How Streams Simplify Java Code

With Streams, we can use functional operations such as: + filter : select based on condition + map : Transform each element + reduce: combine element into single result eg. finding max. + forEach: perform action on each element #JAVA #SpringBoot

💻 Enhancing Problem-Solving Skills with LeetCode (Java) Recently solved a couple of fundamental algorithmic problems that helped strengthen my understanding of two-pointer techniques, array manipulation, and writing efficient solutions. 🔹 Container With Most Water Implemented an optimised two-pointer approach to find the maximum water area between vertical lines. Instead of checking every pair (which would be O(n²)), the solution smartly moves the pointer at the shorter height inward to explore potentially larger areas. Approach: Two Pointers, Greedy Time Complexity: O(n) Space Complexity: O(1) 🔹 3Sum Solved the classic triplet-finding challenge by sorting the array and using two pointers to efficiently search for combinations that sum to zero. Also handled duplicates to avoid repeated triplets. Approach: Sorting + Two Pointers Time Complexity: O(n²) Space Complexity: O(1) (excluding output list) These problems helped sharpen my understanding of pointer movement, edge-case management, and designing clean, efficient solutions. #LeetCode #Java #Algorithms #DSA #Coding #ProblemSolving #SoftwareEngineering #LearningJourney

🚀 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

I solved LeetCode 42: Trapping Rain Water in Java, and it turned out to be one of those problems that looks straightforward at first but hides deep lessons in algorithm design and optimization. The problem asks you to calculate how much water can be trapped between bars of different heights after it rains. It sounds simple, but when you try to implement it, you realize how easily brute-force approaches break down. My initial solution used nested loops to check how much water could be trapped at every index. It worked but ran with O(n²) time complexity, which quickly became inefficient for large inputs. That is when I shifted toward precomputation. The key idea is to figure out how much water each bar can hold by knowing the tallest bar to its left and right. Once that information is known, the amount of trapped water at any position is simply the minimum of those two maximum heights minus the height of the bar itself. To make this efficient, I created two arrays: maxLeft[i] stores the tallest bar to the left of index i. maxRight[i] stores the tallest bar to the right of index i. This approach brought the time complexity down to O(n) while keeping the logic simple and elegant. This problem taught me that performance improvements often come from rethinking the way you process information. Precomputation, caching, and avoiding repetitive operations are not just algorithmic tricks. They are the same concepts that drive efficiency in backend engineering, database optimization, and system design. In many ways, this problem reflects real-world engineering. It is not about how fast you can write code, but how effectively you can anticipate what the system will need before it needs it. That mindset is what separates good developers from great ones. What is one problem that helped you truly understand the balance between simplicity and optimization? #Java #Programming #SoftwareDevelopment #ProblemSolving #Algorithms #Coding #DSA #LeetCode #Engineering #SystemDesign

💻 Strengthening Problem-Solving Skills with LeetCode (Java) Recently explored a few interesting problems that focused on string manipulation, array traversal, and text formatting — each reinforcing clarity and precision in algorithmic thinking: 🔹 Text Justification Implemented a custom text alignment algorithm that evenly distributes spaces between words to fit a given line width. Approach: Greedy + StringBuilder Time Complexity: O(n) 🔹 Two Sum II – Input Array Is Sorted Solved using a two-pointer technique to efficiently find pairs that add up to a target value in a sorted array. Approach: Two Pointers Time Complexity: O(n) 🔹 Is Subsequence Checked whether one string is a subsequence of another using linear traversal and pointer comparison. Approach: Two Pointers Time Complexity: O(n) 🔹 Valid Palindrome Validated alphanumeric palindromes by filtering characters and comparing from both ends. Approach: String cleaning + two-pointer comparison Time Complexity: O(n) These problems helped sharpen my logic-building process and improved my confidence in designing efficient, readable solutions. #LeetCode #Java #ProblemSolving #DSA #Algorithms #Coding #SoftwareEngineering

🚀 Day 145 / 180 of #180DaysOfCode ✅ Revision Highlight: Today, I revisited LeetCode 35 — “Search Insert Position.” 🧩 Problem Summary: Given a sorted array of distinct integers, the task is to return the index of the target if it exists. Otherwise, return the position where it should be inserted to maintain order. The required time complexity is O(log n), making binary search the ideal approach. 💡 Core Concept: This is a classic binary search problem where the goal is not only to locate the target but also determine its correct insert position. It strengthens understanding of: Mid-point evaluation Range adjustments Handling edge cases (target smaller than all elements, larger than all, or between two values) 💻 Tech Stack: Java ⏱ Runtime: 0 ms (Beats 100%) 💾 Memory: 44.58 MB 🧠 Learnings: Revisited binary search fundamentals Reinforced the importance of boundary handling Great quick-revision problem for sharpening algorithmic precision 📈 Progress: This revision improved my command over binary search and edge-case reasoning — vital for tackling more complex algorithmic challenges. #LeetCode #Java #BinarySearch #DSA #ProblemSolving #CodingJourney #SoftwareEngineering #Optimization #180DaysOfCode #LogicBuilding #Revision

🚀 Day 35 of 100 Days of LeetCode 📘 Problem: Binary Tree Inorder Traversal 💻 Language: Java ✅ Status: Accepted — Runtime ⚡ 1 ms Today’s focus was on mastering tree traversal, one of the most essential techniques in data structures 🌳 The goal was simple — visit nodes in order (Left → Root → Right), but the recursive logic behind it reinforced clarity and structured thinking. ✨ Key Learnings: Recursive approach simplifies traversal logic 🧠 Base cases are the backbone of recursion Understanding tree patterns helps in mastering complex problems like BSTs and DFS 💬 “A binary tree teaches one thing — structure leads to clarity.” #Day35 #100DaysOfCode #LeetCode #Java #BinaryTree #Recursion #ProblemSolving #DSA #CodingJourney #SoftwareDevelopment #KeepLearning

Generics in Object oriented programming offers several benefits. To name a few: Type safety - Detects errors at compile time instead of runtime No Casting - no need for manual type casting Code reuse: One generic class/method works for any data type Readability: makes data relationships clearer Performance: no runtime overhead

LeetCode Question #199 — Binary Tree Right Side View Thrilled to share another Accepted Solution (100% Runtime 🚀) on LeetCode! This problem focuses on Binary Trees — specifically, how to capture the view of a tree from its right side 🌳➡️. I implemented a Modified Pre-Order Traversal (Root → Right → Left) in Java, ensuring that the first node at each level (from the right) gets recorded. 💡 Key Idea: Use recursion with a level tracker — when the current level equals the list size, it means we’re seeing the rightmost node for the first time. Here’s the performance snapshot: ⚙️ Runtime: 0 ms (Beats 100% of Java submissions) 💾 Memory: 42.4 MB Every problem like this sharpens my understanding of tree traversal patterns and depth-first search optimization. #LeetCode #Java #DataStructures #BinaryTree #ProblemSolving #CodingJourney #DSA #100PercentRuntime