Top K Frequent Elements Using Heap in DSA Go - Build from Scratch

Top K Frequent Elements Using Heap

📖 Scenario: You are working on a music app that wants to show the top k most played songs from a list of song plays. Each song is represented by an integer ID. Your task is to find the k songs that appear most frequently in the play history.

🎯 Goal: Build a program that finds the top k frequent elements from a list of integers using a heap data structure.

📋 What You'll Learn

Create a slice called plays with the exact values: []int{1,1,1,2,2,3}

Create an integer variable called k and set it to 2

Use a map called frequency to count how many times each song ID appears in plays

Use a min-heap to keep track of the top k frequent elements

Print the top k frequent elements as a slice

💡 Why This Matters

🌍 Real World

Finding the most frequent items is common in recommendation systems, search engines, and analytics dashboards.

💼 Career

Understanding heaps and frequency counting is important for software engineers working on performance-critical data processing and ranking algorithms.

1

Create the plays slice

Create a slice called plays with the exact values []int{1,1,1,2,2,3}.

DSA Go

# Create the plays slice with the given values
# Your code here

Hint

Use plays := []int{1,1,1,2,2,3} to create the slice.

2

Set the value of k

Create an integer variable called k and set it to 2.

DSA Go

package main

func main() {
    plays := []int{1,1,1,2,2,3}
    // Set k to 2
    // Your code here
}

Hint

Use k := 2 to create the variable.

3

Count frequencies and use a min-heap

Create a map called frequency to count how many times each song ID appears in plays. Then use a min-heap to keep track of the top k frequent elements. Use the container/heap package and define a heap type that stores pairs of (frequency, song ID).

DSA Go

package main

import (
    "container/heap"
    "fmt"
)

func main() {
    plays := []int{1,1,1,2,2,3}
    k := 2

    // Create frequency map
    // Your code here

    // Define and use min-heap to find top k frequent
    // Your code here
}

// Define a type for the heap
// Your code here

Hint

Use a map to count frequencies. Use a min-heap to keep only top k elements by frequency.

4

Print the top k frequent elements

Create a slice called result and pop all elements from the heap into it. Then print result.

DSA Go

package main

import (
    "container/heap"
    "fmt"
)

// Pair and MinHeap definitions
// ...

func main() {
    plays := []int{1,1,1,2,2,3}
    k := 2

    frequency := make(map[int]int)
    for _, song := range plays {
        frequency[song]++
    }

    h := &MinHeap{}
    heap.Init(h)

    for val, freq := range frequency {
        heap.Push(h, Pair{freq, val})
        if h.Len() > k {
            heap.Pop(h)
        }
    }

    // Pop all elements from heap into result slice
    // Print result slice
    // Your code here
}

Hint

Pop all elements from the heap and append their values to a slice. Then print the slice.

Practice

1. What is the main purpose of using a heap in the Top K Frequent Elements Using Heap algorithm?

easy

A. To efficiently find and keep track of the top K frequent elements

B. To sort all elements in ascending order

C. To store elements without any order

D. To remove duplicates from the list

Solution

1. Step 1: Understand the role of heap in frequency tracking

   A heap helps keep track of elements based on their frequency efficiently, especially when we want only the top K frequent elements.

2. Step 2: Compare with other options

   Sorting all elements or removing duplicates is not the main goal here; the heap is used for efficient frequency comparison and retrieval.

3. Final Answer:

   To efficiently find and keep track of the top K frequent elements -> Option A

4. Quick Check:

   Heap usage = Efficient top K tracking [OK]

Hint: Heap keeps top K frequent elements efficiently [OK]

Common Mistakes:

• Thinking heap sorts all elements fully
• Confusing heap with simple list storage
• Assuming heap removes duplicates

2. Which of the following Go code snippets correctly initializes a min-heap to store pairs of (frequency, element) for the top K frequent elements problem?

easy

A. heap := new([]int); heap.Init(heap)

B. heap := make([]int, 0); heap.Push(heap, pair)

C. heap := &IntHeap{}; heap.Init(heap)

D. var h IntHeap; heap.Init(&h)

Solution

1. Step 1: Understand Go heap initialization

   In Go, a heap is usually implemented by defining a type that implements heap.Interface, then initializing it with heap.Init(&heap).

2. Step 2: Analyze options

   var h IntHeap; heap.Init(&h) correctly declares a variable of type IntHeap and initializes it by passing its address to heap.Init. Other options misuse pointers or heap methods.

3. Final Answer:

   var h IntHeap; heap.Init(&h) -> Option D

4. Quick Check:

   Correct heap init = var h IntHeap; heap.Init(&h) [OK]

Hint: Use heap.Init with pointer to heap variable [OK]

Common Mistakes:

• Calling heap.Init on non-pointer
• Using make for heap without type
• Incorrect pointer usage with new([]int)

3. What will be the output of the following Go code snippet that finds top 2 frequent elements?

nums := []int{1,1,1,2,2,3}
result := topKFrequent(nums, 2)
fmt.Println(result)

Assuming topKFrequent is correctly implemented using a min-heap.

medium

A. [1, 3]

B. [2, 3]

C. [1, 2]

D. [3, 2]

Solution

1. Step 1: Count frequencies of elements

   1 appears 3 times, 2 appears 2 times, 3 appears 1 time.

2. Step 2: Extract top 2 frequent elements

   The top 2 frequent elements are 1 (3 times) and 2 (2 times). So the result is [1, 2].

3. Final Answer:

   [1, 2] -> Option C

4. Quick Check:

   Top 2 frequent = [1, 2] [OK]

Hint: Count frequencies, pick top K by frequency [OK]

Common Mistakes:

• Confusing element order in output
• Including less frequent elements
• Ignoring frequency counts

4. The following Go code snippet is intended to find the top 3 frequent elements but has a bug. What is the bug?

func topKFrequent(nums []int, k int) []int {
    freqMap := make(map[int]int)
    for _, num := range nums {
        freqMap[num]++
    }
    
    h := &IntHeap{}
    heap.Init(h)
    
    for num, freq := range freqMap {
        heap.Push(h, Pair{freq, num})
        if h.Len() > k {
            heap.Pop(h)
        }
    }
    
    res := make([]int, 0, k)
    for h.Len() > 0 {
        p := heap.Pop(h).(Pair)
        res = append(res, p.num)
    }
    return res
}

medium

A. Result list is in ascending frequency order, needs reversing

B. Heap is not initialized before use

C. Frequency map is not counting correctly

D. Heap size check should be < k, not > k

Solution

1. Step 1: Understand heap usage and result order

   The min-heap keeps smallest frequency on top, so popping elements gives ascending frequency order.

2. Step 2: Identify output order issue

   Appending popped elements directly results in ascending order, but top K frequent elements should be in descending order of frequency.

3. Final Answer:

   Result list is in ascending frequency order, needs reversing -> Option A

4. Quick Check:

   Output order issue = Result list is in ascending frequency order, needs reversing [OK]

Hint: Pop from min-heap gives ascending order; reverse result [OK]

Common Mistakes:

• Ignoring output order of popped elements
• Not initializing heap properly
• Incorrect frequency counting

5. Given a large list of integers where many elements have the same frequency, how should the Top K Frequent Elements Using Heap algorithm be adapted to return the top K elements sorted by frequency and then by element value ascending when frequencies tie?

hard

A. Use a map only without heap to track frequencies

B. Use a heap with custom comparison: first by frequency, then by element value ascending

C. Use a max-heap with frequency only, ignoring element value

D. Sort the entire list first, then pick first K elements

Solution

1. Step 1: Understand tie-breaking requirement

   When frequencies tie, elements should be ordered by their value ascending to maintain consistent order.

2. Step 2: Adapt heap comparison logic

   Implement a custom comparison in the heap that compares frequency first, and if equal, compares element values ascending.

3. Final Answer:

   Use a heap with custom comparison: first by frequency, then by element value ascending -> Option B

4. Quick Check:

   Custom heap comparison handles ties correctly [OK]

Hint: Add tie-breaker in heap comparison for stable ordering [OK]

Common Mistakes:

• Ignoring tie-breaker in heap comparison
• Sorting entire list unnecessarily
• Using max-heap without tie-break logic