AceBase realtime database vs MongoDB

Besides having a client that connects to a remote AceBase server, the database itself is able to run in the browser. It uses the same to codebase with a different storage provider (IndexedDB instead of binary in Node.js), which makes it possible to run a fullblown realtime database in the browser that syncs between multiple tabs, and can additionally be used as local cache database for an AceBase client to enable offline usage.

Realtime data change notifications are blazingly fast which makes synchronization between apps instant and effortless. When used offline, local and remote changes are synchronized upon reconnect without any hassles. Changes are logged on the property level which makes them synchronize extremely efficient and fast.

It takes very little effort to add AceBase to a Node.js or web project, it's ready to use after installing it as a dependency. It uses a API very similar to that of the Firebase realtime database, which makes it familiar and easy to migrate from Firebase to AceBase for a local db instance or fullblown realtime db server.

Miles above other databases in educational resources.

MongoDB queries can be very fast because the data is usually all in one place and can easily be retrieved in a single lookup. But this is true only when the data is truly a document. When it's trying to emulate a relational model it starts to become really slow because it may have to perform many independent queries to retrieve a single document.

As Node.js uses JavaScript there's no need to map the returned JSON data from MongoDB, as JavaScript is a superset of JSON. Essentially solving object-relational impedance mismatch by its very nature. Working with JSON is also easier overall as it more easily fits into how you would represent data on the client.

Mongo is very flexible in that it doesn't require a unified data structure across all objects. So it's rather easy to use.

MongoDB has powerful sharding and scaling capabilities for when the data stored in the database gets so large that a single machine may not be able to store all of it. Sharding solves this problem through horizontal scaling. Mongo gives developers the ability to easily and painlessly add or remove as many machines as needed.