Visual Guide to NoSQL Systems

• Consistency - Every read receives the most recent write or an error
• Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
• Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures

Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.

http://blog.nahurst.com/visual-guide-to-nosql-systems

Note: RDBMSs (MySQL, Postgres, etc) are only featured here for comparison purposes. Also, some of these systems can vary their features by configuration (I use the default configuration here, but will try to delve into others later).

As you can see, there are three primary concerns you must balance when choosing a data management system: consistency, availability, and partition tolerance.

• Consistency means that each client always has the same view of the data.
• Availability means that all clients can always read and write.
• Partition tolerance means that the system works well across physical network partitions.

According to the CAP Theorem, you can only pick two. So how does this all relate to NoSQL systems?

One of the primary goals of NoSQL systems is to bolster horizontal scalability. To scale horizontally, you need strong network partition tolerance which requires giving up either consistency or availability. NoSQL systems typically accomplish this by relaxing relational abilities and/or loosening transactional semantics.

In addition to CAP configurations, another significant way data management systems vary is by the data model they use: relational, key-value, column-oriented, or document-oriented (there are others, but these are the main ones).

• Relational systems are the databases we've been using for a while now. RDBMSs and systems that support ACIDity and joins are considered relational.
• Key-value systems basically support get, put, and delete operations based on a primary key.
• Column-oriented systems still use tables but have no joins (joins must be handled within your application). Obviously, they store data by column as opposed to traditional row-oriented databases. This makes aggregations much easier.
• Document-oriented systems store structured "documents" such as JSON or XML but have no joins (joins must be handled within your application). It's very easy to map data from object-oriented software to these systems.

Now for the particulars of each CAP configuration and the systems that use each configuration:

Consistent, Available (CA) Systems have trouble with partitions and typically deal with it with replication. Examples of CA systems include:

• Traditional RDBMSs like Postgres, MySQL, etc (relational)
• Vertica (column-oriented)
• Aster Data (relational)
• Greenplum (relational)

Consistent, Partition-Tolerant (CP) Systems have trouble with availability while keeping data consistent across partitioned nodes. Examples of CP systems include:

• BigTable (column-oriented/tabular)
• Hypertable (column-oriented/tabular)
• HBase (column-oriented/tabular)
• MongoDB (document-oriented)
• Terrastore (document-oriented)
• Redis (key-value)
• Scalaris (key-value)
• MemcacheDB (key-value)
• Berkeley DB (key-value)

Available, Partition-Tolerant (AP) Systems achieve "eventual consistency" through replication and verification. Examples of AP systems include:

• Dynamo (key-value)
• Voldemort (key-value)
• Tokyo Cabinet (key-value)
• KAI (key-value)
• Cassandra (column-oriented/tabular)
• CouchDB (document-oriented)
• SimpleDB (document-oriented)
• Riak (document-oriented)