Chapter 16 Multi-way Search Trees
Chapter 16
Multi-way Search Trees
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Chapter Objectives
• Examine 2-3 and 2-4 trees
• Introduce the generic concept of a B-tree
• Examine some specialized implementations of B-trees
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Multi-way Search Trees
• In a multi-way search tree, – each node may have more than two child nodes
– there is a specific ordering relationship among the nodes
• In this chapter, we examine three forms of multi-way search trees– 2-3 trees
– 2-4 trees
– B-trees
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2-3 Trees
• A 2-3 tree is a multi-way search tree in which each node has zero, two, or three children
• A node with zero or two children is called a 2-node
• A node with zero or three children is called a 3-node
• A 2-node contains one element and either has no children or two children– Elements of the left sub-tree less than the element
– Elements of the right sub-tree greater than or equal to the element
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2-3 Trees
• A 3-node contains two elements, one designated as the smaller and one as the larger
• A 3-node has either no children or three children
• If a 3-node has children then– Elements of the left sub-tree are less than the smaller
element
– The smaller element is less than or equal to the elements of the middle sub-tree
– Elements of the middle sub-tree are less then the larger element
– The larger element is less than or equal to the elements of the right sub-tree
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2-3 Trees
• All of the leaves of a 2-3 tree are on the same level
• Thus a 2-3 tree maintains balance
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FIGURE 16.1 A 2-3 tree
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Inserting Elements into a 2-3 Tree
• All insertions into a 2-3 tree occur at the leaves– The tree is searched to find the proper leaf for the
new element
• Insertion has three cases– Tree is empty (in which case the new element
becomes the root of the tree)
– Insertion point is a 2-node
– Insertion point is a 3-node
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Inserting Elements into a 2-3 Tree
• The first of these cases is trivial with the element inserted into a new 2-node that becomes the root of the tree
• The second case occurs when the new element is to be inserted into a 2-node
• In this case, we simply add the element to the leaf and make it a 3-node
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FIGURE 16.2 Inserting 27
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Insertion into a 2-3 Tree
• The third case occurs when the insertion point is a 3-node
• In this case – the 3 elements (the two old ones and the new
one) are ordered
– the 3-node is split into two 2-nodes, one for the smaller element and one for the larger element
– the middle element is promoted (or propagated) up a level
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Insertion into a 2-3 Tree
• The promotion of the middle element creates two additional cases:– The parent of the 3-node is a 2-node
– The parent of the 3-node is a 3-node
• If the parent of the 3-node being split is a 2-node then it becomes a 3-node by adding the promoted element and references to the two resulting two nodes
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FIGURE 16.3 Inserting 32
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Insertion into a 2-3 Tree
• If the parent of the 3-node is itself a 3-node then it also splits into two 2-nodes and promotes the middle element again
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FIGURE 16.4 Inserting 57
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FIGURE 16.5 Inserting 25
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Removing Elements from a 2-3 Tree
• Removal of elements is also made up of three cases:– The element to be removed is in a leaf
that is a 3-node
– The element to be removed is in a leaf that is a 2-node
– The element to be removed is in an internal node
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Removing Elements from a 2-3 Tree
• The simplest case is that the element to be removed is in a leaf that is a 3-node
• In this case the element is simply removed and the node is converted to a 2-node
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FIGURE 16.6 Removal from a 2-3 tree (case 1)
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Removing Elements from a 2-3 Tree
• The second case is that the element to be removed is in a leaf that is a 2-node
• This creates a situation called underflow
• We must rotate the tree and/or reduce the tree’s height in order to maintain the properties of the 2-3 tree
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Removing Elements from a 2-3 Tree
• This case can be broken down into four subordinate cases
• The first of these subordinate cases (2.1) is that the parent of the 2-node has a right child that is a 3-node
• In this case, we rotate the smaller element of the 3-node around the parent
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FIGURE 16.7 Removal from a 2-3 tree (case 2.1)
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Removing Elements from a 2-3 Tree
• The second of these subordinate cases (2.2) occurs when the underflow cannot be fixed through a local rotation but there are 3-node leaves in the tree
• In this case, we rotate prior to removal of the element until the right child of the parent is a 3-node
• Then we follow the steps for our previous case (2.1)
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FIGURE 16.8 Removal from a 2-3 tree (case 2.2)
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Removal of Elements from a 2-3 Tree
• The third of these subordinate cases (2.3) occurs when none of the leaves are 3-nodes but there are 3-node internal nodes
• In this case, we can convert an internal 3-node to a 2-node and rotate the appropriate element from that node to rebalance the tree
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FIGURE 16.9 Removal from a 2-3 tree (case 2.3)
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Removing Elements from a 2-3 Tree
• The fourth subordinate case (2.4) occurs when there not any 3-nodes in the tree
• This case forces us to reduce the height of the tree
• To accomplish this, we combine each the leaves with their parent and siblings in order
• If any of these combinations produce more than two elements, we split into two 2-nodes and promote the middle element
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FIGURE 16.10 Removal from a 2-3 tree (case 2.4)
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Removing Elements from a 2-3 Tree
• The third of our original cases is that the element to be removed is in an internal node
• As we did with binary search trees, we can simply replace the element to be removed with its inorder successor
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FIGURE 16.11 Removal from a 2-3 tree (case 3)
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2-4 Trees
• 2-4 Trees are very similar to 2-3 Trees adding the characteristic that a node can contain three elements
• A 4-node contains three elements and has either no children or 4 children
• The same ordering property applies as 2-3 trees
• The same cases apply to both insertion and removal of elements as illustrated on the following slides
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FIGURE 16.12 Insertions into a 2-4 tree
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FIGURE 16.13 Removals from a 2-4 tree
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B-Trees
• Both 2-3 trees and 2-4 trees are examples of a larger class of multi-way search trees called B-trees
• We refer to the maximum number of children of each node as the order of a B-Tree
• Thus 2-3 trees are 3 B-trees and 2-4 trees are 4 B-trees
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B-Trees
• B-trees of order m have the following properties:
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FIGURE 16.14 A B-tree of order 6
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Motivation for B-trees
• B-trees were created to make the most efficient use possible of the relationship between main memory and secondary storage
• For all of the collections we have studied thus far, our assumption has been that the entire collections exists in memory at once
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Motivation for B-trees
• Consider the case where the collection is too large to exist in primary memory at one time
• Depending upon the collection, the overhead associated with reading and writing from files and/or swapping large segments of memory in and out could be devastating
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Motivation for B-trees
• B-trees were designed to flatten the tree structure and to allow for larger blocks of data that could then be tuned so that the size of a node is the same size as a block on secondary storage
• This reduces the number of nodes and/or blocks that must be accessed, thus improving performance
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B*-trees
• A variation of B-trees called B*-trees were created to solve the problem that the B-tree could be half empty at any given time
• B*-trees have all of the same properties as B-trees except that, instead of each node having k children where m/2 ≤ k ≤ m, in a B*-tree, each node has k children where (2m–1)/3 ≤ k ≤ m
• This means that each non-root node is at least two-thirds full
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B+-trees
• Another potential problem for B-trees is sequential access
• B+-trees provide a solution to this problem by requiring that each element appear in a leaf regardless of whether it appears in an internal node
• By requiring this and then linking the leaves together, B+-trees provide very efficient sequential access while maintaining many of the benefits of a tree structure
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FIGURE 16.15 A B+-tree of order 6
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Implementation Strategies for B-trees
• A good implementation strategy for B-trees is to think of each node as a pair of arrays– An array of m-1 elements
– An array of m children
• Then, if we think of the tree itself as one large array of nodes, then the elements stored in the array of children would simply be integer indexes into the array of nodes