How much time do scikit classifiers take to classify? I am planning to use scikit linear support vector machine (SVM) classifier for text classification on a corpus consisting of 1 million labeled documents. What I am planning to do is, when a user enters some keyword, the classifier will first classify it in a category, and then a subsequent information retrieval query will happen in within the documents of that category catagory. I have a few questions:


How do I confirm that classification will not take much time? I don't want users to have to spend time waiting for a classification to finish in order to get better results.
Is using Python's scikit library for websites/web applications suitable for this?
Does anyone know how amazon or flipkart perform classification on user queries, or do they use a completely different logic?
I don't see a huge problem here. So, I would try to answer all of your questions from the production-level point of view: 


  How do I confirm that classification will not take much time?


Take a subset of the corpus data you have (you can randomly do it, no need of sampling), and test your algorithm on it, and they approximate/generalize it to the overall dataset.

(SVM is comparatively faster. Nevertheless, do the above process just to be sure.)

And do test it in the development environment before pushing to production.


  Is using Python's scikit library for websites/web applications
  suitable for this?


Yes, it is. It is already being used by a nice chunk of companies out there.

The third question about Amazon and Flipkart cannot be answered by someone outside their teams.

In addition, I would advise you to use the mapreduce techniques for training your models. And as already advised, pickle your models so that you don't need to train them with every request.