Hello Zahid
Indeed, if you have many network devices, executing this code will take quite a bit of time.
You can make your Python code execute faster by performing tasks concurrently. There are a few approaches you can take to achieve this:
- Multithreading: This approach allows you to run multiple threads in parallel. This is useful for I/O-bound tasks like network requests.
- Multiprocessing: This approach runs multiple processes in parallel, each with its own Python interpreter. It’s suitable for CPU-bound tasks.
- Asyncio: This approach is useful for writing single-threaded concurrent code using async/await syntax. It’s particularly useful for I/O-bound and high-level structured network code.
Since your scenario involves network I/O, using multithreading or asyncio would be appropriate. Rene doesn’t cover these in the Python course. Configuring either one of these is not trivial, so I can’t give specific solutions in a single post. But, you can explore these features to see syntax and methodologies to use. Here are some links to get you started:
If you try them out, let us know how you get along!
I hope this has been helpful!
Laz