How to Implement a Heap Data Structure in Java

  • Post last modified:July 16, 2022
  • Reading time:4 mins read

Introduction

Heap is a tree-based data structure and they are a complete binary tree.

There are generally two types of Heap: 

  • Max heap
    In Max heap, the root should always be maximum compared to the left and right child, and this is true for all subtrees as well.
  • Min heap:
    In Min heap, the root should always be minimum and it is the same for all subtrees as well

Use Case

  • When we need quick access to the highest or lowest element from N numbers of elements we can achieve it in the O(1) operation.
  • Heap is optimized for operations such as Insert, Find Min/Max, and deletion operations compared to other data structures such as LinkedList and Array.
  • Let’s consider one example where we have List of Jobs in the queue where each job has been assigned priority number.
  •  In this case we can always keep the max priority job at the root and find it in O(1) time, while we can remove it as soon as we finish the job and hepify our heap in O(Log N) time to get the next max priority Job.

Heap Implementation In Java

  • In Java one of the common ways to build a heap is to use a PriorityQueue implementation.
  • PriorityQueue expects the comparator where we can define how we plan to prioritize our element. 
  • Default ordering is increasing the order of the element. For example, if we keep adding integers to PriorityQueue then the root element will have a min value and it is true for all the subtrees.
  • In the below example we are adding integers to our PriorityQueue.Since the default order is increasing order, our root will have a minimum number that is 0 in the below case.

Min Heap

Queue<Integer> pq0 = new PriorityQueue<>();
  pq0.add(1);
  pq0.add(2);
  pq0.add(0);

System.out.println("min element -> "+pq0.peek());

If we remove the top element which is our current min, then the heapify will be performed with O(log N) complexity and the second minimum will become the minimum element.

    Queue<Integer> pq0 = new PriorityQueue<>();
        pq0.add(1);
        pq0.add(2);
        pq0.add(0);

        System.out.println("min element -> "+pq0.peek());
        pq0.remove();
        System.out.println("min element -> "+pq0.peek());

Max Heap

  • The next type of heap is the Max heap, where root elements keep the max element and it’s also true for its subtrees.
  • Since the natural order is increasing order in PriorityQueue, hence we need to create comparator logic for returning the max of two elements.
  • Once we have comparator logic we can pass it as a constructor parameter in PriorityQueue.
  • Now our PriorityQueue will keep the elements as Max heap.
    public static void main(String[] args) {
        Queue<Integer> logic = new PriorityQueue<>(new Comparator<Integer>() {
            @Override
            public int compare(Integer i1, Integer i2){
                return i2-i1;
            }
        });

        Queue<Integer> pq = new PriorityQueue<>(logic);
        pq.add(1);
        pq.add(2);
        pq.add(0);

        System.out.println("max element -> "+pq.peek());
    }

If we remove the root element, then the next max will become the root element and will be returned.

        Queue<Integer> logic = new PriorityQueue<>(new Comparator<Integer>() {
            @Override
            public int compare(Integer i1, Integer i2){
                return i2-i1;
            }
        });

        Queue<Integer> pq = new PriorityQueue<>(logic);
        pq.add(1);
        pq.add(2);
        pq.add(0);

        System.out.println("max element -> "+pq.peek());
        pq.remove();
        System.out.println("max element -> "+pq.peek());

Using Lambda

  • We can also replace comparator boilerplate code with one-liner crisp lambda.
// using lambda
Queue<Integer> pq1 = new PriorityQueue<>((a,b)-> b - a);
   pq1.add(2);
   pq1.add(1);
   pq1.add(3);
   pq1.add(4);
   
  System.out.println("pq max-> "+pq.peek());

Conclusion

  • Heap is important data structure to know about since it has good time complexity for insertion/finding/deletion operation compared to other data structures like array and LinkedList,
  • Java provides PriorityQueue implementation which we can use to keep the data in the Heap data structure.
  • We can pass comparator logic as a lambda function to the PriorityQueue to define the element’s priority.

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