Implement Stack using Queues

LeetCode 225 | Difficulty: Easy

Easy

Problem Description

Implement a last-in-first-out (LIFO) stack using only two queues. The implemented stack should support all the functions of a normal stack (push, top, pop, and empty).

Implement the MyStack class:

- `void push(int x)` Pushes element x to the top of the stack.

- `int pop()` Removes the element on the top of the stack and returns it.

- `int top()` Returns the element on the top of the stack.

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

Notes:

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

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

Example 1:

Input
["MyStack", "push", "push", "top", "pop", "empty"]
[[], [1], [2], [], [], []]
Output
[null, null, null, 2, 2, false]

Explanation
MyStack myStack = new MyStack();
myStack.push(1);
myStack.push(2);
myStack.top(); // return 2
myStack.pop(); // return 2
myStack.empty(); // return False

Constraints:

- `1 <= x <= 9`

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

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

Follow-up: Can you implement the stack using only one queue?

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: 124 ms)

Metric	Value
Runtime	124 ms
Memory	23.4 MB
Date	2019-12-31

Solution

public class MyStack {

    private Queue<int> q;

    /** Initialize your data structure here. */
    public MyStack()
    {
        q = new Queue<int>();
    }

    /** Push element x onto stack. */
    public void Push(int x)
    {
        q.Enqueue(x);
        for (int i = 1; i < q.Count; i++)
        {
            q.Enqueue(q.Dequeue());
        }
    }

    /** Removes the element on top of the stack and returns that element. */
    public int Pop()
    {
        return q.Dequeue();
    }

    /** Get the top element. */
    public int Top()
    {
        return q.Peek();
    }

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

/**
 * Your MyStack object will be instantiated and called as such:
 * MyStack obj = new MyStack();
 * obj.Push(x);
 * int param_2 = obj.Pop();
 * int param_3 = obj.Top();
 * 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.