Document Databases
In this section, we compare two popular document databases: CouchDB and MongoDB. Both are open source document databases and are designed to scale across multiple nodes easily. For both, data is stored in documents with self-contained records and no intrinsic relationships. However, MongoDB provides consistency, so each client always has the same view of data. Instead, CouchDB ensures availability. Thus, all clients can always read and write.
To view the query’s results, users of MongoDB can rely on a simple and intuitive query-like language. They are represented as JSON-like structure. On the other hand,
CouchDB is based on a ‘map-reduce’ approach and its view concept. Indeed, queries are done through CouchDB’s ‘views’. They are defined with Javascript to specify the field constraints.
CouchDB stores data to disk by append-only files while MongoDB stores data in the memory-mapped storage engine. It uses memory mapped files for all disk I/O. As an interchange format, CouchDB offers an HTTP API for both data access and administration. MongoDB provides instead a socket-based wire protocol with BSON.
For fault-tolerance, CouchDB supports both master/master and master/slave replication. Replication can be finely tuned via replication filters. On the contrary, MongoDB manages replication using a form of asynchronous master/slave replication called replica sets.
To conclude, both CouchDB and MongoDB have many common features such us replication for fault-tolerance and volatile memory file system for data storage. Both rely on MapReduce paradigm for data processing and have as well a good community support.
However, CouchDB is not adapted to extremely changing data. In fact, while CouchDB requires setting up pre-defined queries, MongoDB is suitable for dynamic queries and ensures a better performance on big databases.
