XML database

There are a number of reasons to directly specify data in XML or other document formats such as JSON. For XML in particular, they include:[1] [2]

• An enterprise may have a lot of XML in an existing standard format
• Data may need to be exposed or ingested as XML, so using another format such as relational forces double-modeling of the data
• XML is very well suited to sparse data, deeply nested data and mixed content (such as text with embedded markup tags)
• XML is human readable whereas relational tables require expertise to access
• Metadata is often available as XML
• Semantic web data is available as RDF/XML
• Provides a solution for Object-relational impedance mismatch[3]

Steve O'Connell gives one reason for the use of XML in databases: the increasingly common use of XML for data transport, which has meant that "data is extracted from databases and put into XML documents and vice-versa".[4] It may prove more efficient (in terms of conversion costs) and easier to store the data in XML format. In content-based applications, the ability of the native XML database also minimizes the need for extraction or entry of metadata to support searching and navigation.

XML-enabled databases typically offer one or more of the following approaches to storing XML within the traditional relational structure:

1. XML is stored into a CLOB (Character large object)
2. XML is `shredded` into a series of Tables based on a Schema[5]
3. XML is stored into a native XML Type as defined by ISO Standard 9075-14[6]

RDBMS that support the ISO XML Type are:

1. IBM DB2 (pureXML[7])
2. Microsoft SQL Server[8]
3. Oracle Database[9]
4. PostgreSQL[10]

Typically an XML-enabled database is best suited where the majority of data are non-XML. For datasets where the majority of data are XML, a native XML database is better suited.

select
   id, vol, xmlquery('$j/name', passing journal as "j") as name 
from
   journals
where 
   xmlexists('$j[licence="CreativeCommons"]', passing journal as "j")

Native XML databases are especially tailored for working with XML data. As managing XML as large strings would be inefficient, and due to the hierarchical nature of XML, custom optimized data structures are used for storage and querying. This usually increases performance both in terms of read-only queries and updates.[11] XML nodes and documents are the fundamental unit of (logical) storage, just as a relational database has fields and rows.

The standard for querying XML data per W3C recommendation is XQuery; the latest version is XQuery 3.1.[12] XQuery includes XPath as a sub-language and XML itself is a valid sub-syntax of XQuery. In addition to XPath, some XML databases support XSLT as a method of transforming documents or query results retrieved from the database.

For data-centric XML datasets, the unique and distinct keyword search method, namely, XDMA[13] for XML databases is designed and developed based on dual indexing and mutual summation.