Kth Largest Element Using Max Heap in DSA Go - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a max heap from the given slice.

DSA Go

h := &MaxHeap[1]

Drag options to blanks, or click blank then click option'

A&nums

Bnums

C{nums}

D*nums

Attempts:

3 left

💡 Hint

Common Mistakes

Using nums without braces

Using &nums which is a pointer to slice, not the slice itself

We need to initialize the MaxHeap struct with the slice nums inside curly braces.

2fill in blank

medium

Complete the code to swap elements at indices i and j in the heap slice.

DSA Go

h.[1](i, j)

Drag options to blanks, or click blank then click option'

ASwap

BswapElements

CSwapElements

Dswap

Attempts:

3 left

💡 Hint

Common Mistakes

Using uppercase Swap which is not defined

Using SwapElements which does not exist

The method to swap elements is named swap with lowercase s as per Go naming conventions.

3fill in blank

hard

Fix the error in the Pop method to correctly remove and return the max element.

DSA Go

func (h *MaxHeap) Pop() int {
    max := h.nums[0]
    h.nums[0] = h.nums[len(h.nums)-1]
    h.nums = h.nums[:[1]]
    h.heapifyDown(0)
    return max
}

Drag options to blanks, or click blank then click option'

Alen(h.nums) - 2

Blen(h.nums) - 1

Clen(h.nums) + 1

Dlen(h.nums)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(h.nums) which does not remove the last element

Using len(h.nums) + 1 which causes runtime error

To remove the last element, slice up to len(h.nums) - 1 is needed.

4fill in blank

hard

Fill both blanks to complete the loop that extracts the kth largest element from the max heap.

DSA Go

for i := 0; i < [1] - 1; i++ {
    h.[2]()
}

Drag options to blanks, or click blank then click option'

Ak

BPop

Cpop

Dlen(h.nums)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(h.nums) instead of k

Using pop instead of Pop which is the correct method name

We loop k-1 times and call Pop method to remove the largest elements before the kth.

5fill in blank

hard

Fill all three blanks to complete the function that returns the kth largest element using max heap.

DSA Go

func KthLargest(nums []int, [1] int) int {
    h := &MaxHeap[2]
    h.buildMaxHeap()
    for i := 0; i < k - 1; i++ {
        h.[3]()
    }
    return h.nums[0]
}

Drag options to blanks, or click blank then click option'

Ak

B{nums}

CPop

Dkth

Attempts:

3 left

💡 Hint

Common Mistakes

Using kth instead of k as parameter

Not initializing MaxHeap with {nums}

Calling pop instead of Pop method

Parameter is k, initialize MaxHeap with {nums}, and call Pop method to remove largest elements.

Practice

1. What is the main idea behind finding the kth largest element using a max heap?

easy

A. Build a max heap from the array and extract the maximum element k times.

B. Build a min heap and extract the minimum element k times.

C. Sort the array and pick the kth element from the start.

D. Use a hash map to count frequencies and find the kth largest.

Solution

1. Step 1: Understand max heap property

   A max heap always keeps the largest element at the root, so extracting the root gives the current largest element.

2. Step 2: Extract max k times

   By removing the root k times, the last extracted element is the kth largest.

3. Final Answer:

   Build a max heap and extract max k times -> Option A

4. Quick Check:

   Max heap extraction = kth largest element [OK]

Hint: Extract max from max heap k times to get kth largest [OK]

Common Mistakes:

• Using min heap instead of max heap
• Sorting and picking wrong index
• Confusing kth largest with kth smallest

2. Which of the following Go code snippets correctly creates a max heap from a slice arr using the container/heap package?

easy

A. heap.Init(&arr) // arr is []int

B. heap.Pop(arr) // arr is []int

C. heap.Push(arr, value) // arr is []int

D. heap.Init(&maxHeap) // maxHeap implements heap.Interface with max-heap logic

Solution

1. Step 1: Understand Go heap package usage

   The Go heap package requires a type implementing heap.Interface to define heap behavior (min or max).

2. Step 2: Use a custom maxHeap type

   To create a max heap, define a type with Len, Less (inverted for max), Swap, Push, Pop methods, then call heap.Init(&maxHeap).

3. Final Answer:

   heap.Init(&maxHeap) where maxHeap implements max-heap interface -> Option D

4. Quick Check:

   Custom heap.Interface needed for max heap [OK]

Hint: Use custom type implementing heap.Interface for max heap [OK]

Common Mistakes:

• Calling heap.Init on a plain slice
• Not implementing Less method for max heap
• Using heap.Push/Pop directly on slice

3. What is the output of the following Go code snippet that finds the 3rd largest element using a max heap?

package main

import (
	"container/heap"
	"fmt"
)

type MaxHeap []int

func (h MaxHeap) Len() int           { return len(h) }
func (h MaxHeap) Less(i, j int) bool { return h[i] > h[j] }
func (h MaxHeap) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }

func (h *MaxHeap) Push(x interface{}) {
	*h = append(*h, x.(int))
}

func (h *MaxHeap) Pop() interface{} {
	n := len(*h)
	x := (*h)[n-1]
	*h = (*h)[:n-1]
	return x
}

func main() {
	arr := []int{4, 1, 7, 3, 8, 5}
	h := MaxHeap(arr)
	heap.Init(&h)

	var kthLargest int
	for i := 0; i < 3; i++ {
		kthLargest = heap.Pop(&h).(int)
	}
	fmt.Println(kthLargest)
}

medium

A. 4

B. 5

C. 7

D. 3

Solution

1. Step 1: Build max heap from array

   Array is [4,1,7,3,8,5]. Max heap root is 8, next largest 7, then 5, etc.

2. Step 2: Pop max 3 times

   First pop: 8, second pop: 7, third pop: 5. The 3rd largest is 5.

3. Final Answer:

   5 -> Option B

4. Quick Check:

   3rd largest element = 5 [OK]

Hint: Pop max heap k times; last popped is kth largest [OK]

Common Mistakes:

• Confusing 3rd largest with 2nd largest
• Not building max heap correctly
• Off-by-one errors in loop

4. Identify the error in this Go code snippet that tries to find the 2nd largest element using a max heap:

package main

import (
	"container/heap"
	"fmt"
)

type MaxHeap []int

func (h MaxHeap) Len() int           { return len(h) }
func (h MaxHeap) Less(i, j int) bool { return h[i] < h[j] }
func (h MaxHeap) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }

func (h *MaxHeap) Push(x interface{}) {
	*h = append(*h, x.(int))
}

func (h *MaxHeap) Pop() interface{} {
	n := len(*h)
	x := (*h)[n-1]
	*h = (*h)[:n-1]
	return x
}

func main() {
	arr := []int{10, 20, 15, 30, 40}
	h := MaxHeap(arr)
	heap.Init(&h)

	heap.Pop(&h)
	secondLargest := heap.Pop(&h).(int)
	fmt.Println(secondLargest)
}

medium

A. Push method does not append correctly.

B. Pop method returns wrong element from the heap.

C. Less method uses < instead of >, so heap is min heap not max heap.

D. heap.Init is called on a slice, not a heap.Interface.

Solution

1. Step 1: Check Less method for heap type

   Less uses < which creates a min heap, but we want a max heap for kth largest.

2. Step 2: Effect on extraction

   Extracting twice from min heap gives 10 and 15, not the largest elements.

3. Final Answer:

   Less method uses < instead of >, making it a min heap -> Option C

4. Quick Check:

   Less method defines heap type [OK]

Hint: Max heap Less must use >, not < [OK]

Common Mistakes:

• Using < in Less for max heap
• Not implementing heap.Interface fully
• Calling heap.Init on plain slice

5. Given a large array of integers, which approach is most efficient to find the 5th largest element using a heap in Go, considering time and space complexity?

hard

A. Build a min heap of size 5 and iterate through array, replacing root if element is larger.

B. Build a max heap of the entire array and pop 5 times.

C. Sort the entire array and pick the 5th largest element.

D. Use a hash map to count frequencies and find the 5th largest.

Solution

1. Step 1: Compare approaches for large arrays

   Building a max heap of entire array is O(n), popping 5 times is O(k log n). Sorting is O(n log n).

2. Step 2: Use min heap of size 5 for efficiency

   Maintaining a min heap of size 5 keeps track of top 5 largest elements in O(n log k) time and O(k) space, which is efficient.

3. Final Answer:

   Use a min heap of size 5 and replace root when larger element found -> Option A

4. Quick Check:

   Min heap size k for kth largest is efficient [OK]

Hint: Use min heap of size k for efficient kth largest in large data [OK]

Common Mistakes:

• Building max heap of entire array wastes space/time
• Sorting entire array is slower for large data
• Using hash map does not help with order