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Types and examples of NoSQL databases

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:

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Detailed Classification

A more detailed classification is the following, based on one from Stephen Yen:<ref>



Type Notable examples of this type
Key-Value Cache Apache Ignite, Coherence, eXtreme Scale, Hazelcast, Infinispan, Memcached, Velocity
Key-Value Store ArangoDB, Aerospike
Key-Value Store (Eventually-Consistent) Oracle NoSQL Database, Dynamo, Riak, Voldemort
Key-Value Store (Ordered) FoundationDB, InfinityDB, LMDB, MemcacheDB
Data-Structures Server Redis
Tuple Store Apache River, GigaSpaces
Object Database Objectivity/DB, Perst, ZopeDB
Document Store ArangoDB, BaseX, Clusterpoint, Couchbase, CouchDB, DocumentDB, IBM Domino, MarkLogic, MongoDB, Qizx, RethinkDB
Wide Column Store Amazon DynamoDB, Bigtable, Cassandra, Druid, HBase, Hypertable
Native Multi-model Database ArangoDB, Cosmos DB, OrientDB

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.<ref>



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.<ref>


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 (SSD) or rotating disks (aka Hard Disk Drive (HDD)).

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 also 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.


Main: Graph database

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, network topologies, etc.

; Graph databases and their query language

! Name !! Language(s) !! Notes
| [[AllegroGraph]] || [[SPARQL]] || [[Resource Description Framework|RDF]] triple store
| [[ArangoDB]] || AQL, [[JavaScript]], [[GraphQL]] || Multi-model DBMS [[Document-oriented database|Document]], [[Graph database]] and [[Key-value store]]
| [[DEX (Graph database)|DEX/Sparksee]] || [[C++]], [[Java (programming language)|Java]], [[.NET Framework|.NET]], [[Python (programming language)|Python]] || [[Graph database]]
| [[FlockDB]] || [[Scala (programming language)|Scala]] || [[Graph database]]
| [[IBM DB2]] || [[SPARQL]] || [[Resource Description Framework|RDF]] triple store added in DB2 10
| [[InfiniteGraph]] || [[Java (programming language)|Java]] || [[Graph database]]
| [[MarkLogic]] || [[Java (programming language)|Java]], [[JavaScript]], [[SPARQL]], [[XQuery]] || Multi-model [[Document-oriented database|document database]] and [[Resource Description Framework|RDF]] triple store
| [[Neo4j]] || [[Cypher Query Language|Cypher]] || [[Graph database]]
| [[Virtuoso Universal Server|OpenLink Virtuoso]] || [[C++]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[SPARQL]] || [[Middleware]] and [[database engine]] hybrid
| [[Oracle Database|Oracle]] || [[SPARQL|SPARQL 1.1]] || [[Resource Description Framework|RDF]] triple store added in 11g
| [[OrientDB]] || [[Java (programming language)|Java]], SQL || Multi-model [[Document-oriented database|document]] and [[graph database]]
| [[Ontotext|OWLIM]] || [[Java (programming language)|Java]], [[SPARQL|SPARQL 1.1]]|| [[Resource Description Framework|RDF]] triple store
| [[Profium|Profium Sense]] || [[Java (programming language)|Java]], [[SPARQL]] || [[Resource Description Framework|RDF]] triple store
| [[sqrrl|Sqrrl Enterprise]] || [[Java (programming language)|Java]] || [[Graph database]]

Object database


Tuple store

Triple/quad store (RDF) database


Multivalue databases

Multimodel database

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Snippet from Wikipedia: NoSQL

NoSQL (originally referring to "non-SQL" or "non-relational") is an approach to database design that focuses on providing a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational 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 a schema, it offers rapid scalability to manage large and typically unstructured data sets. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

Non-relational databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 2000s, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.

Motivations for this approach include simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases), finer control over availability, and limiting the object-relational impedance mismatch. The data structures used by NoSQL databases (e.g. key–value pair, 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.

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), lack of ability to perform ad hoc joins across tables, lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true ACID transactions, 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 as stale read. Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss. Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss. For distributed transaction processing across 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". Few systems maintain both ACID transactions and X/Open XA standards for distributed transaction processing. Interactive relational databases share conformational relay analysis techniques as a common feature. Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to most operating systems.

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nosql.txt · Last modified: 2024/04/28 03:20 by