parallelism. This course begins with an introduction about potential programming speed bottlenecks and solving them. You will delve into Python concepts and create a Wikipedia Reader, Yahoo Finance Reader, Queues, and Master Scheduler. You will build a multi-threaded program to grab data from the Internet and parse and save them into a local database. Implement multiprocessing in Python, which lets us use multiple CPUs in our code. Learn about threading, multiprocessing, asynchronous wait, locking, multiprocessing queues, Pool Map Multiple Arguments, writing asynchronous programs, and combining async and multiprocessing. Upon completion, we can spread our workload over all cores available on the used machine. We will combine both elements, multiprocessing with asynchronous programming, to maximize benefit and CPU resource usage and minimize the time spent waiting for IO responses. You will create multi-threaded, asynchronous, multi-process programs to make programs run faster. All resources are available at: https://github.com/PacktPublishing/Concurrent-and-Parallel-Programming-in-Python

This course is aimed at intermediate- to mastery-level seeking programmers, API developers, web developers, and application developers who know basic- to intermediate-level Python coding beforehand. The topics on concurrency and parallelism expect one to be aware of basic to intermediate understanding of coding on Python. Prior knowledge of basic Python coding is desirable for optimal benefit from this course.