Introduction

Today, I continued my journey into data structures and tackled a concept that’s both powerful and commonly used in many applications: Heaps.

I took time to understand:

What heaps are
The difference between a max heap and a min heap
Common heap operations
Where heaps are used in the real world

Here's everything I learned and how I made sense of it all 👇

🔍 What is a Heap?

A heap is a special kind of binary tree that follows these two rules:

✅ Complete Tree: All levels are fully filled except possibly the last, which must be filled from left to right.
✅ Heap Property: Every parent node must follow a specific rule about its value compared to its children.

There are two main types of heaps: Max Heap and Min Heap.

🔺 Max Heap

The largest element is always at the top (root).
Every parent node is greater than or equal to its children.

🧠 Example:

markdownCopyEdit       100
      /   \
    19     36
   /  \   /  \
  17  3  25   1

Here, 100 is at the top, and every parent is bigger than its children.

🔻 Min Heap

The smallest element is always at the root.
Every parent node is less than or equal to its children.

🧠 Example:

markdownCopyEdit        1
      /   \
     3     6
    / \   / \
   5   9 8  10

Now the smallest number is on top, and all parents are smaller than their children.

🛠️ Heap Operations

Here's what you can do with heaps:

Operation	What it does
Insert	Add an element and "bubble it up" to the right spot
Delete	Remove the root, put the last element on top, then fix the heap
Peek / Top	Check the max (or min) without removing it
Heapify	Turn an array into a valid heap
Extract	Remove the top (max or min) and return it

📦 Real World Uses of Heaps

Heaps are not just for classwork. They're used everywhere:

Priority Queues → Serve urgent tasks first
Heap Sort → Sorting technique using heap
Dijkstra’s Algorithm → Used in shortest path finding (e.g., GPS)
OS Task Scheduling → Manage processes by priority
Median Tracking → Use two heaps to get median in real-time data streams

🧠 What I Learned Today

A heap is a tree with order and shape rules
Max heap puts the biggest number at the top
Min heap puts the smallest number at the top
Operations like insert, delete, heapify help manage the heap
Heaps are very useful in real-life programming

🔚 Wrapping Up

Today’s lesson on heaps was honestly exciting. It felt like organizing a messy shelf: everything in its place, sorted either smallest-to-largest or largest-to-smallest.

Next up, I plan to write Python code to build and practice heap operations myself — and share those in a follow-up blog post.

Today I Studied Heaps — Max Heap, Min Heap, Operations, and Uses