We use rethinkdb in our current project: Python + timeseries data + mining. Pretty happy with it out of the box compared to mysql for speeds.
And that takes into consideration that Rethink uses all cores and Postgres only one. Plus I used jsonb in Postgres to make it totally fair.
And on top I encountered really inconsistent performance. For example when you access a field of an object that doesn't exist, it apparently raises internally an exception and that slows things down a lot. But you don't really see this.
I get it that Rethink is trying to distinguish itself from the other players and find a nice niche for itself but I really wish they improved on the performance and while at it removed the limitation of 32 servers in a cluster.
Oh and the huge blow up of data on disk also needs a lot of work. Rethink used about 5x more disk space in my trials for a table with about 10 million rows. With fewer indexes than PG.
I really like the ops simplicity of Rethink, especially when it comes to clustering. So I have high hopes for it.
The challenge with database systems is that essentially they're complete runtimes -- the user can send a huge variety of queries which are programs that need to be executed by the runtime. So there is no way to fix performance once and for all -- it's a continuous process that never stops. We've worked out a lot of performance issues since then, and keep working on them every day. For anyone reading this, if there is a particular workload that's slow, please let us know and we'll work to prioritize it ASAP.