Concurrent and Parallel Programming in Python
What you'll learn
- How to use concurrency and parallelism in Python
- How to write multi-threaded programs
- How to write multi-process programs
- How to write asynchronous programs
Course content
- Preview13:52
- 18:52Threading in Python
- 14:58Creating a Threading Class
- 14:05Creating a Wikipedia Reader
- 16:23Creating a Yahoo Finance Reader
- 15:06Queues and Master Scheduler
- 21:54Creating a Postgres Worker
- 20:11Integrating the Postgres Worker
- 18:30Yaml File Intro
- 30:54Creating a Yaml Reader
- 28:21Improving Our Wiki Worker
- 29:01Improving All Workers and Adding Monitoring
- 07:13Final Program Cleanup
- 12:06Locking
Requirements
- Basic familiarity with Python
Description
In this course you'll learn how to create multi-threaded, asynchronous, and multi-process programs in Python, so that you can make your programs run even faster.
In applications communicating with other resources, a lot of time is spent just waiting for information to be passed from one place to another. You'll learn how to use multi-threading as well as asynchronous programming to speed up programs that are heavily bottlenecked by IO operations.
We'll go through an introduction first of where potential speed bottlenecks come from as well as how we could solve these issues, and then we'll dive directly into the technical content and build out a multi-threaded program together that grabs data from the internet, parses, and saves it into a local database.
Other programs may be more heavily affected by CPU limitations. We'll also learn how to implement multiprocessing in Python, the library that lets us use multiple CPUs in our Python code. With this we'll be able to spread our workload over all the cores available on the machine we're using.
Finally, we'll also look to combine both elements, taking a look at how we can use multiprocessing together with asynchronous programming to get the most benefit for yourself, maximizing your use of CPU resources and minimizing time spent siting idle waiting for IO response.
You can find the lecture code in the GitHub repository linked in the first lesson.
Who this course is for:
- Python developers that want to make their programs faster by adding concurrency
Instructor
![Maximilian Schallwig](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 5,941 Reviews
- 47,405 Students
- 9 Courses
I've worked for over two years in physics research and mathematical analysis. I participated in two international physics competitions, where my two teammates and I won silver and gold. My thesis was in the field of Quantum Biology, focusing on analyzing the behavior of excitons at room temperature with electronic interaction.
Due to my affinity for math and statistics from my studies in physics, I tend towards data mining, processing, and analysis, which are also the things that I find most exciting.
I enjoy learning new methods and developing my skills, and am constantly studying new literature and documentation to find exciting material that can be applied in the field of data analysis.
If you want to keep up with what else I'm doing in the fields of programming, data, and data science, you can check me out at codingwithmax.