How Much DSA is Required For Front End Developer Interview?

Front-end developer creates the user-facing component such as the user interface(UI) and user experience(UX), that determines how the user interacts with the digital product. Front-end engineer works with generally HTML, CSS, JavaScript, and frameworks like React or Angular. But having a solid foundation in DSA, we can optimize the performance of the website and deliver a great user experience.

In this article, we will explore the importance of DSA, why is it necessary, and How much DSA is required for the front end.

Why DSA is Important for Front-End Developers?

The user interface (UI), user experience (UX), and performance are the most important components of a website. The time it takes to search for an item and the speed at which items are rendered on the website should both be taken into account when evaluating performance. By using the right Data Structures and Algorithms (DSA), the time complexity of an operation like searching, filtering, and traversing data can be decreased, and the performance can be increased significantly. The role of a front-end developer is to focus on how quickly users can search for items and how fast those items are displayed on the website and for that reason Data Structures and Algorithms (DSA), play a significant role in optimizing website performance.

Let’s see a react-based ToDo App project example to understand the importance of performance optimization.

Example – Search Functionality

Search functionality is an important feature used in almost every website. It allows users to find specific items quickly and efficiently and it results in a great user experience. By using the data structure hash table and hash function, we can retrieve search results almost instantly in the shortest possible time. Trie data structure is particularly used for textual data such as words, it enables prefix-based searching and resulting responsive search experience.

jsx

// Task-based Search Functionality
import React, { useState } from 'react';

const SearchBar = ({ searchValue, setSearchValue }) => {
  const handleChange = (e) => {
    setSearchValue(e.target.value);
  };

  return (
    <input
      type="text"
      placeholder="Search..."
      value={searchValue}
      onChange={handleChange}
    />
  );
};

export default SearchBar;

Optimized Rendering of Dynamic Content

Rendering of Dynamic content refers to the website or application that can update dynamically without requiring a full page reload. Dynamic content refers to information that is not static but is generated based on database queries, or real-time updates. We can optimize this operation by using a data structure such as a linked list or balanced binary search trees that can help to manage and render the dynamic content.

jsx

import React from 'react';

const TodoList = React.memo(({ todos }) => {
  return (
    <ul>
      {todos.map((todo) => (
        <li key={todo.id}>{todo.text}</li>
      ))}
    </ul>
  );
});

export default TodoList;

Caching and Memoization

Front-end applications often make API requests to fetch data from servers and we are using Caching and memoization techniques to optimize the performance. By using the data structure like hash maps or LRUs we can store and reuse previously fetched data to avoid redundant requests and enhance overall applications performance.

jsx

import React, { useState, useMemo } from 'react';

const App = () => {
  const [todos, setTodos] = useState([
    { id: 1, text: 'Buy groceries' },
    { id: 2, text: 'Walk the dog' },
    { id: 3, text: 'Do laundry' },
  ]);

  const [searchValue, setSearchValue] = useState('');
  const [newTask, setNewTask] = useState('');

  const filteredTodos = useMemo(() => {
    if (searchValue.trim() === '') {
      return todos;
    }

    return todos.filter((todo) =>
      todo.text.toLowerCase().includes(searchValue.toLowerCase())
    );
  }, [searchValue, todos]);

  const handleAddTask = () => {
    if (newTask.trim() !== '') {
      const newTodo = {
        id: Date.now(),
        text: newTask.trim(),
      };

      setTodos((prevTodos) => [...prevTodos, newTodo]);
      setNewTask('');
    }
  };

  return (
    <div>
      <SearchBar searchValue={searchValue} setSearchValue={setSearchValue} />
      <TodoList todos={filteredTodos} />
      <div>
        <input
          type="text"
          placeholder="Add task..."
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
        />
        <button onClick={handleAddTask}>Add Task</button>
      </div>
    </div>
  );
};

export default App;

Collaboration with Backend Developers

Front-end and Back-end Systems interact on the basis of API Design and Data Exchange. Knowledge of DSA helps front-end engineers to understand the exchange of data and optimized the performance. For example, API response consists of a list of items, so front-end engineers can use their DSA knowledge to search and filter the data in less time complexity to optimize the performance.

The updated code for this ToDo App is below here:

jsx

import React, { useState, useMemo } from 'react';

// Task-based Search Functionality
const SearchBar = ({ searchValue, setSearchValue }) => {
  const handleChange = (e) => {
    setSearchValue(e.target.value);
  };

  return (
    <input
      type="text"
      placeholder="Search..."
      value={searchValue}
      onChange={handleChange}
    />
  );
};

// Optimized Rendering of Dynamic Content
const TodoList = React.memo(({ todos }) => {
  return (
    <ul>
      {todos.map((todo) => (
        <li key={todo.id}>{todo.text}</li>
      ))}
    </ul>
  );
});

// Caching and Memoization
const App = () => {
  const [todos, setTodos] = useState([
    { id: 1, text: 'Buy groceries' },
    { id: 2, text: 'Walk the dog' },
    { id: 3, text: 'Do laundry' },
  ]);

  // State for search value
  const [searchValue, setSearchValue] = useState('');
  const [newTask, setNewTask] = useState('');

  // Filtering todos based on search value using Memoization
  const filteredTodos = useMemo(
    () =>
      todos.filter((todo) =>
        todo.text.toLowerCase().includes(searchValue.toLowerCase())
      ),
    [todos, searchValue]
  );

  // Adding a new task to the To-Do list
  const handleAddTask = () => {
    if (newTask.trim() !== '') {
      const newTodo = {
        id: Date.now(),
        text: newTask.trim(),
      };

      setTodos([...todos, newTodo]);
      setNewTask('');
    }
  };

  return (
    <div>
      <SearchBar searchValue={searchValue} setSearchValue={setSearchValue} />
      <TodoList todos={filteredTodos} />
      <div>
        <input
          type="text"
          placeholder="Add task..."
          value={newTask}
          onChange={(e) => setNewTask(e.target.value)}
        />
        <button onClick={handleAddTask}>Add Task</button>
      </div>
    </div>
  );
};

export default App;

Output

Essential DSA Topics For Front-End Developers

The importance of Data Structures and Algorithms (DSA) can vary based on different companies. For MAANG companies we will have to be well-versed in the DSA concept. On the other hand, for the second-tier company, the level of DSA knowledge is slightly lower and for the startup company, the knowledge of DSA may be at a more basic level. Here’s a list of important DSA topics.

1. Array

• Check if an array is sorted and rotated
• Merge two sorted arrays
• Program for array left rotation by d positions
• Remove duplicates from Sorted Array
• Leaders in an array
• Stock Buy Sell to Maximize Profit
• Trapping Rain Water
• Maximum circular subarray sum
• Median of two Sorted Arrays of Different Sizes
• Length of the longest alternating even odd subarray

2. String

• Check if given strings are rotations of each other or not
• Length Of Last Word in a String
• Check if two given Strings are Isomorphic to each other
• Find maximum occurring character in a string
• Program to reverse a string
• Reverse words in a given string
• Case-specific Sorting of Strings
• Rabin-Karp Algorithm for Pattern Searching
• Find the starting indices of the substrings in string (S) which is made by concatenating all words from a list(L)
• Longest Valid Parentheses

3. Recursion

• Program to Find and Print Nth Fibonacci Numbers
• Josephus Problem
• Lucky Numbers
• Write program to calculate pow(x, n)
• Program for Tower of Hanoi Algorithm

4. Searching

• Search an element in an unsorted array using minimum number of comparisons
• Majority Element
• Find a peak element which is not smaller than its neighbours
• Find the two repeating elements in a given array
• Maximum water that can be stored between two buildings
• Search an element in a sorted and rotated Array
• Search in a sorted 2D matrix
• Maximum sum not exceeding K possible for any rectangle of a Matrix
• k-th smallest absolute difference of two elements in an array
• Split the given array into K sub-arrays such that maximum sum of all sub arrays is minimum

5. Sorting

• Sort a binary array using one traversal and no extra space
• Union and Intersection of two sorted arrays
• Maximum product of a triplet (subsequence of size 3) in array
• Find minimum difference between any two elements (pair) in given array
• Sort an array of 0s, 1s and 2s | Dutch National Flag problem
• Merge 3 Sorted Arrays
• K’th Smallest/Largest Element in Unsorted Array
• Find a triplet that sum to a given value
• Maximum adjacent difference in an array in its sorted form
• Sort elements by frequency

6. Matrix

• Print matrix in snake pattern
• Program to find transpose of a matrix
• Rotate a matrix by 90 degree in clockwise direction without using any extra space
• Print a given matrix in spiral form
• Search in a row wise and column wise sorted matrix
• Check if given Sudoku board configuration is valid or not
• A Boolean Matrix Question

7. Hashing

• Implementing own Hash Table with Open Addressing Linear Probing
• Separate Chaining Collision Handling Technique in Hashing
• Quadratic Probing in Hashing
• Happy Number
• Find winner of an election where votes are represented as candidate names
• Distribute N candies among K people
• Check whether the string can be printed using same row of qwerty keypad
• Longest Consecutive Subsequence

8. Linked List

• Reverse a Linked List
• Remove duplicates from a sorted linked list
• Sort a linked list of 0s, 1s and 2s
• Remove duplicates from an unsorted linked list
• Add two numbers represented by Linked List
• Rotate a Linked List
• LRU Cache Implementation
• Merge K sorted linked lists
• Partitioning a linked list around a given value and keeping the original order
• Pairwise Swap Nodes of a given Linked List

9. Stack

• Delete middle element of a stack
• Reduce string by removing outermost parentheses from each primitive substring
• Implement two Stacks in an Array
• The Stock Span Problem
• Design a stack that supports getMin() in O(1) time and O(1) extra space
• Next Greater Element (NGE) for every element in given Array
• The Celebrity Problem
• Largest Rectangular Area in a Histogram using Stack

10. Queue

• Array implementation of queue
• Queue using Stacks
• Find the arrangement of queue at given time
• Queue – Linked List Implementation
• An Interesting Method to Generate Binary Numbers from 1 to n
• Implement Stack using Queues
• Reversing the first K elements of a Queue

11. Tree

• Postorder Traversal of Binary Tree
• Find the Maximum Depth or Height of given Binary Tree
• Level Order Traversal (Breadth First Search or BFS) of Binary Tree
• Print level order traversal line by line
• Print Left View of a Binary Tree
• Check for Children Sum Property in a Binary Tree
• Convert a Binary Tree into its Mirror Tree
• Vertical width of Binary tree
• Print all nodes at distance k from a given node
• Construct a Binary Tree from Postorder and Inorder
• Flatten a binary tree into linked list
• Maximum Path Sum in a Binary Tree

12. Binary Search Tree

• Searching in Binary Search Tree
• A program to check if a Binary Tree is BST or not
• Print Common Nodes in Two Binary Search Trees
• Construct BST from its given level order traversal
• Lowest Common Ancestor in a Binary Search Tree
• Deletion in Binary Search Tree (BST)
• Find the closest element in Binary Search Tree
• Merge two BSTs with limited extra space
• Two nodes of a BST are swapped, correct the BST

13. Heap

• K-th Greatest Element in a Max-Heap
• Sort a nearly sorted (or K sorted) array
• Find K most occurring elements in the given Array
• K’th largest element in a stream

14. Graph

• Distance of nearest cell having 1 in a binary matrix
• Minimum time required to rot all oranges
• Find the number of islands using DFS
• Level of Each node in a Tree from source node (using BFS)
• Find the number of islands using DFS
• Detect cycle in an undirected graph
• Detect Cycle in a Directed Graph
• Check if removing a given edge disconnects a graph
• Clone an Undirected Graph
• Clone a Directed Acyclic Graph

15. Backtracking

• Combinational Sum
• Iterative Letter Combinations of a Phone Number
• N Queen Problem
• N Queen Problem using Branch And Bound
• Rat in a Maze with multiple steps or jump allowed
• Find Maximum number possible by doing at-most K swaps

16. Dynamic Programming

• Count all possible paths from top left to bottom right of a mXn matrix
• Sum of all substrings of a string representing a number
• Count ways to reach the n’th stair
• Minimum number of jumps to reach end
• Longest Increasing Subsequence (LIS)
• Maximize the number of segments of length p, q and r
• 0/1 Knapsack Problem
• Egg Dropping Puzzle

Conclusion

Based on the Knowledge of Data Structures and Algorithms (DSA), the front-end developer can improve the website performance and enhance the user experience. They can improve search functionality, optimize the rendering of dynamic content, implement caching and memoization techniques, and collaborate effectively with backend developers for efficient data exchange. The above are the most known topics for a front-end developer interview. Having knowledge of Data Structures and Algorithms (DSA) can optimize website applications and meet the expectations of the companies.