You may have dramatically less 'real data' than mongo makes you think you do. I migrated one of our mid sized database out of mongo and into PG a couple years ago. The reduction in size was massive. One table in particular that was storing a small number of numeric fields per doc went from ~10GB to ~50MB. I wouldn't expect this with all datasets of course, but mongo's document + key storage overhead can be massive in some use cases.
In a relational database, you have columns with names and types, and that info is shared by all of the rows.
In Mongo, every cell has to specify its name and type, even if that layout is shared by every other cell in the document.
Mongo's way is more flexible, but it's terrible for storage efficiency.
it doesn’t really have to approach the schemas that way. one would think it would be optimized for repeat schema in the same way one might create the schema definition and then reference it in packing and unpacking the data. seems like if there was schema overhead taking up storage unnecessarily that could be optimized relatively easily.
having schema references also might be a good management tool to understand which records vary potentially due to an application evolving it’s needs.
I don't think that's the problem Mongo is designed to solve. Mongo's promise was the ability to work with un- and semi- structured data, and it would make sense if its optimizations were focused on that problem, not on reducing overhead when someone tries to strongarm it into being MySQL.
Generally speaking, if your data is structured well enough that you can define a schema ahead of time, you're better off with a traditional RDBMS, because that's the problem an RDBMS is designed to solve.
JSON is a great format for simplicity and readability but as a storage format it's hard to come up with one that's more bloated.
It's easy: xml.
I'm not sure exactly what you mean, but that particular collection only had a single index on it (outside the ID column).