JOptimize’s Post

Java and Kotlin run on the same JVM, share the same ecosystem, and can even call each other's code. So why does picking one still feel complicated? Because the right choice depends on what you're building, not which language is objectively better. This decision tree makes it straightforward (see photo below) The full guide by roadmap.sh goes deeper, covering key differences in syntax, null safety, concurrency, and long-term maintenance so you can make a decision now that actually holds up later. Check the link in the comments.

Understanding Bubble Sort vs Selection Sort (Java) Today, I revised two classic sorting algorithms and noted some key differences while implementing them in Java. 🔹 Bubble Sort 1. Time Complexity: O(n²) (Best case O(n) with early stop) 2. Space Complexity: O(1) 3. Multiple swaps can happen in a single pass 4. Large elements move to the end in each pass 5. Optimized using a swap flag to stop early if the array is already sorted 🔹 Selection Sort 1. Time Complexity: O(n²) (Best, Average, Worst) 2. Space Complexity: O(1) 3. Only one swap per pass 4. Smallest element moves to the front in each pass 5. No early termination, even if the array is already sorted Even if the smallest is already at correct position, swap still executes (with same index).

Most Java backends don’t have architecture problems. They have small performance mistakes that go unnoticed… until production. After analyzing dozens of real-world systems, I keep seeing the same ones: • Objects created inside hot loops • Streams used in critical paths • Hidden N+1 queries • Blocking calls in request threads • Excessive logging Nothing crashes. Everything passes code review. But under load → performance drops hard. I wrote a short breakdown with concrete fixes you can apply in minutes 👇 👉 5 Java Backend Performance Mistakes I Keep Seeing in Production If you're working on a Java backend, this will probably save you hours of debugging later. https://lnkd.in/eWRj354Z #java #backend #performance #springboot #softwareengineering

“Java doesn’t have memory leaks.” This is one of the biggest myths in backend development. 👇 Yes, Java has Garbage Collection. But GC only removes objects that are 𝐮𝐧𝐫𝐞𝐚𝐜𝐡𝐚𝐛𝐥𝐞. If your code still holds references… 👉 That memory is never freed. Common real-world mistakes: -Static collections that keep growing -Unclosed DB connections / streams -Listeners or callbacks not removed -Unlimited caching What happens then? 📈 Heap keeps growing ⚠️ Frequent Full GC 💥 Eventually → OutOfMemoryError How to catch it? ✔ Take heap dumps ✔ Analyze using 𝐄𝐜𝐥𝐢𝐩𝐬𝐞 𝐌𝐀𝐓 / 𝐕𝐢𝐬𝐮𝐚𝐥𝐕𝐌 ✔ Check: who is holding the reference? How to fix it? ✔ Remove unused references ✔ Use 𝐖𝐞𝐚𝐤𝐇𝐚𝐬𝐡𝐌𝐚𝐩 where needed ✔ Use 𝐭𝐫𝐲-𝐰𝐢𝐭𝐡-𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ✔ Add limits to caches 💡 Hard truth: Most memory leaks are not JVM problems. They are 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬. Garbage Collection works perfectly… Until your code prevents it. #Java #MemoryLeaks #JVM #Debugging #BackendEngineering

Most teams today are running on Java 17 (LTS) — and for good reason. It’s stable, well-supported, and widely adopted across enterprise systems. But with Java 26 (March 2026) now available, it’s clear where the platform is heading. This isn’t about flashy new syntax. The shift is more subtle — and more important. Java 17 gave us better language design. Java 26 is focused on better system performance. ⸻ What’s new or evolving in Java 26? • Improved Garbage Collection (G1) for better latency and throughput • Early support for HTTP/3, enabling faster and more efficient network communication • Enhancements around AOT (Ahead-of-Time) optimizations, helping reduce startup time • Continued evolution of Vector API and concurrency features, supporting high-performance workloads • Stronger enforcement of code integrity and security constraints ⸻ What does this mean in practice? If you are building large-scale backend systems or microservices: • Startup time and memory efficiency are becoming more critical • Network performance (especially in distributed systems) is gaining importance • Applications are expected to handle more parallel workloads efficiently Java 26 is clearly moving in that direction. ⸻ A realistic perspective Most organizations will continue using Java 17 for production — because it’s LTS and stable. But engineers who start understanding newer Java versions early will be better prepared for: • Performance-focused system design • Modern runtime optimizations • AI and compute-heavy workloads ⸻ My takeaway The conversation is shifting from: “How do we write better code?” to “How does our system perform at scale?” ⸻ Curious to know — Are you still primarily working on Java 17, or have you started exploring newer versions? https://lnkd.in/gv2H-6Rh #java26 #java #softwareengineer

A Java concept that confused me at first: Why were default methods introduced in interfaces? Before Java 8, interfaces could only have: • method declarations (no implementation) Which meant: If you added a new method to an interface, every class implementing it would break. Example: interface PaymentService {   void pay(); } Now if you add: void refund(); All existing classes must implement refund() ❌ This becomes a problem in large systems. 👉 Solution: Default Methods (Java 8) Default methods allow interfaces to provide a default implementation. Example: interface PaymentService {   void pay();   default void refund() {     System.out.println("Default refund logic");   } } Now: • Old classes don’t break ✅ • New behavior can be added safely ✅ 👉 Internal idea (simple) A default method is just a method inside an interface with a body that implementing classes can use or override. 👉 Why this matters It allows Java to evolve without breaking existing code. A simple example is forEach(). It was added later to the Iterable interface, but all existing collection classes could use it without any changes. That’s how default methods help Java grow without breaking old code. Small design decisions like this make a big difference in real systems. Had you faced issues while modifying interfaces in your projects? #Java #BackendEngineering #Java8 #SoftwareEngineering #LearningInPublic

I’ve seen this discussion many times — and the answer is more nuanced than “Streams vs loops”. Yes, streams can introduce overhead in certain scenarios. But the real issue isn’t the syntax. It’s using abstractions without understanding their cost. In performance-sensitive paths, what matters is: allocation patterns GC pressure data size and access patterns Streams can be perfectly fine. Or a bottleneck — depending on context. The dangerous part is optimizing based on assumptions instead of measurements. Clean code vs performance isn’t a trade-off. Blind abstraction vs informed decisions is.

Although streams might use more cpu , they’re incredibly useful when you want to transform from a collection to an array or if you simply want to map a dto from a model

Java Streams are killing your performance Java Streams look clean… but they can silently destroy your performance. I saw this in a production audit last week. A simple loop processing 1M records was replaced with streams “for readability”. // ❌ Stream version list.stream() .filter(x -> x.isActive()) .map(x -> transform(x)) .collect(Collectors.toList()); // ✅ Optimized loop List<Result> result = new ArrayList<>(); for (Item x : list) { if (x.isActive()) { result.add(transform(x)); } } 🚨 What happened in production: • CPU usage increased by 35% • GC pressure exploded • Latency x2 under load ❗ Why? Streams: • Create more objects • Add hidden overhead • Are NOT always optimized by JVM ✅ Fix: • Use streams for readability (small datasets) • Use loops for performance-critical paths • Benchmark before choosing https://www.joptimize.io/ Clean code ≠ fast code. Are you using streams in performance-sensitive code? #JavaDev #SpringBoot #JavaPerformance #Backend #SoftwareEngineering

I just finished the book Java Web Internals. As someone still growing in the Java ecosystem, I’ve often felt like frameworks like Spring or Tomcat were a bit of a "black box." This book completely changed that for me. It’s well-written and focuses on building things from scratch rather than just following recipes. My biggest takeaways: Building from the ground up: Starting with low-level socket programming to build a multithreaded server made the "magic" of web requests finally click. Under the hood: Implementing reflection, annotations, and dependency injection manually gave me a whole new perspective on how modern frameworks actually work. Practical Design: It moves perfectly from basic networking to creating a custom framework, making complex system design feel approachable. If you’ve built a few projects but want to truly understand the "why" behind the tools we use every day, this is a must-read. I cannot thank Packt Vinishka Kalra for sharing the book. Thanks #Java #WebDevelopment #SoftwareEngineering #LearningToCode #SystemDesign #JavaDeveloper