Asad Tahseen’s Post

✨ DAY-31: 🚀 REST API Development in Java – Expectation vs Reality! 🌳💻 When we start building a REST API using Java, everything looks perfectly planned in our mind. Clean architecture, well-structured endpoints, and smooth data flow. 🌱 But once development begins, the reality appears… Debugging issues, fixing errors, handling requests, and managing responses. The code gets messy, coffee consumption increases, and we start questioning every line we wrote. 😅 And then comes the final boss… Production! 🔥 Suddenly unexpected bugs appear, APIs break, logs explode, and developers turn into firefighters trying to fix everything quickly. This meme perfectly shows the journey of every backend developer working with REST APIs in Java. But that’s the beauty of programming — every challenge makes us better developers. 💡 💬 Developers, be honest: Have you ever experienced this while deploying your API? #Java #RESTAPI #BackendDevelopment #ProgrammingHumor #DeveloperLife #CodingMemes #SoftwareDevelopment Clahan Technologies

The worst feeling as a backend developer? Your API works perfectly in local… but breaks in production. That’s when I realized something important: Writing code is easy. Debugging it is the real skill. Early in my backend journey, whenever something broke, my first reaction was panic. But over time I learned something critical: Debugging is not guessing. Debugging is systematic thinking. ⸻ 🧠 My Simple Debugging Process 1️⃣ Read the error carefully Most developers skip this. But stack traces often tell you exactly where the issue is. ⸻ 2️⃣ Check logs first Logs reveal things like: • Null values • API failures • Database issues Good logging can save hours of debugging. ⸻ 3️⃣ Reproduce the issue Try to recreate the problem locally. If you can reproduce it, you are halfway to fixing it. ⸻ 4️⃣ Verify inputs and outputs Check: • Request payload • Service response • Database results Many bugs come from unexpected data. ⸻ 5️⃣ Fix the root cause Avoid quick fixes. Fix the real problem, not just the symptom. ⸻ 💡 Lesson Writing code is only half the job. Understanding why it breaks is what makes you a better engineer. ⸻ Day 11 of becoming production-ready with Spring Boot. What debugging method do you use most? • Logs • Debugger • Print statements 😅 ⸻ #Java #SpringBoot #BackendEngineering #Debugging #SoftwareDevelopment #Coding

The worst feeling as a backend developer? Your API works perfectly in local… but breaks in production. That’s when I realized something important: Writing code is easy. Debugging it is the real skill. Early in my backend journey, whenever something broke, my first reaction was panic. But over time I learned something critical: Debugging is not guessing. Debugging is systematic thinking. ⸻ 🧠 My Simple Debugging Process 1️⃣ Read the error carefully Most developers skip this. But stack traces often tell you exactly where the issue is. ⸻ 2️⃣ Check logs first Logs reveal things like: • Null values • API failures • Database issues Good logging can save hours of debugging. ⸻ 3️⃣ Reproduce the issue Try to recreate the problem locally. If you can reproduce it, you are halfway to fixing it. ⸻ 4️⃣ Verify inputs and outputs Check: • Request payload • Service response • Database results Many bugs come from unexpected data. ⸻ 5️⃣ Fix the root cause Avoid quick fixes. Fix the real problem, not just the symptom. ⸻ 💡 Lesson Writing code is only half the job. Understanding why it breaks is what makes you a better engineer. ⸻ Day 11 of becoming production-ready with Spring Boot. What debugging method do you use most? • Logs • Debugger • Print statements 😅 ⸻ #Java #SpringBoot #BackendEngineering #Debugging #SoftwareDevelopment #Coding

⚡ One Skill That Makes a Strong Backend Developer: Debugging Writing code is important, but debugging is where real learning happens. In backend development with Java and Spring Boot, issues can come from many places: 🔹 Database queries 🔹 API integrations 🔹 Configuration problems 🔹 Exception handling 🔹 Performance bottlenecks Understanding logs, stack traces, and system behavior is essential for identifying the root cause of problems. A developer who can quickly debug and resolve issues adds huge value to any engineering team. Debugging is not just fixing bugs — it’s understanding how systems actually work. What debugging techniques help you solve problems faster? 👇 #Java #SpringBoot #BackendDeveloper #Debugging #SoftwareEngineering

One small backend optimization can save thousands of hours across a system. Recently while working on a Java microservice, we noticed the response latency was slowing down an entire workflow. The root cause was a combination of inefficient database queries and synchronous processing in a high-volume service. After introducing async processing and optimizing the query layer, the response time improved from 5 seconds to around 2.5 seconds. What looked like a small change at the code level actually translated into faster workflows across the platform and protected approximately $300K in annual revenue. Moments like this remind me why I enjoy backend engineering. Behind every API call, there’s an opportunity to improve performance, reliability, and real business outcomes. Curious to hear from other engineers: What’s the most impactful performance improvement you've implemented in a production system? #Java #SpringBoot #Microservices #BackendEngineering #SoftwareEngineering

🧵 If you don't understand thread pools, you don't understand backend performance. Every backend service handles hundreds or thousands of tasks concurrently. Creating a new thread for every request would quickly kill performance and exhaust system resources. That’s why Java uses thread pools. Here are a few every backend engineer should know 👇 1. 🧰 Fixed Thread Pool A fixed number of threads handle incoming tasks. Good for predictable workloads and controlled resource usage. 2.⚡ Cached Thread Pool Creates new threads when needed and reuses idle ones. Useful for short-lived asynchronous tasks. 3. 🔀 ForkJoinPool Designed for parallel workloads that split tasks into smaller subtasks. Common in parallel streams and CPU-heavy operations. 4. 📥 Scheduled Thread Pool Executes tasks after a delay or periodically. Used for cron jobs, background jobs, and maintenance tasks. 5.🚦 Thread Pool Queue Incoming tasks wait in a queue when all threads are busy. Queue strategy can affect: • latency • throughput • system stability 💡 Key idea: Thread pools help reuse threads, control concurrency, and prevent resource exhaustion. Understanding them is critical for building scalable backend services. 💬 Backend engineers: Which thread pool type do you use most in production? #Java #BackendEngineering #Concurrency #Multithreading #SoftwareEngineering

A lot of engineers I know are stressed about not knowing enough. New framework drops. New tool gets hyped. New language everyone is suddenly using. The list never ends. Here is what I actually think after spending time in software development. The fundamentals transfer. Concurrency is concurrency whether you are in Java, Go or Node. A transaction is a transaction whether it is Postgres or MySQL. Debugging is debugging regardless of the stack. The engineers who adapt fastest are not the ones who know the most tools. They are the ones who understand what is actually happening underneath. You do not need to know everything. You need to understand things deeply enough that the new stuff makes sense quickly when you pick it up. The gap between a good engineer and an anxious one is often not skill. It is this. Does this resonate with how you actually feel about it? #SoftwareEngineering #BackendDevelopment #CareerGrowth

If your goal is to become: 👉 Senior Developer 👉 Tech Lead 👉 Architect This Is the ONLY Java Roadmap You Need 👉 https://lnkd.in/dYy-5H33 You don’t need another framework. You need engineering depth. In 2026, growth isn’t about collecting skills. It’s about: Understanding how JVM, memory, and GC interact Knowing why latency appears only in production Reasoning through failures without restarts Making trade-offs instead of chasing best practices Thinking in systems, not features This gap is exactly why I created the Java → Architect Roadmap. 🎥 In 2026, This Is the ONLY Java Roadmap You Need 👉 https://lnkd.in/dYy-5H33 This isn’t a checklist. It’s a thinking upgrade. Built from real production failures — not interview prep blogs. --- If you want to learn backend development through real-world project implementations, follow me or DM me — I’ll personally guide you. 🚀 📘 satyamparmar.blog 🎯 topmate.io/satyam_parmar 🎥 YouTube: Satyverse #Java #SystemDesign #Architecture #BackendEngineering #DistributedSystems #SeniorDeveloper #TechLead #Satyverse

🔐 Ever wondered what it takes to build transactional maps from scratch in Java? Over the past few weeks, I deep-dived into implementing multiple transactional map designs — each with different isolation levels and fascinating trade-offs. Here's what I explored: 🔵 **Optimistic & Pessimistic Maps** — READ COMMITTED globally, SERIALIZABLE per-key. Smart lock upgrading prevents deadlocks, while semantic awareness avoids unnecessary SIZE locks. 🟡 **Read Uncommitted Map** — Dirty reads allowed! Store buffers give you read-your-own-writes consistency without any blocking. ⚪ **Snapshot-based Map** — Copy-on-write via CAS. Beautiful simplicity, brutal under write-heavy workloads ⚡ **Flat Combining Maps** — Instead of threads fighting for locks, they cooperate! One combiner batches and applies operations. Surprisingly, a single combiner outperforms segmented ones due to better amortization. 🕰️ **MVCC Map** — Version chains per key. Readers never block writers, writers never block readers. Pure elegance. Each design reveals a different philosophy about concurrency — there's no silver bullet, only trade-offs! 💬 Which isolation level do YOU prioritize in your systems — performance or consistency? #Java #Concurrency #DatabaseInternals #DataStructures #SoftwareEngineering #BackendDevelopment #SystemsDesign

❇️ 𝐒𝐭𝐫𝐢𝐩𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞 – 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐅𝐥𝐨𝐰 (𝐏𝐚𝐫𝐭 𝟐) ⭐ After that with the help of processStripeResponse() which is present in createPaymentHelper class we can following cases of responses like 🔹 Case 1: 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝟐𝐱𝐱. The JSON response is converted into a Java object using the 𝐉𝐚𝐜𝐤𝐬𝐨𝐧 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 and returned back to service layer. 🔹 Case 2:𝐒𝐭𝐫𝐢𝐩𝐞 𝐀𝐏𝐈 𝐄𝐫𝐫𝐨𝐫 If there are any errors from Client side like 401 Unauthorized , 404 not found. Here we will do all the exception handling using @RestControllerAdvice and Custom exceptions by StripeProviderException. 🔹 Case 3: 𝐈𝐧𝐯𝐚𝐥𝐢𝐝 𝐒𝐭𝐫𝐢𝐩𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 If Stripe returns an unexpected or invalid response, a custom exception is thrown to handle the error. ⭐ After handling the 3 cases we will come back to serviceImpl , now we have method called mapToPaymentResponse() which will Convert Stripe Response into internal response format. ⭐ The Stripe response contains many fields, but we extract only the stripeSessionId and hostedPageUrl and return them to the client. ⭐ Finally After validation, exception handling, and response mapping, the response is returned to the controller, controller will return the response to the Client/End user. ⭐ Now with the help of 𝐡𝐨𝐬𝐭𝐞𝐝𝐏𝐚𝐠𝐞𝐔𝐫𝐥 the client/enduser can now redirect to the Stripe hosted payment page. ⭐ This microservice follows a 𝐥𝐚𝐲𝐞𝐫𝐞𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 including controller, service, helper, and HTTP layers to maintain separation of concerns and clean code structure. Controller - > Service - > Helper - >HTTP Layer This approach ensures 𝐜𝐥𝐞𝐚𝐧 𝐜𝐨𝐝𝐞, 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐜𝐨𝐧𝐜𝐞𝐫𝐧𝐬, 𝐚𝐧𝐝 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Finally, I would like to thank Tausief Shaikh ☑️ for guiding me throughout this project and for his clear explanations, which helped me understand the architecture and implementation effectively. #Java #Microservices #Stripe #BackEndDeveloment #APIDevelopment #SpringBoot