--- title: '230. Kth Smallest Element in a BST' description: Given the root of a binary search tree, and an integer k, return the k^th smallest value (1-indexed) of all the values of the nodes in the tree icon: dot topics: - { name: "Tree", slug: "tree" } - { name: "Depth-First Search", slug: "depth-first-search" } - { name: "Binary Search Tree", slug: "binary-search-tree" } - { name: "Binary Tree", slug: "binary-tree" } video: "5LUXSvjmGCw" issue: "https://github.com/prdlk/leetcode/issues/112" sidebar: label: 'Kth Smallest Element in a BST' badge: 'Medium' --- ::::warning If the BST is modified often (i.e., we can do insert and delete operations) and you need to find the kth smallest frequently, how would you optimize? :::: ### Example 1: - Input: `root = [3,1,4,null,2], k = 1` - Output: `1` ### Example 2: - Input: `root = [5,3,6,2,4,null,null,1], k = 3` - Output: `3` ### Constraints: - The number of nodes in the tree is n. - `1 <= k <= n <= 10^4` - `0 <= Node.val <= 10^4` ## Approach ```mermaid flowchart TD S(["kthSmallest(root, k)"]) --> I["n = 0, stack = [], cur = root"] I --> W{"cur or stack?"} W -- no --> E(["fall off the loop — unreachable, k <= n"]) W -- yes --> L{"cur?"} L -- yes --> P["stack.append(cur), cur = cur.left — push the whole left spine"] P --> L L -- no --> O["cur = stack.pop(), n += 1 — nodes pop in ascending order"] O --> K{"n == k?"} K -- yes --> R(["return cur.val"]) K -- no --> N["cur = cur.right"] N --> W ``` ## Solution ```py # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def kthSmallest(self, root: Optional[TreeNode], k: int) -> int: n = 0 stack = [] cur = root while cur or stack: while cur: stack.append(cur) cur = cur.left cur = stack.pop() n += 1 if n == k: return cur.val cur = cur.right ```