Just one talk this time and it was more of a discussion of the cool things you can do with Postgres JSON fields. These are indeed very cool! Everything I wanted to do with NoSQL historically is now present in a relational database without compromise on performance or functionality, that is an amazing achievement by the Postgres team.
The one thing I did learn is that all the coercion and encoding information is held in the Django model and query logic which means you only have basic types in the column. I previously worked on a codebase that used SQLAlchemy and a custom encoder and decoder which split custom types into a string field with the Python type hint (e.g. Decimal, UUID) and the underlying value. By comparison with the Django implementation which appears to just use strings this is a leaky abstraction where the structure of the data is compromised by the type hint.
Using the Django approach would have been easier when using direct SQL on the database and followed the principle of least surprise.
The speaker was trying to make a case for performing aggregate calculations in the database but via the Django ORM query language which wasn’t entirely convincing. Perhaps if you have a small team but the resulting query language code was more complex that the underlying query and was quite linked to the Postgres implementation so it felt that maybe a view would have been a better approach unless you have very dynamic calculations that are only applied for a fixed timespan.
It was based on an experience report so it clearly worked for the implementing group but if felt like the approach strongly coupled the database, the web framework and the query language.