Stack and Queue are two fundamental linear data structures in C++. A stack follows the Last-In-First-Out principle, like a stack of plates where you add and remove from the top. A queue follows the First-In-First-Out principle, like a line where people enter from one end and exit from the other.
栈是计算机科学中最基本的数据结构之一。它遵循后进先出的原则，就像叠盘子一样，最后放上去的盘子最先被拿下来。在C++中，我们可以使用标准库提供的stack容器来实现栈的功能。栈在程序中有很多应用，比如函数调用管理、表达式求值等。
栈的操作非常简单高效。push操作将元素添加到栈顶，pop操作移除栈顶元素，top操作访问栈顶元素但不移除它。我们还可以用empty检查栈是否为空，用size获取元素个数。所有这些操作的时间复杂度都是O(1)，这使得栈非常高效。
队列是另一种重要的线性数据结构，它遵循先进先出的原则。就像银行排队一样，最先到达的人最先被服务。在队列中，元素从后端插入，从前端删除。C++标准库提供了queue容器来实现队列功能。
队列的操作也很直观。push操作将元素添加到队列后端，pop操作从前端移除元素。front和back分别访问前端和后端元素。empty和size用于检查队列状态。队列的这些操作使得它非常适合需要按顺序处理的场景。
栈和队列在实际编程中有广泛应用。栈常用于函数调用管理、表达式求值、括号匹配等场景。队列则适用于任务调度、广度优先搜索、打印队列等需要按顺序处理的情况。C++中使用这些数据结构非常简单，只需包含相应头文件即可。掌握栈和队列是学好数据结构的重要基础。
Let's compare stack and queue side by side. Stack follows Last-In-First-Out principle with single access point at the top, making it perfect for function calls and undo operations. Queue follows First-In-First-Out principle with two access points, ideal for task scheduling and breadth-first search algorithms. Understanding these differences helps choose the right data structure for specific problems.
Let's see how stack and queue operations work in practice. For the stack, we first push 10, then push 20 on top. When we pop, we remove 20 first because it's the last one added. For the queue, we push 10 first, then push 20 at the back. When we pop, we remove 10 first because it was the first one added. This clearly shows the LIFO versus FIFO behavior.
Stack and queue have numerous real-world applications. Stacks are essential for function call management, expression evaluation, undo operations, and browser navigation. Queues are crucial for CPU scheduling, printer queues, breadth-first search algorithms, and network packet handling. The C++ standard library makes implementing these data structures straightforward with simple push, pop, and access operations. Understanding these fundamental data structures is essential for efficient programming and algorithm design.