The problem is formulated as the identification of the node such that . Initially I was surprised by the performance of the BST algorithm. However, both the Binary search tree algorithm and the Hashset.Contains() method seemed to take the same amount of time. Should we always use a Hash Table because the time complexity of insertion, deletion, and searching is O(1) or we should use BST? O(1) because we don’t create any space to add the node in the given binary tree. ", but "why does knowing that the height of the tree is log2(n) equate to the complexity being O(log(n))?". Binary Search Tree Performance Page 3 Binary search trees, such as those above, in which the nodes are in order so that all links are to right children (or all are to left children), are called skewed trees. Balanced binary search trees have a fairly uniform complexity: each element takes one node in the tree ... but indexed differently from a search tree: you write the key in binary, and go left for a 0 and right for a 1. How come he came up the time coomplexity is log in just by breaking off binary tree and knowing height is log n. I'm guessing this is a key part of the question: you're wondering not just "why is the complexity log(n)? Reference Interview Question. Space Complexity. Both binary search trees and red-black trees maintain the binary search tree property. A binary search tree is a data structure where each node has at most two children. So we have seen the differences between the Binary Search Tree and Hash Table. Time Complexity of a Search in a Binary Tree Suppose we have a key , and we want to retrieve the associated fields of for . Here we visit in linear time. Now, the question that arises here is that when should we use BST over Hash Table and where should we prefer Hash Table over BST? Assume there is an partial ordering on the nodes (I can order them all using a [math]<=[/math] relationship). Average Running Time The average running time of the binary search tree operations is … Linear time complexity means it’s in order of N. READ Segment Tree.