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"Structured storage" redirects here. For the Microsoft technology also known as structured storage, see COM Structured Storage.

A NoSQL (often interpreted as Not Only SQL[1][2]) 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. The data structure (e.g. key-value, graph, or document) differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS. There are differences though, and the particular suitability of a given NoSQL DB depends on the problem it must solve (e.g., does the solution use graph algorithms?).

NoSQL databases are increasingly used in big data and real-time web applications.[3] NoSQL systems are also called "Not only SQL" to emphasize that they may also support SQL-like query languages. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability and partition tolerance. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages, the lack of standardized interfaces, and huge investments in existing SQL.[4] Most NoSQL stores lack true ACID transactions, although a few recent systems, such as FairCom c-treeACE, Google Spanner and FoundationDB, have made them central to their designs.


Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface.[5] Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'",[6] referring to 'No Relational'.

Eric Evans reintroduced the term NoSQL in early 2009 when Johan Oskarsson of wanted to organize an event to discuss open-source distributed databases.[7] The name attempted to label the emergence of an increasing number of non-relational, distributed data stores. Most of the early NoSQL systems did not attempt to provide atomicity, consistency, isolation and durability guarantees, contrary to the prevailing practice among relational database systems.[8]

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, a basic classification is based on data model. A few examples in each category are:

A more detailed classification is the following, by Stephen Yen:[9]

TermMatching Database
Key-Value CacheCoherence, eXtreme Scale, GigaSpaces, GemFire, Hazelcast, Infinispan, JBoss Cache, Memcached, Repcached, Terracotta, Velocity
Key-Value StoreFlare, Keyspace, RAMCloud, SchemaFree
Key-Value Store (Eventually-Consistent)DovetailDB, Dynamo, Riak, Dynomite, MotionDb, Voldemort, SubRecord
Key-Value Store (Ordered)Actord, FoundationDB, Lightcloud, Luxio, MemcacheDB, NMDB, Scalaris, TokyoTyrant
Data-Structures serverRedis
Tuple StoreApache River, Coord, GigaSpaces
Object DatabaseDB4O, Perst, Shoal, ZopeDB,
Document StoreClusterpoint, Couchbase, CouchDB, MarkLogic, MongoDB, XML-databases
Wide Columnar StoreBigTable, Cassandra, Druid, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase


Ben Scofield rated different categories of NoSQL databases as follows: [10]

Data ModelPerformanceScalabilityFlexibilityComplexityFunctionality
Key–Value Storehighhighhighnonevariable (none)
Column-Oriented Storehighhighmoderatelowminimal
Document-Oriented Storehighvariable (high)highlowvariable (low)
Graph Databasevariablevariablehighhighgraph theory
Relational Databasevariablevariablelowmoderaterelational algebra


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:

Compared to relational databases, for example, collections could be considered analogous to tables and documents analogous to 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 using the simple key-document (or key-value) lookup to retrieve a document, the database offers an API or query language that retrieves documents based on their contents.

Document store databases and their query language
BaseXJava, XQueryXML database
CloudantC, Erlang, Java, ScalaJSON store (online service)
ClusterpointC, C++, REST, XML, full text searchXML database with support for JSON, text, binaries
Couchbase ServerC, C++, ErlangSupport for JSON and binary documents
Apache CouchDBErlangJSON database
SolrJavaSearch engine
ElasticSearchJavaJSON, Search engine
eXistJava, XQueryXML database
JackrabbitJavaJava Content Repository implementation
IBM Notes and IBM DominoLotusScript, Java, IBM X Pages, othersMultiValue
MarkLogic ServerJava, REST, XQueryXML database with support for JSON, text, and binaries
MongoDBC++, C#, GoBSON store (binary format JSON)
ObjectDatabase++C++, C#, TScriptBinary Native C++ class structures
Oracle NoSQL DatabaseC, Java
OrientDBJavaJSON, SQL support
CoreFoundation Property listC, C++, Objective-CJSON, XML, binary
SednaC++, XQueryXML database
SimpleDBErlangonline service
TokuMXC++, C#, GoMongoDB with Fractal Tree indexing
OpenLink VirtuosoC++, C#, Java, SPARQLmiddleware and database engine hybrid


Main article: Graph database

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.

Graph databases and their query language
AllegroGraphSPARQLRDF GraphStore
DEX/SparkseeC++, Java, .NET, PythonHigh-performance graph database
IBM DB2SPARQLRDF GraphStore added in DB2 10
InfiniteGraphJavaHigh-performance, scalable, distributed graph database
OWLIMJava, SPARQL 1.1RDF graph store with reasoning
Sones GraphDBC#
Sqrrl EnterpriseJavaDistributed, real-time graph database featuring cell-level security
OpenLink VirtuosoC++, C#, Java, SPARQLmiddleware and database engine hybrid
StardogJava, SPARQLsemantic graph database

Key-value stores[edit]

Key-value (KV) stores use the associative array (also known as a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key-value pairs, such that each possible key appears at most once in the collection.[11][12]

The key-value model is one of the simplest non-trivial data models, and richer data models are often implemented on top of it. The key-value model can be extended to an ordered model that maintains keys in lexicographic order. This extension is powerful, in that it can efficiently process key ranges.[13]

Key-value stores can use consistency models ranging from eventual consistency to serializability. Some support ordering of keys. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks. Here is a list of key-value stores:

KV - eventually consistent[edit]

KV - ordered[edit]

KV - RAM[edit]

KV - solid-state drive or rotating disk[edit]

Object database[edit]

Main article: Object database


Tuple store[edit]

Triple/quad store (RDF) database[edit]


Multivalue databases[edit]

Cell database[edit]

See also[edit]


  1. ^ "NoSQL (Not Only SQL)". "NoSQL database, also called Not Only SQL" 
  2. ^ Martin Fowler. "NosqlDefinition". "many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL" 
  3. ^ "RDBMS dominate the database market, but NoSQL systems are catching up". 21 Nov 2013. Retrieved 24 Nov 2013. 
  4. ^ K. Grolinger, W.A. Higashino, A. Tiwari, M.A.M. Capretz (2013). "Data management in cloud environments: NoSQL and NewSQL data stores". JoCCASA, Springer. Retrieved 8 Jan 2014. 
  5. ^ Lith, Adam; Jakob Mattson (2010). "Investigating storage solutions for large data: A comparison of well performing and scalable data storage solutions for real time extraction and batch insertion of data" (PDF). Göteborg: Department of Computer Science and Engineering, Chalmers University of Technology. p. 70. Retrieved 12 May 2011. "Carlo Strozzi first used the term NoSQL in 1998 as a name for his open source relational database that did not offer a SQL interface[...]" 
  6. ^ "NoSQL Relational Database Management System: Home Page". 2 October 2007. Retrieved 29 March 2010. 
  7. ^ "NoSQL 2009". 12 May 2009. Retrieved 29 March 2010. 
  8. ^ Mike Chapple. "The ACID Model". 
  9. ^ Yen, Stephen. "NoSQL is a Horseless Carriage" (PDF). NorthScale. Retrieved 2014-06-26. .
  10. ^ Scofield, Ben (2010-01-14). "NoSQL - Death to Relational Databases(?)". Retrieved 2014-06-26. 
  11. ^ Sandy (14 January 2011). "Key Value stores and the NoSQL movement". Stackexchange. Retrieved 1 January 2012. "Key-value stores allow the application developer to store schema-less data. This data usually consists of a string that represents the key, and the actual data that is considered the value in the "key-value" relationship. The data itself is usually some kind of primitive of the programming language (a string, an integer, or an array) or an object that is being marshaled by the programming language's bindings to the key-value store. This structure replaces the need for a fixed data model and allows proper formatting." 
  12. ^ Marc Seeger (21 September 2009). "Key-Value Stores: a practical overview". Marc Seeger. Retrieved 1 January 2012. "Key-value stores provide a high-performance alternative to relational database systems with respect to storing and accessing data. This paper provides a short overview of some of the currently available key-value stores and their interface to the Ruby programming language." 
  13. ^ Ilya Katsov (1 March 2012). "NoSQL Data Modeling Techniques". Ilya Katsov. Retrieved 8 May 2014. 
  14. ^ "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010 * OpenLink Virtuoso
  15. ^ Tweed, Rob; George James (2010). "A Universal NoSQL Engine, Using a Tried and Tested Technology" (PDF). p. 25. "Without exception, the most successful and well-known of the NoSQL databases have been developed from scratch, all within just the last few years. Strangely, it seems that nobody looked around to see whether there were any existing, successfully implemented database technologies that could have provided a sound foundation for meeting Web-scale demands. Had they done so, they might have discovered two products, GT.M and Caché.....*" 

Further reading[edit]

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