The four categories of NoSQL databases


Very interesting read on the Monitis Blog about picking the right NoSQL tool. They dive into what it is, what’s possibly wrong with RDBMS, describe the different categories of NoSQL and the pros and cons of the different types.

Most people just see one big pile of NoSQL databases, while there are quite some differences. You couldn’t use a Key-Value store when you need a Graph database for example, while Relational database systems are all quite compatible.

Montis describes the following categories:

1. Key-values Stores

The main idea here is using a hash table where there is a unique key and a pointer to a particular item of data. The Key/value model is the simplest and easiest to implement. But it is inefficient when you are only interested in querying or updating part of a value, among other disadvantages.

Examples: Tokyo Cabinet/Tyrant, Redis, Voldemort, Oracle BDB, Amazon SimpleDBRiak

2. Column Family Stores

These were created to store and process very large amounts of data distributed over many machines. There are still keys but they point to multiple columns. The columns are arranged by column family.

Examples: Cassandra, HBase

3. Document Databases

These were inspired by Lotus Notes and are similar to key-value stores. The model is basically versioned documents that are collections of other key-value collections. The semi-structured documents are stored in formats like JSON. Document databases are essentially the next level of Key/value, allowing nested values associated with each key.  Document databases support querying more efficiently.

Examples: CouchDB, MongoDb

4. Graph Databases

Instead of tables of rows and columns and the rigid structure of SQL, a flexible graph model is used which, again, can scale across multiple machines. NoSQL databases do not provide a high-level declarative query language like SQL to avoid overtime in processing. Rather, querying these databases is data-model specific. Many of the NoSQL platforms allow for RESTful interfaces to the data, while other offer query APIs.

Examples: Neo4J, InfoGrid, Infinite Graph

Montis describes more pros and cons of each type in their post: Picking the Right NoSQL Database Tool

At GroundControl we use MongoDB as our main database because it works really well with for example our experiments that have lots of evidence and learnings.