Jump to content

NoSQL

From Wikipedia, the free encyclopedia
(Redirected fromNon-relational database)
"Structured storage" redirects here. For the Microsoft technology, seeCOM Structured Storage.

NoSQL(originally referring to "non-SQL"or" non-relational ")[1]is an approach todatabasedesign that focuses on providing a mechanism forstorageandretrievalof data that is modeled in means other than the tabular relations used inrelational databases.Instead of the typical tabular structure of a relational database, NoSQL databases house data within one data structure. Since this non-relational database design does not require aschema,it offers rapid scalability to manage large and typically unstructured data sets.[2]NoSQL systems are also sometimes called"Not only SQL"to emphasize that they may supportSQL-like query languages or sit alongside SQL databases inpolyglot-persistentarchitectures.[3][4]

Non-relational databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 2000s,[5]triggered by the needs ofWeb 2.0companies.[6][7]NoSQL databases are increasingly used inbig dataandreal-time webapplications.[8]

Motivations for this approach include simplicity ofdesign,simpler"horizontal" scalingtoclusters of machines(which is a problem for relational databases),[5]finer control overavailability,and limiting theobject-relational impedance mismatch.[9]Thedata structuresused by NoSQL databases (e.g.key–value pair,wide column,graph,ordocument) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as "more flexible" than relationaldatabase tables.[10]

Many NoSQL stores compromise consistency (in the sense of theCAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-levelquery languages(instead of SQL, for instance), lack of ability to perform ad hocjoinsacross tables, lack of standardized interfaces, and huge previous investments in existing relational databases.[11]Most NoSQL stores lack trueACIDtransactions, although a few databases have made them central to their designs.

Instead, most NoSQL databases offer a concept of "eventual consistency",in which database changes are propagated to all nodes" eventually "(typically within milliseconds), so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known asstale read.[12]Additionally, some NoSQL systems may exhibit lost writes and other forms ofdata loss.[13]Some NoSQL systems provide concepts such aswrite-ahead loggingto avoid data loss.[14]Fordistributed transaction processingacross multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Relational databases "do not allow referential integrity constraints to span databases".[15]Few systems maintain bothACIDtransactions andX/Open XAstandards fordistributed transaction processing.[16]Interactive relational databases share conformational relay analysis techniques as a common feature.[17]Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to mostoperating systems.[18]

History

[edit]

The termNoSQLwas used by Carlo Strozzi in 1998 to name his lightweightStrozzi NoSQL open-source relational databasethat did not expose the standardStructured Query Language(SQL) interface, but was still relational.[19]His NoSQLRDBMSis distinct from the around-2009 general concept of NoSQL databases. Strozzi suggests that, because the current NoSQL movement "departs from the relational model altogether, it should therefore have been called more appropriately 'NoREL'",[20]referring to "not relational".

Johan Oskarsson, then a developer atLast.fm,reintroduced the termNoSQLin early 2009 when he organized an event to discuss "open-sourcedistributed, non-relational databases".[21]The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google'sBigtable/MapReduceand Amazon'sDynamoDB.

Types and examples

[edit]

There are various ways to classify NoSQL databases, with different categories and subcategories, some of which overlap. What follows is a non-exhaustive classification by data model, with examples:[22]

Type Notable examples of this type
Key–value cache Apache Ignite,Couchbase,Coherence,eXtreme Scale,Hazelcast,Infinispan,Memcached,Redis,Velocity
Key–value store Azure Cosmos DB,ArangoDB,Amazon DynamoDB,Aerospike,Couchbase,ScyllaDB
Key–value store (eventually consistent) Azure Cosmos DB,Oracle NoSQL Database,Riak,Voldemort
Key–value store (ordered) FoundationDB,InfinityDB,LMDB,MemcacheDB
Tuple store Apache River,GigaSpaces,Tarantool,TIBCOActiveSpaces,OpenLink Virtuoso
Triplestore AllegroGraph,MarkLogic,Ontotext-OWLIM,Oracle NoSQL database,Profium Sense,Virtuoso Universal Server
Object database Objectivity/DB,Perst,ZODB,db4o,GemStone/S,InterSystems Caché,JADE,ObjectDatabase++,ObjectDB,ObjectStore,ODABA,Realm,OpenLink Virtuoso,Versant Object Database
Document store Azure Cosmos DB,ArangoDB,BaseX,Clusterpoint,Couchbase,CouchDB,DocumentDB,eXist-db,Google Cloud Firestore,IBM Domino,MarkLogic,MongoDB,RavenDB,Qizx,RethinkDB,Elasticsearch,OrientDB
Wide-column store Azure Cosmos DB,Amazon DynamoDB,Bigtable,Cassandra,Google Cloud Datastore,HBase,Hypertable,ScyllaDB
Native multi-model database ArangoDB,Azure Cosmos DB,OrientDB,MarkLogic,Apache Ignite,[23][24]Couchbase,FoundationDB,Oracle Database
Graph database Azure Cosmos DB,AllegroGraph,ArangoDB,InfiniteGraph,Apache Giraph,MarkLogic,Neo4J,OrientDB,Virtuoso
Multivalue database D3Pick database,Extensible Storage Engine(ESE/NT),InfinityDB,InterSystems Caché,jBASEPick database,mvBaseRocket Software,mvEnterpriseRocket Software,Northgate Information SolutionsReality (the original Pick/MV Database),OpenQM,Revelation Software's OpenInsight (Windows) and Advanced Revelation (DOS), UniDataRocket U2,UniVerseRocket U2

Key–value store

[edit]
Main article:Key–value database

Key–value (KV) stores use theassociative array(also called 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.[25][26]

The key–value model is one of the simplest non-trivial data models, and richer data models are often implemented as an extension of it. The key–value model can be extended to a discretely ordered model that maintains keys inlexicographic order.This extension is computationally powerful, in that it can efficiently retrieve selective keyranges.[27]

Key–value stores can useconsistency modelsranging fromeventual consistencytoserializability.Some databases support ordering of keys. There are various hardware implementations, and some users store data in memory (RAM), while others onsolid-state drives(SSD) orrotating disks(aka hard disk drive (HDD)).

Document store

[edit]
Main articles:Document-oriented databaseandXML database

The central concept of a document store is that of a "document". While the details of this definition differ among document-oriented databases, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use includeXML,YAML,andJSONandbinaryforms likeBSON.Documents are addressed in the database via a uniquekeythat represents that document. Another defining characteristic of a document-oriented database is anAPIor query language to retrieve documents based on their contents.

Different implementations offer different ways of organizing and/or grouping documents:

  • Collections
  • Tags
  • Non-visiblemetadata
  • Directory hierarchies

Compared to relational databases, 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.

Graph

[edit]
Main article:Graph database

Graph databases are designed for data whose relations are well represented as agraphconsisting of elements connected by a finite number of relations. Examples of data includesocial relations,public transport links, road maps, network topologies, etc.

Graph databases and their query language
Name Language(s) Notes
AllegroGraph SPARQL RDFtriple store
Amazon Neptune Gremlin,SPARQL Graph database
ArangoDB AQL,JavaScript,GraphQL Multi-model DBMSDocument,Graph databaseandKey-value store
Azure Cosmos DB Gremlin Graph database
DEX/Sparksee C++,Java,C#,Python Graph database
FlockDB Scala Graph database
IBM Db2 SPARQL RDFtriple store added in DB2 10
InfiniteGraph Java Graph database
JanusGraph Java Graph database
MarkLogic Java,JavaScript,SPARQL,XQuery Multi-modeldocument databaseandRDFtriple store
Neo4j Cypher Graph database
OpenLink Virtuoso C++,C#,Java,SPARQL Middlewareanddatabase enginehybrid
Oracle SPARQL 1.1 RDFtriple store added in 11g
OrientDB Java,SQL Multi-modeldocumentandgraph database
OWLIM Java,SPARQL 1.1 RDFtriple store
Profium Sense Java,SPARQL RDFtriple store
RedisGraph Cypher Graph database
Sqrrl Enterprise Java Graph database
TerminusDB JavaScript,Python,datalog Open source RDF triple-store and document store[28]

Performance

[edit]

The performance of NoSQL databases is usually evaluated using the metric ofthroughput,which is measured as operations/second. Performance evaluation must pay attention to the rightbenchmarkssuch as production configurations, parameters of the databases, anticipated data volume, and concurrent userworkloads.

Ben Scofield rated different categories of NoSQL databases as follows:[29]

Data model Performance Scalability Flexibility Complexity Data Integrity Functionality
Key–value store high high high none low variable (none)
Column-oriented store high high moderate low low minimal
Document-oriented store high variable (high) high low low variable (low)
Graph database variable variable high high low-med graph theory
Relational database variable variable low moderate high relational algebra

Performance and scalability comparisons are most commonly done using theYCSBbenchmark.

Handling relational data

[edit]

Since most NoSQL databases lack ability for joins in queries, thedatabase schemagenerally 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

[edit]

Instead of retrieving all the data with one query, it is common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries so the cost of 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 and non-normalized data

[edit]

Instead of only storing foreign keys, it is 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.[30]

Nesting data

[edit]

With document databases like MongoDB it is 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.

ACID and join support

[edit]

A database is marked as supportingACIDproperties (Atomicity, Consistency, Isolation, Durability) orjoinoperations if the documentation for the database makes that claim. However, this doesn't necessarily mean that the capability is fully supported in a manner similar to most SQL databases.

Database ACID Joins
Aerospike Yes No
Apache Ignite Yes Yes
ArangoDB Yes Yes
Amazon DynamoDB Yes No
Couchbase Yes Yes
CouchDB Yes Yes
IBM Db2 Yes Yes
InfinityDB Yes No
LMDB Yes No
MarkLogic Yes Yes[nb 1]
MongoDB Yes Yes[nb 2]
OrientDB Yes Yes[nb 3]
  1. ^Joins do not necessarily apply to document databases, but MarkLogic can do joins using semantics.[31]
  2. ^MongoDB did not support joining from a sharded collection until version 5.1.[32]
  3. ^OrientDB can resolve 1:1 joins using links by storing direct links to foreign records.[33]

See also

[edit]

References

[edit]
  1. ^http://nosql-database.org/"NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable".
  2. ^"What Is a NoSQL Database? | IBM".www.ibm.com.12 December 2022.Retrieved9 August2024.
  3. ^"NoSQL (Not Only SQL)".NoSQL database, also called Not Only SQL
  4. ^Fowler, Martin."NosqlDefinition".many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL
  5. ^abLeavitt, Neal (2010)."Will NoSQL Databases Live Up to Their Promise?"(PDF).IEEE Computer.43(2): 12–14.doi:10.1109/MC.2010.58.S2CID26876882.
  6. ^Mohan, C. (2013).History Repeats Itself: Sensible and NonsenSQL Aspects of the NoSQL Hoopla(PDF).Proc. 16th Int'l Conf. on Extending Database Technology.
  7. ^"Amazon Goes Back to the Future With 'NoSQL' Database".WIRED. 19 January 2012.Retrieved6 March2017.
  8. ^"RDBMS dominate the database market, but NoSQL systems are catching up".DB-Engines.com. 21 November 2013.Retrieved24 November2013.
  9. ^NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley Educational Publishers Inc, 2009,ISBN978-0321826626.
  10. ^Vogels, Werner (18 January 2012)."Amazon DynamoDB – a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications".All Things Distributed.Retrieved6 March2017.
  11. ^Grolinger, K.; Higashino, W. A.; Tiwari, A.; Capretz, M. A. M. (2013)."Data management in cloud environments: NoSQL and NewSQL data stores"(PDF).Aira, Springer.Retrieved8 January2014.
  12. ^"Jepsen: MongoDB stale reads".Aphyr.com.20 April 2015.Retrieved6 March2017.
  13. ^"Large volume data analysis on the Typesafe Reactive Platform".Slideshare.net.11 June 2015.Retrieved6 March2017.
  14. ^Fowler, Adam."10 NoSQL Misconceptions".Dummies.com.Retrieved6 March2017.
  15. ^"No! to SQL and No! to NoSQL | So Many Oracle Manuals, So Little Time".Iggyfernandez.wordpress.com.29 July 2013.Retrieved6 March2017.
  16. ^Chapple, Mike."The ACID Model".about.com.Archived fromthe originalon 29 December 2016.Retrieved26 September2012.
  17. ^Fiore, S. (2011).Grid and cloud database management.Springer Science & Business Media. p. 210.
  18. ^Lawrence, Integration and virtualization of relational SQL and NoSQL systems including MySQL and MongoDB (2014). "Integration and virtualization of relational SQL and NoSQL systems including MySQL and MongoDB".International Conference on Computational Science and Computational Intelligence 1.
  19. ^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.Retrieved12 May2011.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[...]
  20. ^"NoSQL Relational Database Management System: Home Page".Strozzi.it. 2 October 2007.Retrieved29 March2010.
  21. ^"NoSQL 2009".Blog.sym-link.com. 12 May 2009. Archived fromthe originalon 16 July 2011.Retrieved29 March2010.
  22. ^Strauch, Christof."NoSQL Databases"(PDF).pp. 23–24.Retrieved27 August2017.
  23. ^https://apacheignite.readme.io/docsIgnite Documentation
  24. ^https://www.infoworld.com/article/3135070/data-center/fire-up-big-data-processing-with-apache-ignite.htmlfire-up-big-data-processing-with-apache-ignite
  25. ^Sandy (14 January 2011)."Key Value stores and the NoSQL movement".Stackexchange.Retrieved1 January2012.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.
  26. ^Seeger, Marc (21 September 2009)."Key-Value Stores: a practical overview"(PDF).Marc Seeger.Retrieved1 January2012.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.
  27. ^Katsov, Ilya (1 March 2012)."NoSQL Data Modeling Techniques".Ilya Katsov.Retrieved8 May2014.
  28. ^"TerminusDB an open-source in-memory document graph database".terminusdb.com.Retrieved16 December2021.
  29. ^Scofield, Ben (14 January 2010)."NoSQL - Death to Relational Databases(?)".Retrieved26 June2014.
  30. ^ "Moving From Relational to NoSQL: How to Get Started".Couchbase.com.Retrieved11 November2019.
  31. ^"Can't do joins with MarkLogic? It's just a matter of Semantics! - General Networks".Gennet.com.Archived fromthe originalon 3 March 2017.Retrieved6 March2017.
  32. ^"Sharded Collection Restrictions".docs.mongodb.com.Retrieved24 January2020.
  33. ^"SQL Reference · OrientDB Manual".OrientDB.com.Retrieved24 January2020.

Further reading

[edit]
[edit]
Retrieved from "https://en.wikipedia.org/w/index.php?title=NoSQL&oldid=1246467749"