Merge Sort Algorithm in DSA Go - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind the Merge Sort algorithm?

Merge Sort divides the list into smaller parts, sorts each part, and then merges them back together in order.

intermediate

What is the time complexity of Merge Sort in the average and worst cases?

The time complexity is O(n log n) for both average and worst cases, where n is the number of elements.

intermediate

Why does Merge Sort use extra space?

Merge Sort uses extra space because it creates temporary arrays to hold sorted parts before merging them.

beginner

Show the result of merging two sorted arrays: [1, 4, 7] and [2, 3, 6].

The merged sorted array is [1, 2, 3, 4, 6, 7].

beginner

What are the two main steps in the Merge Sort algorithm?

1. Divide the array into halves until each part has one element.
2. Merge the parts back together in sorted order.

What technique does Merge Sort use to sort data?

A Dynamic programming

B Greedy approach

C Divide and conquer

D Brute force

Merge Sort splits the array into smaller parts and conquers by merging them sorted.

What is the space complexity of Merge Sort?

A O(n)

B O(1)

C O(log n)

D O(n log n)

Merge Sort requires extra space proportional to the size of the array for merging.

Which of these is NOT true about Merge Sort?

A It sorts in place without extra space.

B It works well on linked lists.

C It has O(n log n) time complexity.

D It is stable.

Merge Sort needs extra space and does not sort in place.

After dividing an array of 8 elements, how many single-element parts do you get?

A 4

B 8

C 2

D 16

Dividing continues until each part has one element, so 8 elements become 8 parts.

What is the first step in the Merge Sort algorithm?

A Swap elements

B Merge two sorted arrays

C Sort the entire array directly

D Divide the array into halves

Merge Sort starts by dividing the array into smaller halves.

Explain how the Merge Sort algorithm sorts an array step-by-step.

Think about breaking down the problem and then combining solutions.

Describe the advantages and disadvantages of using Merge Sort.

Consider speed, memory, and use cases.

Practice

1. What is the main idea behind the Merge Sort algorithm?

easy

A. Compare each element with every other element and swap if needed

B. Pick the smallest element and place it at the beginning repeatedly

C. Divide the list into halves, sort each half, then merge them

D. Use a pivot to partition the list and sort partitions recursively

Solution

1. Step 1: Understand the divide and conquer approach

   Merge Sort splits the list into two halves recursively until single elements remain.

2. Step 2: Merge sorted halves

   It then merges these halves back together in sorted order.

3. Final Answer:

   Divide the list into halves, sort each half, then merge them -> Option C

4. Quick Check:

   Merge Sort = Divide and Conquer [OK]

Hint: Remember: split, sort halves, then merge [OK]

Common Mistakes:

• Confusing Merge Sort with Bubble Sort
• Thinking Merge Sort uses a pivot like Quick Sort
• Believing Merge Sort sorts in place without extra space

2. Which of the following is the correct way to declare a function for merge sort in Go?

easy

A. func mergeSort(arr []int) []int {}

B. func mergeSort(arr int[]) int[] {}

C. func mergeSort(arr array of int) array of int {}

D. func mergeSort(arr []int) int {}

Solution

1. Step 1: Check Go function syntax for slices

   In Go, slices are declared as []int for integer slices.

2. Step 2: Verify return type matches input type

   Merge sort returns a sorted slice, so return type is also []int.

3. Final Answer:

   func mergeSort(arr []int) []int {} -> Option A

4. Quick Check:

   Go slice syntax = []int [OK]

Hint: Go slices use []type syntax, not array of type [OK]

Common Mistakes:

• Using array syntax like int[] which is invalid in Go
• Returning int instead of []int for sorted slice
• Using incorrect parameter types like 'array of int'

3. What will be the output of the following Go code snippet using merge sort?

package main
import "fmt"

func mergeSort(arr []int) []int {
    if len(arr) <= 1 {
        return arr
    }
    mid := len(arr) / 2
    left := mergeSort(arr[:mid])
    right := mergeSort(arr[mid:])
    return merge(left, right)
}

func merge(left, right []int) []int {
    result := []int{}
    i, j := 0, 0
    for i < len(left) && j < len(right) {
        if left[i] < right[j] {
            result = append(result, left[i])
            i++
        } else {
            result = append(result, right[j])
            j++
        }
    }
    result = append(result, left[i:]...)
    result = append(result, right[j:]...)
    return result
}

func main() {
    arr := []int{5, 3, 8, 4, 2}
    sorted := mergeSort(arr)
    fmt.Println(sorted)
}

medium

A. [5 3 8 4 2]

B. [2 3 4 5 8]

C. [8 5 4 3 2]

D. [2 3 5 4 8]

Solution

1. Step 1: Trace the recursive splitting

   The array splits into halves until single elements: [5,3,8,4,2] -> [5,3] and [8,4,2], then further splits.

2. Step 2: Merge sorted halves step-by-step

   Merge [5] and [3] -> [3,5]; merge [8] and [4,2] -> [2,4,8]; finally merge [3,5] and [2,4,8] -> [2,3,4,5,8].

3. Final Answer:

   [2 3 4 5 8] -> Option B

4. Quick Check:

   Sorted array = [2 3 4 5 8] [OK]

Hint: Merge halves sorted individually to get full sorted array [OK]

Common Mistakes:

• Assuming original array prints without sorting
• Mixing up merge order causing wrong output
• Forgetting to append remaining elements after loop

4. Identify the bug in this merge function snippet and select the fix:

func merge(left, right []int) []int {
    result := []int{}
    i, j := 0, 0
    for i < len(left) && j < len(right) {
        if left[i] <= right[j] {
            result = append(result, left[i])
            i++
        } else {
            result = append(result, right[j])
            j++
        }
    }
    result = append(result, left[i:]...)
    result = append(result, right[i:]...)
    return result
}

medium

A. Remove the last two append statements

B. Change comparison from <= to <

C. Initialize result with length of left + right

D. Change last append from right[i:] to right[j:]

Solution

1. Step 1: Analyze the append of remaining elements

   After merging common parts, remaining elements from left[i:] and right[j:] must be appended.

2. Step 2: Spot the incorrect slice used

   The code incorrectly appends right[i:] instead of right[j:], which can cause wrong elements or panic.

3. Final Answer:

   Change last append from right[i:] to right[j:] -> Option D

4. Quick Check:

   Append remaining right elements using j index [OK]

Hint: Use correct index variable when appending leftover elements [OK]

Common Mistakes:

• Using wrong index variable for right slice leftover
• Removing leftover appends causing incomplete merge
• Changing comparison operator unnecessarily

5. You want to sort a large list of 1 million integers using merge sort in Go. Which approach is best to optimize memory usage and performance?

hard

A. Use a bottom-up iterative merge sort to avoid deep recursion

B. Use recursive merge sort with slices copied at each step

C. Use merge sort but create new slices for every merge step

D. Use bubble sort instead for simplicity

Solution

1. Step 1: Understand recursion overhead in large data

   Recursive merge sort can cause deep call stacks and many slice copies, increasing memory use.

2. Step 2: Bottom-up iterative merge sort reduces recursion

   Bottom-up merge sort merges pairs iteratively, reducing recursion and memory overhead.

3. Final Answer:

   Use a bottom-up iterative merge sort to avoid deep recursion -> Option A

4. Quick Check:

   Iterative merge sort = better memory for large data [OK]

Hint: Iterative merge sort saves stack and memory for big lists [OK]

Common Mistakes:

• Ignoring recursion limits on large inputs
• Copying slices unnecessarily increasing memory
• Choosing inefficient sorts like bubble sort for big data