A NoSQL (often interpreted as 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, presumed better horizontal scaling, and presumed finer control over availability. The data structures used by NoSQL databases (e.g. key-value, graph, or document) differ slightly from those used by default in relational databases, making some operations faster in NoSQL and others faster in relational databases. The particular suitability of a given NoSQL database depends on the problem it must solve.
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 (though technically a NewSQL database), FoundationDB and OrientDB have made them central to their designs. (See ACID and JOIN Support.)
Unfortunately, not all NoSQL systems live up to the promised "eventual consistency" an partition tolerace, but in experiments with network partitioning often exhibited lost writes and other forms of data loss.
- 1 History
- 2 Types of NoSQL databases
- 3 Performance
- 4 Handling relational data
- 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 Multimodel database
- 5.11 Correlation database
- 5.12 Cell database
- 5.13 ACID and JOIN Support
- 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 organized 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.
Types of NoSQL databases
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:
- Column: Accumulo, Cassandra, Druid, HBase, Vertica
- Document: Lotus Notes, Clusterpoint, Apache CouchDB, Couchbase, HyperDex, MarkLogic, MongoDB, OrientDB, Qizx
- Key-value: CouchDB, Oracle NoSQL Database, Dynamo, FoundationDB, HyperDex, MemcacheDB, Redis, Riak, FairCom c-treeACE, Aerospike, OrientDB, MUMPS
- Graph: Allegro, Neo4J, InfiniteGraph, OrientDB, Virtuoso, Stardog
- Multi-model: OrientDB, FoundationDB, ArangoDB, Alchemy Database, CortexDB
A more detailed classification is the following, based on one from 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, Hyperdex, Aerospike|
|Key-Value Store (Eventually-Consistent)||DovetailDB, Oracle NoSQL Database 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, Objectivity/DB, Perst, Shoal, ZopeDB,|
|Document Store||Lotus Notes,Clusterpoint, Couchbase, CouchDB, MarkLogic, MongoDB, Qizx, XML-databases|
|Wide Columnar Store||BigTable, Cassandra, Druid, 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|
Performance and scalability comparisons are sometimes done with the YCSB benchmark.
Handling relational data
Since most NoSQL databases lack ability for joins in queries, the database schema generally needs to be designed differently. There are three main techniques for handling relational data in a NoSQL database. (See table Join and ACID Support for NoSQL databases that support joins.)
Instead of retrieving all the data with one query, it's common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries so the cost of having to do additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate.
Instead of only storing foreign keys, it's common to store actual foreign values along with the model's data. For example, each blog comment might include the username in addition to a user id, thus providing easy access to the username without requiring another lookup. When a username changes however, this will now need to be changed in many places in the database. Thus this approach works better when reads are much more common than writes.
With document databases like MongoDB it's common to put more data in a smaller number of collections. For example in a blogging application, one might choose to store comments within the blog post document so that with a single retrieval one gets all the comments. Thus in this approach a single document contains all the data you need for a specific task.
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.
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 - ordered
- Berkeley DB
- FairCom c-treeACE/c-treeRTG
- IBM Informix C-ISAM
KV - RAM
KV - solid-state drive or rotating disk
- Clusterpoint Database Server
- Couchbase Server
- FairCom c-treeACE
- MemcacheDB (using Berkeley DB)
- Oracle NoSQL Database
- OpenLink Virtuoso
- Tokyo Cabinet
- Tuple space
- InterSystems Caché
- NeoDatis ODB
- OpenLink Virtuoso
- Versant Object Database
Triple/quad store (RDF) database
- Apache JENA (It's a framework, not a database)
- Oracle NoSQL database
- Virtuoso Universal Server
- Amazon DynamoDB
- Datastore on Google Appengine
- Clusterpoint database
- Cloudant Data Layer (CouchDB)
- 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. (December 2014)|
|This section is empty. You can help by adding to it. (April 2014)|
ACID and JOIN Support
If a database is marked as supporting ACID or joins, then the documentation for the database makes that claim. The degree to which the capability is fully supported in a manner similar to most SQL databases or the degree to which it meets the needs of a specific application is left up to the reader to assess.
- CAP theorem
- Comparison of object database management systems
- Comparison of structured storage software
- Correlation database
- Distributed cache
- Faceted search
- MultiValue database
- Multi-model database
- "NoSQL (Not Only SQL)".
NoSQL database, also called Not Only SQL
- Fowler, Martin. "NosqlDefinition".
many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL
- "RDBMS dominate the database market, but NoSQL systems are catching up". DB-Engines.com. 21 Nov 2013. Retrieved 24 Nov 2013.
- Grolinger, K.; Higashino, W. A.; Tiwari, A.; Capretz, M. A. M. (2013). "Data management in cloud environments: NoSQL and NewSQL data stores" (PDF). JoCCASA, Springer. Retrieved 8 Jan 2014.
- Martin Zapletal: Large volume data analysis on the Typesafe Reactive Platform, ScalaDays 2015, Slides
- Lith, Adam; Mattson, Jakob (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[...]
- "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Retrieved 29 March 2010.
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- Yen, Stephen. "NoSQL is a Horseless Carriage" (PDF). NorthScale. Retrieved 2014-06-26..
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- "Making the Shift from Relational to NoSQL" (PDF). Couchbase.com. Retrieved December 5, 2014.
- 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 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.
- Seeger, Marc (21 September 2009). "Key-Value Stores: a practical overview" (PDF). 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.
- Katsov, Ilya (1 March 2012). "NoSQL Data Modeling Techniques". Ilya Katsov. Retrieved 8 May 2014.
- "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010.
- Tweed, Rob; James, George (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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- Zicari, Roberto V. (2014). "NoSQL Data Stores – Articles, Papers, Presentations". odbms.org.