[larsmak]

About a year ago I implemented a sub-system relying heavily on MongoDB. The load is not that immense, a couple of hundred requests per minute. The dataset is large however, several hundred GB, spread over millions of documents. Also, updates happens in batch, during the night, while reads are happening all the time. I have not had to touch the system since it was put in production over a year ago - it just runs.

MongoDB is a tool, understand it's strengths and limitations and it will serve you well. We achieves great performance for our use-case by correct schema-design / partitioning of data, and sane use of indexes - which are excellent in MongoDB. If you need to scale large, you need to store the data in such a way that it does not require much resources to fetch them, i.e. you must store the data according to your read-requirements. This is even more true in "key-value" systems like Cassandra, which is more limited in how you can store data. MongoDB is very flexible, so it's a lot easier to shoot yourself in the foot.