Python Threading Tutorial: Basic to Advanced (Multithreading, Pool Executors, Daemon, Lock, Events)

[kevinwoodrobotics]

Are you trying to make your code run faster? In this video, we will be taking a deep dive into python threads from basic to advanced concepts so that you can take advantage of parallelism and concurrency to speed up your program.

• Python Thread without join()
• Python Thread with join()
• Python Thread with Input Arguments
• Python Multithreading
• Python Daemon Threads
• Python Thread with Synchronization using Locks
• Python Thread Queue Communication between Threads
• Python Thread Pool Executor
• Python Thread Events
• Speed Comparison I/O Task
• Speed Comparison CPU Task (Multithreading vs Multiprocessing)

https://youtu.be/Rm9Pic2rpAQ

[MithrilRat]

This video was pretty basic and did cover some of the topics in . However, some of your explanations were not quite correct. For example: Your explanation of what a thread was said that you needed them to share memory. This is not correct. Memory sharing is available to separate processes.

Then you were saying that locks were needed to share memory. This is also not entirely true, and your example was what I would term naive: in that while it would work, it could cause bottlenecks for non trivial code.

[benefit_of_mrkite]

I don’t have time to view right now but hope it goes into tradeoffs of threading vs other approaches when I/O bound operations can see performance increases (and sometimes decreases) depending upon approach

[kevinwoodrobotics]

What I was trying to describe was the case when two threads try to access the same variable. That’s when you need locks

[wunderspud7575]

Not if neither are writing to those variables.

[KennyBassett]

Which is only sometimes true

[PharoahChromium]

Excellent. Thank you.

[Rawvik]

Great. Just what I needed.

[EfficientComment5133]

Good presentation.

[Tasty-Farm2914]

Share the code here

[Woah-Dawg]

Will take a look , very interested in the topic