Is Count Subarrays With K Distinct Integers easy or hard?

The problem is typically labeled Hard because the direct brute-force idea is simple but inefficient. The real challenge is recognizing the atMost(K) − atMost(K−1) transformation and implementing a correct sliding window with frequency tracking.

Count Subarrays With K Distinct Integers Python/Java solution

Most implementations use the sliding window atMost technique. A helper function counts subarrays with at most K distinct values using a hash map and two pointers. The final answer is computed as atMost(K) minus atMost(K-1), and the same logic translates directly to Python, Java, C++, Go, or TypeScript.

How to solve Count Subarrays With K Distinct Integers in O(n)?

Use a sliding window with a frequency hash map to count subarrays with at most K distinct elements. Then compute the result using exactly(k) = atMost(k) − atMost(k−1). Each window expansion and contraction updates the distinct count while maintaining valid boundaries, resulting in linear traversal.

What is the best approach for Count Subarrays With K Distinct Integers?

The most efficient approach uses a sliding window with the formula exactly(k) = atMost(k) − atMost(k−1). A hash map tracks element frequencies while two pointers maintain a valid window. This method processes each element at most twice, giving O(n) time complexity and O(n) space.

Is Count Subarrays With K Distinct Integers asked at Google/Amazon/Meta?

Variants of this problem appear in interviews at companies like Google, Amazon, and Meta because it tests sliding window patterns, hash map usage, and subarray counting logic. Interviewers often expect the optimized O(n) sliding window solution.

What data structure is used in Count Subarrays With K Distinct Integers?

A hash table (or hash map) stores element frequencies inside a sliding window. Two pointers track the window boundaries, while the map quickly updates counts as elements enter or leave the window.

What is the time complexity of Count Subarrays With K Distinct Integers?

The optimal sliding window solution runs in O(n) time because each element is added and removed from the window at most once. The hash map operations are constant on average. Space complexity is O(n) in the worst case if all elements in the array are distinct.

3859. Count Subarrays With K Distinct Integers - Practice Online

You are given an integer array nums and two integers k and m.

Return an integer denoting the count of subarrays of nums such that:

The subarray contains exactly k distinct integers.
Within the subarray, each distinct integer appears at least m times.

Example 1:

Input: nums = [1,2,1,2,2], k = 2, m = 2

Output: 2

Explanation:

The possible subarrays with k = 2 distinct integers, each appearing at least m = 2 times are:

Subarray	Distinct numbers	Frequency
[1, 2, 1, 2]	{1, 2} → 2	{1: 2, 2: 2}
[1, 2, 1, 2, 2]	{1, 2} → 2	{1: 2, 2: 3}

Thus, the answer is 2.

Example 2:

Input: nums = [3,1,2,4], k = 2, m = 1

Output: 3

Explanation:

The possible subarrays with k = 2 distinct integers, each appearing at least m = 1 times are:

Subarray	Distinct numbers	Frequency
[3, 1]	{3, 1} → 2	{3: 1, 1: 1}
[1, 2]	{1, 2} → 2	{1: 1, 2: 1}
[2, 4]	{2, 4} → 2	{2: 1, 4: 1}

Thus, the answer is 3.

Constraints:

1 <= nums.length <= 10⁵
1 <= nums[i] <= 10⁵
1 <= k, m <= nums.length