View source for Document-oriented database

{{about|the software type|deployed applications of the software type|Full-text database}}
{{short description|Type of computer program}}
{{more citations needed|date=June 2025}}

A '''document-oriented database''', or '''document store''', is a [[computer program]] and data storage system designed for storing, retrieving and managing document-oriented information, also known as [[Semi-structured model|semi-structured data]].<ref name = "Drake, DigitalOcean, 2019" >{{ Cite web | url = https://www.digitalocean.com/community/tutorials/a-comparison-of-nosql-database-management-systems-and-models | title = A Comparison of NoSQL Database Management Systems and Models | access-date = 23 August 2019 | first = Mark | last = Drake | date = 9 August 2019 | website = [[DigitalOcean]] | quote = Document-oriented databases, or document stores, are NoSQL databases that store data in the form of documents. Document stores are a type of key-value store: each document has a unique identifier — its key — and the document itself serves as the value. | archive-url = https://web.archive.org/web/20190813163612/https://www.digitalocean.com/community/tutorials/a-comparison-of-nosql-database-management-systems-and-models | archive-date = 2019-08-13  | df = dmy-all }}</ref>

Document-oriented databases are one of the main categories of [[NoSQL]] databases, and the popularity of the term "document-oriented database" has grown<ref>{{cite web|url=http://db-engines.com/en/ranking_categories|title=DB-Engines Ranking per database model category}}</ref> with the use of the term NoSQL itself. [[XML database]]s are a subclass of document-oriented databases that are optimized to work with [[XML]] documents. [[Graph databases]] are similar, but add another layer, the ''relationship'', which allows them to link documents for rapid traversal.

Document-oriented databases are inherently a subclass of the [[Key-value database|key-value store]], another NoSQL database concept. The difference{{contradict inline|reason=If there is a difference between document-oriented databases and key-value stores, then document-oriented databases are not a subclass of the key-value store. If document-oriented databases truly are a subclass of the key-value store, the phrase "the difference" should be rephrased, for example as "the difference between document-oriented databases and other types of key-value stores".|date=December 2022}} lies in the way the data is processed; in a key-value store, the data is considered to be inherently opaque to the database, whereas a document-oriented system relies on internal structure in the ''document'' in order to extract [[metadata]] that the database engine uses for further optimization. Although the difference is often negligible due to tools in the systems,{{efn|To the point that document-oriented and key-value systems can often be interchanged in operation.}} conceptually the document-store is designed to offer a richer experience with modern programming techniques.

Document databases{{efn|And key-value stores in general.}} contrast strongly with the traditional [[relational database]] (RDB). Relational databases generally store data in separate ''tables'' that are defined by the programmer, and a single object may be spread across several tables. Document databases store all information for a given object in a single instance in the database, and every stored object can be different from every other. This eliminates the need for [[object-relational mapping]] while loading data into the database.

== Documents ==
The central concept of a document-oriented database is the notion of a ''document''. While each document-oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format or encoding.<ref name="davoudian"/><ref name="corbellini">{{Cite journal| doi = 10.1016/j.is.2016.07.009| issn = 0306-4379| volume = 63| pages = 1–23| last1 = Corbellini| first1 = Alejandro| last2 = Mateos| first2 = Cristian| last3 = Zunino| first3 = Alejandro| last4 = Godoy| first4 = Daniela| last5 = Schiaffino| first5 = Silvia| title = Persisting big-data: The NoSQL landscape| journal = Information Systems| access-date = 2025-05-25| date = 2017-01-01| url = https://www.sciencedirect.com/science/article/pii/S0306437916303210| hdl = 11336/58462| hdl-access = free}}</ref> Encodings in use include [[XML]], [[YAML]], [[JSON]], as well as binary forms like [[BSON]].<ref name="truica">{{Cite journal| doi = 10.1016/j.bdr.2021.100205| issn = 22145796| volume = 25| pages = 100205| last1 = Truică| first1 = Ciprian-Octavian| last2 = Apostol| first2 = Elena-Simona| last3 = Darmont| first3 = Jérôme| last4 = Pedersen| first4 = Torben Bach| title = The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes| journal = Big Data Research| access-date = 2025-06-02| date = 2021| url = https://linkinghub.elsevier.com/retrieve/pii/S2214579621000228| arxiv = 2102.02246}}</ref>

Documents in a document store are roughly equivalent to the programming concept of an object. They are not required to adhere to a standard schema, nor will they have all the same sections, slots, parts or keys. Generally, programs using objects have many different types of objects, and those objects often have many optional fields. Every object, even those of the same class, can look very different. Document stores are similar in that they allow different types of documents in a single store, allow the fields within them to be optional, and often allow them to be encoded using different encoding systems. For example, the following is a document, encoded in JSON:

<syntaxhighlight lang="json" line="1">
{
    "firstName": "Bob", 
    "lastName": "Smith",
    "address": {
        "type": "Home",
        "street1":"5 Oak St.",
        "city": "Boys",
        "state": "AR",
        "zip": "32225",
        "country": "US"
    },
    "hobby": "sailing",
    "phone": {
        "type": "Cell",
        "number": "(555)-123-4567"
    }
}
</syntaxhighlight>

A second document might be encoded in XML as:
<syntaxhighlight lang="xml">
<contact>
  <firstname>Bob</firstname>
  <lastname>Smith</lastname>
  <phone type="Cell">(123) 555-0178</phone>
  <phone type="Work">(890) 555-0133</phone>
  <address>
    <type>Home</type>
    <street1>123 Back St.</street1>
    <city>Boys</city>
    <state>AR</state>
    <zip>32225</zip>
    <country>US</country>
  </address>
</contact>
</syntaxhighlight>

These two documents share some structural elements with one another, but each also has unique elements. The structure and text and other data inside the document are usually referred to as the document's ''content'' and may be referenced via retrieval or editing methods, (see below). Unlike a relational database where every record contains the same fields, leaving unused fields empty; there are no empty 'fields' in either document (record) in the above example. This approach allows new information to be added to some records without requiring that every other record in the database share the same structure.

Document databases typically provide for additional [[metadata]] to be associated with and stored along with the document content. That metadata may be related to facilities the datastore provides for organizing documents, providing security, or other implementation specific features.

=== CRUD operations ===
The core operations that a document-oriented database supports for documents are similar to other databases, and while the terminology is not perfectly standardized, most practitioners will recognize them as [[CRUD]]:

* Creation (or insertion)
* Retrieval (or query, search, read or find)
* Update (or edit)
* Deletion (or removal)

=== Keys ===
Documents are addressed in the database via a unique ''key'' that represents that document. This key is a simple [[identifier]] (or ID), typically a [[String (computer science)|string]], a [[URI]], or a [[Path (computing)|path]]. The key can be used to retrieve the document from the database. Typically the database retains an [[Database index|index]] on the key to speed up document retrieval, and in some cases the key is required to create or insert the document into the database.

=== Retrieval ===
Another defining characteristic of a document-oriented database is that, beyond the simple key-to-document lookup that can be used to retrieve a document, the database offers an API or query language that allows the user to retrieve documents based on content (or metadata).<ref name="davoudian">{{Cite journal| doi = 10.1145/3158661| issn = 0360-0300| volume = 51| issue = 2| pages = 1–43| last1 = Davoudian| first1 = Ali| last2 = Chen| first2 = Liu| last3 = Liu| first3 = Mengchi| title = A Survey on NoSQL Stores| journal = ACM Computing Surveys| access-date = 2025-05-31| date = 2019-03-31| url = https://dl.acm.org/doi/10.1145/3158661| url-access = subscription}}</ref> For example, you may want a query that retrieves all the documents with a certain field set to a certain value.  The set of query APIs or query language features available, as well as the expected performance of the queries, varies significantly from one implementation to another. Likewise, the specific set of indexing options and configuration that are available vary greatly by implementation.

It is here that the document store varies most from the key-value store. In theory, the values in a key-value store are opaque to the store, they are essentially black boxes. They may offer search systems similar to those of a document store, but may have less understanding about the organization of the content. Document stores use the metadata in the document to classify the content, allowing them, for instance, to understand that one series of digits is a phone number, and another is a postal code. This allows them to search on those types of data, for instance, all phone numbers containing 555, which would ignore the zip code 55555.

=== Editing ===
Document databases typically provide some mechanism for updating or editing the content (or metadata) of a document, either by allowing for replacement of the entire document, or individual structural pieces of the document.

=== Organization ===
Document database implementations offer a variety of ways of organizing documents, including notions of

* Collections: groups of documents, where depending on implementation,<ref name="davoudian"/> a document may be enforced to live inside one collection, or may be allowed to live in multiple collections
* Tags and non-visible metadata: additional data outside the document content 
* Directory hierarchies: groups of documents organized in a tree-like structure, typically based on path or URI

Sometimes these organizational notions vary in how much they are logical vs physical, (e.g. on disk or in memory), representations.

== Relationship to other databases ==

=== Relationship to key-value stores ===

A document-oriented database is a specialized [[Key-value database|key-value store]], which itself is another NoSQL database category.  In a simple key-value store, the document content is opaque. A document-oriented database provides APIs or a query/update language that exposes the ability to query or update based on the internal structure in the ''document''.<ref name="corbellini"/> This difference may be minor for users that do not need richer query, retrieval, or editing APIs that are typically provided by document databases. Modern key-value stores often include features for working with metadata, blurring the lines between document stores.

=== Relationship to search engines ===
Some search engine (aka [[information retrieval]]) systems like [[Apache Solr]] and [[Elasticsearch]] provide enough of the core operations on documents to fit the definition of a document-oriented database.

=== Relationship to relational databases ===

{{cleanup|section|reason="Requires cleanup"|date=July 2016}}

In a relational database, data is first categorized into a number of predefined types, and ''tables'' are created to hold individual entries, or ''records'', of each type. The tables define the data within each record's ''fields'', meaning that every record in the table has the same overall form. The administrator also defines the ''relationships'' between the tables, and selects certain fields that they believe will be most commonly used for searching and defines ''indexes'' on them. A key concept in the relational design is that any data that may be repeated is normally placed in its own table, and if these instances are related to each other, a column is selected to group them together, the ''foreign key''. This design is known as ''[[database normalization]]''.<ref>{{cite web |url=https://support.microsoft.com/en-ca/kb/283878 |title=Description of the database normalization basics |website=Microsoft|date=14 July 2023 }}</ref>

For example, an address book application will generally need to store the contact name, an optional image, one or more phone numbers, one or more mailing addresses, and one or more email addresses. In a canonical relational database, tables would be created for each of these rows with predefined fields for each bit of data: the CONTACT table might include FIRST_NAME, LAST_NAME and IMAGE columns, while the PHONE_NUMBER table might include COUNTRY_CODE, AREA_CODE, PHONE_NUMBER and TYPE (home, work, etc.). The PHONE_NUMBER table also contains a foreign key column, "CONTACT_ID", which holds the unique ID number assigned to the contact when it was created. In order to recreate the original contact, the database engine uses the foreign keys to look for the related items across the group of tables and reconstruct the original data.

In contrast, in a document-oriented database there may be no internal structure that maps directly onto the concept of a table, and the fields and relationships generally don't exist as predefined concepts. Instead, all of the data for an object is placed in a single document, and stored in the database as a single entry. In the address book example, the document would contain the contact's name, image, and any contact info, all in a single record. That entry is accessed through its key, which allows the database to retrieve and return the document to the application. No additional work is needed to retrieve the related data; all of this is returned in a single object.

A key difference between the document-oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in any database, and those documents can change in type and form at any time. If one wishes to add a COUNTRY_FLAG to a CONTACT, this field can be added to new documents as they are inserted, this will have no effect on the database or the existing documents already stored. To aid retrieval of information from the database, document-oriented systems generally allow the administrator to provide ''hints'' to the database to look for certain types of information. These work in a similar fashion to indexes in the relational case. Most also offer the ability to add additional metadata outside of the content of the document itself, for instance, tagging entries as being part of an address book, which allows the programmer to retrieve related types of information, like "all the address book entries". This provides functionality similar to a table, but separates the concept (categories of data) from its physical implementation (tables).

In the classic normalized relational model, objects in the database are represented as separate rows of data with no inherent structure beyond that given to them as they are retrieved. This leads to problems when trying to translate programming objects to and from their associated database rows, a problem known as [[object-relational impedance mismatch]].<ref>{{cite web |url=http://www.agiledata.org/essays/impedanceMismatch.html |title=The Object-Relational Impedance Mismatch |first=Scott |last=Wambler |website=Agile Data|date=22 March 2023 }}</ref> Document stores more closely, or in some cases directly, map programming objects into the store. These are often marketed using the term [[NoSQL]].

== Implementations ==
{{main cat|Document-oriented databases}}

<!--Entries in this list should be "notable" and have a sourced Wikipedia article.-->
{| class="wikitable sortable"
|-
! Name
! Publisher
! License
! Languages supported
! Notes
! [[Representational State Transfer|RESTful]] API
|-
| [[Aerospike (database)|Aerospike]]
| [[Aerospike (company)|Aerospike]]
| {{Open source|[[GNU Affero General Public License|AGPL]]}} and [[proprietary software|Proprietary]]
| [[C (programming language)|C]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Scala (programming language)|Scala]], [[Python (programming language)|Python]], [[Node.js]], [[PHP]], [[Go (programming language)|Go]], [[Rust (programming language)|Rust]], [[Spring Framework]] 
|Aerospike is a flash-optimized and in-memory distributed key value NoSQL database which also supports a document store model.<ref>{{cite web |title=Documentation {{!}} Aerospike - Key-Value Store |url=https://docs.aerospike.com/docs/guide/kvs.html |website=docs.aerospike.com |access-date=3 May 2021}}</ref>
| {{yes}}<ref>{{cite web |title=Documentation {{!}} Aerospike |url=https://docs.aerospike.com/docs/client/rest/index.html |website=docs.aerospike.com |access-date=3 May 2021}}</ref>
|-
| [[AllegroGraph]]
| Franz, Inc.
| {{proprietary}}
| [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Common Lisp (programming language)|Common Lisp]], [[Ruby (programming language)|Ruby]], [[Scala (programming language)|Scala]], [[C Sharp (programming language)|C#]], [[Perl]]
| The database platform supports document store and graph data models in a single database. Supports [[JSON]], [[JSON-LD]], [[Resource Description Framework|RDF]], full-text search, [[ACID]], [[Two-phase commit protocol|two-phase commit]], [[Multi-master replication|Multi-Master Replication]], [[Prolog]] and [[SPARQL]].
| {{yes}}<ref>{{cite web|url=https://franz.com/agraph/support/documentation/current/http-protocol.html|title=HTTP Protocol for AllegroGraph}}</ref>
|-
| [[ArangoDB]]
| ArangoDB
| {{proprietary|[[Business_Source_License|Business Source Licence]]}}
| [[C (programming language)|C]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Node.js]], [[PHP]], [[Scala (programming language)|Scala]], [[Go (programming language)|Go]], [[Ruby (programming language)|Ruby]], [[Elixir]]
| The database system supports document store as well as key/value and graph data models with one database core and a unified query language AQL (ArangoDB Query Language).
| {{yes}}<ref>{{Cite web|url=https://www.arangodb.com/|title=Multi-model highly available NoSQL database|website=ArangoDB}}</ref>
|-
| [[BaseX]]
| BaseX Team
| {{Open source|[[BSD License]]}}
| [[Java (programming language)|Java]], [[XQuery]]
| Support for XML, JSON and binary formats; client-/server based architecture; concurrent structural and full-text searches and updates.
| {{yes}}
|-
| [[InterSystems Caché|Caché]]
| [[InterSystems]] Corporation
| {{proprietary}}
| [[Java (programming language)|Java]], [[C Sharp (programming language)|C#]], [[Node.js]]
| Commonly used in Health, Business and Government applications.
| {{yes}}
|-
| [[Cloudant]]
| Cloudant, Inc.
| {{proprietary}}
| [[Erlang (programming language)|Erlang]], [[Java (programming language)|Java]], [[Scala (programming language)|Scala]], and [[C (programming language)|C]]
| Distributed database service based on [[BigCouch]], the company's [[Open-source model|open source]] fork of the [[Apache Software Foundation|Apache]]-backed [[CouchDB]] project.  Uses JSON model.
| {{yes}}
|-
| [[Clusterpoint|Clusterpoint Database]]
| Clusterpoint Ltd.
| {{proprietary}} with free download
| [[JavaScript]], [[SQL]], [[PHP]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Node.js]], [[C (programming language)|C]], [[C++]], 
| Distributed document-oriented XML / JSON database platform with [[ACID]]-compliant [[transaction processing|transactions]]; [[high-availability]] [[data replication]] and [[sharding]]; built-in [[full-text search]] engine with [[relevance]] [[ranking]]; JS/SQL [[query language]]; [[Geographic information system|GIS]]; Available as pay-per-use [[cloud database|database as a service]] or as an on-premise free software download.
| {{yes}}
|-
| [[Couchbase Server]]
| [[Couchbase, Inc.]]
| {{Open source|[[Apache License]]}}
| [[C (programming language)|C]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Node.js]], [[PHP]], [[SQL]], [[Go (programming language)|Go]], [[Spring Framework]], [[LINQ]]
|Distributed NoSQL Document Database, JSON model and SQL based Query Language.
| {{yes}}<ref>[http://www.couchbase.com/docs/ Documentation] {{webarchive|url=https://web.archive.org/web/20120820182153/http://www.couchbase.com/docs/ |date=2012-08-20 }}. Couchbase. Retrieved on 2013-09-18.</ref>
|-
| [[CouchDB]]
| [[Apache Software Foundation]]
| {{Open source|[[Apache License]]}}
| Any language that can make HTTP requests
| JSON over REST/HTTP with [[Multi-Version Concurrency Control]] and limited [[ACID]] properties. Uses [[map (higher-order function)|map]] and [[fold (higher-order function)|reduce]] for views and queries.<ref>{{Cite web|url=http://couchdb.apache.org/|archive-url=https://web.archive.org/web/20111020074113/http://couchdb.apache.org/docs/overview.html|url-status=dead|title=Apache CouchDB|archive-date=October 20, 2011|website=Apache Couchdb}}</ref>
| {{yes}}<ref>{{cite web|url=http://wiki.apache.org/couchdb/HTTP_Document_API|title=HTTP_Document_API - Couchdb Wiki|access-date=2011-10-14|archive-url=https://web.archive.org/web/20130301093229/http://wiki.apache.org/couchdb/HTTP_Document_API|archive-date=2013-03-01|url-status=dead}}</ref>
|-
| [[CrateDB]]
| Crate.io, Inc.
| {{Open source|[[Apache License]]}}
| [[Java (programming language)|Java]]
| Use familiar SQL syntax for real time distributed queries across a cluster. Based on Lucene / Elasticsearch ecosystem with built-in support for binary objects (BLOBs).
| {{yes}}<ref>{{cite web|url=https://crate.io/docs/stable/sql/rest.html |title=Crate SQL HTTP Endpoint (Archived copy) |access-date=2015-06-22 |url-status=dead |archive-url=https://web.archive.org/web/20150622174526/https://crate.io/docs/stable/sql/rest.html |archive-date=2015-06-22 }}</ref>
|-
| [[Cosmos DB]]
| Microsoft
| {{proprietary}}
| [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Node.js]], [[JavaScript]], [[SQL]]
| Platform-as-a-Service offering, part of the [[Microsoft Azure]] platform. Builds upon and extends the earlier Azure DocumentDB.
| {{yes}}
|-
|[[DocumentDB]]
|[[Amazon Web Services]]
|{{proprietary|[[proprietary software|Proprietary]] online service}}
| various, [[REST]]
| fully managed MongoDB v3.6-compatible database service
|{{yes}}
|-
|[[Amazon DynamoDB|DynamoDB]]
|[[Amazon Web Services]]
|{{proprietary}}
|[[Java (programming language)|Java]], [[JavaScript]], [[Node.js]], [[Go (programming language)|Go]], [[C Sharp (programming language)|C#]] [[.NET Framework|.NET]], [[Perl]], [[PHP]], [[Python (programming language)|Python]], [[Ruby (programming language)|Ruby]], [[Rust (programming language)|Rust]], [[Haskell (programming language)|Haskell]], [[Erlang (programming language)|Erlang]], [[Django (web framework)|Django]], and [[Grails (framework)|Grails]]
|fully managed proprietary [[NoSQL]] [[database]] service that supports [[Attribute–value pair|key–value]] and document data structures
|Yes
|-
|[[Elasticsearch]]
|[[Shay Banon]]
|{{proprietary|Dual-licensed under [[Server Side Public License]] and Elastic license.}}
|[[Java (programming language)|Java]]
|[[JSON]], Search engine.
|{{yes}}
|-
| [[eXist]]
| eXist
| {{Open source|[[LGPL]]}}
| [[XQuery]], [[Java (programming language)|Java]]
| XML over REST/HTTP, WebDAV, Lucene Fulltext search, binary data support, validation, versioning, clustering, triggers, URL rewriting, collections, ACLS, XQuery Update
| {{yes}}<ref>[http://exist-db.org eXist-db Open Source Native XML Database]. Exist-db.org. Retrieved on 2013-09-18.</ref>
|-
| [[Informix]]
| IBM
| {{proprietary|[[proprietary software|Proprietary]], with no-cost editions}}<ref>{{cite web|url=http://www.ibm.com/developerworks/data/library/techarticle/dm-0801doe/|title=Compare the Informix Version 12 editions|website=[[IBM]] |date=22 July 2016}}</ref>
| Various (Compatible with MongoDB API)
| RDBMS with JSON, replication, sharding and ACID compliance.
| {{yes}}
|-
|[[Apache Jackrabbit|Jackrabbit]]
|Apache Foundation
|{{Open source|[[Apache License]]}}
|[[Java (programming language)|Java]]
|[[Java Content Repository]] implementation
|{{dunno}}
|-
| [[HCL Notes]] ([[HCL Domino]])
| [[HCL Technologies|HCL]]
| {{proprietary}}
| [[LotusScript]], [[Java (programming language)|Java]], Notes Formula Language
| [[MultiValue]]
| {{yes}}
|-
| [[MarkLogic]]
| MarkLogic Corporation
| {{proprietary}} with free developer download
|[[Java (programming language)|Java]], [[JavaScript]], [[Node.js]], [[XQuery]], [[SPARQL]], [[XSLT]], [[C++]]
| Distributed document-oriented database for JSON, XML, and [[Resource Description Framework|RDF triples]]. Built-in [[full-text search]], [[ACID]] transactions, [[high availability]] and [[IT disaster recovery|disaster recovery]], certified security.
| {{yes}}
|-
| [[MongoDB]]
| MongoDB, Inc
| {{proprietary|[[Server Side Public License]] for the DBMS, [[Apache 2 License]] for the client drivers<ref>{{cite web|url=http://www.mongodb.org/about/licensing/|title=MongoDB Licensing}}</ref>}}
| [[C (programming language)|C]], [[C++]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[Perl]], [[PHP]], [[Python (programming language)|Python]], [[Go (programming language)|Go]], [[Node.js]], [[Ruby (programming language)|Ruby]], [[Rust (programming language)|Rust]],<ref>{{Cite news|url=https://www.mongodb.com/blog/post/the-new-mongodb-rust-driver|title=The New MongoDB Rust Driver|work=MongoDB|access-date=2018-02-01|language=en-us}}</ref> [[Scala (programming language)|Scala]]<ref>{{cite web|url=http://docs.mongodb.org/ecosystem/drivers/community-supported-drivers/|title=Community Supported Drivers Reference}}</ref>
| Document database with replication and sharding, [[BSON]] store (binary format [[JSON]]).
| {{yes}}<ref name="docs">{{Cite web|url=https://docs.mongodb.com/ecosystem/tools/http-interfaces/|title=HTTP Interface — MongoDB Ecosystem|website=MongoDB Docs}}</ref><ref name="github">{{Cite web|url=https://github.com/mongodb/docs-ecosystem| title=MongoDB Ecosystem Documentation|date=June 27, 2019|website=GitHub}}</ref>
|-
| [[MUMPS]] Database
| {{dunno}}
| {{proprietary|[[proprietary software|Proprietary]] and [[GNU Affero General Public License|AGPL]]}}<ref>{{cite web|url=http://sourceforge.net/projects/fis-gtm/|title=GT.M High end TP database engine|date=26 September 2023 }}</ref>
| [[MUMPS]]
| Commonly used in health applications.
| {{dunno}}
|-
| [[ObjectDatabase++]]
| Ekky Software
| {{proprietary}}
| [[C++]], [[C Sharp (programming language)|C#]], [[TScript]]
| Binary Native C++ class structures
| {{dunno}}
|-
|[[Virtuoso Universal Server|OpenLink Virtuoso]]
|OpenLink Software
|{{Open source|[[GPLv2]] and [[proprietary software|Proprietary]]}}
|[[C++]], [[C Sharp (programming language)|C#]], [[Java (programming language)|Java]], [[SPARQL]]
|[[Middleware]] and [[database engine]] hybrid
|{{yes}}
|-
| [[OrientDB]]
| Orient Technologies
| {{Open source|[[Apache License]]}}
| [[Java (programming language)|Java]]
| JSON over HTTP, SQL support, [[ACID]] transactions
| {{yes}}
|-
|[[Oracle NoSQL Database]]
|Oracle Corp
|{{Open source|[[Apache License]] and [[proprietary software|Proprietary]]}}
|C, C#, Java, Python, node.js, Go
|Shared nothing, horizontally scalable NoSQL Database supporting schema-less JSON, fixed schema tables, and key/value pairs.  ACID transactions are also supported.| Shared nothing, horizontally scalable database with support for schema-less JSON, fixed schema tables, and key/value pairs.   Also supports ACID transactions.
|Yes
|-
| [[Qizx]]
| [[Qualcomm]]
| {{proprietary}}
| [[REST]], [[Java (programming language)|Java]], [[XQuery]], [[XSLT]], [[C (programming language)|C]], [[C++]], [[Python (programming language)|Python]]
| Distributed document-oriented [[XML database]] with integrated [[full-text search]]; support for [[JSON]], text, and binaries.
|{{yes}}
|-
| [[Redis|RedisJSON]]
| [[Redis]]
| Redis Source Available License (RSAL)
| [[Python (programming language)|Python]]
| [[JSON]] with integrated [[full-text search]].<ref>{{cite web |title=RedisJSON - a JSON data type for Redis |url=https://oss.redis.com/redisjson/}}</ref>
|{{yes}}
|-
| [[RethinkDB]]
| {{dunno}}
|{{Open source|[[Apache License]]}}<ref>{{cite web |title=Transferring copyright to The Linux Foundation, relicensing RethinkDB under ASLv2.|url=https://github.com/rethinkdb/rethinkdb/commit/b0ec8bc5a874d5241d8af1166d664083edc5f750#diff-97d9303acdfc078a050e61dc5c1a9a76 |website=github.com |access-date=27 January 2020}}</ref>
| [[C++]], [[Python (programming language)|Python]], [[JavaScript]], [[Ruby (programming language)|Ruby]], [[Java (programming language)|Java]]
| Distributed document-oriented [[JSON]] database with replication and sharding.
|{{no}}
|-
|[[SAP HANA]]
|[[SAP SE|SAP]]
|{{proprietary}}
|[[SQL]]-like language
|[[ACID]] transaction supported, [[JSON]] only
|{{yes}}
|-
| [[Sedna (database)|Sedna]]
|sedna.org
|{{Open source|[[Apache License]]}}
|[[C++]], [[XQuery]]
|[[XML database]]
|{{no}}
|-
| [[SimpleDB]]
|[[Amazon Web Services]]
| {{proprietary|[[proprietary software|Proprietary]] online service}}
|[[Erlang (programming language)|Erlang]]
|
|{{dunno}}
|-
|[[Apache Solr]]
|[[Apache Software Foundation]]
|{{Open source|[[Apache License]]<ref>{{cite web |title=solr/LICENSE.txt at main · apache/solr · GitHub.|url=https://github.com/apache/solr/blob/main/LICENSE.txt |website=github.com |access-date=24 December 2022}}</ref>}}
|[[Java (programming language)|Java]]
|[[JSON]], [[Comma-separated values|CSV]], [[XML]], and a few other formats.<ref>{{cite web |title=Response Writers :: Apache Solr Reference Guide.|url=https://solr.apache.org/guide/solr/latest/query-guide/response-writers.html |website=solr.apache.org |access-date=24 December 2022}}</ref> Search engine.
|{{yes}}<ref>{{cite web |title=Managed Resources :: Apache Solr Reference Guide.|url=https://solr.apache.org/guide/solr/latest/configuration-guide/managed-resources.html |website=solr.apache.org |access-date=24 December 2022}}</ref>
|-
| [[TerminusDB]]
| TerminusDB
| {{Open source|[[Apache License]]}}
|[[Python (programming language)|Python]], [[Node.js]], JavaScript
|The database system supports document store as well as graph data models with one database core and a unified, [[datalog]] based query language WOQL (Web Object Query Language).<ref>{{Cite web |title=TerminusDB and open-source in-memory document-oriented graph database |url=https://terminusdb.com/products/terminusdb/ |access-date=2023-08-09 |website=terminusdb.com}}</ref>
| {{yes}}
|}

===XML database implementations===
{{Further|XML database}}
Most XML databases are document-oriented databases.

== See also ==
* [[Database theory]]
* [[Data hierarchy]]
* [[Data analysis]]
* [[Full-text search]]
* [[In-memory database]]
* [[Internet Message Access Protocol]] (IMAP)
* [[Machine-readable document]]
* [[Multi-model database]]
* [[NoSQL]]
* [[Object database]]
* [[Online database]]
* [[Real-time database]]
* [[Relational database]]
* [[Content management system]]

==Notes==
{{notelist}}

==References==
{{Reflist}}

==Further reading==
* Assaf Arkin. (2007, September 20). [https://web.archive.org/web/20080327222152/http://blog.labnotes.org/2007/09/20/read-consistency-dumb-databases-smart-services/ Read Consistency: Dumb Databases, Smart Services.]


==External links==
* [http://db-engines.com/en/ranking/document+store DB-Engines Ranking of Document Stores] by popularity, updated monthly

{{Database models}}
{{Databases}}

[[Category:Document-oriented databases| ]]
[[Category:Data management]]
[[Category:Database management systems]]
[[Category:Types of databases]]
[[Category:Data analysis]]
[[Category:Databases]]