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NoSQL showdown: MongoDB vs. Couchbase

Which NoSQL database has richer querying, indexing, and ease of use?

By Rick Grehan, InfoWorld, 03/21/13

Page 2 of 7

This is not to say that Couchbase is hobbled. You can easily manage your Couchbase cluster -- adding, deleting, and fetching documents -- from the Couchbase Management GUI, for which MongoDB has no counterpart. Indeed, if you prefer management via GUI consoles, score one for Couchbase Server. If, however, you favor life at the command line, you will be tipped in MongoDB's direction.

The cloud-based MongoDB Monitoring Service (MMS), which gathers statistics, is not meant to be a full-blown database management interface. But MongoDB's environment provides a near seamless connection between the data objects abstracted in the mongo shell and the database entities they model. This is particularly apparent when you discover that MongoDB allows you to create indexes on a specific document field using a single function call, whereas indexes in Couchbase must be created by more complex mapreduce operations.

In addition, while Couchbase documents are described via JSON, MongoDB documents are described in BSON; the latter notation includes a richer number of useful data types, such as 32-bit and 64-bit integer types, date types, and byte arrays. Both support geospatial data and queries, but this support in Couchbase is currently in an experimental phase and likely won't stay there long. New in version 2.4, MongoDB's full text search capability is also integrated with the database. A similar capability is available in Couchbase Server, but requires a plug-in for the elasticsearch tool.

Both Couchbase Server and MongoDB provide data safety via replication, both within a cluster (where live documents are protected from loss by the invisible creation of replica documents) and outside of a cluster (through cross data-center replication). Also, both provide access parallelism through sharding. However, where both Couchbase and MongoDB support hash sharding, MongoDB supports range sharding and "tag" sharding. This is a two-edged sword. On the one hand, it puts a great deal of flexibility at a database administrator's fingertips. On the other hand, its misuse can result in an imbalanced cluster.

Mapreduce is a key tool used in both Couchbase and MongoDB, but for different purposes. In MongoDB, mapreduce serves as the means of general data processing, information aggregating, and analytics. In Couchbase, it is the means of creating indexes for the purpose of querying data in the database. (We suspect that this, like the poorer document handling, is an effect of Couchbase's only having recently morphed into a document database.) As a result, it's easier to create indexes and perform ad hoc queries in MongoDB.

Couchbase's full incorporation of Memcached has no counterpart in MongoDB, and Memcached is a powerful adjunct as general object caching system for high-throughput, data-intensive Internet and intranet applications. If your application needs a Memcache server with your database, then look no further than Couchbase.

In general, the two systems are neck-and-neck in terms of features provided, though the ways those features are implemented may differ. Further, the advantages that one might hold over the other will certainly come and go as development proceeds. Both provide database drivers and client frameworks in all the popular programming languages, both are open source, both are easily installed, and both enjoy plenty of online documentation and active community support. As is typical for such well-matched systems, the best advice anyone could give for determining one over the other will be that you install them both and try them out.

For more enterprise computing news, visit InfoWorld. Story copyright InfoWorld Media Group, Inc.