I have no experience with RethinkDB, so maybe they did it right, but I have quite a bit with Mongo. MongoDB's querying is nowhere near as powerful as SQL. And

I have no experience with RethinkDB, so maybe they did it right, but I have quite a bit with Mongo. MongoDB's querying is nowhere near as powerful as SQL. And understanding its limitations and pain points are essential when structuring your data. Otherwise you'll never really scale unless you've got data that's embarrassingly easy to query (eg: filtering on 1 or 2 indexed fields). Being able to write JS map-reduce queries is fine when you need to do stuff ad-hoc, but none of it can be used in production at scale. Then there's the aggregate framework which helps quite a bit but still doesn't offer the same level of performance I'd expect from Postgres, MSSQL, or Oracle. Things get really yucky when you have to start unpacking arrays - especially since there's a memory limit on $sort and $group. There are further issues related to skipping being slow on large collections because it must step through. And to cope with these limitations you need to invest a ton of time into coding around them. The freedom of the database being schemaless? Gone. You must carefully structure data and decide up-front how it needs to be queried - or cope with poor performance. And those complex queries, when written in ugly JSON, aren't the most human-readable things in the world. Certainly not any better than SQL. I'd rather just invest the time in learning SQL than partake in the mental gymnastics necessary squeeze high-performance non-trivial queries out of MongoDB. I understand MongoDB is a big hit with people who use it for low volume internal tools or people building MVPs. I can totally understand how its an awesome tool for those. But rusty old SQL starts looking better when your app scales and the business demands change.