I used MongoDB at a company where engineering policy was strictly that MongoDB was our only allowable database, including in cases where it was clearly not the best choice.
Were there good aspects? Sure... kind of. It was super super easy to just throw data into the database. Need to add a field? Who cares, just add it. Need to remove a field? Who cares, just remove it -- as long as the calling code is null-safe. And it was likewise super easy to store denormalized data for fast lookups when it made sense to do so, as well as deeply-nested things and arbitrary JSON blobs synced from our CMS. And queries could be stored and manipulated as actual Python dicts instead of opaque strings, whereas you normally need an ORM or query builder to do that in SQL. And you could get the best-of-both-worlds-ish with the ODMantic framework, where you could spit out a warning (but not an error) and recover gracefully if there happened to be bad data in the database.
Basically it allows you to forego any foresight or serious design, and instead just throw shit together. Which is great for getting a prototype going really fast and giving the appearance of a highly productive team. That is, until you run out of new microservices to prototype and now you have to start adding features to existing code and fixing bugs. IMO it was completely "penny-wise and pound-foolish" with respect to developer time, but I can at least see the appeal if you're a particular type of engineer operating under a particular set of incentives.
As for using MongoDB for things it was actually meant for (writing a ton of schemaless JSON blobs really fast and figuring out reads later), I have no idea because we never used it for that and had nothing really resembling that use case.
Were there good aspects? Sure... kind of. It was super super easy to just throw data into the database. Need to add a field? Who cares, just add it. Need to remove a field? Who cares, just remove it -- as long as the calling code is null-safe. And it was likewise super easy to store denormalized data for fast lookups when it made sense to do so, as well as deeply-nested things and arbitrary JSON blobs synced from our CMS. And queries could be stored and manipulated as actual Python dicts instead of opaque strings, whereas you normally need an ORM or query builder to do that in SQL. And you could get the best-of-both-worlds-ish with the ODMantic framework, where you could spit out a warning (but not an error) and recover gracefully if there happened to be bad data in the database.
Basically it allows you to forego any foresight or serious design, and instead just throw shit together. Which is great for getting a prototype going really fast and giving the appearance of a highly productive team. That is, until you run out of new microservices to prototype and now you have to start adding features to existing code and fixing bugs. IMO it was completely "penny-wise and pound-foolish" with respect to developer time, but I can at least see the appeal if you're a particular type of engineer operating under a particular set of incentives.
As for using MongoDB for things it was actually meant for (writing a ton of schemaless JSON blobs really fast and figuring out reads later), I have no idea because we never used it for that and had nothing really resembling that use case.