Mongo is part of the large NoSQL movement sweeping the web community.
NoSQL is a movement promoting a loosely defined class of non-relational data stores that break with a long history of relational databases. These data stores may not require fixed table schemas, usually avoid join operations and typically scale horizontally. Academics and papers typically refer to these databases as structured storage.
From NoSQL‚ Wikipedia
MongoDB is a designed to be a scalable, high performance database. It is not designed for situations where strict formats need to be applied. It supports atomic inserts which makes it highly suited for real time data aggregates. It has support for standard indexes, sharding, and even commercial support. It models itself after the idea that databases are specializing and you can’t create something to please everyone. It uses the JSON document data model to provide cross service support easily and fast. By removing transactional semantics from standard database design you strip most of the “fat” from traditional SQL processes. This comes at a cost however, you can’t create foreign keys or constrain the data like you can in SQL. Systems modeled after transactional systems (think banking or corporate records) won’t be efficient in Mongo as they are in other systems.
Node’s goal is to provide an easy way to build scalable network programs. In the “hello world” web server example above, many client connections can be handled concurrently. Node tells the operating system (through epoll, kqueue, /dev/poll, or select) that it should be notified when a new connection is made, and then it goes to sleep. If someone new connects, then it executes the callback. Each connection is only a small heap allocation.
Node has some really cool stuff going on under the hood. I haven’t had enough of a chance to play with it, though I did create a small server for a project I’m working on. Requests dropped from ~40ms to <10ms consistently, no matter how many times I hit it up with requests.