[Ixiaus]

I came into a company that was attempting to use MongoDB for their soft-real time time-series data (I would consider it between low and medium scale) and it was atrocious, in too many ways.

It doesn't scale without tremendous effort and implicit schemas are a very dangerous thing to introduce into your application, they're insidious, and require enormous diligence in the application to codify the schemas (I would only feel comfortable using a loose document store with something like Haskell in which I can model the schema with strong types).

I've encountered MongoDB in three different companies / products and expended much effort to immediately move away from it in every case. In each case, the solution (which has been different each time) was far more appropriate to what was needed.

There's a sad inclination by developers to pick "one ring to rule them all" tools and MongoDB I believe even sells itself that way. It is not.

[ak217]

What were the more appropriate solutions?

[Ixiaus]

Postgres for structured data, Riak for high-volume and flatter data, and TempoDB for high-volume time-series data.