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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] 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.[5] 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.[6] Classification 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, Couchbase, MarkLogic, MongoDB Key-value: Dynamo, FoundationDB, MemcacheDB, Redis, Riak, FairCom c-treeACE Graph: Allegro, Neo4J, OrientDB, Virtuoso A more detailed classification is the following, by Stephen Yen:[7] 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 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, Perst, Shoal, ZopeDB, Document Store Clusterpoint, CouchDB, MarkLogic, MongoDB, XML-databases Wide Columnar Store BigTable, Cassandra, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase Performance Ben Scofield rated different categories of NoSQL databases as follows: [8] 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 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 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 Cloudant C, Erlang, Java, Scala JSON store (online service) Clusterpoint C, C++, REST, XML, full text search XML database with support for JSON, text, binaries Couchbase Server C, C++, Erlang Support for JSON and binary documents Apache CouchDB Erlang JSON database djondb[9][10][11] C++ JSON, ACID Document Store Solr Java Search engine ElasticSearch Java JSON, Search engine eXist Java, XQuery XML database Jackrabbit Java Java Content Repository implementation IBM Notes and IBM Domino LotusScript, Java, IBM X Pages, others MultiValue 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 Oracle NoSQL Database C, Java OrientDB Java JSON, SQL support CoreFoundation Property list C, C++, Objective-C JSON, XML, binary Sedna C++, XQuery 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 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 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 Dynamo Riak[15] KV - immediately consistent FairCom c-treeACE KV - ordered Berkeley DB FairCom c-treeACE/c-treeRTG FoundationDB IBM Informix C-ISAM InfinityDB MemcacheDB NDBM KV - RAM Coherence FairCom c-treeACE GemFire Hazelcast memcached redis OpenLink Virtuoso XAP KV - solid-state drive or rotating disk Aerospike BigTable CDB Clusterpoint XML database Couchbase Server GT.M[16] FairCom c-treeACE Hibari Keyspace LevelDB MemcacheDB (using Berkeley DB) MongoDB Oracle NoSQL Database Tarantool Tokyo Cabinet Tuple space OpenLink Virtuoso Object database Main article: Object database db4o GemStone/S InterSystems Caché JADE NeoDatis ODB ObjectDatabase++ ObjectDB Objectivity/DB ObjectStore ODABA Perst OpenLink Virtuoso Versant Object Database WakandaDB ZODB Tabular Apache Accumulo BigTable Apache Hbase Hypertable Mnesia OpenLink Virtuoso Tuple store GigaSpaces Apache River Tarantool OpenLink Virtuoso Triple/Quad Store (RDF) database Apache JENA MarkLogic Ontotext-OWLIM Oracle NoSQL database SparkleDB Virtuoso Universal Server Stardog Hosted Amazon DynamoDB Cloudant Data Layer (CouchDB) Datastore on Google Appengine Freebase OpenLink Virtuoso Multivalue databases D3 Pick database Extensible Storage Engine (ESE/NT) InfinityDB InterSystems Caché Northgate Information Solutions Reality, the original Pick/MV Database OpenQM Revelation Software's OpenInsight Rocket U2 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.[1] 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.[2] 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. |
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