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XML database

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An XML database is a data persistence software system that allows data to be specified, and sometimes stored, in XML format. This data can be queried, transformed, exported and returned to a calling system. XML databases are a flavor of document-oriented databases which are in turn a category of NoSQL database.

Rationale for XML in databases

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][needs update] 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

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.

Example of XML Type Query in IBM DB2 SQL

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

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 contrast to XML enabled databases, native databases provide full support for XQuery. In addition to XPath, some XML databases support XSLT as a method of transforming documents or query results retrieved from the database.

Language features

Name License Native Language XQuery 3.1 XQuery 3.0 XQuery 1.0 XQuery Update XQuery Full Text EXPath Extensions EXQuery Extensions XSLT 2.0 XForms 1.1 XProc 1.0
BaseX BSD Java Yes Yes Yes Yes Yes Yes Yes Yes Yes No
eXist GNU LGPL Java Partial Partial Yes Proprietary Proprietary Yes Yes Yes Yes Yes
MarkLogic Server Commercial C++ No Partial Yes Proprietary Proprietary No No Yes Yes No
OpenText xDB Commercial Java Partial Partial Yes Yes Yes No No No No No
Oracle Berkeley DB XML Commercial
Qizx Commercial Java No No Yes Yes Yes No No Yes No No
Sedna Apache License 2.0

Supported APIs

Name XQJ XML:DB RESTful RESTXQ WebDAV
BaseX Yes Yes Yes Yes Yes
eXist Yes Yes Yes Yes Yes
MarkLogic Server Yes No Yes Yes Yes
Qizx No No Yes No No
Sedna Yes Yes No No No

Data-centric XML datasets

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.

References

  1. ^ Nicola, Matthias (28 September 2010). "5 Reasons for Storing XML in a Database". Native XML Database. Retrieved 17 March 2015.
  2. ^ Feldman, Damon (11 April 2013). Moving from Relational Modeling to XML and MarkLogic Data Models. MarkLogic World. Retrieved 17 March 2015.
  3. ^ [NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley Educational Publishers Inc, 2009] ISBN 978-0321826626
  4. ^ O'Connell, Steve (2005). Section 9.2. Advanced Databases Course Notes (Syllabus). Southampton, England: University of Southampton.
  5. ^ "XML Schema Storage and Query: Basic". Oracle XML DB Developer's Guide, 10g Release 2. Oracle Corporation. August 2005. Retrieved 17 March 2015.. Section Creating XMLType Tables and Columns Based on XML Schema
  6. ^ "ISO/IEC 9075-14:2011: Information technology -- Database languages -- SQL -- Part 14: XML-Related Specifications (SQL/XML)". International Organization for Standardization. 2011. Retrieved 17 March 2015.
  7. ^ "pureXML overview -- DB2 as an XML database". IBM Knowledge Center. IBM. Retrieved 17 March 2015.
  8. ^ "Using XML in SQL Server". Microsoft Developer Network. Microsoft Corporation. Retrieved 17 March 2015.
  9. ^ "XMLType Operations". Oracle XML DB Developer's Guide, 10g Release 2. Oracle Corporation. August 2005. Retrieved 17 March 2015.
  10. ^ "8.13. XML Type". PostgreSQL 9.6 Documentation. Retrieved 1 April 2017.
  11. ^ Matthias, Nicola (22 August 2010). "XML versus Relational Database Performance". Native XML Database. Retrieved 28 Jun 2017.
  12. ^ "XQuery 3.1 Recommendation". 2017-03-21.
  13. ^ Selvaganesan, S.; Haw, Su-Cheng; Soon, Lay-Ki (2014). "XDMA: A Dual Indexing and Mutual Summation Based Keyword Search Algorithm for XML Databases". International Journal of Software Engineering and Knowledge Engineering. 24 (4): 591–615. doi:10.1142/s0218194014500223.
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