pystore | Fast data store for Pandas time-series data | Database library

I'm developing a Django app. Use-case scenario is this:

50 users, each one can store up to 300 time series and each time serie has around 7000 rows.

Each user can ask at any time to retrieve all of their 300 time series and ask, for each of them, to perform some advanced data analysis on the last N rows. The data analysis cannot be done in SQL but in Pandas, where it doesn't take much time... but retrieving 300,000 rows in separate dataframes does!

Users can also ask results of some analysis that can be performed in SQL (like aggregation+sum by date) and that is considerably faster (to the point where I wouldn't be writing this post if that was all of it).

Browsing and thinking around, I've figured storing time series in SQL is not a good solution (read here).

Ideal deploy architecture looks like this (each bucket is a separate server!):

Problem: time series in SQL are too slow to retrieve in a multi-user app.

Researched solutions (from this article):

• PyStore: https://github.com/ranaroussi/pystore

• Arctic: https://github.com/manahl/arctic

Here are some problems:

1) Although these solutions are massively faster for pulling millions of rows time series into a single dataframe, I might need to pull around 500.000 rows into 300 different dataframes. Would that still be as fast?

This is the current db structure I'm using: