Race Condition and How to Solve it - threading.Lock() | 2MinutesPy

#racecondition #concurrency #threading #threads #python #pythonprogramming #2minutespy
🖐Hey, have you ever heard of "race condition"?
Race condition?? What is that? You might ask the same question, don't worry, in this video, we'll see the race condition and how to solve it.
Race condition occurs when we run concurrent or multi-threaded programs as they access shared resources simultaneously leading to unpredictable/inconsistent results.
We can solve it using the threading.Lock() that restricts multiple threads to access shared resources simultaneously.
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@2MinutesPy

[@champfisk5613]

Now this is practical and good

[@2MinutesPy]

Thanks

[@sachinpal5823]

Nice topic🎉

[@2MinutesPy]

Thanks 😊

[@cyberlando]

I remember reading in a book that you can parallelize work in python like that …. Python doesn’t have true multithreading capabilities isn’t that correct ?

[@2MinutesPy]

Yeah, it's right and all of this because of GIL but still you can perform parallelize task

[@KasimAhmic]

Python’s threading module allows you to run many tasks in parallel, but with only one of those tasks being executed at a time. Python handles switching between the tasks whenever there is “downtime” within the tasks. This downtime usually comes in the form of IO operations like reading/writing to a file, making network requests, etc. What Python can do is pause execution of one task that is already waiting for the OS to finish writing to a file (for example), and resume execution of another task that isn’t waiting on anything. Now if each task is doing something synchronous or CPU intensive, say encryption/decryption, Python’s threading module will give you no benefit whatsoever as each task will block the next until it is done with the encryption/decryption. In this case, there is no downtime and Python is unable to pause/resume execution of the tasks. To combat this limitation, Python also has the multiprocessing module which allows you to spawn separate Python interpreters in completely separate processes and enables communication between said processes. This allows you to run as many tasks as your computer can handle (usually limited by core count) with all of them executing simultaneously at the cost of increased CPU and memory usage. As a general rule of thumb, if you have an IO heavy application (i.e. one that spends most of its time idle, waiting on external systems) the threading module is for you. On the other hand, if you have a largely synchronous application where you spend most of your time executing Python code, multiprocessing is the way to speed things up.

[@2MinutesPy]

Great, that's some valuable info

[@FeverYonge]

Make a video about concurrent.future use for accessing (login and do stuff) multiple network devices at same time, without waiting for the first task to finish

[@2MinutesPy]

Yeah, sure