Editing B+ tree (section)

=== Database systems ===
[[Relational database management system]]s such as [[IBM Db2]],<ref name="DMS">{{cite book
|first1=Ramakrishnan |last1=Raghu |first2=Gehrke |last2=Johannes |title=Database Management Systems |publisher=McGraw-Hill Higher Education |year=2000 |edition=2nd |page=267 }} {{ISBN missing|date=March 2025}}</ref> [[Informix]],<ref name="DMS" /> [[Microsoft SQL Server]],<ref name="DMS" /> [[Oracle Database|Oracle 8]],<ref name="DMS" /> [[Adaptive Server Enterprise|Sybase ASE]],<ref name="DMS" /> and [[SQLite]]<ref name="SQLite">[http://sqlite.org/version3.html SQLite Version 3 Overview]</ref>{{full citation needed |date=March 2025}} support this type of tree for table indices, though each such system implements the basic B+ tree structure with variations and extensions.  Many [[NoSQL]] database management systems such as [[CouchDB]]<ref name="CouchDB">[http://guide.couchdb.org/draft/btree.html CouchDB Guide]</ref>{{full citation needed |date=March 2025}}{{efn |See note after 3rd paragraph. }} and [[Tokyo Cabinet]]<ref name="TC">[http://1978th.net/tokyocabinet/ Tokyo Cabinet reference] {{webarchive |url=https://web.archive.org/web/20090912082150/http://1978th.net/tokyocabinet/ |date=September 12, 2009 }}</ref> also support this type of tree for data access and storage.

Finding objects in a [[High-dimensional statistics|high-dimensional database]] that are comparable to a particular query object is one of the most often utilized and yet expensive procedures in such systems.{{citation needed|date=June 2022}} In such situations, finding the closest neighbor using a B+ tree is productive.<ref>{{Cite book |title=Database Systems for Advanced Applications |year=2010 |location=Japan}}</ref>{{full citation needed |date=March 2025}}

==== iDistance ====
{{further|iDistance}}

B+ tree is efficiently used to construct an indexed search method called iDistance. iDistance searches for k nearest neighbors (kNN) in high-dimension metric spaces. The data in those high-dimension spaces is divided based on space or partition strategies, and each partition has an index value that is close with the respect to the partition. From here, those points can be efficiently implemented using B+ tree, thus, the queries are mapped to single dimensions ranged search. In other words, the iDistance technique can be viewed as a way of accelerating the sequential scan. Instead of scanning records from the beginning to the end of the data file, the iDistance starts the scan from spots where the nearest neighbors can be obtained early with a very high probability.<ref>{{Cite journal |last1=Jagadish |first1=H. V. |last2=Ooi |first2=Beng Chin |last3=Tan |first3=Kian-Lee |last4=Yu |first4=Cui |last5=Zhang |first5=Rui |date=June 2005 |title=iDistance: An adaptive B+-tree based indexing method for nearest neighbor search |url=https://dl.acm.org/doi/10.1145/1071610.1071612 |journal=ACM Transactions on Database Systems |language=en |volume=30 |issue=2 |pages=364–397 |doi=10.1145/1071610.1071612 |s2cid=967678 |issn=0362-5915|url-access=subscription }}</ref>

==== NVRAM ====
{{further|Non-volatile random-access memory}}

Nonvolatile random-access memory (NVRAM) has been using B+ tree structure as the main memory access technique for the Internet Of Things (IoT) system because of its non static power consumption and high solidity of cell memory.  B+ can regulate the trafficking of data to memory efficiently. Moreover, with advanced strategies on frequencies of some highly used leaf or reference point, the B+ tree shows significant results in increasing the endurance of database systems.<ref>{{Cite journal |last1=Dharamjeet |last2=Chen |first2=Tseng-Yi |last3=Chang |first3=Yuan-Hao |last4=Wu |first4=Chun-Feng |last5=Lee |first5=Chi-Heng |last6=Shih |first6=Wei-Kuan |date=December 2021 |title=Beyond Write-Reduction Consideration: A Wear-Leveling-Enabled B⁺-Tree Indexing Scheme Over an NVRAM-Based Architecture |url=https://ieeexplore.ieee.org/document/9314895 |journal=IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |volume=40 |issue=12 |pages=2455–2466 |doi=10.1109/TCAD.2021.3049677 |s2cid=234157183 |issn=0278-0070|url-access=subscription }}</ref>