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+---
+id: contains-duplicate-II
+title: Contains Duplicate II(LeetCode)
+sidebar_label: 0219-Contains Duplicate II
+tags:
+  - Array
+  - Hash Table
+  - Sliding Window
+description: Given an integer array nums and an integer k, return true if there are two distinct indices i and j in the array such that nums[i] == nums[j] and abs(i - j) <= k.
+---
+
+## Problem Statement
+
+Given an integer array `nums` and an integer `k`, return `true` if there are two distinct indices `i` and `j` in the array such that `nums[i] == nums[j]` and `abs(i - j) <= k`.
+
+### Examples
+
+**Example 1:**
+
+```plaintext
+Input: nums = [1,2,3,1], k = 3
+Output: true
+```
+
+**Example 2:**
+
+```plaintext
+Input: nums = [1,0,1,1], k = 1
+Output: true
+```
+
+**Example 3:**
+
+```plaintext
+Input: nums = [1,2,3,1,2,3], k = 2
+Output: false
+```
+
+### Constraints
+
+- `1 <= nums.length <= 105`
+- `109 <= nums[i] <= 109`
+- `0 <= k <= 105`
+
+## Solution
+
+We can solve this problem using a HashSet or a HashMap to keep track of the elements within the sliding window of size k. Here, we discuss Java, C++, Python, and JavaScript solutions for this problem.
+
+### Approach 1: Java Solution Using HashSet
+
+#### Algorithm
+
+1. Initialize an empty HashSet.
+2. Traverse the array with a loop.
+3. For each element:
+* If it is already in the HashSet, return 'true'.
+* Add the element to the HashSet.
+* If the HashSet size exceeds 'k', remove the oldest element.
+4. If no duplicate is found within the given range, return 'false'.
+
+#### Implementation
+
+```Java
+class Solution {
+    public boolean containsNearbyDuplicate(int[] nums, int k) {
+        if (nums == null || nums.length < 2 || k == 0) return false;
+        HashSet<Integer> hset = new HashSet<Integer>();
+        for (int j = 0; j < nums.length; j++) {
+            if (!hset.add(nums[j])) return true;
+            if (hset.size() >= k + 1) {
+                hset.remove(nums[j - k]);
+            }
+        }
+        return false;
+    }
+}
+```
+
+### Complexity Analysis
+
+- **Time complexity**: O(N)
+- **Space complexity**: O(k)
+
+### Approach 2: C++ Solution Using Unordered Set
+
+#### Algorithm
+
+1. Initialize an unordered set.
+2. Traverse the array with a loop.
+3. For each element:
+* Check if it is in the set. If yes, return 'true'.
+* Insert the element into the set.
+* If the set size exceeds 'k', remove the oldest element.
+4. If no duplicate is found, return 'false'.
+
+#### Implementation 
+
+```C++
+class Solution {
+public:
+    bool containsNearbyDuplicate(vector<int>& nums, int k) {
+        unordered_set<int> hset;
+        for (int idx = 0; idx < nums.size(); idx++) {
+            if (hset.count(nums[idx])) return true;
+            hset.insert(nums[idx]);
+            if (hset.size() > k) {
+                hset.erase(nums[idx - k]);
+            }
+        }
+        return false;
+    }
+};
+```
+
+### Approach 3: Python Solution Using Dictionary
+
+#### Algorithm
+
+1. Initialize an empty dictionary.
+2. Traverse the array with a loop.
+3. For each element:
+* Check if the element exists in the dictionary and the difference between the current index and the stored index
+  is less than or equal to 'k'. If yes, return 'true'.
+* Update the dictionary with the current element and its index.
+4. If no duplicate is found, return 'false'.
+
+#### Implementation 
+
+```Python
+class Solution:
+    def containsNearbyDuplicate(self, nums: List[int], k: int) -> bool:
+        hset = {}
+        for idx in range(len(nums)):
+            if nums[idx] in hset and abs(idx - hset[nums[idx]]) <= k:
+                return True
+            hset[nums[idx]] = idx
+        return False
+```
+
+### Complexity Analysis
+
+- **Time complexity**: O(N)
+- **Space complexity**: O(k)
+
+### Approach 4: JavaScript Solution Using HashMap
+
+#### Algorithm
+
+1. Initialize an empty Map.
+2. Traverse the array with a loop.
+3. For each element:
+* Check if it exists in the map and the difference between indices is less than or equal to 'k'. If yes, return
+  'true'.
+* Update the map with the current element and its index.
+4. If no duplicate is found, return 'false'.
+
+#### Implementation 
+
+```Javascript
+var containsNearbyDuplicate = function(nums, k) {
+    const hasmap = new Map();
+    for (let idx = 0; idx < nums.length; idx++) {
+        if (hasmap.has(nums[idx]) && idx - hasmap.get(nums[idx]) <= k) {
+            return true;
+        }
+        hasmap.set(nums[idx], idx);
+    }
+    return false;
+};
+```
+
+### Complexity Analysis
+
+- **Time complexity**: O(N)
+- **Space complexity**: O(k)
+
+
+### Conclusion
+
+All four solutions utilize a similar strategy of keeping track of the elements within a sliding window of size 'k'. 
+This ensures that we can efficiently determine if any two indices have the same value and are within 'k' distance of 
+each other. Each solution is optimal with a time complexity of O(n) and a space complexity of O(k).