Fraud Transaction Alert System Built with Java & Spring Boot

Day 6 — The Race Condition Bug Nobody talks about this in backend systems… Everything works perfectly… Until two users hit the same API at the same time. And suddenly: • Duplicate orders • Negative stock • Payment processed twice No errors. No crashes. Just silent data corruption. ⸻ 💥 The Problem Two threads access and modify shared data at the same time without proper control. Example: if (stock > 0) { stock–; } Looks correct, right? But under concurrency: Thread A reads stock = 1 Thread B reads stock = 1 Both decrement 👉 Final stock = -1 ⸻ ⚠️ Why it’s dangerous • Hard to reproduce (only happens under load) • Works fine in local testing • Fails silently in production • Direct impact on money and trust ⸻ ✅ The Fix Use proper concurrency control: • Optimistic locking (@Version) • Pessimistic locking (DB level) • Atomic operations • Synchronized blocks / locks • Idempotency for critical APIs ⸻ 🧠 Real lesson If your system handles payments, orders, or inventory… You’re already exposed to race conditions. It’s not “if” it will happen. It’s “when”. ⸻ 👇 Have you ever faced a race condition in production? What was your fix? ⸻ #Backend #Java #Microservices #SystemDesign #Concurrency #SpringBoot #CodingBugs #TechLeadership

⚠️ We removed @Transactional… nothing broke. That was the problem. It looked like a small cleanup. We had a method doing multiple DB operations. Someone asked: 👉 "Do we really need @Transactional here?" At first, it felt like a safe change. So we removed it. Everything worked fine in testing. No errors. No failures. But in production… things started getting weird. * Orders were created… without payments * Some updates were saved… others silently failed * Data looked fine until users started noticing No crashes. No alerts. Just inconsistent data. That is when it clicked. Without @Transactional, each DB call commits independently. So if something fails in between…there is no rollback. You don’t get failure. You get partial success Transactions don’t protect success. They protect you from partial failure. Now Iam more careful. Not every method needs it. But removing it without understanding the boundary…is a silent risk. And in production… partial failure is the default. #SpringBoot #Java #SpringFramework #Microservices #100DaysOfCode

I am excited to share a major milestone in my transition into Java Backend Development! 🚀 Before jumping straight into powerful frameworks, I wanted to deeply understand how enterprise architecture and build tools actually work under the hood. So, I built VaultBank—a fully functional, persistent Banking CLI application written in pure Java. Instead of writing a single script, I structured this project exactly how a real-world enterprise backend is designed. Here are the key technical concepts I implemented: 🔹 Maven Multi-Module Architecture: Designed a Parent POM to manage dependencies for separated core (business logic) and cli (user interface) modules. 🔹 Separation of Concerns: Isolated the strict banking rules and data state from the terminal interface using a dedicated Service Layer. 🔹 Data Serialization & File I/O: Integrated Google Gson to deserialize/serialize Java objects, ensuring account balances and transaction histories survive application restarts via a database.json file. 🔹 Automated Regression Testing: Wrote JUnit 5 tests in an isolated src/test environment to mathematically prove the banking logic is flawless before every build. 🔹 Fat JAR Packaging: Used the Maven Shade Plugin to bundle the application into a standalone, executable JAR file. Taking the time to build this without Spring Boot gave me a profound appreciation for what Maven actually does—from dependency management and the build lifecycle to keeping production code perfectly separated from testing logic. Now that the foundation is rock solid, the next stop is turning this architecture into a REST API with Spring Boot! ☕💻 #Java #BackendDevelopment #Maven #SoftwareEngineering #CareerTransition #JUnit #LearningInPublic

Another weekend side project. I built Yeu-Pay, a fintech REST API to explore the core mechanics of digital ledger systems and financial workflows. What I focused on : Authentication RSA-signed JWTs with Spring Security for stateless, verifiable identity Ledger design Clear modeling of Accounts, Transactions, Payments, and Invoices with enforced invariants Transaction integrity Atomic operations and consistency across related financial updates Card Provisioning Virtual card issuance with controlled balance and lifecycle states Persistence PostgreSQL + Flyway for explicit, versioned schema evolution Observability Actuator for visibility into system health and metrics Stack Java 21, Spring Boot, Spring Security, PostgreSQL, Docker Compose, Open to feedback, suggestions, or discussion. Repository: https://lnkd.in/dnhVA8cq #FinTech #BackendEngineering #SoftwareEngineering #Java #SpringBoot #SystemDesign #APIDesign #PostgreSQL #Docker #CloudNative

Excited to share that I along with my teammates Rushab Engolla and Ali Shaikh, successfully developed a Full-Stack Online Banking System focused on secure banking operations, clean architecture, and real-world financial transaction management. 💳🏦 This project was built using Java Spring Boot, MySQL, JDBC, and HTML/CSS, following a structured MVC Architecture to ensure scalability, maintainability, and efficient backend processing. 🔹 Key Features of the System: •Secure user authentication and session management •Deposit, withdrawal, and fund transfer functionalities •Real-time transaction history tracking •Admin dashboard and reporting system •Customer account management •Banking operations with structured database integration 🔹 Technical Concepts Implemented: •MVC Architecture •Object-Oriented Programming (OOP) •Inheritance, Polymorphism, Encapsulation, and Abstraction •JDBC-based DAO Layer for database interaction •RESTful backend handling with Spring Boot •MySQL database integration One of the highlights of this project was implementing a well-structured backend using DAO, Service, and Controller layers, which helped us understand enterprise-level application development and software design principles. Through this project, we strengthened our understanding of: •Full Stack Development •Backend Architecture Design •Database Management •Secure Authentication Handling •Transaction Processing Systems •Java Spring Boot Development Grateful to my teammates for their collaboration, dedication and teamwork throughout the development journey. #Java #SpringBoot #FullStackDevelopment #MySQL #JDBC #BankingSystem #SoftwareDevelopment #OOP #BackendDevelopment #WebDevelopment #MVCArchitecture #Technology #StudentProject #Innovation

One of the most abused annotations in Spring is @Transactional. People treat it like a “wrap everything and forget” switch. I’ve seen methods like this: 👉 save to DB 👉 call external payment API 👉 send email 👉 call another microservice 👉 update DB again All inside ONE transaction. Looks clean. Feels safe. It’s not. Because the moment you mix DB + external calls inside a transaction, things start getting messy: ⚠️ external APIs are slow ⚠️ transactions stay open longer (locks, performance issues) ⚠️ if API fails → DB rolls back, but external system doesn’t ⚠️ debugging becomes painful Now you have inconsistent systems and no clear recovery path. The better way? Keep @Transactional boring. ✔️ only DB operations inside ✔️ keep it short ✔️ do external calls before or after ✔️ think about failure scenarios explicitly @Transaction is not a safety net. It’s a boundary. Use it wrong → hidden bugs Use it right → stable systems Learned this the hard way 🙂 #Java #SpringBoot #BackendEngineering #SystemDesign #Microservices #CodeQuality

Spring Developers Adopt @TransactionalEventListener for Reliable Post-Commit Actions 📌 Spring developers are now embracing @TransactionalEventListener to ensure critical post-commit actions-like emails or cache updates-run reliably only after database transactions succeed. This prevents inconsistent states and enhances data integrity in complex workflows. By decoupling side effects from core logic, teams gain both reliability and performance, especially when paired with async processing for long-running tasks. 🔗 Read more: https://lnkd.in/dUFppiHb #Springframework #Databasetransaction #Postcommitaction

🚀 Day 11/45 – Backend Engineering (Transactions) Today I focused on how transactions ensure data consistency in backend systems. 💡 What I learned: 🔹 Problem: If multiple DB operations are involved and one fails: 👉 System can end up in inconsistent state ❌ 🔹 Example: Deduct money from Account A Add money to Account B If second step fails: 👉 Money lost ❌ 🔹 Solution: Transactions All operations succeed OR none Ensures atomicity 🔹 ACID properties: Atomicity Consistency Isolation Durability 🔹 In Spring Boot: @Transactional annotation Automatic rollback on failure Can control propagation & isolation 🛠 Practical: Tested transaction rollback scenarios to ensure consistency when failures occur. 📌 Real-world impact: Transactions help: Prevent data corruption Maintain consistency Build reliable backend systems 🔥 Takeaway: If your system can leave data half-updated, it’s not production-ready. Currently building a production-ready backend system — sharing real backend lessons daily. https://lnkd.in/gJqEuQQs #Java #SpringBoot #BackendDevelopment #Transactions #Database

𝐅𝐫𝐨𝐦 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐧 𝐑𝐞𝐝𝐢𝐬 & 𝐑𝐚𝐛𝐛𝐢𝐭𝐌𝐐 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 → 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐒𝐦𝐚𝐫𝐭 𝐐𝐮𝐞𝐮𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐨𝐫 I’ve spent a good amount of time working with 𝐑𝐞𝐝𝐢𝐬 and 𝐑𝐚𝐛𝐛𝐢𝐭𝐌𝐐 in real systems — from setup (manual + K8s) to security(TLS), performance testing, load testing, and stability analysis. Also worked on optimizing consumers using 𝐛𝐚𝐭𝐜𝐡 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 to improve throughput. One thing I kept noticing: • Most systems are tightly coupled to a specific queue backend • Switching infra often means rewriting business logic So I started building something to solve that. 𝐀 𝐒𝐦𝐚𝐫𝐭 𝐐𝐮𝐞𝐮𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐨𝐫. The idea is simple: • Plug in 𝐑𝐞𝐝𝐢𝐬 𝐒𝐭𝐫𝐞𝐚𝐦𝐬 or 𝐑𝐚𝐛𝐛𝐢𝐭𝐌𝐐 • Keep the 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐥𝐨𝐠𝐢𝐜 𝐮𝐧𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐖𝐡𝐚𝐭 𝐈’𝐯𝐞 𝐛𝐮𝐢𝐥𝐭 𝐬𝐨 𝐟𝐚𝐫: • Adapter-based design to abstract queue backends • Clean configuration management using Pydantic • Minimal message model (keeping performance in mind) • FastAPI-based producer to simulate real workloads 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 When you’ve dealt with: • Failovers • Load spikes • Throughput bottlenecks You start valuing 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐢𝐬𝐨𝐥𝐚𝐭𝐢𝐨𝐧 more than anything. This project is my attempt to bring those learnings into a clean, reusable system. Next steps: • Batching (based on my RabbitMQ work) • Retry strategies + DLQ • Performance benchmarking across backends Curious — what has worked better for you in production: Redis or RabbitMQ? #Backend #SystemDesign #Python #Redis #RabbitMQ #DistributedSystems #SoftwareEngineering

☕ Understanding @Transactional in Spring Boot One annotation that quietly protects your data integrity: @Transactional It ensures multiple DB operations either: ✅ All succeed ❌ Or all rollback No partial data corruption. 🔍 Real Example @Transactional public void transferMoney(Account a, Account b, int amount) { withdraw(a, amount); deposit(b, amount); } If deposit fails → Spring rolls back withdraw automatically. 🧩 What Happens Under the Hood Spring creates a proxy around the method: 1️⃣ Start transaction 2️⃣ Execute method 3️⃣ Commit if success 4️⃣ Rollback if exception 🚨 Critical Rules Many Developers Miss • Works only on public methods • Works only on Spring-managed beans • Self-invocation bypasses transaction • RuntimeException triggers rollback by default 🧠 Production Insight Transactions define system consistency boundaries. Too large → locks & slow DB Too small → inconsistent state 💡 Best Practice Keep transactions: • Short • Focused • Database-only @Transactional is not just annotation magic — it’s a core reliability guarantee in backend systems. #SpringBoot #Java #BackendEngineering #Transactions #LearnInPublic