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NoSQL

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

A NoSQL database provides a mechanism for storage and retrieval of data that employs less constrained consistency models than traditional relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are often highly optimized key–value stores intended for simple retrieval and appending operations, with the goal being significant performance benefits in terms of latency and throughput. 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.

History

Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface.[1] Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.[2]

Eric Evans (then a Rackspace employee) reintroduced the term NoSQL in early 2009 when Johan Oskarsson of Last.fm wanted to organize an event to discuss open-source distributed databases.[3] 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.[4]

Taxonomy

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:

Classification based on data model

Stephen Yen in his blog post "NoSQL is a Horseless Carriage" suggests the following:[5]

Term Matching Database
KV Store Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB)
KV Store - Eventually consistent Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB
KV Store - Hierarchical GT.m, Cache
KV Store - Ordered TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
KV Cache Memcached, Repcached, Coherence, Hazelcast, Infinispan, EXtremeScale, JBossCache, Velocity, Terracotta
Tuple Store Gigaspaces, Coord, Apache River
Object Database ZopeDB, DB40, Shoal
Document Store CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris
Wide Columnar Store BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI

Classification based on feature

Ben Scofield categorized NoSQL databases based on nonfunctional categories (“(il)ities“) plus a rating of their feature coverage: [citation needed]

Data Model Performance Scalability Flexibility Complexity Functionality
Key–value Stores high high high none variable (none)
Column Store high high moderate low minimal
Document Store high variable (high) high low variable (low)
Graph Database variable variable high high graph theory
Relational Database variable variable low moderate relational algebra.
See also: Comparison of structured storage software

Examples

Document store

Main articles: Document-oriented database and XML database

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 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. Some NoSQL document stores offer an alternative way to retrieve information using MapReduce techniques, in CouchDB the usage of MapReduce is mandatory if you want to retrieve documents based on the contents, this is called "Views" and it's an indexed collection with the results of the MapReduce algorithms.

Name Language Notes
BaseX Java, XQuery XML database
Cloudant Erlang, Java, Scala, C JSON store (online service)
Clusterpoint C++ XML, geared for Full text search
Couchbase Server Erlang, C, C++ Support for JSON and binary documents
Apache CouchDB Erlang JSON database
djondb[6][7][8] C++ JSON, ACID Document Store
ElasticSearch Java JSON, Search engine
eXist Java, XQuery XML database
Jackrabbit Java Java Content Repository implementation
IBM Lotus Notes and Lotus Domino LotusScript, Java, IBM X Pages, others MultiValue
MarkLogic Server XQuery, Java, REST XML database with support for JSON, text, and binaries
MongoDB C++, C#, Go BSON store (binary format JSON)
Oracle NoSQL Database Java, C
OrientDB Java JSON, SQL support
CoreFoundation Property list C, C++, Objective-C JSON, XML, binary
Sedna XQuery, C++ 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

Graph

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

Key–value stores

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.[9][10] The following types exist:

KV - eventually consistent

KV - hierarchical

KV - cache in RAM

KV - solid state or rotating disk

KV - ordered

Object database

Main article: Object database

Tabular

Tuple store

Triple/Quad Store (RDF) database

Hosted

Multivalue databases

Cell database

See also

References

  1. ^ a b Lith, Adam (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[...] {{cite web}}: Unknown parameter |coauthors= ignored (|author= suggested) (help) Cite error: The named reference ":0" was defined multiple times with different content (see the help page).
  2. ^ "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Retrieved 29 March 2010.
  3. ^ "NoSQL 2009". Blog.sym-link.com. 12 May 2009. Retrieved 29 March 2010.
  4. ^ Mike Chapple. "The ACID Model".
  5. ^ A Yes for a NoSQL Taxonomy. High Scalability (2009-11-05). Retrieved on 2013-09-18.
  6. ^ The enterprise class NoSQL database. djondb. Retrieved on 2013-09-18.
  7. ^ http://tinman.cs.gsu.edu/~raj/8711/sp13/djondb/Report.pdf
  8. ^ Undefined Blog: Meeting with DjonDB. Undefvoid.blogspot.com. Retrieved on 2013-09-18.
  9. ^ 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. {{cite web}}: External link in |location= (help)CS1 maint: location (link)
  10. ^ Marc Seeger (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. {{cite web}}: External link in |location= (help)CS1 maint: location (link)
  11. ^ "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010.
  12. ^ Tweed, Rob (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.....* {{cite web}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); line feed character in |quote= at position 82 (help)

Further reading


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