The Proper Way To Scale Python Tornado Application

I am searching for some way to scale one instance of tornado application to many. I have 5 servers and want to run at each 4 instances of application. The main issue I don't know h

Solution 1:

I'm actually looking at something similar and the thought I have come up with is this. Use the Python Multiprocessing module ( http://docs.python.org/library/multiprocessing.html ) to link the processes together in that way on the individual servers. Then use a memcached server for session specific data. (SessionIDs, IP information, information used to tie the session to a specific user and to the thread of activity they are using) The rest being data driven from a DB instance.

Solution 2:

What you could do is for each server you run a memcached instance and a tornado instance. Make the memcached instances "Master replicate" with each other using repcached so each instance of tornado can access memcached data from its machine. Four servers for the tornado and memcached instances and the fifth to run haproxy to load balance the others.

www.haproxy.org/

repcached.lab.klab.org/