NoSQL

All unapproved Citizendium articles may contain errors of fact, bias, grammar etc. A version of an article is unapproved unless it is marked as citable with a dedicated green template at the top of the page, as in this version of the 'Biology' article. Citable articles are intended to be of reasonably high quality. The participants in the Citizendium project make no representations about the reliability of Citizendium articles or, generally, their suitability for any purpose. This article is currently being developed as part of an Eduzendium student project. The course homepage can be found at CZ:Special_Topics_2010. To provide students with experience in collaboration, you are warmly invited to join in here, or to leave comments on the discussion page. The anticipated date of course completion is 13 August 2010. One month after that date at the latest, this notice shall be removed. Besides, many other Citizendium articles welcome your collaboration!

NoSQL refers to a number of non-relational distributed database architectures. NoSQL architectures usually store data as key-value pairs, rather than supporting relations. Some systems eliminate the guarantee of consistency (instead promising eventual consistency) in order to increase scalability. The distributed nature of NoSQL architectures makes such data stores highly scalable and fault-tolerant.

NoSQL vs. RDBMS

In many cases, NoSQL databases can process data more quickly than traditional relational database management systems. One reason for this is that data representation in NoSQL databases is much simpler than in relational systems. For example, a table in a relational database might have many columns, but data in a key-value store will always have only two parts: the key and the value. In addition, many NoSQL databases do not fully support ACID transactions. While this allows faster performance, it can also be risky in applications where precision is needed, such as in banking applications.

Types of NoSQL Databases

Key-value Store

A key-value store maintains data as a pair consisting of an indexed key and a value. In general, key-value stores provide a single operation: fetching a single value using its key. Some key-value store implementations include mechanisms for performing a join on two distinct tables. Examples of key-value stores include Oracle's BerkeleyDB and Amazon's Dynamo.

BerkeleyDB

BerkeleyDB is an open source, transactional, embedded database engine. It is available as a library that can be included in any application. Data are represented as key/value pairs. The keys and values in BerkeleyDB can be any objects supported by the programming language. Data are stored in files on disk, as a single file for each key-value store. BerkeleyDB also provides the option to maintain data stores in memory only, if the store is small enough to fit in main memory.

BerkeleyDB provides a number of features competitive with relational databases, including support for transactions, two-phase locking, joins, and write ahead logging. These features make BerkeleyDB very reliable. The BerkeleyDB engine is used in a number of applications. The MySQL database management system offers BerkeleyDB as an option for the storage engine.

BerkeleyDB in itself does not provide a method for distributing data, but using a distributed hash table, it is possible to distribute data across multiple BerkeleyDB instances.

Column-oriented Databases

The column oriented database stores entries by column as opposed to row-oriented databases. This optimizes the SQL databases by maximizing disk performance and making it easier to query many rows for smaller sets of data. Examples of open-source and commercial column oriented databases includes: Cassandra(Facebook), Big Table(Google), Hypertable(Open-source implementation of Big Table), Hbase(Open-source implementation of Big Table), etc.