DSA Mastery

Stacks and Queues: Implementing Undo/Redo & Message Buffers

2 Views Updated 5/6/2026

Stacks & Queues

Stacks and Queues are "Restricted" data structures. They don't allow you to access elements in the middle. They are designed for specific data flow patterns.

1. Stack (LIFO - Last In First Out)

Think of a stack of plates. You can only add/remove from the top.

  • Push: Add an item.
  • Pop: Remove the top item.
  • Peek: Look at the top item without removing it.
Usecase: Undo/Redo logic, Browser History, Recursive function calls.

2. Queue (FIFO - First In First Out)

Think of a line at a grocery store. The first person to join is the first person served.

  • Enqueue: Add to the back.
  • Dequeue: Remove from the front.
Usecase: Background task processing, Print Spoolers, Breadth-First Search (BFS).

4. Interview Mastery

Q: "How can you implement a Queue using only two Stacks?"

Architect Answer: "You use one stack for 入队 (Enqueue) and one for 出队 (Dequeue). When you want to dequeue, and the 'Dequeue Stack' is empty, you pop everything from the 'Enqueue Stack' and push it into the 'Dequeue Stack'. This reverses the order, effectively turning LIFO into FIFO. This is a common logic test that evaluates your understanding of data flow manipulation."

DSA Mastery
1. Algorithmic Foundations
Big O Notation: Analyzing Time and Space Complexity Memory Management: Stack vs Heap in C# Recursion: The foundation of modern algorithms
2. Linear Data Structures
Arrays Deep Dive: Static vs Dynamic (List<T> Internals) Linked Lists: Singly, Doubly, and Circular Stacks and Queues: Implementing Undo/Redo & Message Buffers Hash Tables: Handling Collisions like a Pro
3. Non-Linear Data Structures
Binary Trees & BST: Searching at Log(N) speed Balanced Trees: AVL and Red-Black Trees Internals Heaps: Implementing a Priority Queue Tries (Prefix Trees): Optimizing Auto-complete features Graphs (Part 1): Representation (Matrix vs List) Graphs (Part 2): BFS vs DFS Traversal
4. Searching & Sorting
Binary Search: The power of Divide & Conquer Elementary Sorts: Bubble, Selection, and Insertion Merge Sort: Stable sorting for massive datasets Quick Sort: In-place sorting and Pivot selection Heap Sort: Leveraging the Priority Queue
5. Algorithmic Patterns
Sliding Window Pattern: Optimizing array performance Two Pointers Pattern: Reversing and Finding cycles Fast & Slow Pointers (Hare & Tortoise) Backtracking: Solving Sudoku and N-Queens
6. Dynamic Programming (DP)
Memoization vs Tabulation: Top-down vs Bottom-up The Knapsack Problem: 0/1 DP optimization Longest Common Subsequence (LCS) Matrix Chain Multiplication
7. Advanced Graphs & Interview
Dijkstra's Algorithm: Shortest path in weighted graphs Prim's and Kruskal's: Minimum Spanning Trees Disjoint Set Union (DSU) / Union-Find DSA Interview: FAANG Style Coding Challenges