Dzhamal Kichukov’s Post

Today was a good day—but also a reality check. I spent hours diving into the Spring Framework and backend fundamentals. On paper, concepts like REST APIs, annotations, and dependency injection look simple. But when you actually try to understand how everything connects… it’s a different story. What really clicked today was the difference between tight coupling and loose coupling. It’s not just theory—it completely changes how you design scalable systems. Also explored autowiring and components in Spring Boot. Slowly starting to see how real-world applications are structured. Still learning. Still figuring things out. But one thing is clear: Consistency matters more than intensity. Back at it again tomorrow. #Java #SpringBoot #BackendDevelopment #LearningInPublic #DeveloperJourney #Consistency #Tech

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

Ever noticed how one small backend concept can quietly make your entire application more reliable? 💡 Today I explored something powerful in Spring Boot — "OncePerRequestFilter". At first, it looks like just another filter. But when you start building real-world applications with JWT authentication, request logging, security checks, and tracing, this class becomes a game changer. The best part? It ensures your filter logic runs only once for every HTTP request. Sounds simple, right? But imagine a production system where the same request gets internally forwarded and your filter executes multiple times. That could mean: 🔁 repeated token validation 📝 duplicate logs ⚠️ unnecessary processing 🐢 slower performance This is where "OncePerRequestFilter" saves the day. It brings consistency, security, and cleaner request flow to your application. One of the most common use cases is in JWT authentication: ➡️ Read token from header ➡️ Validate it ➡️ Authenticate user ➡️ Set security context ➡️ Pass request forward And all of this happens exactly once. For me, this is one of those concepts that proves: Great backend engineering is often about controlling what should happen once — and only once. Small concept. Big impact. 🚀 Backend developers, where have you used "OncePerRequestFilter" in your projects? Would love to learn your real-world use cases in the comments 👇 #Java #SpringBoot #SpringSecurity #BackendDevelopment #JavaDeveloper #SoftwareEngineering #CodingJourney #TechLearning #JWT #WebDevelopment #Programming #Developers #unique2

Spring doesn’t just create beans- it manages their entire lifecycle. Many developers stop at "@Autowired", "@Qualifier", and "@Primary". But to build reliable and production-ready applications, understanding the Spring Bean Lifecycle is essential. ------ 🧠 What happens behind the scenes? A Spring bean goes through multiple stages: • Instantiation • Dependency Injection • Initialization (e.g., "@PostConstruct") • Ready for use • Destruction (e.g., "@PreDestroy") ------ 🔥 Key idea: • "@PostConstruct" → Used for initialization after dependencies are injected • "@PreDestroy" → Used for cleanup before the bean is destroyed ----- 💡 Why this matters: Proper lifecycle management helps you: ✔ Avoid resource leaks ✔ Manage connections effectively ✔ Write cleaner and more maintainable code ✔ Build stable, production-ready applications ----- 🎯 Best practice: Avoid placing heavy logic (such as database calls) inside constructors. Instead, use lifecycle annotations to handle initialization and cleanup in a structured way. ----- 📌 Takeaway: If your Spring knowledge ends at dependency injection, you’re only scratching the surface. 👉 Understanding the lifecycle is what separates good developers from great ones. #SpringBoot #SpringFramework #JavaDeveloper #BackendDevelopment #SoftwareEngineering #CleanCode #CodingBestPractices #LearnInPublic #Developers #Java

Most developers encounter their first race condition and think: "That's weird. Let me just run it again." It works. So they move on. That is the most dangerous moment in a developer's career. Here's the thing about concurrency bugs: They don't fail consistently. They fail probabilistically — based on timing, load, and thread scheduling that you cannot fully control or predict. Which means: — They pass code review — They pass unit tests — They pass load tests — They go live — And then they wait The classic setup: Two threads. One shared resource. No synchronization. Thread A reads a value → 100 Thread B reads the same value → 100 Thread A writes 110 Thread B writes 90 The final value is 90. Thread A's update is silently gone. No error. No exception. Just wrong data. In a low-traffic system this might never happen. In a system handling thousands of requests per second, it's inevitable. Three mistakes developers make with concurrency: Assuming "it's unlikely" means "it won't happen." Unlikely × millions of requests = guaranteed eventually. Testing for volume instead of contention. You can have 10K RPS with zero race conditions if all requests touch different data. The danger is concurrent access to the same resource. Adding synchronization as an afterthought. Retrofitting thread safety into existing code is significantly harder than designing for it upfront. What actually works: — Optimistic locking for low-contention scenarios: let conflicts happen, detect them, retry cleanly — Idempotent operations: design every write so running it twice produces the same result as running it once — Immutability where possible: a value that can't change can't have a race condition — Version your mutable state: know exactly which version of a record you're modifying Concurrency is one of those topics that feels theoretical until it isn't. The best time to think about it is before you write the code. The second best time is right now. What's your go-to strategy for handling concurrent writes? #java #concurrency #multithreading #softwareengineering #systemdesign #distributedsystems #backenddevelopment

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

🥤 𝕊𝕡𝕣𝕚𝕟𝕘 𝕧𝕤 𝕊𝕡𝕣𝕚𝕟𝕘 𝔹𝕠𝕠𝕥 — 𝕋𝕙𝕚𝕟𝕜 𝕠𝕗 𝕚𝕥 𝕝𝕚𝕜𝕖 𝕥𝕙𝕚𝕤 When I first learned backend development, i thought Spring and Spring Boot were basically the same thing.. They’re not. And this small confusion can slow you down more than you think. Let’s simplify it 👇 : 🧰 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Gives you all the tools you need: Dependency injection. Configuration. Flexibility. Full control. 👉 But… you have to assemble everything yourself. ⚡ 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 Takes those same tools and says: “Let me set things up for you.” Auto-configuration. Embedded server. Minimal setup. Production-ready faster. 💡 𝐓𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐢𝐧 𝐨𝐧𝐞 𝐬𝐞𝐧𝐭𝐞𝐧𝐜𝐞: 👉 Spring = control. 👉 Spring Boot = speed. Now here’s what many developers miss: Using Spring Boot without understanding Spring is like using a machine.. without knowing how it works. It works fine — until something breaks. That’s why strong backend engineers: ✔️ Use Spring Boot for productivity. ✔️ Understand Spring for control. The goal is not to choose one over the other. It’s to use both — the right way. #Java #Spring #SpringBoot #BackendDevelopment #SoftwareEngineering #Microservices #Programming #JavaDeveloper

🚀 Spring Framework Deep Dive – Day 18 🚨 Your Spring Boot app crashed in production. No alerts. No warnings. No idea why. You know what was missing? Logging. I've seen developers spend HOURS debugging... when proper logging would have shown the answer in SECONDS. Here's everything you need to know 👇 🔹 What is Logging? → Recording everything that happens inside your app → Helps debug issues in seconds not hours → Tracks user actions and system behavior → Non-negotiable in production applications 🔹 Spring Boot uses SLF4J + Logback by default → Zero setup needed ✔ → Just inject the logger and start logging 🔹 5 Log Levels you MUST know: 🔵 TRACE → Every single step (dev only) 🟢 DEBUG → Detailed debugging info 🟡 INFO → General events (default) ⭐ 🟠 WARN → Something might go wrong 🔴 ERROR → Something broke — fix NOW 🚀 Real-world example: Banking app 🏦 👉 INFO — "User John logged in successfully" 👉 WARN — "Login failed 3 times for John" 👉 ERROR — "Payment service not responding" 👉 DEBUG — "Processing transaction ID: 10234" 🔥 The hard truth: Without logging — you are flying blind in production. With logging — you know EXACTLY what happened and when. 💡 Simple way to remember: TRACE → DEBUG → INFO → WARN → ERROR Least detail →→→→→→→→ Most critical Logging + Exception Handling = Rock-solid production app 🚀 More deep dives coming 🚀 💬 Have you ever spent hours debugging something that logging would have solved in seconds? Drop your story 👇 #SpringBoot #JavaDeveloper #BackendDevelopment #Logging #FullStackDeveloper #OpenToWork #Java #100DaysOfCode

🚀 Most developers learn Spring Boot annotations... But very few understand how to build clean and scalable backend systems. That’s where Spring Boot features make all the difference 👇 🌱 5 Spring Boot Features Every Developer Should Know 1️⃣ Dependency Injection ↳ Let Spring manage object creation 👉 Cleaner & loosely coupled code 2️⃣ Spring Data JPA ↳ Write less SQL, manage data faster 👉 Faster development 3️⃣ Profiles ↳ Separate dev, test, prod configs 👉 Better environment management 4️⃣ Global Exception Handling ↳ Handle errors in one place 👉 Clean APIs & better responses 5️⃣ Actuator ↳ Monitor app health & metrics 👉 Production-ready applications 💡 Here’s the truth: Great backend developers don’t just write APIs... They build maintainable systems. #SpringBoot #Java #BackendDevelopment #Programming #SoftwareEngineer #Coding #Developers #Tech #Microservices #JavaDeveloper