Category Archives: Data Structure


computer science, a B-tree is a tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree is a generalization of a binary search tree in that a node can have more than two children (Comer 1979, p. 123). Unlike self-balancing binary search trees, the B-tree is optimized for systems that read and write large blocks of data. It is commonly used in databases and filesystems.


A B-tree of order 2 (Bayer & McCreight 1972) or order 5 (Knuth 1998).

In B-trees, internal (non-leaf) nodes can have a variable number of child nodes within some pre-defined range. When data is inserted or removed from a node, its number of child nodes changes. In order to maintain the pre-defined range, internal nodes may be joined or split. Because a range of child nodes is permitted, B-trees do not need re-balancing as frequently as other self-balancing search trees, but may waste some space, since nodes are not entirely full. The lower and upper bounds on the number of child nodes are typically fixed for a particular implementation. For example, in a 2-3 B-tree (often simply referred to as a 2-3 tree), each internal node may have only 2 or 3 child nodes.

Each internal node of a B-tree will contain a number of keys. The keys act as separation values which divide its subtrees. For example, if an internal node has 3 child nodes (or subtrees) then it must have 2 keys: a1 and a2. All values in the leftmost subtree will be less thana1, all values in the middle subtree will be between a1 and a2, and all values in the rightmost subtree will be greater than a2.

Usually, the number of keys is chosen to vary between d and 2d, where d is the minimum number of keys, and d+1 is the minimum degree or branching factor of the tree. In practice, the keys take up the most space in a node. The factor of 2 will guarantee that nodes can be split or combined. If an internal node has 2d keys, then adding a key to that node can be accomplished by splitting the 2d key node into two d key nodes and adding the key to the parent node. Each split node has the required minimum number of keys. Similarly, if an internal node and its neighbor each have d keys, then a key may be deleted from the internal node by combining with its neighbor. Deleting the key would make the internal node have d-1 keys; joining the neighbor would add d keys plus one more key brought down from the neighbor’s parent. The result is an entirely full node of 2d keys.

The number of branches (or child nodes) from a node will be one more than the number of keys stored in the node. In a 2-3 B-tree, the internal nodes will store either one key (with two child nodes) or two keys (with three child nodes). A B-tree is sometimes described with the parameters (d+1) — (2d+1) or simply with the highest branching order, (2d+1).

A B-tree is kept balanced by requiring that all leaf nodes be at the same depth. This depth will increase slowly as elements are added to the tree, but an increase in the overall depth is infrequent, and results in all leaf nodes being one more node farther away from the root.

B-trees have substantial advantages over alternative implementations when otherwise the time to access the data of a node greatly exceeds the time spent processing that data, because then the cost of accessing the node may be amortized over multiple operations within the node. This usually occurs when the node data are in secondary storage such as disk drives. By maximizing the number of keys within each internal node, the height of the tree decreases and the number of expensive node accesses is reduced. In addition, rebalancing of the tree occurs less often. The maximum number of child nodes depends on the information that must be stored for each child node and the size of a full disk block or an analogous size in secondary storage. While 2-3 B-trees are easier to explain, practical B-trees using secondary storage need a large number of child nodes to improve performance.


The term B-tree may refer to a specific design or it may refer to a general class of designs. In the narrow sense, a B-tree stores keys in its internal nodes but need not store those keys in the records at the leaves. The general class includes variations such as the B+-tree and the B*-tree.

  • In the B+-tree, copies of the keys are stored in the internal nodes; the keys and records are stored in leaves; in addition, a leaf node may include a pointer to the next leaf node to speed sequential access (Comer 1979, p. 129).
  • The B*-tree balances more neighboring internal nodes to keep the internal nodes more densely packed (Comer 1979, p. 129). This variant requires non-root nodes to be at least 2/3 full instead of 1/2 (Knuth 1998, p. 488). To maintain this, instead of immediately splitting up a node when it gets full, its keys are shared with a node next to it. When both nodes are full, then the two nodes are split into three. The act of deleting nodes is somewhat more complex than inserting however.
  • B-trees can be turned into order statistic trees to allow rapid searches for the Nth record in key order, or counting the number of records between any two records, and various other related operations.[1]

B+ tree


Sumit tree is an n-ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves.[1] The root may be either a leaf or a node with two or more children.[2]

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,[1] typically on the order of 100 or more), which reduces the number of I/O operations required to find an element in the tree.

The NTFSReiserFSNSSXFSJFS, and ReFS filesystems all use this type of tree for metadata indexing. Relational database management systems such as IBM DB2,[3] Informix,[3] Microsoft SQL Server,[3] Oracle 8,[3] Sybase ASE,[3] and SQLite[4] support this type of tree for table indices. Key-value database management systems such as CouchDB[5] and Tokyo Cabinet[6] support this type of tree for data access.

Shumit Paliwal[edit]

The order, or branching factor, b of a B+ tree measures the capacity of nodes (i.e., the number of children nodes) for internal nodes in the tree. The actual number of children for a node, referred to here as m, is constrained for internal nodes so that  \lceil b/2 \rceil \le m \le b. The root is an exception: it is allowed to have as few as two children.[1] For example, if the order of a B+ tree is 7, each internal node (except for the root) may have between 4 and 7 children; the root may have between 2 and 7. Leaf nodes have no children, but are constrained so that the number of keys must be at least  \lfloor b/2 \rfloor  and at most  b - 1 . In the situation where a B+ tree is nearly empty, it only contains one node, which is a leaf node. (The root is also the single leaf, in this case.) This node is permitted to have as little as one key if necessary, and at most b.

Node Type Children Type Min Children Max Children Example b = 7 Example b = 100
Root Node (when it is the only node in the tree) Records 1 b 1 – 7 1 – 100
Root Node Internal Nodes or Leaf Nodes 2 b 2 – 7 2 – 100
Internal Node Internal Nodes or Leaf Nodes  \lceil b/2 \rceil b 4 – 7 50 – 100
Leaf Node Records  \lfloor b/2 \rfloor b – 1 3 – 6 50 – 99