A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are often highly optimizedkey–value stores intended primarily for simple retrieval and appending operations, whereas an RDBMS is intended as a general purpose data store. There will thus be some operations where NoSQL is faster and some where an RDBMS is faster. NoSQL databases are finding significant and growing industry use in big dataand real-time web applications.^[1] NoSQL systems are also referred to as "Not only SQL" to emphasize that they may in fact allow SQL-like query languages to be used. In the context of the CAP theorem, NoSQL stores often compromise consistency in favor of availability and partition tolerance. Barriers to the greater adoption of NoSQL data stores in practice include: the lack of full ACID transaction support, the use of low-level query languages, the lack of standardized interfaces, and the huge investments already made in SQL by enterprises. ^[2]

History[edit]

Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface.^[3] Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.^[4]

Eric Evans (then a Rackspace employee) reintroduced the term NoSQL in early 2009 when Johan Oskarsson of Last.fm wanted to organize an event to discuss open-source distributed databases.^[5] The name attempted to label the emergence of a growing number of non-relational, distributed data stores that often did not attempt to provide atomicity, consistency, isolation and durability guarantees that are key attributes of classic relational database systems.^[6]

Taxonomy[edit]

There have been various approaches to classify NoSQL databases, each with different categories and subcategories. Because of the variety of approaches and overlaps it is difficult to get and maintain an overview of non-relational databases. Nevertheless, the basic classification that most would agree on is based on data model. A few of these and their prototypes are:

• Column: HBase, Accumulo, Cassandra
• Document: MarkLogic, MongoDB, Couchbase
• Key-value: Dynamo, Riak, Redis, MemcacheDB, Project Voldemort
• Graph: Neo4J, OrientDB, Allegro, Virtuoso

Classification based on data model[edit]

Stephen Yen in his blog post "NoSQL is a Horseless Carriage" suggests the following:^[7]

Classification based on feature[edit]

Ben Scofield categorized NoSQL databases based on nonfunctional categories (“(il)ities“) plus a rating of their feature coverage:^{[citation needed]}

It has been suggested that this article be merged into Comparison of structured storage software. (Discuss) Proposed since March 2011.

Examples[edit]

Document store[edit]

The central concept of a document store is the notion of a "document". While each document-oriented database implementation differs on the details of this definition, in general, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use include XML, YAML, and JSON as well as binary forms like BSON, PDF and Microsoft Office documents (MS Word, Excel, and so on).

Different implementations offer different ways of organizing and/or grouping documents:

• Collections
• Tags
• Non-visible Metadata
• Directory hierarchies

Compared to relational databases, for example, collections could be considered as tables as well as documents could be considered as records. But they are different: every record in a table has the same sequence of fields, while documents in a collection may have fields that are completely different.

Documents are addressed in the database via a unique key that represents that document. One of the other defining characteristics of a document-oriented database is that, beyond the simple key-document (or key–value) lookup that you can use to retrieve a document, the database will offer an API or query language that will allow retrieval of documents based on their contents.

Graph[edit]

This kind of database is designed for data whose relations are well represented as a graph (elements interconnected with an undetermined number of relations between them). The kind of data could be social relations, public transport links, road maps or network topologies, for example.

Key–value stores[edit]

Key–value stores allow the application to store its data in a schema-less way. The data could be stored in a datatype of a programming language or an object. Because of this, there is no need for a fixed data model.^[11][12] The following types exist:

KV - eventually consistent[edit]

• Apache Cassandra
• Dynamo
• Hibari
• OpenLink Virtuoso
• Project Voldemort
• Riak^[13]

KV - hierarchical[edit]

• GT.M^[14]
• InterSystems Caché

KV - cache in RAM[edit]

• memcached
• redis
• OpenLink Virtuoso
• Hazelcast
• Oracle Coherence

KV - solid state or rotating disk[edit]

• Aerospike
• BigTable
• CDB
• Couchbase Server
• Keyspace
• LevelDB
• MemcacheDB (using Berkeley DB)
• MongoDB
• OpenLink Virtuoso
• Tarantool
• Tokyo Cabinet
• Tuple space
• Oracle NoSQL Database

KV - ordered[edit]

• Berkeley DB
• FoundationDB
• IBM Informix C-ISAM
• InfinityDB
• MemcacheDB
• NDBM

Object database[edit]

• db4o
• GemStone/S
• InterSystems Caché
• JADE
• NeoDatis ODB
• ObjectDB
• Objectivity/DB
• ObjectStore
• ODABA
• Perst
• OpenLink Virtuoso
• Versant Object Database
• WakandaDB
• ZODB

Tabular[edit]

• Apache Accumulo
• BigTable
• Apache Hbase
• Hypertable
• Mnesia
• OpenLink Virtuoso

Tuple store[edit]

• Apache River
• OpenLink Virtuoso
• Tarantool

Triple/Quad Store (RDF) database[edit]

• SparkleDB
• Virtuoso Universal Server
• Ontotext-OWLIM
• Apache JENA
• Oracle NoSQL database
• MarkLogic

Hosted[edit]

• Freebase
• OpenLink Virtuoso
• Datastore on Google Appengine
• Amazon DynamoDB
• Cloudant Data Layer (CouchDB)

Multivalue databases[edit]

• Northgate Information Solutions Reality, the original Pick/MV Database
• Extensible Storage Engine (ESE/NT)
• OpenQM
• Revelation Software's OpenInsight
• Rocket U2
• D3 Pick database
• InterSystems Caché
• InfinityDB

Cell database[edit]

NoSQL databases on the cloud[edit]

NoSQL databases can be run on-premises, but are also often run on IaaS or PaaS platforms like Amazon Web Services, RackSpace or Heroku. There are three common deployment models for NoSQL on the cloud:

• Virtual machine image - cloud platforms allow users to rent virtual machine instances for a limited time. It is possible to run a NoSQL database on these virtual machines. Users can upload their own machine image with a database installed on it, use ready-made machine images that already include an optimized installation of a database, or install the NoSQL database on a running machine instance.
• Database as a service - some cloud platforms offer options for using familiar NoSQL database products as a service, such as MongoDB, Redis and Cassandra, without physically launching a virtual machine instance for the database. The database is provided as a managed service, meaning that application owners do not have to install and maintain the database on their own, and pay according to usage. Some database as a service providers provide additional features, such as clustering or high availability, that are not available in the on-premise version of the database (see the table below for several examples).
• Native cloud NoSQL databases - some providers offer a NoSQL database service which is available only on the cloud. A well-known example is Amazon’sSimpleDB, a simple NoSQL key-value store. SimpleDB cannot be installed on a local machine and cannot be used on any cloud platform except Amazon’s.

The following table provides notable examples of NoSQL databases available on the cloud in each of these deployment models:

See also[edit]

• CAP theorem
• Comparison of object database management systems
• Comparison of structured storage software
• Faceted search
• Triplestore
• Distributed cache

References[edit]

Further reading[edit]

• Pramod Sadalage and Martin Fowler (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley. ISBN 0-321-82662-0.
• Christof Strauch (2012). "NoSQL Databases".
• Moniruzzaman AB, Hossain SA (2013). "NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison".
• Kai Orend (2013). Analysis and Classification of NoSQL Databases and Evaluation of their Ability to Replace an Object-relational Persistence Layer.
• Ganesh Krishnan, Sarang Kulkarni, Dharmesh Kirit Dadbhawala. "Method and system for versioned sharing, consolidating and reporting information".
• Sugam Sharma. "A Brief Review on Modern NoSQL Data Models, Handling Big Data".

External links[edit]

• Christoph Strauch. "NoSQL whitepaper". Hochschule der Medien, Stuttgart.
• Stefan Edlich. "NoSQL database List".
• Peter Neubauer (2010). "Graph Databases, NOSQL and Neo4j".
• Sergey Bushik (2012). "A vendor-independent comparison of NoSQL databases: Cassandra, HBase, MongoDB, Riak". NetworkWorld.