Is Largest Subarray Length K easy or hard?

Largest Subarray Length K is classified as Easy. The implementation is short, but the key insight is recognizing that distinct values allow a greedy choice based only on the first element. Once that observation is made, the solution reduces to a single linear scan.

Largest Subarray Length K Python/Java solution

Most implementations follow the same pattern across languages: iterate through indices 0 to n-k, track the index with the largest value, and return nums[start:start+k]. This logic works identically in Python, Java, C++, Go, TypeScript, and Rust with linear O(n) time complexity.

How to solve Largest Subarray Length K in O(n)?

Iterate through the range of valid starting indices from 0 to n-k. Track the index containing the largest element in that range. Once the scan finishes, return the subarray nums[maxIndex : maxIndex + k]. The distinct-value constraint guarantees this start index produces the lexicographically largest sequence.

What is the best approach for Largest Subarray Length K?

The optimal approach is a greedy scan. Because all numbers in the array are distinct, the lexicographically largest subarray must start with the largest possible first element. Scan indices from 0 to n-k, find the maximum value, and return the k-length slice starting there. This runs in O(n) time and O(1) extra space.

Is Largest Subarray Length K asked at Google/Amazon/Meta?

Problems combining array traversal with greedy selection appear frequently in interviews at large tech companies such as Google, Amazon, and Meta. While this exact problem may vary, the core pattern—identifying the best window using a greedy observation—is a common interview theme.

What data structure is used in Largest Subarray Length K?

The problem primarily uses a simple array traversal. No advanced data structures are required for the optimal solution. A few integer variables track the maximum value and its index while scanning the array.

What is the time complexity of Largest Subarray Length K?

The optimal greedy solution runs in O(n) time because it scans the valid starting positions once to find the maximum element. Space complexity is O(1) since only a few variables are used besides the returned subarray. A brute force method that compares every k-length subarray takes O(n * k) time.

Description Hints 2 Solution Video 8:37

1708. Largest Subarray Length K

Easy65.7% acceptance1 companyPremium elsewhere, free here

An array A is larger than some array B if for the first index i where A[i] != B[i], A[i] > B[i].

For example, consider 0-indexing:

[1,3,2,4] > [1,2,2,4], since at index 1, 3 > 2.
[1,4,4,4] < [2,1,1,1], since at index 0, 1 < 2.

A subarray is a contiguous subsequence of the array.

Given an integer array nums of distinct integers, return the largest subarray of nums of length k.

Example 1:

Input: nums = [1,4,5,2,3], k = 3
Output: [5,2,3]
Explanation: The subarrays of size 3 are: [1,4,5], [4,5,2], and [5,2,3].
Of these, [5,2,3] is the largest.

Example 2:

Input: nums = [1,4,5,2,3], k = 4
Output: [4,5,2,3]
Explanation: The subarrays of size 4 are: [1,4,5,2], and [4,5,2,3].
Of these, [4,5,2,3] is the largest.

Example 3:

Input: nums = [1,4,5,2,3], k = 1
Output: [5]

Constraints:

1 <= k <= nums.length <= 10⁵
1 <= nums[i] <= 10⁹
All the integers of nums are unique.

Follow up: What if the integers in nums are not distinct?

Asked at 1 company

Google

Topics

Array Greedy

Hints open one at a time

Hint 1

Search for the largest integer in the range [0, n - k]

Hint 2

This integer is the first element in the subarray. You should take it with the k - 1 elements after it.

8:37

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Examples

Case 1Case 2Case 3

Input

[1,4,5,2,3]
3

Input

[1,4,5,2,3]
4

Input

[1,4,5,2,3]
1

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