Document-oriented database
From Wikipedia, the free encyclopedia
This article is about the software type. For
usage/deployment instances, see Full text database.
A document-oriented database is a computer
program designed for storing, retrieving, and managing document-oriented
information, also known as semi-structured data. Document-oriented
databases are one of the main categories of NoSQL databases and
the popularity of the term "document-oriented database" (or
"document store") has grown[1]
with the use of the term NoSQL itself. In contrast to relational databases and their notion of
"Relation", i.e., a tuple (or row) of related strong-typed data
items, these systems are designed around an abstract notion of a
"document".
Document-oriented databases are inherently a subclass
of the key-value store, another NoSQL database concept.
The difference 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 order to extract metadata that the
database engine uses for further optimization. Although the difference is often
moot due to tools in the systems, and the two can often be interchanged in
operation, conceptually the document-store is designed to offer a richer
experience with modern programming techniques.
XML databases are a specific subclass of
document-oriented databases.
Contents
- 1 Documents
- 2 Comparison with relational databases
- 3 Implementations
- 4 See also
- 5 References
- 6 Further reading
Documents
The central concept of a document-oriented database are the documents,
which is used in usual English sense of a group of data that encodes some sort
of user-readable information. This contrasts with the value in the
key-value store, which is assumed to be opaque data. The basic concept that
makes a database document-oriented as opposed to key-value is the idea that the
documents include internal structure that the database engine can use to
further automate the storage and provide more value.
To understand the difference, consider this text document:
Bob Smith
123 Back St.
Boys, AR, 32225
US
Although it is clear to the reader that this document contains the address
for a contact, there is no information within the document that
indicates that, nor information on what the individual fields represent. This
file could be stored in a key-value store, but the semantic content that this is
an address may be lost, and the database has no way to know how to optimize or
index this data by itself. For instance, there is no way for the database to
know that "AR" is the state, it is simply a piece of data in a string
that also includes the city and zip code. Even the format might change, one
might have a PO Box or suite number that adds another line to the address,
which places the state information in the 4th line instead of 3rd. Without
additional information, parsing this data can be complex.
Now consider the same document marked up in pseudo-XML:
<address>
<firstname>Bob</firstname>
<lastname>Smith</lastname>
<street1>123 Back St.</street1>
<city>Boys</city>
<state>AR</state>
<zip>32225</zip>
<country>US</country>
</address>
In this case, the document includes both data and the metadata explaining
each of the fields. A key-value store receiving this document would simply
store it. In the case of a document-store, the system understands that address
documents may have a country field, allowing the programmer to "find all
the addresses where the state is 'AR'". Additionally, the programmer can
provide hints based on the document type or fields within it, for instance,
they may tell the engine to place all address documents in a separate physical
store, or to make an index on the state field for performance reasons. All of
this can be done in a key-value store as well, and the difference lies
primarily in how much programming effort is needed to add these indexes and
other features; in a document-store this is normally almost entirely automated.
Now consider a slightly more complex example, one that is more realistic:
<contact>
<firstname>Bob</firstname>
<lastname>Smith</lastname>
<email
type=Home>bob.smith@gmaile.com</email>
<phone
type=Cell>(123) 456-7890</phone>
<phone
type=Work>(890) 765-4321</phone>
<address>
<type>Home</type>
<street1>123 Back St.</street1>
<city>Boys</city>
<state>AR</state>
<zip>32225</zip>
<country>US</country>
</address>
</contact>
With similar hints, the document store will know that this is a contact
entry and allow searches for things like "find all my contacts with a work
phone number but no work email".
The usefulness of this sort of introspection
of the data is not lost on the designers of other database systems. Many
key-value stores include some or all of the functionality of dedicated from the
start document stores, and a number of relational databases, notably PostgreSQL
and Informix,
have added functionality to make these sorts of operations possible. It is not
the ability to provide these functions that define the document-orientation,
but the ease with which these functions can be implemented and used; a
document-oriented database is designed from the start to work with complex
documents, and will (hopefully) make it easier to access this functionality
than a system where this was added after the fact.
Documents inside a document-oriented database are similar, in some ways,
to records or rows in relational databases, but they have vastly more internal
structure (the extent the database itself is aware of that structure, and can
use it, varies). Documents, particularly in XML, TeX, and other high-end
formats, do adhere to a formal schema; but many documents do not, or if they
do, the schema is not explicit. For example, the following is a document:
<Article>
<Author>
<FirstName>Bob</FirstName>
<Surname>Smith</Surname>
</Author>
<Abstract>This paper concerns....</Abstract>
<Section
n="1"><Title>Introduction</Title>
<Para>...
</Section>
</Article>
A second document, even of the same genre and schema, may have a far
different number and arrangement of sections, paragraphs, and the like; it may
have multiple co-authors; it may have much other metadata such as copyright or
publication information, bibliographic references to other documents (in the
same or other databases, or in no database at all), and so on.
Two such documents typically share many structural elements with one
another, but each may also have elements the other does not. Unlike a relational
database where every record contains the identical sequence of fields (a few of
which may be empty or hold missing value indicators), document structures
generally allow for an unbounded number of hierarchically-organized components,
with extensive repetition. It would be absurd, for example, to design a
database with table for "sections," that tried to provide as many
fields as the number of paragraphs in the longest section one will ever see
(not to mention the many other kinds of document components that appear within
sections). Even if one did, naming fields in a relation something like
"p1", "p2",... does not, so far as the database is
concerned, indicate that those fields have anything to do with one another, or
belong in a certain meaningful order. In order to avoid confusion with the
quite different notion of database "fields", document databases may
refer to the parts of documents as "components" or
"elements".
Practically any "document" containing metadata can be managed in
this fashion, and common examples include XML, YAML, JSON, and BSON. Some
document-oriented databases include functionality to help map data lacking
clearly defined metadata. For instance, many engines include functionality to
index PDF or TeX documents, or may
include predefined document formats that are in turn based on XML, like MathML,
JATS or DocBook. Some
allow documents to be mapped onto a more suitable format using a schema
language such as DTD, XSD, Relax NG, or Schematron.
Others may include tools to map enterprise data, like column-delimited text
files, into formats that can be read more easily by the database engine. Still
others take the opposite route, and are dedicated to one type of data format, JSON. JSON is widely
used in online programming for interactive web pages and mobile apps, and a
niche has appeared for document stores dedicated to efficiently handling them.
Some of the most popular Web sites are document databases. The many
collections of articles at pubmed.gov
or major journal publishers; Wikipedia and its kin; and even search engines
(though many of those store links to indexed documents, rather than the full
documents themselves).
Keys and retrieval
Documents may be addressed in the database via a unique key that
represents that document. This key is often a simple string, a URI, or a path. The key
can be used to retrieve the document from the database. Typically, the database
retains an index on the key to speed up document retrieval. The most primitive
document databases may do little more than that. However, modern
document-oriented databases provide far more, because they extract and index
all kinds of metadata, and usually also the entire data content, of the
documents. Such databases offer a query language that allows the user to
retrieve documents based on their content. For example, you may want to
retrieve all the documents whose date falls within some range, that contains a
citation to another document, etc.. 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 the next.
Organization
Implementations offer a variety of ways of organizing documents, including
notions of:
- Collections
- Tags
- Non-visible Metadata
- Directory hierarchies
- Buckets
Comparison with relational databases
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 relations 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 placed in its own table, and if these
instances are related to each other, a field is selected to group them
together, the foreign key.
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
solution, tables would be created for each of these records with predefined
fields for each bit of data; the CONTACT table might include FIRST_NAME,
LAST_NAME and IMAGE fields, 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 field, "CONTACT_ID",
which holds a the unique ID number assigned to the contact when it was created.
In order to recreate the original contact, the system has to search through all
of the tables and collect the information back together using joins.
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
relations 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 a key, some unique bit of data, 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, simply add this field to new documents as they are inserted, this will
have no effect on the database or the existing documents already stored, they
simply won't have this field. This indicates an advantage of the document-based
model; optional fields are truly optional, a contact that does not include a
mailing address simply does not have a mailing address, there is no need to
check another table to see if there are entries.
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. In the address book example,
the design might add hints for the first and last name fields. When the
document is inserted into the database (or later modified), the database engine
looks for these bits of information and indexes them, in the same fashion as
the relational model. Additionally, most document-oriented databases allow
documents to have a type associated with them, like "address book
entry", 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).
All of this is predicated on the ability of the database engine to examine
the data in the document and extract fields from the formatting, its metadata.
This is easy in the case of, for example, an XML document or HTML page, where markup
tags clearly identify various bits of data. Document-oriented databases may
include functionally to automatically extract this sort of information from a
variety of document types, even those that were not originally designed for
easy access in this manner. In other cases the programmer has to provide this
information using their own code. In contrast, a relational database relies on
the programmer to handle all of these tasks, breaking down the document into
fields and providing those to the database engine, which may require separate
instructions if the data spans tables.
Document-oriented databases normally map more cleanly onto existing
programming concepts, like object-oriented programming (OOP). OOP
systems have a structure somewhere between the relational and document models;
they have predefined fields but they may be empty, they have a defined
structure but that may change, they have related data store in other objects,
but they may be optional, and collections of other data are directly linked to
the "master" object, there is no need to look in other collections to
gather up related information. Generally, any object that can be archived
to a document can be stored directly in the database and directly retrieved.
Most modern OOP systems include archiving systems as a basic feature.
The relational model stores each part of the object as a separate concept
and has to split out this information on storage and recombine it on retrieval.
This leads to a problem known as object-relational impedance
mismatch, which requires considerable effort to overcome. Object-relational mapping systems, which
solve these problems, are often complex and have a considerable performance
overhead. This problem simply doesn't exist in a document-oriented system, and
more generally, in NoSQL systems as a whole.
Implementations
Main category: Document-oriented databases
Name
|
Publisher
|
License
|
Language
|
Notes
|
RESTful API
|
C, C++ & Javascript
|
A distributed multi model, high-performance document
store and graph database.
|
Yes [2]
| |||
Support for XML, JSON and binary formats;
client-/server based architecture; concurrent structural and full-text
searches and updates; REST APIs.
|
Yes
| ||||
JSON over HTTP
|
Yes
| ||||
Distributed database service based on BigCouch, the
company's open source fork of the Apache-backed CouchDB project.
|
Yes
| ||||
Free license
|
Distributed XML and JSON database server with secure
high-performance ACID-compliant
transactions; built-in full
text search; database as a service[3]
|
Yes
| |||
Distributed NoSQL Document Database.
|
Yes [4]
| ||||
JSON over REST/HTTP with Multi-Version Concurrency Control
and limited ACID
properties. Uses map and reduce for views and queries.[5]
|
Yes [6]
| ||||
Dotissi SRL
|
Commercial
|
C# - .NET, Windows Store, Windows Phone,
Xamarin.iOS, Xamarin.Android, Unity3D, Mono; Java - Android
|
Privacy aware cloud-mobile database, with client
libraries for Windows Store, Windows Phone, Xamarin Android, Xamarin iOS,
Android, Unity3D (iOS, Android, Windows Store, Windows Phone)
|
Yes
| |
XML over REST/HTTP, WebDAV, Lucene Fulltext search,
validation, versioning, clustering, triggers, URL rewriting, collections,
ACLS, XQuery Update
|
Yes [7]
| ||||
FleetDB
|
A JSON-based schema-free database optimized for
agile development.
|
(unknown)
| |||
IBM
|
Various (Compatible with MongoDB API)
|
RDBMS with JSON, replication, sharding and ACID
compliance
|
(unknown)
| ||
Inquire
|
unknown
|
In the mid-80's this was the dominant
document-oriented commercial database, widely successful. The company seems
to have gone out of business in 2005.
|
(unknown)
| ||
IBM
|
LotusScript, Java, Lotus @Formula
|
(unknown)
| |||
MarkLogic Corporation
|
Distributed document-oriented database with Multi-Version Concurrency Control,
integrated Full text search and ACID-compliant
transaction semantics
|
Yes
| |||
MongoDB, Inc
|
GNU AGPL v3.0 for the DBMS, Apache
2 License for the client drivers[8]
|
Document database with replication and sharding
|
Optional [9]
| ||
Commonly used in health applications.
|
(unknown)
| ||||
JSON over HTTP
|
Yes
| ||||
Distributed document-oriented database with
integrated Full text search
|
Yes
| ||||
Proprietary and modified Affero GPL[13]
|
Yes
| ||||
Key-value store supporting lists and sets with
binary-safe protocol
|
(unknown)
| ||||
GNU APGL for the DBMS, Apache
2 License for the client drivers
|
(unknown)
| ||||
Rocket Software
|
UniData, UniVerse
|
Yes (Beta)
| |||
Distributed, real-time database featuring cell-level
security and massive scalability.
|
Yes
| ||||
Secure web-based data collection and management
platform tailored for research.
|
(Unknown)
|
XML database implementations
Further information: XML database
Most XML databases are document-oriented databases.
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