I doubt this is even possible, but here is the problem and proposed solution (the feasibility of the proposed solution is the object of this question):



I have some "global data" that needs to be available for all requests. I'm persisting this data to Riak and using Redis as a caching layer for access speed (for now...). The data is split into about 30 logical chunks, each about 8 KB.
Each request is required to read 4 of these 8KB chunks, resulting in 32KB of data read in from Redis or Riak. This is in ADDITION to any request-specific data which would also need to be read (which is quite a bit).
Assuming even 3000 requests per second (this isn't a live server so I don't have real numbers, but 3000ps is a reasonable assumption, could be more), this means 96KBps of transfer from Redis or Riak in ADDITION to the already not-insignificant other calls being made from the application logic. Also, Python is parsing the JSON of these 8KB objects 3000 times every second.



All of this - especially Python having to repeatedly deserialize the data - seems like an utter waste, and a perfectly elegant solution would be to just have the deserialized data cached in an in-memory native object in Python, which I can refresh periodically as and when all this "static" data becomes stale. Once in a few minutes (or hours), instead of 3000 times per second.
But I don't know if this is even possible. You'd realistically need an "always running" application for it to cache any data in its memory. And I know this is not the case in the nginx+uwsgi+python combination (versus something like node) - python in-memory data will NOT be persisted across all requests to my knowledge, unless I'm terribly mistaken.
Unfortunately this is a system I have "inherited" and therefore can't make too many changes in terms of the base technology, nor am I knowledgeable enough of how the nginx+uwsgi+python combination works in terms of starting up Python processes and persisting Python in-memory data - which means I COULD be terribly mistaken with my assumption above!



So, direct advice on whether this solution would work + references to material that could help me understand how the nginx+uwsgi+python would work in terms of starting new processes and memory allocation, would help greatly. 
P.S:

Have gone through some of the documentation for nginx, uwsgi etc but haven't fully understood the ramifications per my use-case yet. Hope to make some progress on that going forward now
If the in-memory thing COULD work out, I would chuck Redis, since I'm caching ONLY the static data I mentioned above, in it. This makes an in-process persistent in-memory Python cache even more attractive for me, reducing one moving part in the system and at least FOUR network round-trips per request.
"python in-memory data will NOT be persisted across all requests to my knowledge, unless I'm terribly mistaken."
you are mistaken.
the whole point of using uwsgi over, say, the CGI mechanism is to persist data across threads and save the overhead of initialization for each call. you must set processes = 1 in your .ini file, or, depending on how uwsgi is configured, it might launch more than 1 worker process on your behalf. log the env and look for 'wsgi.multiprocess': False and 'wsgi.multithread': True, and all uwsgi.core threads for the single worker should show the same data.
you can also see how many worker processes, and "core" threads under each, you have by using the built-in stats-server.
that's why uwsgi provides lock and unlock functions for manipulating data stores by multiple threads.
you can easily test this by adding a /status route in your app that just dumps a json representation of your global data object, and view it every so often after actions that update the store.
Persistent in-memory Python object for nginx/uwsgi server