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Design Circular Queue

Design your implementation of the circular queue. The circular queue is a linear data structure in which the operations are performed based on FIFO (First In First Out) principle, and the last position is connected back to the first position to make a circle. It is also called "Ring Buffer".

One of the benefits of the circular queue is that we can make use of the spaces in front of the queue. In a normal queue, once the queue becomes full, we cannot insert the next element even if there is a space in front of the queue. But using the circular queue, we can use the space to store new values.

Implement the MyCircularQueue class:

  • MyCircularQueue(k) Initializes the object with the size of the queue to be k.
  • int Front() Gets the front item from the queue. If the queue is empty, return -1.
  • int Rear() Gets the last item from the queue. If the queue is empty, return -1.
  • boolean enQueue(int value) Inserts an element into the circular queue. Return true if the operation is successful.
  • boolean deQueue() Deletes an element from the circular queue. Return true if the operation is successful.
  • boolean isEmpty() Checks whether the circular queue is empty or not.
  • boolean isFull() Checks whether the circular queue is full or not.

You must solve the problem without using the built-in queue data structure in your programming language. 

 

Example 1:

Input
["MyCircularQueue", "enQueue", "enQueue", "enQueue", "enQueue", "Rear", "isFull", "deQueue", "enQueue", "Rear"]
[[3], [1], [2], [3], [4], [], [], [], [4], []]
Output
[null, true, true, true, false, 3, true, true, true, 4]

Explanation
MyCircularQueue myCircularQueue = new MyCircularQueue(3);
myCircularQueue.enQueue(1); // return True
myCircularQueue.enQueue(2); // return True
myCircularQueue.enQueue(3); // return True
myCircularQueue.enQueue(4); // return False
myCircularQueue.Rear();     // return 3
myCircularQueue.isFull();   // return True
myCircularQueue.deQueue();  // return True
myCircularQueue.enQueue(4); // return True
myCircularQueue.Rear();     // return 4

 

Constraints:

  • 1 <= k <= 1000
  • 0 <= value <= 1000
  • At most 3000 calls will be made to enQueue, deQueue, Front, Rear, isEmpty, and isFull.

Solution Explanation: Design Circular Queue

This problem requires designing a circular queue data structure. A circular queue is a linear data structure that uses a FIFO (First-In, First-Out) principle, but unlike a standard queue, it reuses space at the beginning of the queue once the end is reached, forming a circular buffer.

Approach: Array-Based Implementation

The most efficient way to implement a circular queue is using an array. We'll track:

  • q (array): Stores the queue elements.
  • front (integer): Index of the front element (the next element to be dequeued).
  • size (integer): Number of elements currently in the queue.
  • capacity (integer): Maximum capacity of the queue.

Methods:

  1. __init__(k) (Constructor): Initializes the queue with capacity k.

  2. enQueue(value): Adds an element to the rear of the queue.

    • Checks if the queue is full (isFull()). If full, returns false.
    • Otherwise, adds the element at index (front + size) % capacity (using the modulo operator to wrap around the array for circularity), increments size, and returns true.

  3. deQueue(): Removes and returns the element at the front of the queue.

    • Checks if the queue is empty (isEmpty()). If empty, returns false.
    • Otherwise, increments front (modulo capacity for circularity), decrements size, and returns true.

  4. Front(): Returns the value of the front element.

    • If the queue is empty, returns -1.
    • Otherwise, returns q[front].

  5. Rear(): Returns the value of the rear element.

    • If the queue is empty, returns -1.
    • Otherwise, returns q[(front + size - 1) % capacity].

  6. isEmpty(): Checks if the queue is empty (size == 0).

  7. isFull(): Checks if the queue is full (size == capacity).

Time and Space Complexity

  • Time Complexity: All operations (enQueue, deQueue, Front, Rear, isEmpty, isFull) have a time complexity of O(1) because they involve constant-time array access and arithmetic operations.

  • Space Complexity: O(k), where k is the queue's capacity, due to the array used to store the elements.

Code Examples (Python, Java, C++, Go, TypeScript, Rust)

The code examples in various languages are provided in the original response. They all implement the array-based approach described above. The key is to correctly handle the circularity using the modulo operator (%) to wrap around the array when accessing indices. Each example uses slightly different naming conventions and syntax but embodies the same underlying algorithm.