The central concept of a document-o

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, or metadata, 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 and add it to an index of states, it is simply a piece of data in a string that also includes the city and zip code. It is possible to add additional logic to deconstruct the string into fields, to extract the state by looking for the middle item of three comma separated values in the 3rd line, but this is not a simple task. For instance, if another line is added to the address, adding a PO Box or suite number for instance, the state information is in the 4th line instead of 3rd. Without additional information, parsing free form data of this sort can be complex.

Now consider the same document marked up in pseudo-XML:


Bob
Smith
123 Back St.
Boys
AR
32225
US

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 contact documents may have a state field, allowing the programmer to "find all the s where the 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 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:


Bob
Smith
bob.smith@example.com
(123) 555-0178
(890) 555-0133

Home
123 Back St.
Boys
AR
32225
US


In this case a number of the fields are either repeated or split out into separate containers in the case of . With similar hints, the document store will allow searches for things like "find all my s with a of type but does not have an of type ". This is not unlike other database systems in terms of retrieval. What is different is that these fields are defined by the metadata in the document itself. There is no need to pre-define these fields in the database.

This is another major advantage of the document-oriented concept; a single database can contain both of these objects in the same store, and more generally, every document in the database can have a different format. It is very common for a particular type of document to differ from instance to instance; one might have a work email, another might not, one might have a single address, another might have several. More widely, the database can store completely unrelated documents, yet still understand that parts of the data within them are the same. For instance, one could construct a query that would look for any document that has the 'AR', it doesn't matter that the documents might be s or es, or if the is within an or not.

In addition to making it easier to handle different types of data, the metadata also allows the document format to be changed at any time without affecting the existing records. If one wishes to add an field to their contact book application some time in the future, they simply add it. Existing documents will still work fine without being changed in the database, they simply won't have an image. Fields can be added at any time, anywhere, with no need to change the physical storage.

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[citation needed].

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, including 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[edit]
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[edit]
Implementations offer a variety of ways of organizing documents, including notions of:

Collections
Tags
Non-visible Metadata
Directory hierarchies
Buckets
Comparison with relational databases[edit]
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 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, a