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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 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. NoSQL databases are often highly optimized key–value stores intended primarily for simple retrieval and appending operations, whereas an RDBMS is intended as a general purpose data store. There will thus be some operations where NoSQL is faster and some where an RDBMS is faster. NoSQL databases are finding significant and growing industry use in big data and real-time web applications.[1] 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. [2]

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.[3] Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.[4]

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.[5] 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.[6]

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:[7]

Term Matching Database
KV Cache Memcached, Repcached, Coherence, Infinispan, eXtreme Scale, JBoss Cache, Velocity, Terracotta, Gigaspaces XAP
KV Store Keyspace, Flare, SchemaFree, RAMCloud
KV Store - Eventually consistent Dynamo, Voldemort, Dynomite, SubRecord, MotionDb, DovetailDB
Data-structures server Redis
KV Store - Ordered TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
Tuple Store Gigaspaces, Coord, Apache River
Object Database ZopeDB, DB4O, Shoal, Perst
Document Store MarkLogic, CouchDB, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Persevere, Riak Basho, Scalaris
Wide Columnar Store BigTable, HBase, 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 low 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.

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[8][9][10] 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)
ObjectDatabase++ C++, C#, TScript Binary Native C++ class structures
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

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.[11][12] 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

NoSQL databases on the cloud

Main article: Cloud database

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
Virtual machine image MongoDB MongoDB - machine images for Amazon EC2[15] and Windows Azure[16] None
  • Database and machine image - open source
  • Amazon/Azure instances - pay per use
Virtual Machine Image Redis
  • Redis - standard open source installation
  • Script for installation on Amazon EC2 [17]
  • Recommended installation on Windows Azure [18]
 None
  • Database and machine image - open source
  • Amazon/Azure instances - pay per use
Virtual machine image Cassandra Apache Cassandra - machine image for Amazon EC2[19] None
  • Database and machine image - open source
  • Amazon instances - pay per use
Database as a Service MongoDB Mongolab[20] - available on Amazon, Google, Joyent, Rackspace and Windows Azure
  • Managed service
  • High availability
  • Automatic failover
  • Pre-configured clustering
  • Free up to 500MB (on disk)[21]
  • Paid plans based on architecture and storage size
Database as a Service Redis/Memcached Amazon Web Services - ElastiCache[22]
  • Managed service
  • Automatic healing of failed nodes
  • Resilient system to prevent overloaded DBs
  • Performance monitoring
  • Free for 750 hours on micro instance[23]
  • Pay per use for machine utilization, no separate charge for data usage[24]
Database as a Service Redis RedisToGo[25] - available on Amazon EC2, RackSpace, Heroku, AppHarbor, Orchestra
  • Managed service
  • Daily backups
  • API enabling creation, deletion, or download of Redis instances
  • Free up to 5MB (memory)
  • Paid plans based on memory usage
Database as a Service Redis Redis Cloud (Garantia Data)[26] - available on Amazon EC2, Windows Azure, Heroku, Cloud Foundry, OpenShift, AppFog, AppHarbor
  • Managed service
  • Automatic scaling, unlimited Redis nodes
  • High availability
  • Built-in clustering
  • Free up to 25MB (memory)[27]
  • Pay per use
Database as a Service Cassandra Instaclustr[28] - available on Amazon EC2, RackSpace, Windows Azure, Joyent, Google Compute Engine
  • Managed service
  • Performance tuning
  • Monitoring
  • Automated backups
  • DataStax OpsCenter for cluster administration

Paid plans based on disk storage, memory usage and CPU cores[29]

Native cloud NoSQL database Amazon SimpleDB Amazon Web Services
  • Managed service
  • High availability
  • Unlimited scale
  • Data durability
  • Free for 750 hours on micro instance[30]
  • Pay per use - separate charge for machine utilization and data usage[30]
Native cloud NoSQL database Google App Engine Datastore[31] Google
  • No planned downtime
  • Atomic transactions
  • High availability of reads and writes
  • Free with quota system limiting instance hours, storage and throughput[32]
  • Pay per use based on instance hours, storage, throughput and other parameters
Native cloud NoSQL database SalesForce Database.com[33] SalesForce
  • Unlimited scale
  • Access to SalesForce meta data
  • Social API
  • Support for mobile clients
  • Multi-tenancy
  • Free up to 100K records and 50K transactions[34]
  • Pay per use based on users, number of records and transactions

See also

References

  1. ^ "RDBMS dominate the database market, but NoSQL systems are catching up". DB-Engines.com. 21 November 2013. Retrieved 24 November 2013.
  2. ^ K. Grolinger, W.A. Higashino, A. Tiwari, M.A.M. Capretz (2013). "Data management in cloud environments: NoSQL and NewSQL data stores" (PDF). JoCCASA, Springer. Retrieved 8 January 2014.{{cite web}}: CS1 maint: multiple names: authors list (link)
  3. ^ 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)
  4. ^ "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Retrieved 29 March 2010.
  5. ^ "NoSQL 2009". Blog.sym-link.com. 12 May 2009. Retrieved 29 March 2010.
  6. ^ Mike Chapple. "The ACID Model".
  7. ^ A Yes for a NoSQL Taxonomy. High Scalability (2009-11-05). Retrieved on 2013-09-18.
  8. ^ The enterprise class NoSQL database. djondb. Retrieved on 2013-09-18.
  9. ^ http://tinman.cs.gsu.edu/~raj/8711/sp13/djondb/Report.pdf
  10. ^ Undefined Blog: Meeting with DjonDB. Undefvoid.blogspot.com. Retrieved on 2013-09-18.
  11. ^ 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)
  12. ^ 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)
  13. ^ "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010.
  14. ^ 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)
  15. ^ "Neo4J in the Cloud", Neo4J Wiki, Retrieved 2011-11-10.
  16. ^ "MongoDB on Azure, MongoDB.org, Retrieved 2011-11-10.
  17. ^ "Install Redis.sh", GitHub Gist, Retrieved 2013-12-29.
  18. ^ "Running Redis on a CentOS Linux VM in Windows Azure", Thomas Conté's MSDN Weblog, Retrieved 2013-12-29.
  19. ^ "Setting up Cassandra in the Cloud", Cassandra Wiki, Retrieved 2011-11-10.
  20. ^ "MongoLab Product Overview", MongoLab.com, Retrieved 2013-12-29.
  21. ^ "MongoLab Plans and Pricing", MongoLab.com, Retrieved 2013-12-29.
  22. ^ "Amazon ElastiCache", Amazon Web Services, Retrieved 2013-12-29.
  23. ^ "Amazon ElastiCache Free Usage Tier", Amazon Web Services, Retrieved 2013-12-29.
  24. ^ "Amazon ElastiCache Pricing", Amazon Web Services, Retrieved 2013-12-29.
  25. ^ "RedisToGo Documentation", RedisToGo.com, Retrieved 2013-12-29.
  26. ^ Redis Cloud by Garantia Data, Redis-Cloud.com, Retrieved 2013-12-29.
  27. ^ "Garantia Data Pricing", GarantiaData.com, Retrieved 2013-12-29.
  28. ^ "Instaclustr Managed Apache Cassandra Hosting", Instaclustr.com, Retrieved 2013-12-29.
  29. ^ Instaclustr Providers & Pricing, Instaclustr.com, Retrieved 2013-12-29.
  30. ^ a b Amazon SimpleDB Pricing, Amazon Web Services, Retrieved 2013-12-29.
  31. ^ "Java Datastore API", Google App Engine, Retrieved 2013-12-29.
  32. ^ App Engine Pricing, Google Cloud Platform, Retrieved 2013-12-29.
  33. ^ "How it works", Database.com, Retrieved 2013-12-29.
  34. ^ "Database.com Pricing", Database.com, Retrieved 2013-12-29.

Further reading

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