To implement fast product search in PHP, you can use hash tables and binary search trees: Hash tables use hash functions to find products in constant time, while binary search trees use binary search algorithms to find products in logarithmic time. commodity. In practical applications, a hash table is used to quickly retrieve product information. If there are too many conflicts or there are too many products in the table, a binary search tree is used for more accurate results.
PHP realizes fast product search
Preface
On large e-commerce websites, fast Finding products is crucial and directly affects user experience and conversion rates. This article will introduce how to use advanced algorithms in PHP to achieve fast product search and provide a practical case.
Hash table
A hash table is a data structure that uses a hash function to map keys to values. In product lookup, we can use the product ID as the key and the product information as the value. This way we can look up items by key in constant time, no matter how many items are contained in the hash table.
Code example
class HashTable { private $table = []; public function set($key, $value) { $this->table[$key] = $value; } public function get($key) { return $this->table[$key] ?? null; } } $hashTable = new HashTable(); $hashTable->set(1, ['name' => 'Product 1', 'price' => 10.99]); $product = $hashTable->get(1);
Binary search tree
Binary search tree is a binary tree in which each node The values are greater than the values of all its left child nodes and smaller than the values of all its right child nodes. This way, we can use the binary search algorithm to find items in logarithmic time.
Code Example
class Node { public $value; public $left; public $right; } class BinarySearchTree { private $root; public function insert($value) { $newNode = new Node(); $newNode->value = $value; if (!$this->root) { $this->root = $newNode; } else { $this->_insert($newNode, $this->root); } } private function _insert($newNode, $node) { if ($newNode->value < $node->value) { if (!$node->left) { $node->left = $newNode; } else { $this->_insert($newNode, $node->left); } } else { if (!$node->right) { $node->right = $newNode; } else { $this->_insert($newNode, $node->right); } } } public function search($value) { $node = $this->root; while ($node) { if ($node->value == $value) { return $node; } elseif ($node->value < $value) { $node = $node->right; } else { $node = $node->left; } } return null; } } $binarySearchTree = new BinarySearchTree(); $binarySearchTree->insert(['name' => 'Product 1', 'price' => 10.99]); $product = $binarySearchTree->search(['name' => 'Product 1']);
Practical Case
Let us consider a real situation, such as a large e-commerce website. Users enter a query string in the search bar and need to find relevant products among millions of products.
We can use a hash table to store all product information in memory. When a user enters a query string, we can use a hash function to map the string to an item ID and quickly retrieve item information.
If there are too many items in the hash table, or there are too many collisions in the hash function, we can use a binary search tree as an auxiliary data structure. We can store the item ID and related information in a binary search tree and use the binary search tree to find more accurate results after the ID is retrieved from the hash table.
By combining hash tables and binary search trees, we can implement a fast and efficient product search system to improve user experience.
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