B+ ағашы: құрылымы және қолданылуы

An m ary rooted tree

A B+ tree is an m ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves. The root may be either a leaf or a node with two or more children. A B+ tree can be viewed as a B tree in which each node contains only keys (not key–value pairs), and to which an additional level is added at the bottom with linked leaves. The primary value of a B+ tree is in storing data for efficient retrieval in a block oriented storage context — in particular, filesystems. This is primarily because unlike binary search trees, B+ trees have very high fanout (number of pointers to child nodes in a node,

By definition, each value contained within the B+ tree is a key contained in exactly one leaf node. Each key is required to be directly comparable with every other key, which forms a total order. This enables each leaf node to keep all of its keys sorted at all times, which then enables each internal node to construct an ordered collection of intervals representing the contiguous extent of values contained in a given leaf. Internal nodes higher in the tree can then construct their own intervals, which recursively aggregate the intervals contained in their own child internal nodes. Eventually, the root of a B+ Tree represents the whole range of values in the tree, where every internal node represents a subinterval. For this recursive interval information to be retained, internal nodes must additionally contain copies of keys for representing the least element within the interval covered by the child with index i (which may itself be an internal node, or a leaf). Where m represents the actual number of children for a given internal node.

The leaves (the bottom most index blocks) of the B+ tree are often linked to one another in a linked list; this makes range queries or an (ordered) iteration through the blocks simpler and more efficient (though the aforementioned upper bound can be achieved even without this addition). This does not substantially increase space consumption or maintenance on the tree. This illustrates one of the significant advantages of a B+tree over a B tree; in a B tree, since not all keys are present in the leaves, such an ordered linked list cannot be constructed. A B+tree is thus particularly useful as a database system index, where the data typically resides on disk, as it allows the B+tree to actually provide an efficient structure for housing the data itself (this is described in APFS uses B+ trees to store mappings from filesystem object IDs to their locations on disk, and to store filesystem records (including directories), though these trees' leaf nodes lack sibling pointers.

Relational database management systems such as IBM Db2, Informix, 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 and Tokyo Cabinet also support this type of tree for data access and storage. Finding objects in a 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. In such situations, finding the closest neighbor using a B+ tree is productive.

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.

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.