1. What search optimization algorithms are there?
Search optimization algorithm refers to an algorithm that can improve search efficiency. Common search optimization algorithms include:
- Binary search : Binary search is a search algorithm based on the divide and conquer idea. It first reduces the search range by half, and then searches within the reduced range, and so on until the target element is found or the target element is judged not to exist. The average time complexity of binary search is O(log n), where n is the size of the data set.
- Hash table : A hash table is a lookup algorithm based on a hash function. It stores data in a hash table, with each element corresponding to a hash value. When searching for an element, first calculate the hash value of the element, and then directly search for the corresponding element in the hash table. The average time complexity of a hash table is O(1), but in the worst case, the time complexity is O(n).
- B-tree : B-tree is a multi-fork search tree, which can improve search efficiency. Each node of the B-tree can store multiple elements, and the number of subtrees of each node is fixed. When searching for an element, you can start from the root node and search downwards layer by layer based on the value of the element until you find the target element or determine that the target element does not exist. The average time complexity of B-tree is O(log n).
- B+ tree : B+ tree is a variant of B-tree, which is more suitable for sequential search. Each node of the B+ tree only stores the value of the element, and the subtree of each node is stored in a linked list. When searching for an element, you can start from the root node and sequentially traverse the value of each node until the target element is found or the target element is judged not to exist. The average time complexity of a B+ tree is O(log n).
In practical applications, appropriate search optimization algorithms can be selected based on the characteristics of the data set. For situations where the data set is small and the data is evenly distributed, binary search or hash table can be used. For situations where the data set is large and the data distribution is uneven, B-tree or B+ tree can be used.