Binary Heaps and Heapsort Algorithm

Hello people…! In this post I will talk about the Binary Heaps and Heapsort Algorithm implemented using structures and not arrays. This is post is the successor to my previous post on Binary Heaps. Using arrays to code Binary Heaps is very comfortable for the programmer. But it is not fit for the day-to-day applications. This is because the Binary Heap is used to implement the Priority Queue and it is meant to grow, not stay of fixed size. But given that it is meant to grow, we cannot use STL Vector for this. This is because the Vector keeps re-allocating memory as it grows. So, it is slow. Here we will learn the proper structure based implementation of the Binary Heaps.

Now, currently we have the knowledge of the Binary Heap, its structure, working and other properties. But we implemented this using arrays. Now if we were to implement using a structure, there is one challenge that we will face. That is to ensure the Structural Property of the heap. Why? For insertion into an array, we created a node at the end which itself ensured the Structural Property is not violated and then we bubbled upwards to ensure that the Heap Property is not violated at any point. In the array, we knew exactly where we are supposed to insert the node. But in a tree coded using structures how will you find where to insert the new element such that the tree remains a complete binary tree..? The same goes when we want to delete the root. How do we know where the last node is. For this, we will use the binary representation of the size of the heap to traverse to the location we want.

Size does matter !

While inserting itself we could keep a track of the heap size. So, this takes constant time. While inserting, we must search for the appropriate location. For this, we will convert the size into its binary representation and begin the traversal from the root. How? Keep reading the bits of the binary representation you just generated, if you encounter ‘0’ go to the left child of the parent, if it is a ‘1’ go to the right child of the parent. I know it sounds really weird, but that’s the best thing we can do. Let’s take up an example to understand this better. Consider the Binary Heap below –

Binary Heap Insertion - 1

Binary Heap Insertion – 1

Now consider the binary representation of the number (HeapSize + 1) = 8, which is “1000”. We omit the first bit (left-to-right) because that bit says “come to the root”.

  • The Binary String left is “000”. The leftmost bit is ‘0’. So, go to the Left Child. You come to Node 4.
  • The Binary String left is “00”. The leftmost bit is ‘0’. So, go to the Left Child. You come to Node 1.
  • The Binary String left is “0”. There is only one bit left. This bit doesn’t tell you to “go left” or “go right”, it tells you to “insert it to left” or “insert it to right”. As this bit is “0”, we place the new node as the left child of Node 1.

This is how you must use the binary representation of the (HeapSize + 1) to get to the location you want to insert the node. This is process is depicted in the sketch below –

Binary Heap Insertion - 2

Binary Heap Insertion – 2

Every time you insert a node, you will go all the way down the tree, you will not perform any insertions in the middle, so the traversal always takes O(log n), once inserted, you travel up, ensuring that the heap property holds good, so you have to travel all the way up again, so it is another O(log n) operation. So, the over all process takes O(log n), which is same as in the case of an array implementation.

But for deletion, we are supposed to find something else. We are supposed to find out the last node. In the sketch above, before the insertion of 8, we are supposed to somehow find out that the last node is Node 5. There are a couple of methods by which we can accomplish this –

  1. BFS or DFS – By using the Breadth First Search or the Depth First Search we can find out which is the last node in the Binary Heap by searching the whole tree for a node at the maximum depth which is encountered as last as possible. The time complexity of BFS and DFS is O(|E| + |V|). We would have N vertices and what about the number the edges? We would have N – 1 edges, because every node is pointed by a child pointer, except the root. So, the overall complexity becomes O(N + N), which is O(N).
  2. Binary Traversal – This is the same traversal as we did in the case of Insertion. I just called it Binary Traversal. But the method which we discussed about gave us the Node to be inserted but not the last node. Can you think of a way of modifying the procedure such that it would give us the last node directly…? Think about this for a couple of minutes and you will figure out the simple logic behind it. For those who can’t, don’t worry I will discuss it, when I do, try to figure how silly a thing you missed…! The logic is simple, you used the binary representation of (HeapSize + 1) to get to the location where you were supposed to insert a node, if you use the binary representation of (HeapSize) itself for traversal, you will end up at the last node…! Try working out this on paper and you will realise the logic behind it. As, we would traverse the whole tree from root to leaf, this takes O(log N), which is way faster than BFS and DFS which was an O(N) operation. This kind of Binary Traversal is depicted in the sketch below –
Binary Heap Insertion - 3

Binary Heap Insertion – 3

The next thing that we must know about the structure implementation is that unlike the array implementation, where we could construct the heap using build heap procedure, we cannot do that here. The only way to construct the heap from a given collection of numbers is to keep inserting them one-by-one. As we discussed that in insertion, we would have to go all the way down and come up, inserting a node in the structure implementation will be slower than inserting into a heap implemented using arrays.

So, now let us plan what we would be writing as our code. First things first..! The structure of our nodes in the heap would be –

struct node
{
	int value;
	struct node * parent;
	struct node * leftChild;
	struct node * rightChild;
};

Apart from the standard variables that we see in many structure related codes, we would be needing the parent pointer here. This is because, without that, we cannot travel up the tree, which is needed in insertion and deletion.

Heapsort – If you have understood the concepts of constructing the Binary Heap, finding the Last Node and Extracting maximum element, then Heapsort becomes obvious. You just have to keep extracting the maximum element and store it in an array or a list. Then, you will have a sorted array. This essentially destroys the entire heap.

Take a break and come back to code the binary heap. If you have already coded the binary search tree or a binary tree before this should be relatively easy for you. If you have not, then this will be challenging, and I’m sure you’ll be proud of your efforts once you have successfully coded this data structure. You will spend 80% of the time debugging your code, and trust me that’s the way programming is done. I can’t discuss the issues that you might encounter while coding but you can always comment them if you need help. Once you are able to code Insertion and Deletion, Heap Sort should be easy. If you get stuck, you can always refer to my code below –

    

The code is big and that is how it should be and moreover, it is well commented with information. If you still have doubts regarding the code or any concept, feel free to comment them. I hope my post has helped you to get a complete idea about the Binary Heap. If it did, let me know by commenting…! Keep practising… Happy Coding…! 🙂

Binary Heaps

Hello people…! In this post I will talk about Binary Heaps, or, more casually called as Heaps. Binary Heaps are used to implement Priority Queues which are associated with Scheduling. Binary Heaps is a starting step to learn many advanced heap related Data Structures that are used in many algorithms. So let’s understand the need of the situation by taking up a scenario.

Let’s say that the CPU has to execute ‘n’ programs that take their own pre-defined time to get completed. Then one strategy to schedule them is the Shortest Remaining Processing Time (SRPT) first method. As the name suggests, we look at all the available programs, then pick up that program which takes the minimum time to get executed. After completing that program, then the next minimum is supposed to be picked up. So if we observe what operations are we doing on the data we have, they would be –

  • Finding the minimum of given elements.
  • Deleting the minimum element (after a program gets executed).
  • Inserting a new element (if a new program enters the scheduling).

So, we must choose such a Data Structure which takes the minimum to these operations so that we can come up with an efficient solution to the scenario. Binary Heap is one such Data Structure that does these operations in pretty fast time. Now, what exactly is the Binary Heap…? What does it look like…? The Binary Heap is actually a Binary Tree has two very important properties –

  • Structural Property – The value of every node in the Binary Tree should be less than or equal to the values of its children nodes.
  • Heap Property – The Binary Tree should be fully-filled up to the 2nd last level and the last level must be “left-filled”.

To understand these two properties let’s take up an example –

Heap Property in Binary Heaps

Heap Property in Binary Heaps

So, as we can see, the last level is what is called “left-filled”, i.e., all the nodes are to the left and all the empty spaces to the right. The heap property is also depicted. The parent is smaller than both of its children. Now, having introduced this structure to you. Can you think where the smallest element of the heap would be….? At the top of course…! This is due to the very definition of the Heap Property. If the parent has the lower number, then the ancestor of all the nodes should surely have the least number. But this does not say that the leaves at the last level have the greatest number. Think about it for a while looking at the picture and you will understand it.

Now, the Binary Heap is although depicted as a tree, it is implemented as an array…! Just like the Fenwick Tree. The Binary Tree structure of the allows this. Look at the sketch below –

Storing a Binary Heap

Storing a Binary Heap

As, you can see, the nodes of the heap are put into an array in the left-to-right order at each level. Why is this so…? This is the way we can access all the nodes in a heap. How…? If we take any element arr[i] –

  • Parent of Element at i → arr[ i2 ]
  • Left Child of Element at → arr[(2 ✕ i)]
  • Right Child of Element at → arr[(2 ✕ i) + 1]

Looking at the picture, we can verify the above statements. For example, take the node 9 (arr[6]) which is at level 3. Its parent is arr[ 62 ] which is arr[3], i.e., node 6 at level 2, as it is in the structure. The left child of arr[6] is arr[(2 ✕ 6)], which is, arr[12], node 20 and like this even the right child can be verified. The way to find the parent is actually floor( i2 ), but as both ‘i’ and 2 are integers the result comes as it is desired. Now, we must discuss the five important operations in a binary heap –

  • Getting minimum element
  • Inserting a new element
  • Heapify
  • Deleting a new Element
  • Build Heap

Minimum Element – As you can clearly see, the minimum element would be the topmost element of the heap which is the first element of the array. So this operation takes O(1) time.

Inserting an Element – Recall that the last level of the heap must be left-filled. So, where ever you add a node, the resulting structure should be such that the last level will have an extra element. We do this, by first adding the new element to the end, then to ensure that the Structural Property is satisfied we keep swapping the element with its parent until the parent node to be swapped is less than the inserted element. So, we kind of insert the element at the bottom and we “move up” the heap. This process is illustrated in the sketch below.

Inserting an element into a Binary Heap

Inserting an element into a Binary Heap

This is how we insert a node into a heap. In the worst case, we would have to travel all the way up which costs us (log n) swaps, which is the height of the tree. So, the insertion operation has a complexity of O(log n).

Heapify – The heapify operation is as the name suggests, converting some “non-heap” thing into a heap. Well, what this non-heap thing is that it is a parent node, where the left and the right children are heaps, but the structure as a whole isn’t a heap. How is that possible…? This parent node we are talking about, its value could be greater than one or both of its children. In such a case, the smaller valued child is swapped with the parent and this is carried on down the tree until the heap property holds true for the whole structure. To understand this, observe the sketch below.

Heapify in Binary Min Heap

The process of heapify is clearly shown in the sketch, how we keep swapping the child nodes till the heap property is satisfied for the whole structure. This too takes O(log n) time in the worst case, when we would have to travel the whole till the leaves. Where is this process used anyway…? It is used to build a heap, which is our next discussion.

Deleting an Element – Deleting an element is a little tricky. Structurally, when an element is deleted, the last level will lose the right-most node. So what we do is, we swap the last element and the node to be deleted, then remove the last element. Based on the newly swapped element, we either bubble it up (as we did it in Insertion) or we bubble it down (which is simply to use heapify on the swapped element). Why would we get these two cases? Let us take some examples to understand this better. Study the sketch below –

Deletion in Binary Min Heap

So this is the case where we would have to call heapify on the swapped node. As we can see, this case arises when the swapped element is greater than both its children (greater than its parent). Now, the sketch below depicts the other case where we would have to bubble it upwards like we did it in insertion –

binary-min-heap-delete-bubble-up

So this case arises when the swapped element is lesser than its parent. So, while deletion the conditions are –

if (swappedElement > parent) {
	heapify(swappedElement)
} else {
	bubble up swappedElement
}

Build Heap – Build heap is constructing the heap from a given set of randomly arranged nodes in a binary tree. The build heap process uses the heapify to construct the heap “down-to-up”. Now, if you remember the principle of the heapify process, we were able to do that only because the left-subtree and the right-subtree were heaps. Think of the second last level in a heap, the left and the right nodes are leaves, i.e., they have no children, so, they are heaps by themselves. Why is a leaf by itself a heap…? This is because the left child and the right child don’t exist, so we can claim that the parent node (the leaf), satisfies the heap property. And for a leaf, the structural property also holds good because the second-last level, (the level of the leaf node), is fully filled, and the last level (the level of the leaf’s children), is left-filled (actually there are no nodes to be left-filled). Well, most probably, you won’t understand this in the first reading, give it another couple of readings and observe the picture below.

Build Heap Step-by-Step

Build Heap Step-by-Step

I hope the process of the Build Heap is clear. At every level we use the heapify process to gradually build the heap “down-up”. So what is the asymptotic time complexity of Build Heap…? We use heapify for all the nodes except the nodes in the last level,

Nodes till the 2nd Last level = 12 ✕ (Total number of Nodes ⇒ N)
Complexity of the Heapify procedure = O(log N)
Complexity of Build Heap = O(N log N)

But we can argue that the Build Heap process will only take O(N). Now, look at the above sketch once again. If you feel the text is too small, open the image by right-clicking, its bigger than you think…! Now,

The (n + 1)4 nodes at level 3 took 1 swap ⇒ (n + 1)4 ✕ 1
The (n + 1)8 nodes at level 2 took 2 swaps ⇒ (n + 1)8 ✕ 2
The (n + 1)16 nodes at level 1 took 3 swaps ⇒ (n + 1)16 ✕ 3

As, you can see, we have a sort-of progression here. If we were to sum them up, taking (n + 1) common,

⇒ (N + 1) ✕ (14 + 28 + 316 + …)
⇒ (N + 1) ✕ Σ i2i
⇒ (N + 1) ✕ 2       { Σ i2i = 2 }
⇒ O(N)

Therefore, the Build Heap takes O(N) time. The principle behind this is that for most of the time Heapify works on smaller values of ‘N’. These are the most important operations of a Binary Heap. We have discussed the implementation of Binary Heaps using arrays and accessing the parent and child. Now, try to code the above-stated operations at least 3 times…! You have to try. There is no better way of knowing a Data Structure than trying to code it. After you have given your everything, if you succeed, you are brilliant…! If not, look at my code below and see for yourselves how close you were…!

    

One thing I would like to point out is that my heap is an array of structures. Just an integer would suffice, but I made it an array of structures so that it becomes a little generic. So, tomorrow, if you want to you this code as a Priority Queue, you needn’t change anything (or at most the names) in the code. Or, if you wanted to store more information about each node of the Binary Heap, you can simply add attributes to the struct.
Next, for my deletion operation, I swapped the last element and the element to be deleted and called the heapify procedure. This is because, the heapify procedure, checks if the Heap Property is violated and makes the necessary corrections. As the first element of the heap is the smallest, it can be accessed directly by heap[1]. Remember, the arrays in my code are 1-indexed, and all the loops and conditions go with it.
The code is pretty well tested. You can download the input file here.

There is one last topic to discuss before I conclude.

Max Heap – Whatever heaps we have been seeing till now are all Min Heaps. This is because the Heap Property is such that the minimum element is put on top. What if the Heap Property would just be the reverse of it…? Something like –

Heap Property – Every parent node must have a value greater than or equal to both of its child node.

In this case, by the Heap property, the maximum element would be put on the top and so will be for all the sub-trees or sub-heaps in the structure. This is a Max Heap. Actually, the very first heap that we used in our Build Heap sketch is a Max Heap. Have a close look at it and you will understand the difference. Max Heap differ from Min Heaps only by the Heap Property, due to which the other differences arise.

Whoo…! This has become a really long post…! Thanks a lot to stay with me till the end. I hope this helps you to get started with heaps. It may not be easy to code the Binary Heap, but that’s how you grow your skills in programming. You won’t learn anything if you sit for hours and write the Hello World program over-and-over again. You need to challenge yourself…! Feel free to comment your doubts..! Keep practicing…! Happy Coding…! 🙂