On Thu, Mar 28, 2019 at 5:33 PM Jonathan Vanasco <[email protected]> wrote:
>
> I gave up on attempts to do something similar a while back, because it became 
> to problematic to examine all the SqlAlchemy objects – and the existing query 
> – in an effort to construct the joins and query correctly.
>
> I would up using a two-phase approach. phase 1 analyzes the 'requested 
> metrics' to figure out which tables and columns are needed, and raises an 
> error if things look bad. phase 2 generates the query.  I use a python dict 
> to store metadata about the query as it is analyzed, using the tables as keys 
> and building an array of the columns - this way i only join the table once.  
> based on what tables are needed in the dict, or other data on the metrics I 
> pre-calculate, i know how to structure the joins. this approach is somewhat 
> restricting, but works very well, is quick to deploy and easy to maintain.

is this some standard thing you're both doing?   I didn't see anything
about joins or query analyzing.    you often have answers for
questions where I don't understand what theyre asking!

>
> --
> SQLAlchemy -
> The Python SQL Toolkit and Object Relational Mapper
>
> http://www.sqlalchemy.org/
>
> To post example code, please provide an MCVE: Minimal, Complete, and 
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-- 
SQLAlchemy - 
The Python SQL Toolkit and Object Relational Mapper

http://www.sqlalchemy.org/

To post example code, please provide an MCVE: Minimal, Complete, and Verifiable 
Example.  See  http://stackoverflow.com/help/mcve for a full description.
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