Is Maximum Sum of Distinct Subarrays With Length K easy or hard?

The problem is rated Medium because it combines two constraints: fixed window size and element uniqueness. Recognizing the sliding window pattern is straightforward, but correctly maintaining the frequency map and window sum requires careful implementation.

Maximum Sum of Distinct Subarrays With Length K Python/Java solution

Implement the sliding window approach with a dictionary or HashMap to track counts and a variable for the current window sum. Move the right pointer to add elements and shrink from the left when the window exceeds size k. When the window size is k and all elements are distinct, update the maximum sum. The same logic works in Python, Java, C++, and JavaScript with minor syntax differences.

How to solve Maximum Sum of Distinct Subarrays With Length K in O(n)?

Use a sliding window of size k and maintain a hash map that stores frequencies of elements in the current window. Keep a running sum of the window elements. When the window grows beyond k, remove the leftmost element and update both the sum and its frequency in the map. Whenever the window size equals k and the map size equals k, update the maximum sum.

What is the best approach for Maximum Sum of Distinct Subarrays With Length K?

The best approach uses a sliding window combined with a hash map to track element frequencies. As the window moves across the array, update the running sum and maintain counts in the map. When the window size equals k and the number of unique elements is also k, the subarray is valid. This method runs in O(n) time with O(k) extra space.

Is Maximum Sum of Distinct Subarrays With Length K asked at Google/Amazon/Meta?

Sliding window problems with uniqueness constraints frequently appear in interviews at companies like Amazon, Google, and Meta. Variants include longest substring with distinct characters or maximum sum subarray with constraints. This problem tests understanding of hash maps, window maintenance, and linear-time optimization.

What data structure is used in Maximum Sum of Distinct Subarrays With Length K?

A hash map (or dictionary) is used to store frequency counts of elements inside the current sliding window. This allows constant-time updates when elements enter or leave the window. The approach also maintains a running sum and two pointers representing the window boundaries.

What is the time complexity of Maximum Sum of Distinct Subarrays With Length K?

The optimal sliding window solution runs in O(n) time because each element enters and leaves the window at most once. Hash map updates and lookups are O(1) on average. The auxiliary space complexity is O(k) since the map stores at most k elements.

Description Hints 3 Solution Video 16:53

2461. Maximum Sum of Distinct Subarrays With Length K

Medium43.5% acceptance10 companies

You are given an integer array nums and an integer k. Find the maximum subarray sum of all the subarrays of nums that meet the following conditions:

The length of the subarray is k, and
All the elements of the subarray are distinct.

Return the maximum subarray sum of all the subarrays that meet the conditions. If no subarray meets the conditions, return 0.

A subarray is a contiguous non-empty sequence of elements within an array.

Example 1:

Input: nums = [1,5,4,2,9,9,9], k = 3
Output: 15
Explanation: The subarrays of nums with length 3 are:
- [1,5,4] which meets the requirements and has a sum of 10.
- [5,4,2] which meets the requirements and has a sum of 11.
- [4,2,9] which meets the requirements and has a sum of 15.
- [2,9,9] which does not meet the requirements because the element 9 is repeated.
- [9,9,9] which does not meet the requirements because the element 9 is repeated.
We return 15 because it is the maximum subarray sum of all the subarrays that meet the conditions

Example 2:

Input: nums = [4,4,4], k = 3
Output: 0
Explanation: The subarrays of nums with length 3 are:
- [4,4,4] which does not meet the requirements because the element 4 is repeated.
We return 0 because no subarrays meet the conditions.

Constraints: