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DSA Goprogramming~20 mins

Min Heap vs Max Heap When to Use Which in DSA Go - Compare & Choose

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Challenge - 5 Problems
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🧠 Conceptual
intermediate
2:00remaining
When to use a Min Heap?
Which scenario is best suited for using a Min Heap?
ASorting elements in descending order efficiently
BFinding the largest element quickly in a dynamic dataset
CFinding the smallest element quickly in a dynamic dataset
DStoring elements without any order requirement
Attempts:
2 left
💡 Hint
Think about which heap type keeps the smallest element at the top.
🧠 Conceptual
intermediate
2:00remaining
When to use a Max Heap?
Which use case is best for a Max Heap?
AFinding the largest element quickly
BFinding the smallest element quickly
CImplementing a queue with FIFO order
DStoring elements in random order
Attempts:
2 left
💡 Hint
Max Heap keeps the largest element at the root.
❓ Predict Output
advanced
3:00remaining
Output of Min Heap after insertions
What is the printed state of the Min Heap after inserting these elements in order: 7, 3, 5, 1?
DSA Go
package main

import (
	"container/heap"
	"fmt"
)

type IntHeap []int

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

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

func (h *IntHeap) Pop() interface{} {
	old := *h
	n := len(old)
	x := old[n-1]
	*h = old[0 : n-1]
	return x
}

func main() {
	h := &IntHeap{}
	heap.Init(h)
	heap.Push(h, 7)
	heap.Push(h, 3)
	heap.Push(h, 5)
	heap.Push(h, 1)

	for h.Len() > 0 {
		fmt.Print(heap.Pop(h))
		if h.Len() > 0 {
			fmt.Print(" -> ")
		}
	}
	fmt.Println(" -> null")
}
A1 -> 5 -> 3 -> 7 -> null
B7 -> 5 -> 3 -> 1 -> null
C3 -> 1 -> 5 -> 7 -> null
D1 -> 3 -> 5 -> 7 -> null
Attempts:
2 left
💡 Hint
Min Heap pops elements in ascending order.
❓ Predict Output
advanced
3:00remaining
Output of Max Heap after insertions
What is the printed state of the Max Heap after inserting these elements in order: 2, 9, 4, 6?
DSA Go
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{} {
	old := *h
	n := len(old)
	x := old[n-1]
	*h = old[0 : n-1]
	return x
}

func main() {
	h := &MaxHeap{}
	heap.Init(h)
	heap.Push(h, 2)
	heap.Push(h, 9)
	heap.Push(h, 4)
	heap.Push(h, 6)

	for h.Len() > 0 {
		fmt.Print(heap.Pop(h))
		if h.Len() > 0 {
			fmt.Print(" -> ")
		}
	}
	fmt.Println(" -> null")
}
A6 -> 9 -> 4 -> 2 -> null
B9 -> 6 -> 4 -> 2 -> null
C2 -> 4 -> 6 -> 9 -> null
D4 -> 6 -> 9 -> 2 -> null
Attempts:
2 left
💡 Hint
Max Heap pops elements in descending order.
🚀 Application
expert
3:00remaining
Choosing Heap Type for Median Maintenance
You want to maintain the median of a stream of numbers efficiently. Which combination of heaps should you use?
AOne Min Heap for the larger half and one Max Heap for the smaller half
BTwo Min Heaps, one for odd and one for even indexed numbers
CTwo Max Heaps, one for positive and one for negative numbers
DOne Max Heap for the larger half and one Min Heap for the smaller half
Attempts:
2 left
💡 Hint
Think about splitting the data into two halves around the median.