Reviewing Data Structures - Building a Hash Tree

This article explores a set data structure with O(1) time complexity that is implemented manually without relying on the standard library. The author proposes the BinaryHashTrie (binary hash prefix tree) approach, whose core idea is to hash the input string, then use bit operations to parse the hash value bit by bit, mapping it to the left and right child node paths of a binary tree. Specifically, the implementation determines the traversal direction by checking the least significant bit of the hash value and recurses deeper until reaching a node where the hash value is zero, at which point a termination state is marked to complete the add and lookup operations. This structure avoids the complexity of handling collisions in traditional hash tables. Although there is a risk of false positives due to hash collisions (judging a non-existent element as existing), it offers significant performance advantages in scenarios with low precision requirements (such as quickly checking user registration status). Real-world testing shows that its execution speed is significantly superior to linked list structures, making it suitable for acceleration scenarios that demand extremely high speed and can tolerate a very low error rate.