NoSQL

"التخزين المهيكل" redirects here. For تقنية ميكروسوفت المعروفة أيضاً بالتخزين المهيكل, see COM Structured Storage.

NoSQL (تختصر عادة إلى Not Only SQL[1][2][unreliable source?])، هي قاعدة بيانات توفر آلية لتخزين واسترجاع البيانات that is modeled in means other than the tabular relations المستخدمة في قواعد البيانات العلائقية. 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 some faster in relational databases. The particular suitability of a given NoSQL database depends on the problem it must solve.

قواعد بيانات NoSQL تستخدم بشكل متزايد في البيانات الضخمة وتطبيقات real-time web.[3] يطلق على أنظمة NoSQL أيضاً "Not only SQL" للتأكيد على أنها يمكنها أيضاً أن تعدم لغات الاستعلام الشبيهة بSQL-. 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) and FoundationDB, have made them central to their designs.

معوقات الاعتماد

Barriers to wider NoSQL adoption include their use of low-level query languages instead of SQL, inability to perform ad hoc joins across tables, lack of standardized interfaces, and significant investments already made in relational databases.[5] Some NoSQL systems risk losing data through lost writes or other forms, though features like write-ahead logging—a method to record changes before they’re applied—can help prevent this.[6][7] For distributed transaction processing across multiple databases, keeping data consistent is a challenge for both NoSQL and relational systems, as relational databases cannot enforce rules linking separate databases, and few systems support both ACID transactions and X/Open XA standards for managing distributed updates.[8][9] Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to most operating systems.[10]

التاريخ

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.[11] His NoSQL RDBMS is 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'",[12] referring to "not relational".

Johan Oskarsson, then a developer at Last.fm, reintroduced the term NoSQL in early 2009 when he organized an event to discuss "open-source distributed, non-relational databases".[13] The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google's Bigtable/MapReduce and Amazon's DynamoDB.

أنواع وأمثلة

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:[14]

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, TIBCO ActiveSpaces, 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, Indexed Database API
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,[15][16] Couchbase, FoundationDB, Oracle Database, AgensGraph
Graph database Azure Cosmos DB, AllegroGraph, ArangoDB, Apache Giraph, GUN (Graph Universe Node), InfiniteGraph, MarkLogic, Neo4J, OrientDB, Virtuoso
Multivalue database D3 Pick database, Extensible Storage Engine (ESE/NT), InfinityDB, InterSystems Caché, jBASE Pick database, mvBase Rocket Software, mvEnterprise Rocket Software, Northgate Information Solutions Reality (the original Pick/MV Database), OpenQM, Revelation Software's OpenInsight (Windows) and Advanced Revelation (DOS), UniData Rocket U2, UniVerse Rocket U2

Key–value store

المقالة الرئيسية: Key–value database

Key–value (KV) stores use the associative 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.[17][18]

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.[19]

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 store data in memory (RAM), while others on solid-state drives (SSD) or rotating disks (aka hard disk drive (HDD)).

Document store

المقالة الرئيسية: Document-oriented database and XML 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 include XML, YAML, and JSON and binary forms like BSON. Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve 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, 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

المقالة الرئيسية: Graph database

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

Graph databases and their query language
Name Language(s) Notes
AgensGraph Cypher Multi-model graph database
AllegroGraph SPARQL RDF triple store
Amazon Neptune Gremlin, SPARQL Graph database
ArangoDB AQL, JavaScript, GraphQL Multi-model DBMS Document, Graph database and Key-value store
Azure Cosmos DB Gremlin Graph database
DEX/Sparksee C++, Java, C#, Python Graph database
FlockDB Scala Graph database
GUN (Graph Universe Node) JavaScript Graph database
IBM Db2 SPARQL RDF triple store added in DB2 10
InfiniteGraph Java Graph database
JanusGraph Java Graph database
MarkLogic Java, JavaScript, SPARQL, XQuery Multi-model document database and RDF triple store
Neo4j Cypher Graph database
OpenLink Virtuoso C++, C#, Java, SPARQL Middleware and database engine hybrid
Oracle SPARQL 1.1 RDF triple store added in 11g
OrientDB Java, SQL Multi-model document and graph database
OWLIM Java, SPARQL 1.1 RDF triple store
Profium Sense Java, SPARQL RDF triple store
RedisGraph Cypher Graph database
Sqrrl Enterprise Java Graph database
TerminusDB JavaScript, Python, datalog Open source RDF triple-store and document store[20]

تطبيقات قواعد بيانات NoSQL


المصطلح قاعدة البيانات المطابقة
Key-Value Cache Coherence, eXtreme Scale, GigaSpaces, GemFire, Hazelcast, Infinispan, JBoss Cache, Memcached, Repcached, Terracotta, Velocity
Key-Value Store Flare, Keyspace, RAMCloud, SchemaFree, Hyperdex
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, Couchbase, CouchDB, MarkLogic, MongoDB, XML-databases
Wide Columnar Store BigTable, Cassandra, Druid, HBase, Hypertable, KAI, KDI, OpenNeptune, Qbase

الآداء

نموذج البيانات الآداء التدرجية المرونة التعقيد التشغيل
Key–Value Store مرتفع مرتفع مرتفع لا يوجد متغير (لا يوجد)
Column-Oriented Store مرتفع مرتفع متوسط منخفض اسمي
Document-Oriented Store مرتفع متغير (مرتفع) مرتفع منخفض متغير (منخفض)
قاعدة بيانات المخططات متغير متغير مرتفع مرتفع نظرية المخططات
قاعدة البيانات العلائقية متغير متغير منخفض متوسط الجبر العلائقي
انظر أيضاً: مقارنة برامج التخزين المهيكلة

معالجة البيانات العلائقية

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.)

الاستعلامات المتعددة

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/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.[21]

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 needed for a specific task.

ACID and join support

A database is marked as supporting ACID properties (atomicity, consistency, isolation, durability) or join operations 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 نعم لا
AgensGraph نعم نعم
Apache Ignite نعم نعم
ArangoDB نعم نعم
Amazon DynamoDB نعم لا
Couchbase نعم نعم
CouchDB نعم نعم
IBM Db2 نعم نعم
InfinityDB نعم لا
LMDB نعم لا
MarkLogic نعم نعم[nb 1]
MongoDB نعم نعم[nb 2]
OrientDB نعم نعم[nb 3]
  1. ^ Joins do not necessarily apply to document databases, but MarkLogic can do joins using semantics.[22]
  2. ^ MongoDB did not support joining from a sharded collection until version 5.1.[23]
  3. ^ OrientDB can resolve 1:1 joins using links by storing direct links to foreign records.[24]

أمثلة

تخزين الوثائق

المقالة الرئيسية: Document-oriented database and XML database


الاسم اللغة هوامش
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
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

المخططات

المقالة الرئيسية: قاعدة بيانات المخططات


الاسم اللغة هوامش
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

KV - eventually consistent

KV - ordered

KV - RAM

KV - solid-state drive or rotating disk

Object database

المقالة الرئيسية: Object database

Tabular

Tuple store

Triple/quad store (RDF) database

Hosted

Multivalue databases

قاعدة البيانات متعددة النماذج

قاعدة البيانات التصحيحية

قاعدة البيانات الخلوية

انظر أيضاً

المصادر

  1. ^ "NoSQL (Not Only SQL)". NoSQL database, also called Not Only SQL
  2. ^ Martin Fowler. "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 Nov 2013. Retrieved 24 Nov 2013.
  4. ^ 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 Jan 2014.{{cite web}}: CS1 maint: multiple names: authors list (link)
  5. ^ 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. Retrieved 8 January 2014.
  6. ^ "Large volume data analysis on the Typesafe Reactive Platform". Slideshare.net. 11 June 2015. Retrieved 2017-03-06.
  7. ^ Fowler, Adam. "10 NoSQL Misconceptions". Dummies.com. Retrieved 2017-03-06.
  8. ^ "No! to SQL and No! to NoSQL". Iggyfernandez.wordpress.com. 29 July 2013. Retrieved 2017-03-06.
  9. ^ Chapple, Mike. "The ACID Model". about.com. Archived from the original on 29 December 2016. Retrieved 26 September 2012.
  10. ^ 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.
  11. ^ 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[...]
  12. ^ "NoSQL Relational Database Management System: Home Page". Strozzi.it. 2 October 2007. Retrieved 29 March 2010.
  13. ^ "NoSQL 2009". Blog.sym-link.com. 12 May 2009. Archived from the original on 16 July 2011. Retrieved 29 March 2010.
  14. ^ Strauch, Christof. "NoSQL Databases" (PDF). pp. 23–24. Retrieved 2017-08-27.
  15. ^ https://apacheignite.readme.io/docs Ignite Documentation
  16. ^ https://www.infoworld.com/article/3135070/data-center/fire-up-big-data-processing-with-apache-ignite.html fire-up-big-data-processing-with-apache-ignite
  17. ^ Sandy (14 January 2011). "Key Value stores and the NoSQL movement". 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.
  18. ^ Seeger, Marc (21 September 2009). "Key-Value Stores: a practical overview" (PDF). 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.
  19. ^ Katsov, Ilya (1 March 2012). "NoSQL Data Modeling Techniques". Ilya Katsov. Retrieved 8 May 2014.
  20. ^ "TerminusDB an open-source in-memory document graph database". terminusdb.com. Retrieved 2021-12-16.
  21. ^ "Moving From Relational to NoSQL: How to Get Started". Couchbase.com. Retrieved 11 November 2019.
  22. ^ "Can't do joins with MarkLogic? It's just a matter of Semantics! - General Networks". Gennet.com. Archived from the original on 3 March 2017. Retrieved 2017-03-06.
  23. ^ "Sharded Collection Restrictions". docs.mongodb.com. Retrieved 2020-01-24.
  24. ^ "SQL Reference · OrientDB Manual". OrientDB.com. Retrieved 2020-01-24.
  25. ^ "Riak: An Open Source Scalable Data Store". 28 November 2010. Retrieved 28 November 2010 * OpenLink Virtuoso
  26. ^ Tweed, Rob; George James (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)

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