“The right tool for the right job.” If such wisdom holds true anywhere, it certainly holds true with the choice of database a developer picks for a given application. Document databases, one of the family of data products collectively referred to as “NoSQL,” are for developers who want to focus on their application rather than the database technology.
With a document database, data is not stored in tables with distinct column types. Instead, it is stored in freeform “documents” with any number of fields and any number of nested structures. Such documents are typically represented as JSON, and updated either by way of APIs or by sending JSON to a REST endpoint. Most every modern programming language supports JSON and REST, so working with a document database feels more like working natively with those data structures than working with a traditional database.
This schemaless design, as it is called, has its limitations. A developer must do more work to ensure that inserted data is consistent, because such consistency isn’t always guaranteed by the database itself. SQL, the standard-issue and widely understood language for database work, isn’t supported by most document databases, so those with existing database expertise must start from scratch. But the convenience, speed, scalability, and versatility of a document database is hard to beat when you’re writing an application that needs a protean, free-form data structure.
Here we’ve profiled seven of the best known and most widely used document databases. Four of the seven—CouchDB, Couchbase Server, MongoDB, and RethinkDB—are open source projects with few or no practical barriers to getting started; Couchbase and MongoDB are also available in supported enterprise editions under commercial licenses. The other three—Amazon DynamoDB, Google Firebase, and IBM Cloudant—are hosted services from major cloud vendors, where close integration with other services in those clouds is a big draw.