In computer science, a B-tree is a tree data structure that keeps data sorted and allows searches, insertions, and deletions in logarithmic amortized time. It is most commonly used in databases and filesystems.
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.
A B-tree is kept balanced by requiring that all external nodes are 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 hop further removed from the root.
B-trees have substantial advantages over alternative implementations when node access times far exceed access times within nodes. This usually occurs when most nodes are in secondary storage such as hard drives. By maximizing the number of child nodes within each internal node, the height of the tree decreases, balancing occurs less often, and efficiency increases. Usually this value is set such that each node takes up a full disk block or an analogous size in secondary storage. While 2-3 B-trees might be useful in main memory, and are certainly easier to explain, if the node sizes are tuned to the size of a disk block, the result might be a 257-513 B-tree.
A B-tree of order m (the maximum number of children for each node) is a tree which satisfies the following properties :
The B-tree's creators, Rudolf Bayer and Ed McCreight, have not explained what, if anything, the B stands for. The most common belief is that B stands for balanced, as all the leaf nodes are at the same level in the tree. B may also stand for Bayer, Branching Tree, or for Boeing, because they were working for Boeing Scientific Research Labs at the time.
Internal nodes in a B-tree — nodes which are not leaf nodes — are usually represented as an ordered set of elements and child pointers. Every internal node contains a maximum of U children and — other than the root — a minimum of L children. For all internal nodes other than the root, the number of elements is one less than the number of child pointers; the number of elements is between L-1 and U-1. The number U must be either 2L or 2L-1; thus each internal node is at least half full. This relationship between U and L implies that two half-full nodes can be joined to make a legal node, and one full node can be split into two legal nodes (if there is room to push one element up into the parent). These properties make it possible to delete and insert new values into a B-tree and adjust the tree to preserve the B-tree properties.
Leaf nodes have the same restriction on the number of elements, but have no children, and no child pointers.
The root node still has the upper limit on the number of children, but has no lower limit. For example, when there are fewer than L-1 elements in the entire tree, the root will be the only node in the tree, and it will have no children at all.
A B-tree of depth n+1 can hold about U times as many items as a B-tree of depth n, but the cost of search, insert, and delete operations grows with the depth of the tree. As with any balanced tree, the cost grows much more slowly than the number of elements.
Some balanced trees store values only at the leaf nodes, and so have different kinds of nodes for leaf nodes and internal nodes. B-trees keep values in every node in the tree, and may use the same structure for all nodes. However, since leaf nodes never have children, a specialized structure for leaf nodes in B-trees will improve performance.
Binary search is typically (but not necessarily) used within nodes to find the separation values and child tree of interest.
All insertions happen at the leaf nodes.
If the splitting goes all the way up to the root, it creates a new root with a single separator value and two children, which is why the lower bound on the size of internal nodes does not apply to the root. The maximum number of elements per node is U-1. When a node is split, one element moves to the parent, but one element is added. So, it must be possible to divide the maximum number U-1 of elements into two legal nodes. If this number is odd, then U=2L and one of the new nodes contains (U-2)/2 = L-1 elements, and hence is a legal node, and the other contains one more element, and hence it too is legal. If U-1 is even, then U=2L-1, so there are 2L-2 elements in the node. Half of this number is L-1, which is the minimum number of elements allowed per node.
An improved algorithm supports a single pass down the tree from the root to the node where the insertion will take place, splitting any full nodes encountered on the way. This prevents the need to recall the parent nodes into memory, which may be expensive if the nodes are on secondary storage. However, to use this improved algorithm, we must be able to send one element to the parent and split the remaining U-2 elements into two legal nodes, without adding a new element. This requires U = 2L rather than U = 2L-1, which accounts for why some textbooks impose this requirement in defining B-trees.
There are two popular strategies for deletion from a B-Tree.
The algorithm below uses the former strategy.
There are two special cases to consider when deleting an element:
Each of these cases will be dealt with in order.
In the second case, one of the two child nodes contains more than the minimum number of elements. Then a new separator for those subtrees must be found. Note that the largest element in the left subtree is the largest element which is still less than the separator. Likewise, the smallest element in the right subtree is the smallest element which is still greater than the separator. Both of those elements are in leaf nodes, and either can be the new separator for the two subtrees.
The only other case to account for is when the root has no elements and one child. In this case it is sufficient to replace it with its only child.
It is possible to modify the above algorithm to, when trying to find extra elements for a deficient node, examine other siblings, and if one has more than the minimum number of values rearrange values across a larger number of siblings to make up the deficit in one.
Similarly, when a node is split, extra elements can be moved to nearby, less populated siblings; or the split can involve a number of siblings, redistributing elements among them rather than splitting a node.
In practice, the most common use of B-trees involves keeping the nodes on secondary storage, where it is slow to access a node which is not already being used. Using only two-ways splits and combines helps decrease the number of nodes needed for many common situations, but may be useful in others.
For example, in a 2-3 B-tree, adding an element to a node with three child nodes, and thus two separator values, involves three values — the two separators and the new value. The median becomes the new separator in the parent, and each of the other two becomes the sole elements in nodes with one value and two children. Generally, if U is odd, each of the two new nodes has (U+1)/2 children. If U is even, one has U/2 children and the other U/2+1.
If full nodes are split into exactly two nodes, L must be small enough to allow for the sizes after a node is split. But it is possible to split full nodes into more than two new nodes. Choosing to split a node into more than two nodes would require a lower value of L for the same value of U.
As L gets smaller, it allows for more unused space in the nodes. This might decrease the frequency of node splitting, but it is also likely to increase the amount of memory needed to store the same number of values, and the number of nodes that have to be examined for any particular operation.