Pandas as fast data storage for Flask application

Question

I\'m impressed by the speed of running transformations, loading data and ease of use of Pandas and want to leverage all these nice properties (amongst others) to mo

Answer 1

Answers to some aspects of what you're asking for:

It's not quite clear from your description whether you have the tables in your SQL database only, stored as HDF5 files or both. Something to look out for here is that if you use Python 2.x and create the files via pandas' HDFStore class, any strings will be pickled leading to fairly large files. You can also generate pandas DataFrame's directly from SQL queries using read_sql, for example.

If you don't need any relational operations then I would say ditch the postgre server, if it's already set up and you might need that in future keep using the SQL server. The nice thing about the server is that even if you don't expect concurrency issues, it will be handled automatically for you using (Flask-)SQLAlchemy causing you less headache. In general, if you ever expect to add more tables (files), it's less of an issue to have one central database server than maintaining multiple files lying around.

Whichever way you go, Flask-Cache will be your friend, using either a memcached or a redis backend. You can then cache/memoize the function that returns a prepared DataFrame from either SQL or HDF5 file. Importantly, it also let's you cache templates which may play a role in displaying large tables.

You could, of course, also generate a global variable, for example, where you create the Flask app and just import that wherever it's needed. I have not tried this and would thus not recommend it. It might cause all sorts of concurrency issues.