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. The data structure (e.g., tree, graph, key-value) 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 to be solved (e.g., does the solution use tree algorithms?). The appearance of mature NoSQL databases has reduced the rationale for Java content repository (JCR) implementations.
NoSQL databases are finding significant and growing industry use in big data and real-time web applications. 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. 
- 1 History
- 2 Taxonomy
- 3 Classification based on data model
- 4 Classification based on feature
- 5 Examples
- 5.1 Document store
- 5.2 Graph
- 5.3 Key–value stores
- 5.4 Object database
- 5.5 Tabular
- 5.6 Tuple store
- 5.7 Triple/Quad Store (RDF) database
- 5.8 Hosted
- 5.9 Multivalue databases
- 5.10 Cell database
- 6 NoSQL databases on the cloud
- 7 See also
- 8 References
- 9 Further reading
- 10 External links
Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface. Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.
Eric Evans reintroduced the term NoSQL in early 2009 when Johan Oskarsson of Last.fm wanted to organize an event to discuss open-source distributed databases. 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.
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: Accumulo, Cassandra, HBase
- Document: Clusterpoint, Couchbase, MarkLogic, MongoDB
- Key-value: Dynamo, MemcacheDB, Project Voldemort, Redis, Riak
- Graph: Allegro, Neo4J, OrientDB, Virtuoso
Classification based on data model
Stephen Yen in his blog post "NoSQL is a Horseless Carriage" suggests the following:
|KV Cache||Coherence, eXtreme Scale, GigaSpaces, Hazelcast, Infinispan, JBoss Cache, Memcached, Repcached, Terracotta, Velocity|
|KV Store||Flare, Keyspace, RAMCloud, SchemaFree|
|KV Store - Eventually consistent||DovetailDB, Dynamo, Dynomite, MotionDb, Voldemort, SubRecord|
|KV Store - Ordered||Actord, Lightcloud, Luxio, MemcacheDB, NMDB, Scalaris, TokyoTyrant|
|Tuple Store||Apache River, Coord, GigaSpaces|
|Object Database||DB4O, Perst, Shoal, ZopeDB,|
|Document Store||Clusterpoint, CouchDB, MarkLogic, MongoDB, Riak, XML-databases|
|Wide Columnar Store||BigTable, Cassandra, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase|
Classification based on feature
Ben Scofield categorized NoSQL databases based on nonfunctional categories (“(il)ities“) plus a rating of their feature coverage:
|Key–value Stores||high||high||high||moderate||associative array|
|Column Store||high||high||moderate||low||columnar database|
|Document Store||high||variable (high)||high||low||object model, based on document object model or markup language|
|Graph Database||variable||variable||high||high||graph theory|
|Relational Database||variable||variable||low||moderate||relational algebra|
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:
- 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.
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.
|DEX/Sparksee||C++, Java, .NET, Python||High-performance graph database|
|IBM DB2||SPARQL||RDF GraphStore added in DB2 10|
|InfiniteGraph||Java||High-performance, scalable, distributed graph database|
|OWLIM||Java, SPARQL 1.1||RDF graph store with reasoning|
|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 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. The following types exist:
KV - eventually consistent
KV - hierarchical
KV - cache in RAM
KV - solid state or rotating disk
- Clusterpoint XML database
- Couchbase Server
- MemcacheDB (using Berkeley DB)
- Oracle NoSQL Database
- Tokyo Cabinet
- Tuple space
- OpenLink Virtuoso
KV - ordered
- InterSystems Caché
- NeoDatis ODB
- OpenLink Virtuoso
- Versant Object Database
Triple/Quad Store (RDF) database
- Amazon DynamoDB
- Cloudant Data Layer (CouchDB)
- Datastore on Google Appengine
- OpenLink Virtuoso
- D3 Pick database
- Extensible Storage Engine (ESE/NT)
- InterSystems Caché
- Northgate Information Solutions Reality, the original Pick/MV Database
- Revelation Software's OpenInsight
- Rocket U2
NoSQL databases on the cloud
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’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.
The following table provides 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||
|Virtual machine image||Cassandra||Apache Cassandra - machine image for Amazon EC2||None||
|Database as a Service||Cassandra||Instaclustr - available on Amazon EC2, RackSpace, Windows Azure, Joyent, Google Compute Engine||
Paid plans based on disk storage, memory usage and CPU cores
|Native cloud NoSQL database||Google App Engine Datastore||
|Virtual machine image||MongoDB||MongoDB - machine images for Amazon EC2 and Windows Azure||None||
|Database as a Service||MongoDB||MongoLab - available on Amazon, Google, Joyent, Rackspace and Windows Azure||
|Database as a Service||Redis/Memcached||Amazon Web Services - ElastiCache||
|Virtual Machine Image||Redis||None||
|Database as a Service||Redis||RedisToGo - available on Amazon EC2, RackSpace, Heroku, AppHarbor, Orchestra||
|Database as a Service||Redis||Redis Cloud (Redis Labs) - available on Amazon EC2, Windows Azure, Heroku, Cloud Foundry, OpenShift, AppFog, AppHarbor||
|Native cloud NoSQL database||SalesForce Database.com||SalesForce||
- CAP theorem
- Comparison of object database management systems
- Comparison of structured storage software
- Faceted search
- Distributed cache
- "RDBMS dominate the database market, but NoSQL systems are catching up". DB-Engines.com. 21 Nov 2013. Retrieved 24 Nov 2013.
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- Sandy (14 January 2011). "Key Value stores and the NoSQL movement". http://dba.stackexchange.com/questions/607/what-is-a-key-value-store-database: 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 to be 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."
- Marc Seeger (21 September 2009). "Key-Value Stores: a practical overview". http://blog.marc-seeger.de/2009/09/21/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."
- "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010 * OpenLink Virtuoso
- 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.....*"
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- Instaclustr Providers & Pricing, Instaclustr.com, Retrieved 2013-12-29.
- "Java Datastore API", Google App Engine, Retrieved 2013-12-29.
- App Engine Pricing, Google Cloud Platform, Retrieved 2013-12-29.
- "Neo4J in the Cloud", Neo4J Wiki, Retrieved 2011-11-10.
- "MongoDB on Azure, MongoDB.org, Retrieved 2011-11-10.
- "MongoLab Product Overview", MongoLab.com, Retrieved 2013-12-29.
- "MongoLab Plans and Pricing", MongoLab.com, Retrieved 2013-12-29.
- "Amazon ElastiCache", Amazon Web Services, Retrieved 2013-12-29.
- "Amazon ElastiCache Free Usage Tier", Amazon Web Services, Retrieved 2013-12-29.
- "Amazon ElastiCache Pricing", Amazon Web Services, Retrieved 2013-12-29.
- "Install Redis.sh", GitHub Gist, Retrieved 2013-12-29.
- "Running Redis on a CentOS Linux VM in Windows Azure", Thomas Conté's MSDN Weblog, Retrieved 2013-12-29.
- "RedisToGo Documentation", RedisToGo.com, Retrieved 2013-12-29.
- Redis Cloud by Redis Labs, Redis-Cloud.com, Retrieved 2013-12-29.
- "Garantia Data Pricing", GarantiaData.com, Retrieved 2013-12-29.
- "How it works", Database.com, Retrieved 2013-12-29.
- "Database.com Pricing", Database.com, Retrieved 2013-12-29.
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- Dan McCreary & Ann Kelly (2013). Making Sense of NoSQL: A guide for managers and the rest of us. ISBN 9781617291074.
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