MngoDB can be a great alternative to MySQL, but it’s not really appropriate for the scale-out applications targeted by Cassandra. Still, as early members of the NoSQL category, the two do draw comparisons.

One important limitation in MongoDB is database-level locking. That is, only one writer may modify a given database at a time. Support for collection- level (a set of documents, analogous to a relational table) locking is planned. With either database- or collection-level locking, other writers or readers are locked out. Even a small number of writes can produce stalls in read performance.

Cassandra uses advanced concurrent structures to provide row-level isolation without locking. Cassandra even eliminated the need for row-level locks for index updates in the recent 1.2 release.

A more subtle MongoDB limitation is that when adding or updating a field in a document, the entire document must be re-written. If you pre-allocate space for each document, you can avoid the associated fragmentation, but even with pre-allocation updating your document gets slower as it grows.

Cassandra’s storage engine only appends updated data, it never has to re- write or re-read existing data. Thus, updates to a Cassandra row or partition stay fast as your dataset grows

Source : http://www.datastax.com/dev/blog/2012-in-review-performance