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NoSQL

Википедиа — Чөлөөт нэвтэрхий толь
03:15, 8 Дөрөвдүгээр сар 2017-ий байдлаарх ErkaIS (хэлэлцүүлэг | оруулсан хувь нэмэр) хэрэглэгчийн хийсэн залруулга

NoSQL ("SQL бус", "Дан ганц SQL ч биш" гэж бас өөрөөр хэлэгддэг)[1] database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the "NoSQL" moniker until a surge of popularity in the early twenty-first century,[./NoSQL#cite_note-leavitt-2 [2]][2] triggered by the needs of Web 2.0 companies such as Facebook, Google, and Amazon.com.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages.[7][8]

Motivations for this approach include: simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) 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 relational database tables.[9]

Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)

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 as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Fortunately, some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases "do not allow referential integrity constraints to span databases."[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing.

Түүх

The term NoSQL was used by Carlo Strozzi in 1998 to name his lightweight, Strozzi NoSQL open-source relational database that did not expose the standard Structured Query Language (SQL) interface, but was still relational.[15] His NoSQL RDBMS is distinct from the circa-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'",[16] referring to 'No Relational'.

Johan Oskarsson, дараа нь хөгжүүлэгч үед Өнгөрсөн.fm, эргүүлэн хугацааны NoSQL 2009 оны эхээр тэр үед зохион байгуулсан үйл явдлын талаар хэлэлцэх "нээлттэй эх тараасан, төрийн бус харилцааны мэдээллийн сан".[17] нэр оролдсон шошго бий-ийн тоо нэмэгдэх бус харилцааны, тараасан мэдээллийг хадгалдаг, түүний дотор нээлттэй эх клоноо Google ' s BigTable/MapReduce, Amazon-ийн Dynamo. Хамгийн эрт NoSQL систем биш үү хангах оролдлого atomicity, тууштай, тусгаарлах, эдэлгээний баталгаа, эсрэг зонхилох дадлага дунд relational database systems.[18]

Үндэслэн 2014 орлого, NoSQL зах зээлийн удирдагчид MarkLogic, MongoDBба Datastax.[19] дээр Үндэслэн 2015 алдартай зэрэглэл, хамгийн алдартай NoSQL өгөгдлийн сан байгаа MongoDB, Apache Cassandra, Redis.[20]

Төрөл, жишээ NoSQL өгөгдлийн сан

There have been various approaches to classify NoSQL databases, each with different categories and subcategories, some of which overlap. What follows is a basic classification by data model, with examples:

  • Column: Accumulo, Cassandra, Druid, HBase, Vertica, SAP HANA
  • Document: Apache CouchDB, ArangoDB, Clusterpoint, Couchbase, DocumentDB, HyperDex, IBM Domino, MarkLogic, MongoDB, OrientDB, Qizx, RethinkDB
  • Key-value: Aerospike, ArangoDB, Couchbase, Dynamo, FairCom c-treeACE, FoundationDB, HyperDex, InfinityDB, MemcacheDB, MUMPS, Oracle NoSQL Database, OrientDB, Redis, Riak, Berkeley DB
  • Graph: AllegroGraph, ArangoDB, InfiniteGraph, Apache Giraph, MarkLogic, Neo4J, OrientDB, Virtuoso, Stardog
  • Multi-model: Alchemy Database, ArangoDB, CortexDB, Couchbase, FoundationDB, InfinityDB, MarkLogic, OrientDB

A more detailed classification is the following, based on one from Stephen Yen:[21]

Type Examples of this type
Key-Value Cache Coherence, eXtreme Scale, GigaSpaces, GemFire, Hazelcast, Infinispan, JBoss Cache, Memcached, Repcached, Terracotta, Velocity
Key-Value Store ArangoDB, Flare, Keyspace, RAMCloud, SchemaFree, Hyperdex, Aerospike, quasardb
Key-Value Store (Eventually-Consistent) DovetailDB, Oracle NoSQL Database, Dynamo, Riak, Dynomite, MotionDb, Voldemort, SubRecord
Key-Value Store (Ordered) Actord, FoundationDB, InfinityDB, Lightcloud, LMDB, 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 ArangoDB, Clusterpoint, Couchbase, CouchDB, DocumentDB, IBM Domino, MarkLogic, MongoDB, Qizx, RethinkDB, XML-databases
Wide Column Store BigTable, Cassandra, Druid, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase

Correlation databases are model-independent, and instead of row-based or column-based storage, use value-based storage.

Key-value store

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.[22][23]

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 in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges.[24]

Key-value stores can use consistency models ranging from eventual consistency to serializability. Some databases support ordering of keys. There are various hardware implementations, and some users maintain data in memory (RAM), while others employ solid-state drives or rotating disks.

Examples include ArangoDB, InfinityDB, Oracle NoSQL Database, Redis, and dbm.

Document store

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. 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 in addition to the key lookup performed by a key-value store, the database offers an API or query language that retrieves documents based on their contents.

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.

Graph

This kind of database is designed for data whose relations are well represented as a graph consisting of elements interconnected with a finite number of relations between them. The type of data could be social relations, public transport links, road maps or network topologies.

Graph databases and their query language
Name Language(s) Notes
AllegroGraph SPARQL RDF triple store
ArangoDB AQL, JavaScript Multi-model DBMS Document, Graph database and Key-value store
DEX/Sparksee C++, Java, .NET, Python Graph database
FlockDB Scala Graph database
IBM DB2 SPARQL RDF triple store added in DB2 10
InfiniteGraph Java Graph database
MarkLogic Java, JavaScript, SPARQL, XQuery Multi-model document database and RDF triple store
Neo4j Cypher Graph database
OWLIM Java, SPARQL 1.1 RDF triple store
Oracle SPARQL 1.1 RDF triple store added in 11g
OrientDB Java, SQL Multi-model document and graph database
Sqrrl Enterprise Java Graph database
OpenLink Virtuoso C++, C#, Java, SPARQL Middleware and database engine hybrid
Stardog Java, SPARQL Graph database

Object database

  • db4o
  • GemStone/S
  • InterSystems Caché
  • JADE
  • ObjectDatabase++
  • ObjectDB
  • Objectivity/DB
  • ObjectStore
  • ODABA
  • Perst
  • OpenLink Virtuoso
  • Versant Object Database
  • ZODB

Tabular

  • Apache Accumulo
  • BigTable
  • Apache Hbase
  • Hypertable
  • Mnesia
  • OpenLink Virtuoso

Tuple store

  • Apache Голын
  • GigaSpaces
  • Tarantool
  • TIBCO ActiveSpaces
  • OpenLink Virtuoso

Triple/quad store (RDF) database

  • AllegroGraph
  • Apache JENA (Энэ нь хамрах хүрээ, мэдээллийн сан)
  • MarkLogic
  • Ontotext-OWLIM
  • Oracle NoSQL өгөгдлийн сан
  • Virtuoso Бүх Нийтийн Сервер
  • Stardog

Hosted

  • Amazon DynamoDB
  • Amazon SimpleDB
  • Datastore on Google Appengine
  • Clusterpoint database
  • Cloudant Data Layer (CouchDB)
  • Freebase
  • Microsoft Azure Tables[25]
  • Microsoft Azure DocumentDB[26]
  • OpenLink Virtuoso

Multivalue databases

  • D3 Pick database
  • Extensible Storage Engine (ESE/NT)
  • InfinityDB
  • InterSystems Caché
  • jBASE Pick database
  • Northgate Information Solutions Reality, the original Pick/MV Database
  • OpenQM
  • Revelation Software's OpenInsight
  • Rocket U2

Multimodel database

  • ArangoDB
  • Couchbase
  • FoundationDB
  • MarkLogic
  • OrientDB

Performance

Ben Scofield rated different categories of NoSQL databases as follows:[27]

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.

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 is 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 and non-normalized data

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.[28]

Nesting data

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

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
Aerospike Тийм Үгүй
ArangoDB Тийм Тийм
CouchDB Тийм Тийм
c-treeACE Тийм Тийм
HyperDex Тийм[nb 1] Тийм
InfinityDB Тийм Үгүй
LMDB Тийм Үгүй
MarkLogic Тийм Тийм[nb 2]
OrientDB Тийм Тийм
  1. ↑ HyperDex currently offers ACID support via its Warp extension, which is a commercial add-on.
  2. ↑ Joins do not necessarily apply to document databases, but MarkLogic can do joins using semantics.[29]

See also

  • CAP theorem
  • Comparison of object database management systems
  • Comparison of structured storage software
  • Correlation database
  • Distributed cache
  • Faceted search
  • MultiValue database
  • Multi-model database
  • Triplestore
  • Schema-agnostic databases

References

  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. ↑ 2.0 2.1 Leavitt, Neal (2010). "Will NoSQL Databases Live Up to Their Promise?" (PDF). IEEE Computer.
  3. ↑ Mohan, C. (2013). "History Repeats Itself: Sensible and NonsenSQL Aspects of the NoSQL Hoopla" in Proc. 16th Int'l Conf. on Extending Database Technology.. 
  4. ↑ "NOSQL meetup Tickets, Thu, Jun 11, 2009 at 10:00 AM". Татаж авсан: 2017-03-06.
  5. ↑ "Amazon Goes Back to the Future With 'NoSQL' Database". WIRED. 2012-01-19. Татаж авсан: 2017-03-06.
  6. ↑ "RDBMS dominate the database market, but NoSQL systems are catching up". DB-Engines.com. 21 Nov 2013. Татаж авсан: 24 Nov 2013.
  7. ↑ "NoSQL (Not Only SQL)". NoSQL database, also called Not Only SQL
  8. ↑ Fowler, Martin. "NosqlDefinition". many advocates of NoSQL say that it does not mean a "no" to SQL, rather it means Not Only SQL
  9. ↑ Vogels, Werner (2012-01-18). "Amazon DynamoDB – a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications". All Things Distributed. Татаж авсан: 2017-03-06.
  10. ↑ Grolinger, K. (2013). "Data management in cloud environments: NoSQL and NewSQL data stores" (PDF). Aira, Springer. Татаж авсан: 8 Jan 2014.
  11. ↑ "Jepsen: MongoDB stale reads". 2015-04-20. Татаж авсан: 2017-03-06.
  12. ↑ "Large volume data analysis on the Typesafe Reactive Platform". Татаж авсан: 2017-03-06.
  13. ↑ Fowler, Adam. "10 NoSQL Misconceptions". Татаж авсан: 2017-03-06.
  14. ↑ "No! to SQL and No! to NoSQL | So Many Oracle Manuals, So Little Time". Татаж авсан: 2017-03-06.
  15. ↑ 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. Татаж авсан: 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[...]
  16. ↑ "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Татаж авсан: 29 March 2010.
  17. ↑ "NoSQL 2009". Blog.sym-link.com. 12 May 2009. Татаж авсан: 29 March 2010.
  18. ↑ Chapple, Mike. "The ACID Model".
  19. ↑ "Hadoop-NoSQL-rankings". Татаж авсан: 2015-11-17.
  20. ↑ "DB-Engines Ranking". Татаж авсан: 2015-07-31.
  21. ↑ Yen, Stephen. "NoSQL is a Horseless Carriage" (PDF). NorthScale. Татаж авсан: 2014-06-26.
  22. ↑ Sandy (14 January 2011). "Key Value stores and the NoSQL movement". Stackexchange. Татаж авсан: 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}}: More than one of |author= and |last= specified (help)
  23. ↑ Seeger, Marc (21 September 2009). "Key-Value Stores: a practical overview" (PDF). Marc Seeger. Татаж авсан: 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.
  24. ↑ Katsov, Ilya (1 March 2012). "NoSQL Data Modeling Techniques". Ilya Katsov. Татаж авсан: 8 May 2014.
  25. ↑ "Table storage | Microsoft Azure". Татаж авсан: 2017-03-06.
  26. ↑ "DocumentDB – NoSQL Database Service | Microsoft Azure". Татаж авсан: 2017-03-06.
  27. ↑ Scofield, Ben (2010-01-14). "NoSQL - Death to Relational Databases(?)". Татаж авсан: 2014-06-26.
  28. ↑ "Making the Shift from Relational to NoSQL" (PDF). Couchbase.com. Татаж авсан: December 5, 2014.
  29. ↑ "Can’t do joins with MarkLogic? It’s just a matter of Semantics! - General Networks". Gennet.com. Татаж авсан: 2017-03-06.

Further reading

  • Sadalage, Pramod (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley. ISBN 0-321-82662-0.
  • McCreary, Dan (2013). Making Sense of NoSQL: A guide for managers and the rest of us. ISBN 9781617291074.
  • Wiese, Lena (2015). Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. DeGruyter/Oldenbourg. ISBN 978-3-11-044140-6.
  • Strauch, Christof (2012). "NoSQL Databases" (PDF).
  • Moniruzzaman, A. B. (2013). "NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison". {{cite journal}}: Cite journal requires |journal= (help)
  • Orend, Kai (2013). "Analysis and Classification of NoSQL Databases and Evaluation of their Ability to Replace an Object-relational Persistence Layer". {{cite journal}}: Cite journal requires |journal= (help)
  • Krishnan, Ganesh. "Method and system for versioned sharing, consolidating and reporting information".
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