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A NoSQL or Not Only SQL 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.[1] 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.[2] 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.[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] 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.[5] 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.[6]


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 examples in each category are:

A more detailed classification is the following, by Stephen Yen:[7]

Term Matching Database
Key-Value Cache Coherence, eXtreme Scale, GigaSpaces, GemFire, Hazelcast, Infinispan, JBoss Cache, Memcached, Repcached, Terracotta, Velocity
Key-Value Store Flare, 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 server Redis
Tuple Store Apache River, Coord, GigaSpaces
Object Database DB4O, Perst, Shoal, ZopeDB,
Document Store Clusterpoint, CouchDB, MarkLogic, MongoDB, XML-databases
Wide Columnar Store BigTable, Cassandra, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase


Ben Scofield rated different categories of NoSQL databases as follows: [8]

Data Model Performance Scalability Flexibility Complexity Functionality
Key–Value Store high high high none variable (none)
Column-Oriented Store high high moderate low minimal
Document-Oriented Store high variable (high) high low variable (low)
Graph Database variable variable high high graph theory
Relational Database variable variable low moderate relational 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:

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

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

Name Language Notes
BaseX Java, XQuery XML database
Cloudant C, Erlang, Java, Scala JSON store (online service)
Clusterpoint C, C++, REST, XML, full text search XML database with support for JSON, text, binaries
Couchbase Server C, C++, Erlang Support for JSON and binary documents
Apache CouchDB Erlang JSON database
djondb[9][10][11] C++ JSON, ACID Document Store
Solr Java Search engine
ElasticSearch Java JSON, Search engine
eXist Java, XQuery XML database
Jackrabbit Java Java Content Repository implementation
IBM Notes and IBM Domino LotusScript, Java, IBM X Pages, others MultiValue
MarkLogic Server Java, REST, XQuery XML database with support for JSON, text, and binaries
MongoDB C++, C#, Go BSON store (binary format JSON)
ObjectDatabase++ C++, C#, TScript Binary Native C++ class structures
Oracle NoSQL Database C, Java
OrientDB Java JSON, SQL support
CoreFoundation Property list C, C++, Objective-C JSON, XML, binary
Sedna C++, XQuery XML database
SimpleDB Erlang online service
TokuMX C++, C#, Go MongoDB with Fractal Tree indexing
OpenLink Virtuoso C++, C#, Java, SPARQL middleware and database engine hybrid


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.

Main article: Graph database

Graph Databases and Their Query Language

Name Language(s) Notes
AllegroGraph SPARQL RDF GraphStore
DEX/Sparksee C++, Java, .NET, Python High-performance graph database
FlockDB Scala
IBM DB2 SPARQL RDF GraphStore added in DB2 10
InfiniteGraph Java High-performance, scalable, distributed graph database
Neo4j Java
OWLIM Java, SPARQL 1.1 RDF graph store with reasoning
OrientDB Java
Sones GraphDB C#
Sqrrl Enterprise Java Distributed, real-time graph database featuring cell-level security
OpenLink Virtuoso C++, C#, Java, SPARQL middleware and database engine hybrid

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.[12][13]

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.[14]

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 - immediately 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]

NoSQL databases on the cloud[edit]

Main article: Cloud database

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 available only on the cloud. A well-known example is Amazon’s SimpleDB, 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.

Notable examples of NoSQL databases available on the Cloud in each of these deployment models

Deployment Model Database Technology Provider Cloud-Specific Features Pricing Model
Native cloud NoSQL database Amazon SimpleDB Amazon Web Services
  • Managed service
  • High availability
  • Unlimited scale
  • Data durability
  • Free for 750 hours on micro instance[17]
  • Pay per use - separate charge for machine utilization and data usage[17]
Virtual machine image Cassandra Apache Cassandra - machine image for Amazon EC2[18] None
  • Database and machine image - open source
  • Amazon instances - pay per use
Database as a Service Cassandra Instaclustr[19] - available on Amazon EC2, RackSpace, Windows Azure, Joyent, Google Compute Engine
  • Managed service
  • Performance tuning
  • Monitoring
  • Automated backups
  • DataStax OpsCenter for cluster administration

Paid plans based on disk storage, memory usage and CPU cores[20]

Native cloud NoSQL database Google App Engine Datastore[21] Google
  • No planned downtime
  • Atomic transactions
  • High availability of reads and writes
  • Free with quota system limiting instance hours, storage and throughput[22]
  • Pay per use based on instance hours, storage, throughput and other parameters
Virtual machine image MongoDB MongoDB - machine images for Amazon EC2[23] and Windows Azure[24] None
  • Database and machine image - open source
  • Amazon/Azure instances - pay per use
Database as a Service MongoDB MongoLab[25] - available on Amazon, Google, Joyent, Rackspace and Windows Azure
  • Managed service
  • High availability
  • Automatic failover
  • Pre-configured clustering
  • Free up to 500MB (on disk)[26]
  • Paid plans based on architecture and storage size
Database as a Service Redis/Memcached Amazon Web Services - ElastiCache[27]
  • Managed service
  • Automatic healing of failed nodes
  • Resilient system to prevent overloaded DBs
  • Performance monitoring
  • Free for 750 hours on micro instance[28]
  • Pay per use for machine utilization, no separate charge for data usage[29]
Virtual Machine Image Redis
  • Redis - standard open source installation
  • Script for installation on Amazon EC2 [30]
  • Recommended installation on Windows Azure [31]
  • Database and machine image - open source
  • Amazon/Azure instances - pay per use
Database as a Service Redis RedisToGo[32] - available on Amazon EC2, RackSpace, Heroku, AppHarbor, Orchestra
  • Managed service
  • Daily backups
  • API enabling creation, deletion, or download of Redis instances
  • Free up to 5MB (memory)
  • Paid plans based on memory usage
Database as a Service Redis Redis Cloud (Redis Labs)[33] - available on Amazon EC2, Google Compute Engine, Windows Azure, Heroku, Cloud Foundry, OpenShift, AppFog, AppHarbor
  • Managed service
  • Automatic scaling, unlimited Redis nodes
  • High availability
  • Built-in clustering
  • Free up to 25MB (memory)[34]
  • Pay per use
Native cloud NoSQL database SalesForce[35] SalesForce
  • Unlimited scale
  • Access to SalesForce meta data
  • Social API
  • Support for mobile clients
  • Multi-tenancy
  • Free up to 100K records and 50K transactions[36]
  • Pay per use based on users, number of records and transactions

See also[edit]


  1. ^ "RDBMS dominate the database market, but NoSQL systems are catching up". 21 Nov 2013. Retrieved 24 Nov 2013. 
  2. ^ 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. 
  3. ^ 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[...]" 
  4. ^ "NoSQL Relational Database Management System: Home Page". 2 October 2007. Retrieved 29 March 2010. 
  5. ^ "NoSQL 2009". 12 May 2009. Retrieved 29 March 2010. 
  6. ^ Mike Chapple. "The ACID Model". 
  7. ^ Yen, Stephen. "NoSQL is a Horseless Carriage" (PDF). NorthScale. Retrieved 2014-06-26. .
  8. ^ Scofield, Ben (2010-01-14). "NoSQL - Death to Relational Databases(?)". Retrieved 2014-06-26. 
  9. ^ The enterprise class NoSQL database. djondb. Retrieved on 2013-09-18.
  10. ^
  11. ^ Undefined Blog: Meeting with DjonDB. Retrieved on 2013-09-18.
  12. ^ 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." 
  13. ^ 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." 
  14. ^ Ilya Katsov (1 March 2012). "NoSQL Data Modeling Techniques". Ilya Katsov. Retrieved 8 May 2014. 
  15. ^ "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010 * OpenLink Virtuoso
  16. ^ 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.....*" 
  17. ^ a b Amazon SimpleDB Pricing, Amazon Web Services, Retrieved 2013-12-29.
  18. ^ "Setting up Cassandra in the Cloud", Cassandra Wiki, Retrieved 2011-11-10.
  19. ^ "Instaclustr Managed Apache Cassandra Hosting",, Retrieved 2013-12-29.
  20. ^ Instaclustr Providers & Pricing,, Retrieved 2013-12-29.
  21. ^ "Java Datastore API", Google App Engine, Retrieved 2013-12-29.
  22. ^ App Engine Pricing, Google Cloud Platform, Retrieved 2013-12-29.
  23. ^ "Neo4J in the Cloud", Neo4J Wiki, Retrieved 2011-11-10.
  24. ^ "MongoDB on Azure,, Retrieved 2011-11-10.
  25. ^ "MongoLab Product Overview",, Retrieved 2013-12-29.
  26. ^ "MongoLab Plans and Pricing",, Retrieved 2013-12-29.
  27. ^ "Amazon ElastiCache", Amazon Web Services, Retrieved 2013-12-29.
  28. ^ "Amazon ElastiCache Free Usage Tier", Amazon Web Services, Retrieved 2013-12-29.
  29. ^ "Amazon ElastiCache Pricing", Amazon Web Services, Retrieved 2013-12-29.
  30. ^ "Install", GitHub Gist, Retrieved 2013-12-29.
  31. ^ "Running Redis on a CentOS Linux VM in Windows Azure", Thomas Conté's MSDN Weblog, Retrieved 2013-12-29.
  32. ^ "RedisToGo Documentation",, Retrieved 2013-12-29.
  33. ^ Redis Cloud by Redis Labs,, Retrieved 2013-12-29.
  34. ^ "Garantia Data Pricing",, Retrieved 2013-12-29.
  35. ^ "How it works",, Retrieved 2013-12-29.
  36. ^ " Pricing",, Retrieved 2013-12-29.

Further reading[edit]

External links[edit]