> Every MongoDB project I've touched has run into a schema version issue. Sure, you can put freeform data into Mongo. But when you're querying your database, wi

> Every MongoDB project I've touched has run into a schema version issue. Sure, you can put freeform data into Mongo. But when you're querying your database, will a record made yesterday have the same shape as one made six months ago? How do you ensure that? You write model level code to normalize your data at run time, or you write migrations. While our project found that Mongo wasn't a good fit, this was a big reason why we leaned towards Mongo. We had log data that evolved over years and has lots of weird quirks over the years so having one place to deal with all of those was great. Ultimately though we found Mongo's scalability in its batch processing to be lackluster (there's only so much you can do with a small cluster of machines) and fell back to Hadoop. I can't speak towards Mongo vs. Postgres, but coming from a hacked up MariaDB-based sharding system I loved how easy shard management was in Mongo and felt Mongo had a lot of tools for prototyping and allowing me to try something in a new paradigm with fairly low effort. Overall I found Mongo pleasant and flexible but it was a real PITA when you were trying to optimize for a particular use case