Implement Queue using Stacks

LeetCode 232 | Difficulty: Easy

Easy

Problem Description

Implement a first in first out (FIFO) queue using only two stacks. The implemented queue should support all the functions of a normal queue (push, peek, pop, and empty).

Implement the MyQueue class:

- `void push(int x)` Pushes element x to the back of the queue.

- `int pop()` Removes the element from the front of the queue and returns it.

- `int peek()` Returns the element at the front of the queue.

- `boolean empty()` Returns `true` if the queue is empty, `false` otherwise.

Notes:

- You must use **only** standard operations of a stack, which means only `push to top`, `peek/pop from top`, `size`, and `is empty` operations are valid.

- Depending on your language, the stack may not be supported natively. You may simulate a stack using a list or deque (double-ended queue) as long as you use only a stack's standard operations.

Example 1:

Input
["MyQueue", "push", "push", "peek", "pop", "empty"]
[[], [1], [2], [], [], []]
Output
[null, null, null, 1, 1, false]

Explanation
MyQueue myQueue = new MyQueue();
myQueue.push(1); // queue is: [1]
myQueue.push(2); // queue is: [1, 2] (leftmost is front of the queue)
myQueue.peek(); // return 1
myQueue.pop(); // return 1, queue is [2]
myQueue.empty(); // return false

Constraints:

- `1 <= x <= 9`

- At most `100` calls will be made to `push`, `pop`, `peek`, and `empty`.

- All the calls to `pop` and `peek` are valid.

Follow-up: Can you implement the queue such that each operation is amortized O(1) time complexity? In other words, performing n operations will take overall O(n) time even if one of those operations may take longer.

Topics: Stack, Design, Queue

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Approach

Stack

Use a stack (LIFO) to track elements that need future processing. Process elements when a "trigger" condition is met (e.g., finding a smaller/larger element). Monotonic stack maintains elements in sorted order for next greater/smaller element problems.

When to use

Matching brackets, next greater element, evaluating expressions, backtracking history.

Design

Choose the right data structures to meet the required time complexities for each operation. Consider hash maps for O(1) access, doubly-linked lists for O(1) insertion/deletion, and combining structures for complex requirements.

When to use

Implementing a data structure or system with specific operation time requirements.

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Solutions

Solution 1: C# (Best: 96 ms)

Metric	Value
Runtime	96 ms
Memory	24 MB
Date	2020-01-01

Solution

public class MyQueue {

    int top = -1;   
    private Stack<int> s1 = new Stack<int>();
    private Stack<int> s2 = new Stack<int>();
    /** Initialize your data structure here. */
    public MyQueue()
    {
        
    }

    /** Push element x to the back of queue. */
    public void Push(int x)
    {
        s1.Push(x);
        if(s1.Count == 1) top = x;
    }

    /** Removes the element from in front of queue and returns that element. */
    public int Pop()
    {
        while(s1.Count != 0)
        {
            s2.Push(s1.Pop());
            
        }
        var popped = s2.Pop();
        top = s2.Count != 0 ? s2.Peek() : -1;
        while(s2.Count != 0)
        {
            s1.Push(s2.Pop());
        }
        return popped;

    }

    /** Get the front element. */
    public int Peek()
    {
        return s1.Count != 0 ? top : -1;
    }

    /** Returns whether the queue is empty. */
    public bool Empty()
    {
        return s1.Count == 0;
    }
}

/**
 * Your MyQueue object will be instantiated and called as such:
 * MyQueue obj = new MyQueue();
 * obj.Push(x);
 * int param_2 = obj.Pop();
 * int param_3 = obj.Peek();
 * bool param_4 = obj.Empty();
 */

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Complexity Analysis

Approach	Time	Space
Stack	$O(n)$	$O(n)$

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Interview Tips

Key Points

• Start by clarifying edge cases: empty input, single element, all duplicates.
• Think about what triggers a pop: is it finding a match, or finding a smaller/larger element?
• Clarify the expected time complexity for each operation before choosing data structures.