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B-tree

The idea behind B-trees is that inner nodes can have a variable number of child nodes within some pre-defined range. This causes B-trees to not need re-balancing frequently, unlike AVL trees. The lower and upper bounds on the number of child nodes are fixed for a particular implementation. For example, in a 2-3 B-tree (often simply 2-3 tree), each internal node may have only 2 or 3 child nodes. A node is considered to be in an illegal state if it has an invalid number of child nodes.

The "B" stands for "balanced" because all the leaf nodes appear at the same level in the tree.

See also AVL tree, binary tree, binary space partitioning, red-black tree, skip list.

Table of contents

1 Inner node structures 2 Steps for deletion 3 Steps for insertion 4 Searching 5 Notes 6 References 7 External links

Inner node structures

Generally speaking, the "separation values" can simply be the values of the tree.

Each inner node has separation values which divide its sub-trees. For example, if an inner node has three child nodes (or sub-trees) then it must have two separation values a₁ and a₂. All values less than a₁ will be in the leftmost sub-tree, values between a₁ and a₂ will be in the middle sub-tree, and values greater than a₂ will be in the rightmost sub-tree.

Steps for deletion

1. If after removing the desired node, no inner node is in an illegal state then the process is finished.
2. If some inner node is in an illegal state then there are two possible cases:
   1. Its sibling node (a child of the same parent node) can transfer one of its child nodes to the current node and return it to a legal state. If so, after updating the separation values in the parent and the two siblings the operation ends.
   2. Its sibling does not have an extra child because it is on the lower bound too. In that case both these nodes are merged into a single node and the action is transferred to the parent node, since it has had a child node removed.

Steps for insertion

1. If after inserting the node into the appropriate position, no inner node is in an illegal state then the process is finished.
2. If some node has more than the maximum amount of child nodes then it is split into two nodes, each with the minimum amount of child nodes. This process continues action recursively in the parent node.

Searching

Searching is performed very similar to a binary tree search, simply by following the separation values until the value is found or the end of the tree is reached.

Notes

Suppose L is the least number of children a node is allowed to have, while U is the most number. Then each node will always have between L and U children, inclusively, with one exception: the root node may have anywhere from 2 to U children inclusively, or in other words, it is exempt from the lower bound restriction, instead having a lower bound of its own (2). This allows the tree to hold small numbers of elements. The root having one child makes no sense, since the subtree attached to that child could simply be attached to the root. Giving the root no children is also unnecessary, since a tree with no elements is typically represented as having no root node.

Robert Tarjan proved that the amortized number of splits/merges is 2.

References

• Rudolf Bayer, Binary B-Trees for Virtual Memory, ACM-SIGFIDET Workshop 1971, San Diego, California, Session 5B, p. 219-235.
• Rudolf Bayer and McCreight, E. M Organization and Maintenance of Large Ordered Indexes. Acta Informatica 1, 173-189, 1972.

• Donald E. Knuth, "The Art of Computer Programming", second edition, volume 3, section 6.2.4, 1997.

• http://www.bluerwhite.org/btree
• B-Tree animation (Java Applet)
• NIST's Dictionary of Algorithms and Data Structures: B-tree
• B-tree algorithms
• B-tree variations