Spring Boot Validation: @Size Annotation for Length Control

👉Arrays vs ArrayLists. Looks similar. But difference matters! Many people start with Arrays. Fixed size. Simple syntax. Everything feels under control. Then comes ArrayList. Dynamic size. Easy to use. No need to worry about capacity. Sounds like ArrayList is better, right? Not always. That’s where the realisation comes in. 👉Arrays are faster. They use fixed memory. Better when size is known. 👉ArrayLists are flexible. They resize automatically. But come with slight overhead. Same data. Different behavior. And that difference shows up in performance. The real takeaway is simple. ✨Use Arrays when size is fixed and performance matters. ✨Use ArrayList when flexibility is needed. Don’t just learn syntax — understand use cases. 👉Because in the end, choosing the right structure > writing more code. 👉 When do you prefer Arrays over ArrayList? #DSA #Java #CodingJourney #SoftwareDevelopment #CareerGrowth

🚀 Day 15 – ArrayList vs LinkedList (It’s Not Just About Syntax) Today I explored the real difference between "ArrayList" and "LinkedList". List<Integer> list1 = new ArrayList<>(); List<Integer> list2 = new LinkedList<>(); At first glance, both implement "List"… but internally they are very different. --- 💡 ArrayList ✔ Backed by a dynamic array ✔ Fast for random access (get by index) ✔ Slower for insert/delete in middle (shifting required) --- 💡 LinkedList ✔ Uses a doubly linked list ✔ Faster for insert/delete (no shifting) ✔ Slower for access (traversal needed) --- ⚠️ Real insight: In most real-world scenarios, "ArrayList" is preferred due to better cache locality and overall performance. 👉 "LinkedList" is useful only when: - Frequent insertions/deletions in the middle - Less need for random access --- 💡 Takeaway: Don’t choose based on interface—choose based on use case and internal behavior #Java #BackendDevelopment #Collections #ArrayList #LearningInPublic

🔥 𝗗𝗮𝘆 𝟵𝟯/𝟭𝟬𝟬 — 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟭𝟲𝟬𝟴. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗿𝗿𝗮𝘆 𝗪𝗶𝘁𝗵 𝗫 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗼𝗿 𝗘𝗾𝘂𝗮𝗹 𝗫 | 🟢 Easy | Java A self-referential condition — x elements must be ≥ x. Elegant problem, elegant solution. 🎯 🔍 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Find x such that exactly x elements in the array are ≥ x. Return -1 if no such x exists. ⚡ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 — 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗖𝗼𝘂𝗻𝘁 + 𝗦𝘂𝗳𝗳𝗶𝘅 𝗦𝘂𝗺 ✅ Cap all values at n (array length) — anything larger contributes the same way ✅ Build a frequency count array of size n+1 ✅ Traverse from right to left, accumulating a running suffix sum ✅ When suffix sum == current index i → x = i is the answer! 💡 𝗪𝗵𝘆 𝗰𝗮𝗽 𝗮𝘁 𝗻? x can never exceed n (can't have more elements than the array size). So values above n are equivalent — capping them avoids index overflow and keeps the logic clean. 📊 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 ⏱ Time: O(n) — two passes 📦 Space: O(n) — frequency array No sorting. No binary search. Just a clever frequency count + suffix accumulation. Sometimes the cleanest approach is right under your nose. 🧠 📂 𝗙𝘂𝗹𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gm2c4-6x 𝟳 𝗺𝗼𝗿𝗲 𝗱𝗮𝘆𝘀. 𝗦𝗼 𝗰𝗹𝗼𝘀𝗲 𝘁𝗼 𝟭𝟬𝟬! 💪 #LeetCode #Day93of100 #100DaysOfCode #Java #DSA #Arrays #FrequencyCount #CodingChallenge #Programming

Day 77/100 Completed ✅ 🚀 Solved LeetCode – Search a 2D Matrix II (Java) ⚡ Implemented an efficient approach by starting from the top-right corner of the matrix and eliminating rows or columns based on comparison with the target. This reduces the search space at every step, achieving O(m + n) time complexity. 🧠 Key Learnings: • Smart traversal in a sorted 2D matrix • Eliminating search space using row & column properties • Moving left (col--) when value is greater • Moving down (row++) when value is smaller • Better than brute-force (O(m × n)) approach 💯 This problem improved my understanding of matrix traversal strategies and how to optimize searching using sorted properties. 🔗 Profile: https://lnkd.in/gaJmKdrA #leetcode #datastructures #algorithms #java #matrix #problemSolving #optimization #arrays #100DaysOfCode 🚀

Some of the hardest problems become manageable once you recognize a repeating pattern. 🚀 Day 105/365 — DSA Challenge Solved: Subarrays with K Different Integers Problem idea: We need to count subarrays that contain exactly k distinct integers. Efficient approach: Use the powerful trick: subarrays with exactly k distinct = subarrays with ≤ k distinct − subarrays with ≤ (k − 1) distinct Steps: 1. Use a sliding window with a hashmap to track frequency of elements 2. Expand window by moving right pointer 3. If distinct count exceeds k, shrink window from the left 4. Count valid subarrays ending at each index 5. Subtract results to get exact count This pattern converts a hard problem into a manageable one. ⏱ Time: O(n) 📦 Space: O(n) Day 105/365 complete. 💻 260 days to go. Code: https://lnkd.in/dad5sZfu #DSA #Java #SlidingWindow #HashMap #LeetCode #LearningInPublic

If your class name looks like CoffeeWithMilkAndSugarAndCream… you’ve already lost. This is how most codebases slowly break: You start with one clean class. Then come “small changes”: add logging add validation add caching So you create: a few subclasses… then a few more or pile everything into if-else Now every change touches existing code. And every change risks breaking something. That’s not scaling. That’s slow decay. The Decorator Pattern fixes this in a simple way: Don’t modify the original class. Wrap it. Start with a base object → then layer behavior on top of it. Each decorator: adds one responsibility doesn’t break existing code can be combined at runtime No subclass explosion. No god classes. No fragile code. Real-world example? Java I/O does this everywhere: you wrap streams on top of streams. The real shift is this: Stop thinking inheritance. Start thinking composition. Because most “just one more feature” problems… are actually design problems. Have you ever seen a codebase collapse under too many subclasses or flags? #DesignPatterns #LowLevelDesign #SystemDesign #CleanCode #Java #SoftwareEngineering #OOP Attaching the decorator pattern diagram with a simple example.

🚀 Day 7 – Exception Handling: More Than Just try-catch Today I focused on how exception handling should be used in real applications—not just syntax. try { int result = 10 / 0; } catch (Exception e) { System.out.println("Error occurred"); } This works… but is it the right approach? 🤔 👉 Catching generic "Exception" is usually a bad practice 💡 Better approach: ✔ Catch specific exceptions (like "ArithmeticException") ✔ Helps in debugging and handling issues more precisely ⚠️ Another insight: Avoid using exceptions for normal flow control Example: if (value != null) { value.process(); } 👉 is better than relying on exceptions 💡 Key takeaway: - Exceptions are for unexpected scenarios, not regular logic - Proper handling improves readability, debugging, and reliability Small changes here can make a big difference in production code. #Java #BackendDevelopment #ExceptionHandling #CleanCode #LearningInPublic

Hi everyone 👋 Continuing the Spring Boot Validation Series Part 29 👇 📌 Validation Annotations – @Min & @Max @Min and @Max are used to validate numeric values 👇 🔹 Why do we use them? Sometimes we need to restrict numbers to a certain range. Age should be at least 18 Quantity should not exceed 100 @Min and @Max help enforce these rules automatically. 🔹 Simple Example public class User { @Min(18) private int age; @Max(100) private int score; } @PostMapping("/users") public String createUser(@Valid @RequestBody User user) { return "User created"; } 👉 If age < 18 or score > 100 → validation fails ❌ 🔹 Important Points Works with numeric types: int, long, double, BigDecimal Often combined with @NotNull to ensure value is provided @NotNull @Min(1) @Max(100) private Integer quantity; 🔹 In simple words @Min → number must be greater than or equal to specified value @Max → number must be less than or equal to specified value 👉 🧠 Quick Understanding @Min(value) → minimum allowed value @Max(value) → maximum allowed value Works with all numeric types Often combined with @NotNull #SpringBoot #Java #Validation #MinMaxAnnotation #BackendDevelopment #LearningInPublic

#Post11 In the previous post(https://lnkd.in/dynAvNrN), we saw how to create threads in Java. Now let’s talk about a problem. If creating threads is so simple… why don’t we just create a new thread every time we need one? Let’s say we are building a backend system. For every incoming request/task, we create a new thread: new Thread(() -> { // process request }).start(); This looks simple. But this approach breaks very quickly in real systems because of below mentioned problems. Problem 1: Thread creation is expensive Creating a thread is not just creating an object. It involves: • Allocating memory (stack) • Registering with OS • Scheduling overhead Creating thousands of threads = performance degradation Problem 2: Too many threads → too much context switching We already saw this earlier(https://lnkd.in/dYG3v-vb). More threads does NOT mean more performance. Instead: • CPU spends more time switching • Less time doing actual work Problem 3: No control over thread lifecycle When you create threads manually: • No limit on number of threads • No reuse • Hard to manage failures This quickly becomes difficult to manage as the system grows. So what’s the solution? Instead of creating threads manually: we use something called the Executor Framework. In simple words consider the framework to be like: Earlier, we were manually hiring a worker (thread) for every task. With Executor, we have a team of workers (thread pool), and we just assign tasks to them. Key idea Instead of: Creating a new thread for every task We do: Submit tasks to a pool of reusable threads This is exactly what Java provides using: Executor Framework Key takeaway Manual thread creation works for learning, but does not scale in real-world systems. Thread pools help: • Control number of threads • Reduce overhead • Improve performance We no longer manage threads directly — we delegate that responsibility to the Executor Framework. In the next post, we’ll see how Executor Framework works and how to use it in Java. #Java #Multithreading #Concurrency #BackendDevelopment #SoftwareEngineering

Understanding the Magic Under the Hood: How the JVM Works ☕️⚙️ Ever wondered how your Java code actually runs on any device, regardless of the operating system? The secret sauce is the Java Virtual Machine (JVM). The journey from a .java file to a running application is a fascinating multi-stage process. Here is a high-level breakdown of the lifecycle: 1. The Build Phase 🛠️ It all starts with your Java Source File. When you run the compiler (javac), it doesn't create machine code. Instead, it produces Bytecode—stored in .class files. This is the "Write Once, Run Anywhere" magic! 2. Loading & Linking 🔗 Before execution, the JVM's Class Loader Subsystem takes over: • Loading: Pulls in class files from various sources. • Linking: Verifies the code for security, prepares memory for variables, and resolves symbolic references. • Initialization: Executes static initializers and assigns values to static variables. 3. Runtime Data Areas (Memory) 🧠 The JVM manages memory by splitting it into specific zones: • Shared Areas: The Heap (where objects live) and the Method Area are shared across all threads. • Thread-Specific: Each thread gets its own Stack, PC Register, and Native Method Stack for isolated execution. 4. The Execution Engine ⚡ This is the powerhouse. It uses two main tools: • Interpreter: Quickly reads and executes bytecode instructions. • JIT (Just-In-Time) Compiler: Identifies "hot methods" that run frequently and compiles them directly into native machine code for massive performance gains. The Bottom Line: The JVM isn't just an interpreter; it’s a sophisticated engine that optimizes your code in real-time, manages your memory via Garbage Collection (GC), and ensures platform independence. Understanding these internals makes us better developers, helping us write more efficient code and debug complex performance issues. #Java #JVM #SoftwareEngineering #Programming #BackendDevelopment #TechExplainers #JavaVirtualMachine #CodingLife