"Java Code for Jump Search Algorithm"

🚀 Day 75 / 100 Days of Code 🌳 Problem: Check if a Binary Tree is Univalue A tree is univalue if all nodes contain the same value. ✅ My Approach Recursively check both left and right subtree If a node doesn't match the root value → return false Base case: null nodes naturally return true Result: 0 ms runtime, beating 100% submissions 🚀 💡 Takeaway Recursion really shines when validating conditions across tree structures. 🧩 Tech Stack: Java </> Algorithm | Recursion | Trees #100DaysOfCode #LeetCode #Java #DataStructures #CodingJourney #ProblemSolving #LinkedList

👉 Why should a Functional Interface contain only one abstract method? 📌 Ex - interface Interf { public void m1(int i) } Interf I = i -> System.out.println(i * i); I.m1(10); I.m2(20); 👉 A functional interface must have exactly one abstract method because a lambda expression is meant to represent a single functionality or behavior. When you write a lambda expression, it needs to map to one specific abstract method in the interface. If the interface had multiple abstract methods, the compiler wouldn’t know which method the lambda is referring to — leading to ambiguity. That’s why a functional interface can have only one abstract method, and it is the only type of interface that can be used with lambda expressions in Java. 👉 Why can’t a Functional Interface have two abstract methods? 📌 Ex - interface Interf { public void m1(int i); public void m2(int i); } Interf I = i -> System.out.println(i * i); 💡Compile Time Error - Interf is not a Functional Interface - Multiple non-overriding abstract method in interface Interf 👉 If an interface contains two abstract methods, a lambda expression won’t know which method to map to, because there are multiple choices. That’s why a Functional Interface must contain exactly one abstract method this ensures that the lambda expression can clearly represent that single method. #OOP #Abstraction #CleanCode #LearningJava #Programming #Developers #String #CodingTips #CoreJava #Learning #JavaDeveloper #CodingPractice #Interface #JavaInterviewPreparation #LearnToCode #JavaInterviewPreparation #JavaFresher #Java #Java8 #FunctionalInterface #LambdaExpressions

🔥 #100DaysOfDSA — Day 29/100 Topic: Binary Search in Java ⚡ 💡 What I Did Today: Today, I implemented one of the most powerful and efficient searching algorithms — Binary Search 🔍 Instead of checking every element one by one like Linear Search, Binary Search smartly divides the array in half each time — reducing the time complexity drastically! 🧠 Logic Used: Works only on sorted arrays ✅ Find the mid index: mid = (start + end) / 2 If key == numbers[mid] → found the element 🎯 If key > numbers[mid] → search in right half Else → search in left half Continue until the element is found or the range is empty 📊 Example: Input → {4, 5, 6, 10, 18, 61, 122} Key → 61 ✅ Output → index for key is: 5 ⚙️ Time Complexity: O(log n) — super fast compared to O(n) of Linear Search 💨 ✨ Takeaway: Binary Search is a fundamental algorithm that teaches the power of divide and conquer. Mastering this concept builds the foundation for more advanced topics like search trees, sorting, and algorithm optimization 🚀 #100DaysOfCode #Day29 #Java #DSA #BinarySearch #ProblemSolving #CodingJourney #LearnInPublic #DeveloperLife #CodeNewbie

💻 DSA – Day 13 : Strings & StringBuilder Today I explored Strings in Java, a core concept used in almost every application — from input handling to algorithms, data processing, and problem-solving. Strings look simple, but there's a lot happening under the hood. 🌈 What I learned today 🔹 ❓ What are Strings? A sequence of characters stored as an object in Java. Strings are immutable, which means once created, they cannot be changed. 🔹 ⌨️ Input & Output Took string inputs using Scanner and printed them with basic operations. 🔹 📏 String Length Used .length() to find the number of characters. 🔹 ➕ String Concatenation Joined strings using +, concat(), and StringBuilder. 🔹 🔡 charAt() Method Accessed characters using index values. 🔹 🔄 Check if a String is Palindrome Compared characters from both ends to verify if the string reads the same backwards. 🔹 🧭 Shortest Path Problem Calculated the shortest path (N/E/W/S directions) using coordinate logic. 🔹 ⚖️ String compare() Compared strings lexicographically using .compareTo(). 🔹 ✂️ substring() Function Extracted specific parts of the string. 🔹 🏆 Print Largest String Compared strings alphabetically to find the largest one. 🔹 🔒 Why Strings Are Immutable? Because they are stored in the String Pool and designed for security, caching, and thread-safety. 🧵 StringBuilder – Faster & Mutable 🔹 Unlike Strings, StringBuilder is mutable 🔹 Perfect for modifying strings repeatedly (loops, concatenation, dynamic strings) ✨ Extra Concepts Learned 🔹 🔤 Convert Letters to Uppercase Converted the first letter of each word to uppercase (title case-style logic). 🔹 📦 String Compression Implemented character frequency compression like: aaabbccc → a3b2c3 🚀 Loving the learning curve Strings are everywhere — mastering them builds a strong foundation for upcoming DSA topics. #DSA #Strings #Java #StringBuilder #CodingJourney #100DaysOfCode #LearningInPublic #ProblemSolving #Day13

🚀 Mastering Graphs in DSA — My Java Journey 💻 As a Java coder, one of the toughest yet most rewarding parts of DSA for me was Graphs. They’re not just data structures — they’re networks of logic and relationships. At first, I struggled with directions, cycles, and visualizing connections. But once I shifted focus from memorizing algorithms to understanding patterns, everything clicked 🔥 🧠 My Step-by-Step Graph Learning Path 1️⃣ Foundation Learn what vertices (V) & edges (E) represent Understand adjacency list vs matrix Implement BFS & DFS void bfs(int V, ArrayList<ArrayList<Integer>> adj) { boolean[] vis = new boolean[V]; Queue<Integer> q = new LinkedList<>(); q.add(0); vis[0] = true; while (!q.isEmpty()) { int node = q.poll(); System.out.print(node + " "); for (int it : adj.get(node)) if (!vis[it]) { vis[it] = true; q.add(it); } } } 2️⃣ Key Algorithms to Master Cycle Detection (BFS / DFS) Topological Sort Shortest Path (BFS, Dijkstra, Bellman-Ford) Minimum Spanning Tree (Kruskal, Prim) 3️⃣ Visualization Tools Use VisuAlgo.net or GraphOnline — seeing BFS, DFS, and Dijkstra in action builds real intuition ⚙️ 💡 My Learnings ✅ Don’t rush — draw the graph before coding ✅ Focus on patterns (Traversal, Shortest Path, Cycle, MST) ✅ Build reusable Java templates ✅ 1 solid problem per concept > 10 random ones Graphs aren’t hard — they’re dense. Once the patterns click, you start seeing logic differently. Keep going — the clarity is worth it 💪 #Java #DSA #GraphAlgorithms #BFS #DFS #Dijkstra #CodingJourney #LearningInPublic #ProblemSolving #FullStackDeveloper #WomenInTech

Hey, Everyone! I built a Java program using Spring Boot that analyzes multiple-choice questions without any external help. A question analyzer that examines patterns, evaluates option structures, and attempts to identify the most likely answer through algorithmic reasoning alone. No Google. No databases. No knowledge base. Just pure pattern analysis. It's about as accurate as flipping a coin 😅 Confidence scores hover around 40-50%, basically saying "I have no idea." But I learned: • Why AI models need training data • Sometimes the simplest problems are the hardest I'm thinking about transforming this into an AI-powered system. The gap between rule-based systems and true AI understanding is fascinating. What's your experience bridging traditional programming with AI capabilities? Would love to hear your thoughts! #Java #SpringBoot #AIIntegration #SoftwareDevelopment #LearningInPublic #SpringAI #TechJourney

Day 37/100 ✅ Binary Tree Postorder Traversal Today I solved the Binary Tree Postorder Traversal problem using a simple recursive approach in Java. In postorder traversal, we visit nodes in the order Left → Right → Root. This helps in many real-world tree-based algorithms like expression tree evaluations or file system traversal. 💡 Approach: I used recursion — first visiting the left child, then the right child, and finally adding the node’s value to the result list. It’s clean, elegant, and easy to understand! ✅ Key Learning: Understanding traversal patterns (Preorder, Inorder, Postorder) is essential for mastering tree-based problems. Each pattern teaches how to process data at different stages of recursion. #LeetCode #Java #CodingJourney #DSA #BinaryTree #Recursion #PostorderTraversal #100DaysOfCode

🔍 Problem Statement: Given an integer array sorted in non-decreasing order, return an array of the squares of each number sorted in non-decreasing order. 🧠 Key Idea: Since the array is already sorted, negative numbers can affect the order after squaring. To handle this efficiently, I used the two-pointer approach — one starting from the beginning and one from the end — comparing squares and filling the result array from the back. ⚙️ Time Complexity: O(n) 🗂️ Space Complexity: O(n) 💬 Code (Java): import java.util.Arrays; class Solution { public int[] sortedSquares(int[] nums) { int n = nums.length; int[] res = new int[n]; int left = 0, right = n - 1, index = n - 1; while (left <= right) { int lsq = nums[left] * nums[left]; int rsq = nums[right] * nums[right]; if (lsq > rsq) { res[index--] = lsq; left++; } else { res[index--] = rsq; right--; } } return res; } } ✨ Learning: Improved my understanding of two-pointer technique. Reinforced the importance of optimizing sorting-related problems. #Java #DSA #Coding #ProblemSolving #StriversSheet #LearningEveryday

🚀 How a Single Annotation Made Our Java Backend 50x Faster Sometimes, performance issues hide in plain sight. In our case, it was a seemingly harmless @Transactional annotation. Here’s what happened 👇 We had: @Transactional @Query("SELECT u FROM User u WHERE u.id = :id") Optional<User> findById(@Param("id") Long id); This annotation was silently creating proxies, starting unnecessary transactions, and performing dirty checks — all for a simple read query. The fix? @Transactional(readOnly = true) public interface UserRepository extends JpaRepository<User, Long> {} 💡 Instant impact: 10ms → 0.2ms per query (50x faster!) 📊 Key takeaways: Use @Transactional(readOnly = true) for queries — avoids unnecessary flush checks Don’t annotate repository methods with @Transactional unless needed Always profile before guessing — tools like JProfiler, YourKit, or async-profiler reveal hidden bottlenecks Micro-optimizations scale — saving milliseconds per request can mean hours of CPU time saved daily Sometimes, small changes lead to massive performance wins. ⚡ #Java #SpringBoot #Performance #BackendDevelopment #CodeOptimization #TechLearning #SpringDataJPA

Day 47/100 – #100DaysOfCode 🚀 | #Java #Arrays #InPlaceAlgorithms ✅ Problem Solved: First Missing Positive (LeetCode 41) 🧩 Problem Summary: Given an unsorted array, find the smallest missing positive integer. The solution must run in O(n) time and constant extra space. 💡 Approach Used: This is not a direct sorting or hashing problem — the key is index positioning. Logic: The answer must lie within 1 to n+1. Place each positive number x at index x-1 if possible. Then scan to find the first index where nums[i] != i+1. Steps: Iterate and swap values into their correct position. Ignore values ≤0 or >n. Final scan to find the first missing positive. ⚙️ Time Complexity: O(n) 📦 Space Complexity: O(1) ✨ Takeaway: This problem teaches how index-based hashing can eliminate the need for extra space — very common in interview-style optimization problems. #Java #LeetCode #Algorithm #Array #ProblemSolving #100DaysOfCode #CodingChallenge