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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 (often interpreted as Not only SQL[1][2]) 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 structures used by NoSQL databases (e.g. key-value, graph, or document) differ from those used 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.

NoSQL databases are increasingly used in big data and real-time web applications.[3] 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.[4] 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.)

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.[5] Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'",[6] 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.[7] 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.[8]

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:

A more detailed classification is the following, based on one from Stephen Yen:[9]

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, Hyperdex, Aerospike
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, 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

Performance

Ben Scofield rated different categories of NoSQL databases as follows: [10]

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

Performance and scalability comparisons are sometimes done with the YCSB benchmark.

See also: Comparison of structured storage software

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.)

Multiple queries

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.

Caching/replication/non-normalized data

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.[11]

Nesting data

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.

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.

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 with support for JSON, text, and binaries
Cloudant C, Erlang, Java, Scala JSON store (online service)
Clusterpoint Database C, C++, REST, SQL, Java, Php, .NET, Python, Node.js XML, JSON, text, binaries, search engine, online service
Couchbase Server C, C++, Erlang Support for JSON and binary documents
CouchDB Erlang JSON database
Elasticsearch Java JSON, Search engine
eXist Java, XQuery XML database with support for JSON, text, and binaries
HyperDex C, C++, Node.js, Go, Python, Ruby Support for JSON and binary documents
IBM Notes and IBM Domino LotusScript, Java, IBM X Pages, others MultiValue
Jackrabbit Java Java Content Repository implementation
JSON
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
OrientDB Java JSON, SQL support
Qizx C++, Java, Python, REST, XQuery XML database with support for JSON, text, and binaries
Sedna C++, XQuery XML database
SimpleDB Erlang online service
Solr Java Search engine
TokuMX C++, C#, Go MongoDB with Fractal Tree indexing
OpenLink Virtuoso C++, C#, Java, SPARQL middleware and database engine hybrid

Graph

Main article: Graph database

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
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
Stardog Java, SPARQL semantic graph database

Key-value stores

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

KV - ordered

KV - RAM

KV - solid-state drive or rotating disk

Object database

Main article: Object database

Tabular

Tuple store

Triple/quad store (RDF) database

Hosted

Multivalue databases

Multimodel database

Correlation database

Cell database

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.

Database ACID Joins
CouchDB Yes Yes
OrientDB Yes Yes
c-treeACE Yes Yes
FoundationDB Yes Yes
HyperDex Yes Yes
InfinityDB Yes No

See also

References

  1. ^ "NoSQL (Not Only SQL)". NoSQL database, also called Not Only SQL
  2. ^ Fowler, Martin. "NosqlDefinition". many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL
  3. ^ "RDBMS dominate the database market, but NoSQL systems are catching up". DB-Engines.com. 21 November 2013. Retrieved 24 November 2013.
  4. ^ 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 January 2014.
  5. ^ 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[...]
  6. ^ "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Retrieved 29 March 2010.
  7. ^ "NoSQL 2009". Blog.sym-link.com. 12 May 2009. Retrieved 29 March 2010.
  8. ^ Chapple, Mike. "The ACID Model".
  9. ^ Yen, Stephen. "NoSQL is a Horseless Carriage" (PDF). NorthScale. Retrieved 26 June 2014..
  10. ^ Scofield, Ben (14 January 2010). "NoSQL - Death to Relational Databases(?)". Retrieved 26 June 2014.
  11. ^ "Making the Shift from Relational to NoSQL" (PDF). Couchbase.com. Retrieved 5 December 2014.
  12. ^ 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. {{cite web}}: External link in |location= (help)CS1 maint: location (link)
  13. ^ 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. {{cite web}}: External link in |location= (help)CS1 maint: location (link)
  14. ^ Katsov, Ilya (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.
  16. ^ 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.....* {{cite web}}: line feed character in |quote= at position 82 (help)

Further reading

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