Java Top K Frequent Elements Solution with HashMap and Min Heap

🚀 Day 9 — LeetCode Progress (Java) 🔹 Next Greater Element I → Built a mapping using HashMap by scanning the second array and identifying the next greater value for each element, then used it to construct the result efficiently without repeated searches. ━━━━━━━━━━━━━━━━━━━━ Today’s focus was on improving array traversal logic and understanding how mapping relationships between elements can simplify future lookups. Instead of brute force comparisons for every query, pre-processing the array helped reduce unnecessary checks and made the solution cleaner. 💭 Takeaways: • Precomputing relationships using hashing can drastically reduce complexity. • Writing readable loops is just as important as getting the correct answer. • Small optimizations in logic structure make solutions easier to scale later. Consistency continues. One step closer to stronger fundamentals. 🔥 #LeetCode #DSA #Java #CodingJourney

🚀 Day 32 Out of #365DaysOfCode -LeetCode ✅ LeetCode Problem Solved: Delete the Middle Node of a Linked List I solved the Delete the Middle Node problem on LeetCode by analyzing the length of the linked list and removing the middle element in a clean and structured way. 🔹 Approach Used: First traversed the linked list to calculate its total size Identified the middle index using size / 2 Traversed again to reach the node just before the middle Adjusted pointers to delete the middle node safely 🔹 Key Concepts Practiced: Linked List traversal Pointer manipulation Edge case handling (empty list or single node) 🔹 Complexity Analysis: Time Complexity: O(n) Space Complexity: O(1) 📌 This problem helped strengthen my understanding of linked list operations and pointer-based problem solving in Java. #LeetCode #Java #LinkedList #DSA #ProblemSolving #CodingPractice #InterviewPreparation #LearningJourney Github link: https://lnkd.in/gGUy_MKZ

Day 3 of Daily DSA 🚀 Solved LeetCode 1: Two Sum Approach: Used a HashMap to store numbers with their indices. For each element, checked if the complement (target - current) already exists. Complexity: • Time: O(n) • Space: O(n) Performance: Runtime: 2 ms (Beats 99.15%) Memory: 47.34 MB Focusing on writing clean and efficient solutions before over-optimizing. Consistency > Intensity 💯 #DSA #LeetCode #Java #ProblemSolving #Consistency

Ever wondered how to transform a collection of data into a single, powerful result with just a few lines of code? Enter the reduce() method in Java’s Stream API – your secret weapon for folding streams into summaries like sums, products, or custom aggregates! Imagine you have a list of numbers [2, 3, 4, 5, 6]. Using reduce(), we can sum them up starting from an initial value of 0. List<Integer> numbers = Arrays.asList(2, 3, 4, 5, 6); Optional<Integer> sum = numbers.stream() .reduce(0, (x, y) -> x + y); System.out.println(sum.get()); // Output: 20 #Java #StreamAPI #FunctionalProgramming #JavaTips #SoftwareDevelopment

Ever wondered where your Java variables and objects actually live? 🧠 Stack Memory Used for method calls and local variables. It’s fast and cleared automatically after method execution. 📦 Heap Memory Used for objects and instance variables. It’s shared across the application and managed by the Garbage Collector. Stack → execution Heap → objects Understanding this difference really helped me write cleaner code and debug issues faster. #Java #CoreJava #JVM #JavaDeveloper #BackendDevelopment #CodingJourney

Day 46 — Hashing & Collision Handling (Java) Implemented a hash table from scratch to understand how hashing actually works under the hood—no HashMap shortcuts. What the code does (clearly and correctly): Takes hash table size and elements as user input Applies a simple hash function (key % size) Demonstrates collisions when multiple keys map to the same index Resolves collisions using chaining (LinkedList) Prints the final hash table bucket-wise Complexity (straight facts): Average insert/search: O(1) Worst case (all collisions): O(n) Space: O(n) This isn’t about reinventing HashMap. It’s about understanding why collisions happen and how they’re handled, which is exactly what interviews test. Verified with input: 10 15 20 25 30 35, size = 5 → collisions at index 0. #Java #DSA #DataStructureAndAlgorithm #Hashing #CollisionHandling #LinkedList #CoreJava #ProblemSolving #CodingPractice

Day: 23/365 Problem: Minimum Cost to Convert String I Medium Key takeaways/Learnings from this problem: 1. Floyd–Warshall is clutch when you need all-pairs minimum cost, especially with character-to-character conversions. 2. Precomputing the cheapest path once saves you from doing repeated work for every character in the string. 3.This problem is a good reminder that classic graph algos + small constraints = clean and elegant solution. #POTD #365DaysOfCode #DSA #Java #ProblemSolving #Consistency 🥷

Java☕ — HashMap vs ConcurrentHashMap clarified 🔥 Earlier, I thought: “HashMap works fine… why another map?” Then I learned about multi-threading. #Java_Code Map<String, Integer> map = new HashMap<>(); ❌ Not thread-safe #Java_Code Map<String, Integer> map = new ConcurrentHashMap<>(); ✅ Thread-safe The difference is not syntax — it’s behavior. 📌HashMap 📝Faster 📝Unsafe in multi-threading 📌ConcurrentHashMap 📝Thread-safe 📝Uses bucket-level locking 📝No ConcurrentModificationException Big realization for me: Thread safety is not optional in real applications. If multiple threads touch shared data — design matters. #Java #ConcurrentHashMap #Multithreading #BackendDevelopment #LearningJava