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. 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. 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.
- 1 History
- 2 Classification
- 3 Performance
- 4 Examples
- 4.1 Document store
- 4.2 Graph
- 4.3 Key-value stores
- 4.4 Object database
- 4.5 Tabular
- 4.6 Tuple store
- 4.7 Triple/Quad Store (RDF) database
- 4.8 Hosted
- 4.9 Multivalue databases
- 4.10 Cell database
- 5 NoSQL databases on the cloud
- 6 See also
- 7 References
- 8 Further reading
- 9 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'", referring to 'No Relational'.
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 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.
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:
- Column: Accumulo, Cassandra, HBase
- Document: Clusterpoint, Couchdb, Couchbase, MarkLogic, MongoDB
- Key-value: Dynamo, FoundationDB, MemcacheDB, Redis, Riak, FairCom c-treeACE
- Graph: Allegro, Neo4J, OrientDB, Virtuoso, Stardog
A more detailed classification is the following, by Stephen Yen:
|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|
|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: 
|Key–Value Store||high||high||high||none||variable (none)|
|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|
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 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
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
|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|
|Stardog||Java, SPARQL||semantic graph database|
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.
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.
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
KV - immediately consistent
- FairCom c-treeACE
KV - ordered
KV - RAM
KV - solid-state drive or rotating disk
- Clusterpoint XML database
- Couchbase Server
- FairCom c-treeACE
- MemcacheDB (using Berkeley DB)
- Oracle NoSQL Database
- Tokyo Cabinet
- Tuple space
- OpenLink Virtuoso
- InterSystems Caché
- NeoDatis ODB
- OpenLink Virtuoso
- Versant Object Database
Triple/Quad Store (RDF) database
- Apache JENA
- Oracle NoSQL database
- Virtuoso Universal Server
- 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
|This section is empty. You can help by adding to it. (April 2014)|
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 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||
|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, Google Compute Engine, 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
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