Understanding the Stack Data Structure and Removing Adjacent Duplicates from a String

In this article, we will first explore the stack data structure and how it works. Then, we'll dive into how stacks can be used to solve a common problem—removing adjacent duplicates from a string—and show how this technique can be applied in various practical use cases.

What is a Stack?

A stack is a fundamental data structure that follows the Last-In-First-Out (LIFO) principle. The last element added to the stack is the first one to be removed. This structure is akin to a stack of plates: you add a plate at the top and take the one from the top when needed.

Basic Stack Operations:

• Push: Add an element to the stack.

• Pop: Remove the top element from the stack.

• Peek/Top: View the top element of the stack without removing it.

• isEmpty: Check if the stack is empty.

In JavaScript, we can use an array to simulate a stack with the and methods.

Problem: Removing Adjacent Duplicates from a String

The task is to remove adjacent duplicate characters from a string. When two consecutive characters are identical, we should remove both and continue this process until no adjacent duplicates remain.

Example:

• Input:

• Output:

The goal is to clean up the string by removing consecutive duplicate characters, making it more compact and easier to process.

Stack-Based Approach in JavaScript

We can solve this problem efficiently using a stack. By processing each character and utilizing the LIFO nature of the stack, we can eliminate adjacent duplicates in one pass.

JavaScript Code Example

How It Works for the Input "abbaca":

1. Start with an empty stack:

2. → stack is empty, so push :

3. → stack top is , so push :

4. → stack top is , so pop :

5. → stack top is , so pop :

6. → stack is empty, so push :

7. → stack top is , so push :

8. The stack now contains . We join the characters to get the string .

Time and Space Complexity

• Time Complexity: The time complexity is O(n), where is the length of the input string. Each character is processed exactly once, and both push and pop operations take constant time.

• Space Complexity: The space complexity is O(n), where in the worst case, all characters are unique and stored in the stack.

Practical Use Cases of Removing Adjacent Duplicates from a String

Retail: Simplifying Product Identifiers and Codes

In the retail domain, product identifiers, barcodes, or stock keeping units (SKUs) may sometimes contain redundant or repetitive characters due to human error or automatic generation processes. Cleaning up these redundant characters can ensure more consistent and standardized codes.

Example: A retail inventory system might receive product codes like . By applying the adjacent duplicate removal algorithm, the code is cleaned to , ensuring uniqueness and easier lookup.

Scenario:

When managing large inventories, redundant product codes or tags can cause confusion. Cleaning product identifiers ensures accurate stock management, better categorization, and consistency in product catalogs across retail systems.

Ecommerce: Optimizing User Input in Search Queries

In ecommerce platforms, users often input search queries that may contain accidental duplicate characters (e.g., instead of ). Removing these duplicates can help normalize the search input, ensuring the system processes queries more efficiently.

Example: A search engine on an ecommerce platform might clean up the query into to ensure better product matching.

Scenario:

When users enter search terms on an ecommerce platform, removing adjacent duplicates can standardize queries before performing the search. This improves the chances of returning relevant search results and enhances user experience by eliminating common typos or mistakes.

Conclusion

The technique of removing adjacent duplicates from a string, while simple in concept, plays a significant role in many practical scenarios. Whether it’s simplifying product identifiers, optimizing search queries, cleaning customer feedback, or standardizing transaction data, this approach helps ensure data consistency, accuracy, and improved system efficiency. By leveraging the stack-based solution, businesses can enhance the user experience, improve data processing, and maintain cleaner data systems.