Multithreading for parallel processing

As we are now focusing on writing our scripts efficiently, a major aspect of this is how efficiently, quickly, and correctly we fetch the information. When we use the for loop, we parse through each item one by one, which is fine if we get results quickly.

Now, if each item in a for loop is a router from which we need to get the output of show version, and if each router takes around 10 seconds to log in, gather the output, and log out, and we have around 30 routers that we need to get this information from, we would need 10*30 = 300 seconds for the program to complete the execution. If we are looking for more advanced or complex calculations on each output, which might take up to a minute, then it will take 30 minutes for just 30 routers.

This starts becoming very inefficient when our complexity and scalability grows. To help with this, we need to add parallelism to our programs. What this simply means is, we log in simultaneously on all 30 routers,...