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The AVL tree is a balanced binary search tree that ensures fast and efficient data operations. To achieve balance, it performs left- and right-turn operations, adjusting subtrees that violate balance. AVL trees utilize height balancing to ensure that the height of the tree is always small relative to the number of nodes, enabling logarithmic time complexity (O(log n)) lookup operations and maintaining data structure efficiency even on large data sets.
PHP data structure: The balance of the AVL tree, maintaining an efficient and orderly data structure
AVL (Adelson-Velsky and Landis) tree is a binary search tree that is balanced to ensure fast and efficient search, insertion, and deletion operations. The key is height balancing, ensuring that the height of the tree (the distance from the root node to the deepest leaf node) is always small relative to the number of nodes in the tree.
To achieve the balance of the AVL tree, we need to perform two main operations:
Implementing AVL tree
We start with a simple binary search tree class:
class BinarySearchTree { protected $root; // 插入节点 public function insert($value) { // ... } // 查找节点 public function search($value) { // ... } }
In order to implement AVL tree, we need Add the following functionality:
class AVLTree extends BinarySearchTree { // 获取节点的高度 public function height(Node $node) { // ... } // 检查节点是否平衡 public function isBalanced(Node $node) { // ... } // 左旋节点 public function leftRotate(Node $node) { // ... } // 右旋节点 public function rightRotate(Node $node) { // ... } }
Practical case
Let us use an AVL tree to store a set of integers and perform lookup operations:
$avlTree = new AVLTree(); $avlTree->insert(10); $avlTree->insert(5); $avlTree->insert(15); $avlTree->insert(3); $avlTree->insert(7); $avlTree->insert(12); $avlTree->insert(17); // 查找值 12 $result = $avlTree->search(12); if ($result) { echo "找到值 " . $result->value . PHP_EOL; } else { echo "未找到值 12" . PHP_EOL; }
In a well-balanced In an AVL tree, even if the amount of data is large, the search operation can be completed efficiently within logarithmic time complexity (O(log n)), keeping the data structure fast and efficient.
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