At least in my experience it is all still the case today.
If you are dealing exclusively with documents I find MongoDB to be faster, better and easier to use. If my data model is hybrid or purely relational then I would use PostgreSQL.
After all these years I am still waiting for an horizontal scalability solution for PostgreSQL that is up to the level of a modern database. It's 2023 and for many of us in enterprise environments it's a mandatory NFR.
But it seems like you're not interested in a having an actual technical discussion.
It has serious limitations and needs a lot of elbow grease to run at scale. With some of the alternatives (for example MongoDB) you basically just add a few more machines and you are good.
In my opinion Citus is still only a hack which is only useful if for some reason you can't move away from Postgres but you have reached the limit of what you can get out of a single machine.
There are many examples but let's take one: star schemas.
I can implement that in MongoDB such that a query can be done in O(1) whereas implementing this in a relational database requires n joins which in turn requires n foreign key indexes and n row fetches. In many cases this approaches O(n). I have personally seen improvements of 10-100x for this use case when n is large.
Also there is a reason that the standard now in enterprise companies are data lakes and not EDWs. Because the cost and effort to model all of your data is prohibitive especially when 99% of the data ends up never being queried.
If you are dealing exclusively with documents I find MongoDB to be faster, better and easier to use. If my data model is hybrid or purely relational then I would use PostgreSQL.
After all these years I am still waiting for an horizontal scalability solution for PostgreSQL that is up to the level of a modern database. It's 2023 and for many of us in enterprise environments it's a mandatory NFR.
But it seems like you're not interested in a having an actual technical discussion.