Node process with dedicated memory in Python
Simple answer : stop wasting your time trying to do complicated things that will never work right with your typical web server and store your data in a database (doesn't have to be a mysql database FWIW). 
Longest answer: in a production environment you typically have several parallel (sub)processes handling incoming requests, and any of those process can serve any user at any time, so keeping your data in memory in a process will never work reliably. This is by design and this is a sane design, so trying to fight against it is just a waste of time and energy. Web server processes are not meant to persist data before requests, that's what your database is for, so use it.
I'm developing with Apache and Django an web application application where users interacts with a data model (C++ implementation wrapped into Python). 
To avoid load / save data in a file or database after each user operation, I prefer keep data model in memory as long as the user is connected to the app. 
Until now, data models are stored into a variable attached to web service. As Python running under Apache has sometimes strange behavior, I'd prefer execute user operation into separated python process, today on same server, maybe tomorrow on a different node. 
I'm under the impression that Distributed computing library (dispy, dask distributed) does not enable to keep memory attached to a node. Does anyone have a solution / idea about what libraries could I use ?