Jatin Mehta’s Post

Is it a 6 or a 9? 👀 Two people. Same number. Different perspectives. In software development, I see this all the time: Frontend says it’s a bug. Backend says it’s working fine. Both are right — from their side. The real problem? Missing clarity. ✔ Align on the same data ✔ Check logs, payloads, contracts ✔ Remove ambiguity Because good engineering is not about arguing who is right — it’s about making things clear enough that no one has to argue. Have you faced this in your project? 🤔 #Angular #Java #Debugging #SoftwareEngineering #TeamWork

🚀 From Writing APIs to Thinking in Systems For a long time, my focus as a backend developer was simple: Write APIs. Fix bugs. Deliver features. But recently, while building a microservices-based system, something changed. I started thinking beyond code: • What happens if a service goes down? • How do systems handle failures gracefully? • How do multiple services communicate reliably? That’s when concepts like API Gateway, Circuit Breakers, and JWT authentication started making real sense. It made me realize: 👉 Writing code is important 👉 But designing systems is what makes you grow as an engineer Still learning, still improving—but enjoying the process of thinking at a deeper level. What was that one moment that changed how you think about software? #SoftwareEngineering #BackendDevelopment #Microservices #LearningJourney #Java

Most developers misuse @Transactional and don’t even realize it 👇 At first, I thought adding @Transactional everywhere would make my code “safe”. It doesn’t. Here’s what actually happens: ❌ Transaction won’t work on private methods ❌ Internal method calls skip Spring proxy ❌ Rollback doesn’t trigger for checked exceptions by default Example mistake: Calling a transactional method inside the same class → No transaction applied Why? 👉 Spring uses proxies, not direct method execution What I do now: ✅ Keep transactions in service layer ✅ Avoid internal method calls ✅ Understand rollback behavior Small annotation. Big production bugs if misunderstood. Save this before your next backend issue. What’s a mistake you learned the hard way? 👇 #SpringBoot #Java #BackendDevelopment #CleanCode #SoftwareEngineering

A subtle Spring behavior that causes real production issues: @Transactional propagation. Most people rely on the default propagation without thinking about transaction boundaries. Example: Method A → @Transactional (REQUIRED) calls Method B → @Transactional (REQUIRES_NEW) What actually happens? Method B runs in a NEW transaction. So even if Method A fails and rolls back, Method B can still commit ❌ Result: Partial data committed → inconsistent state Fix: • Use REQUIRED if operations must succeed or fail together • Use REQUIRES_NEW only when you intentionally need an independent transaction (e.g., audit/logging) • Define transaction boundaries clearly at the service layer Seen this during backend development while handling dependent operations. Lesson: Don’t rely on defaults — design your transaction boundaries consciously. #SpringBoot #Java #Transactions #Microservices #Backend #SoftwareEngineering

Today I was watching Johnny English, and one particular scene caught my attention. The main character chooses an old, reliable car over several new, flashy options. It made me reflect on something I’ve often wondered since I started working with Java — why is Java still so relevant in enterprise systems despite so many modern alternatives? The answer, I believe, lies beyond trends or developer convenience. In enterprise systems, stability, security, and long-term reliability matter more than anything else. When applications handle financial transactions, sensitive user data, or mission-critical workflows, technology decisions are rarely about what’s newest — they’re about what’s dependable. Java’s mature ecosystem, strong backward compatibility, predictable behavior, and architectural strengths have made it a trusted choice for decades. Concepts like immutability, structured exception handling, and robust concurrency models all contribute to building stable systems at scale. Sometimes the “old car” is still the right choice. As I continue deepening my understanding of backend engineering, I’m realizing that enterprise technology is less about hype and more about trust, architecture, and long-term thinking. Curious to hear your thoughts — what do you think keeps certain technologies relevant in enterprise systems for so long? #BackendEngineering #Java #SystemDesign

If your code doesn’t use #tryLock(), it can freeze forever. Not crash. Not fail. Just… hang. That’s a #deadlock — and most developers don’t see it coming. 🔹 The Classic Mistake synchronized(lock1) { synchronized(lock2) { // do something } } Somewhere else: synchronized(lock2) { synchronized(lock1) { // do something } } 💥 Both threads are now stuck forever. 🔹 What just happened? Thread A → holds `lock1`, waiting for `lock2` Thread B → holds `lock2`, waiting for `lock1` 👉 No progress → #system #freeze 🔹 The Smarter Approach if(lock.tryLock()) { try { if(lock2.tryLock()) { try { // safe execution } finally { lock2.unlock(); } } } finally { lock.unlock(); } } ✔ Avoids waiting forever ✔ Gives control back to your code ✔ Helps prevent deadlocks 🔹 4 Conditions That Cause Deadlock 1️⃣ Mutual Exclusion → Resource can be used by one thread at a time 2️⃣ Hold and Wait → Thread holds one lock while waiting for another 3️⃣ No Preemption → Locks cannot be forcibly taken away 4️⃣ Circular Wait → Threads wait in a circular chain 👉 Break ANY one of these → deadlock is prevented 🔹 Real Insight Deadlocks don’t throw errors. They silently kill your system performance. 🔹 Some Interview Questions 1️⃣ What is a deadlock? 2️⃣ What are the 4 necessary conditions for deadlock? 3️⃣ How does `tryLock()` help prevent deadlocks? 4️⃣ What is lock ordering and why is it important? 5️⃣ How would you debug a deadlock in production? 🔹 Takeaway 👉 `synchronized` can trap you 👉 `tryLock()` gives you an escape 💬 Have you ever faced a system freeze with zero errors? #Java #Multithreading #Concurrency #Backend #InterviewPrep

💡 Thread pools were a nightmare. I’m glad they’re finally dying. I used to spend way too much time obsessing over thread pool tuning. Seriously. Calculating the "perfect" number of threads to stop a Spring Boot app from falling over under heavy I/O felt like a dark art. If you haven't had a minor panic attack over a ThreadPoolExecutor rejection policy at 4 PM on a Friday... have you even done backend dev? But honestly? That era is ending. Lately, I’ve been stripping out that complexity. By just toggling spring.threads.virtual.enabled=true in my Spring Boot 3.x builds, the whole "thread-per-request" bottleneck just... vanishes. It’s weird how simple it feels. Why I’m actually excited about this (and why you should be too): No more "Callback Hell": I’m done with the mental gymnastics of reactive programming. I want to write code that I can actually read. Sequential, clean, and simple. RAM that doesn't quit: Handling massive concurrent loads without the memory footprint of traditional threads is a lifesaver for cloud costs. Actually debuggable: Have you ever tried to trace a reactive stream? It's a mess. Debugging a virtual thread stack trace actually makes sense. The most impactful move I’ve made this year wasn't building something more complex. It was making my stack simpler so the JVM does the heavy lifting. Java is in a new golden age—if you’re still stuck in the Java 8/11/17 mindset, you’re missing the best part of the job. 👉 Are you still babysitting thread pools? Or have you moved to Virtual Threads yet? #Java26 #SpringBoot3 #SpringBoot4 #ProjectLoom #Backend #SoftwareEngineering #CloudComputing #JavaDeveloper

What I’ll learned in a couple of months using Claude: I have some Dunning-Kruger regarding Claude: 1) For things I don’t know, or certainly don’t like doing, I trust it a lot. A lot of this is “full stack” developer stuff, which is NOT what I do. But Claude does it generally pretty well… as far as I can tell. 2) For things I do know fairly well, it can be very frustrating seeing it try, but not quite “getting there”. However I am certainly more on the side of algoritym and HPC, than “Full Stack”. 
Unit tests, documentation, pipelines it does pretty good on though.
 And a “Subject Matter Expert” (SME) colleague who is “not a software developer”, has been doing some pretty impressive work using Claude as his offsider developer.

Claude’s /insights can be pretty useful. And I attempted giving it “a personality”, which has made the interaction a lot less grating. I’ve blown through ~3.2B tokens in about 6-8 weeks. Mostly on the “full stack” side of things, documentation, unit tests, pipelines and yaml, etc. And some actual code restructuring and minor refactoring. I gave up on using it to make performance improvements after things went 4x slower. Doing the performance work mostly by hand I did get a 4x performance improvement. So in my example it’s 16x faster to put work into “hot spots” and lean to using Claude for all the drudgery work… and restricting it to doing light work on actual “code”. There’s p’rolly a lot more to learn, but I have good traction, and pull on the reins both quickly and with force, when it trys to run wild doing what it thing is best.

Earlier today I asked our Year Up instructor Matthew C. a simple question. I told him I wanted explore into Fullstack and DevOps as a career. What should I be exploring? He pointed me toward exploring JavaFX as we are still in Week 2 of Java base on my previous experience working with React and the Next framework in web development and he gave me great insights into why companies may choose electron for their uses. But before I even got there, we had an assignment. A theater reservation system. The program takes a guest's name, a date, and a ticket count and outputs a formatted confirmation message. Simple enough on the surface. The name had to come out as Last, First. The date had to reformat from MM/DD/YYYY to YYYY-MM-DD etc". Small things but it forced me to actually think about how data flows and how you format it for real output in a real world application Once I had that working in the terminal I thought, what if I can turned this into something you could actually see and use. Like one of those kiosks at the airport or a point of sale machine like Toast. So after class I opened IntelliJ and just started experimenting with JavaFX. It did not go smoothly. Version mismatches, null pointers, wrong field names. Every time I thought I was done something else broke. The window opened once. Crashed. Opened again, figure out whats wrong with the code. Until finally it just worked. It looked like something a real business would use. That eureka moment was great honestly and felt satisfying after hours of figuring out "What the heck is wrong now" Thank you Matthew C. for answering my question in a way that sent me down a rabbit hole that taught me more in one day than I expected. #Java #YearUpUnited #SoftwareDevelopment #ApplicationDevelopment #BuildingInPublic

I increased concurrency to speed up a bulk workflow. It worked… until it didn’t. At higher volumes, things started failing with: PrematureCloseException — connections closing before response That’s when I realized: this wasn’t a performance problem anymore — it was a system pressure problem. What actually fixed it: * reducing unsafe parallelism * treating concurrency as a budget, not a goal * tuning chunk size for stability * adding retry with backoff (not blind retries) * fixing connection pool behavior * preserving partial failures instead of failing everything The biggest lesson? > More concurrency doesn’t always mean more throughput. Full debugging story: https://lnkd.in/g_Mq45kw #SpringBoot #Java #Backend #SystemDesign #DistributedSystems #Debugging