I’m trying to solve a problem, where I have many (on the order of ten thousand) URLs, and need to download the content from all of them. I’ve been doing this in a “for link in links:” loop up till now, but the amount of time it’s taking is now too long. I think it’s time to implement a multithreaded or multiprocessing approach. My question is, what is the best approach to take?
I know about the Global Interpreter Lock, but since my problem is network-bound, not CPU-bound, I don’t think that will be an issue. I need to pass data back from each thread/process to the main thread/process. I don’t need help implementing whatever approach (Terminate multiple threads when any thread completes a task covers that), I need advice on which approach to take. My current approach:
data_list = get_data(...)
output = []
for datum in data:
output.append(get_URL_data(datum))
return output
There’s no other shared state.
I think the best approach would be to have a queue with all the data in it, and have several worker threads pop from the input queue, get the URL data, then push onto an output queue.
Am I right? Is there anything I’m missing? This is my first time implementing multithreaded code in any language, and I know it’s generally a Hard Problem.
For your specific task I would recommend a multiprocessing worker pool. You simply define a pool and tell it how many processes you want to use (one per processor core by default) as well as a function you want to run on each unit of work. Then you ready every unit of work (in your case this would be a list of URLs) in a list and give it to the worker pool.
Your output will be a list of the return values of your worker function for every item of work in your original array. All the cool multi-processing goodness will happen in the background. There is of course other ways of working with the worker pool as well, but this is my favourite one.
Happy multi-processing!