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Bhanu Bisht

Template

Heap / Priority Queue Template: Pattern, Code & Cheat Sheet

The Heap / Priority Queue pattern is one of the most frequently tested coding interview patterns. Efficiently extract min/max elements using heap data structure. This template gives you a reusable code skeleton, pseudocode, and implementation in multiple languages so you can solve 10+ problems using this single mental model.

Difficulty: Medium | Time Complexity: O(n log k) | Space Complexity: O(k)

When to Use This Template

Use the Heap / Priority Queue template when you see these signals in a problem:

1.K-th element — Problems asking to k-th element

2.Merge K lists — Problems asking to merge k lists

3.Median finding — Problems asking to median finding

Prerequisites: Arrays, Trees concept

Problem count on W Code: 10 problems across Easy, Medium, and Hard difficulty levels.

If the problem does not match these signals, consider alternative patterns.

Pseudocode Template

function heap_priority_queueSolve(input):
    // Initialize data structures
    result = initial_value
    
    // Core logic for Heap / Priority Queue
    for each element in input:
        process(element)
        update(result)
    
    return result

Python Implementation

python
def solve(input_data):
    """Heap / Priority Queue solution template."""
    result = []
    # Implement heap / priority queue logic here
    for item in input_data:
        # Process each item
        result.append(item)
    return result

Java Implementation

java
public Object solve(Object[] input) {
    // Heap / Priority Queue template
    // Implement core logic here
    return null;
}

C++ Implementation

cpp
auto solve(vector<int>& input) {
    // Heap / Priority Queue template
    // Implement core logic
    return result;
}

Variations & Adaptations

The Heap / Priority Queue pattern has several variations you should master:

Variation 1: Min Heap

This variation is useful when the problem specifically requires min heap. Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 2: Max Heap

This variation is useful when the problem specifically requires max heap. Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 3: K-way Merge

This variation is useful when the problem specifically requires k-way merge. Adapt the main template by modifying the core loop/recursion logic accordingly.

Variation 4: Median Finding (Two Heaps)

This variation is useful when the problem specifically requires median finding (two heaps). Adapt the main template by modifying the core loop/recursion logic accordingly.

Common Mistakes & Edge Cases

When implementing Heap / Priority Queue, watch out for:

1.Off-by-one errors — Carefully handle boundary conditions in loops

2.Missing base case — Ensure recursion terminates properly

3.Integer overflow — Use appropriate data types for large inputs

4.Empty input — Handle null/empty arrays, strings, or graphs

5.Single element — Test with minimal inputs

Edge cases to always test:

•Empty input → expected output

•Single element → correct result

•All same elements → no infinite loop

•Maximum constraints → within time limit

Step-by-Step Problem Solving Guide

1.Identify the pattern: Does the problem involve K-th element, Merge K lists, Median finding?

2.Choose the template: Select the variation from above that best fits

3.Define variables: Map problem-specific entities to template variables

4.Implement the core logic: Fill in the template skeleton

5.Handle edge cases: Add boundary checks

6.Verify with examples: Trace through sample inputs

7.Analyze complexity: Confirm O(n log k) time and O(k) space

Frequently Asked Questions

What problems can I solve with the Heap / Priority Queue template?

The Heap / Priority Queue template applies to 10+ problems including: K-th element, Merge K lists, Median finding. Look for problems that involve efficiently extract min/max elements using heap data structure..

What is the time complexity of Heap / Priority Queue?

The typical time complexity is O(n log k) with space complexity of O(k). Some variations may differ.

What should I learn before Heap / Priority Queue?

Prerequisites include: Arrays, Trees concept. Make sure you are comfortable with these concepts first.

How do I recognize a Heap / Priority Queue problem in an interview?

Key signals: the problem involves k-th element; the problem involves merge k lists; the problem involves median finding. Practice identifying these cues by solving at least 10 problems using this pattern.

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