Handling Concurrency with Distributed Locking in Distributed Systems

Want to be a great backend engineer? Learn: 1. Programming Fundamentals Language depth, data structures, algorithms, concurrency, memory basics 2. APIs and Contracts REST, gRPC basics, versioning, idempotency, pagination, error handling 3. Databases SQL, joins, indexes, transactions, query optimization, schema design 4. Caching Redis, cache invalidation, TTLs, read-through vs write-through, hot key problems 5. Async and Messaging Queues, Kafka/RabbitMQ, retries, DLQs, at-least-once delivery, idempotent consumers 6. Distributed Systems Basics Replication, partitioning, consistency, leader-follower, network failures, backpressure 7. Reliability Engineering Timeouts, retries, circuit breakers, rate limiting, graceful degradation 8. Observability Logs, metrics, tracing, p95/p99 latency, debugging production issues 9. Infrastructure and Deployment Docker, CI/CD, Linux basics, cloud services, rollbacks, blue-green/canary deploys 10. Security Auth, authorization, secrets management, input validation, encryption, secure defaults Preparing for interviews? Start revising these today 𝗜’𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗶𝗻 𝗗𝗲𝗽𝘁𝗵 𝗝𝗮𝘃𝗮 𝗦𝗽𝗿𝗶𝗻𝗴𝗯𝗼𝗼𝘁 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗚𝘂𝗶𝗱𝗲, 𝟏𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dfhsJKMj keep learning, keep sharing ! #java #backend #javaresources

Reflecting on the evolution of backend engineering, it's evident that the right technology stack can significantly enhance system reliability and speed. Over the past few years, I have explored remarkable technologies while developing high-throughput distributed systems. Here are the core technologies I currently leverage to build scalable, production-grade architectures: 🏗️ Distributed Microservices & Messaging Building services that handle over 100,000 daily requests requires a resilient communication layer. - Java (Spring Boot) & Python (FastAPI/Flask): My preferred choices for creating modular, high-performance services. - Apache Kafka & RabbitMQ: Crucial for event-driven architectures, I recently observed a reduction in message delays from 8 minutes to 90 seconds using Kafka. - gRPC & REST: Facilitating seamless service-to-service communication. ⚡ Performance & Data Persistence Efficiency lies in the details of the database and caching layers. - PostgreSQL & MySQL: Optimizing complex queries to decrease execution time from seconds to milliseconds. - Redis: My top choice for caching, significantly cutting latency and reducing repeated database reads by tens of thousands per day. ☁️ Cloud & Reliability Scalability is only as effective as the infrastructure that supports it. - AWS (EC2, S3, Lambda, RDS): Utilizing cloud-native tools for global deployment and scaling. - Kubernetes & Docker: Standardizing environments and automating container orchestration. - Prometheus & ELK Stack: Implementing real-time monitoring to establish circuit breakers and prevent hours of potential downtime. As technology continues to evolve, the objective remains consistent: to build systems that are both reliable and fast. #SoftwareEngineering #BackendDeveloper #Java #Python #Microservices #CloudComputing #Kafka #SystemDesign #TechStack #DellTechnologies

🚀 Backend Learning | Caching Patterns for High-Performance Systems While working on backend systems, I recently explored different caching strategies used to improve performance and scalability. 🔹 The Problem: • Frequent database hits increasing latency • High load under traffic • Need for faster response times 🔹 What I Learned: • Cache Aside (Lazy Loading): Load data into cache on demand • Write Through: Write to cache and DB simultaneously • Write Back (Write Behind): Write to cache first, DB updated later 🔹 Key Insights: • Cache Aside → Simple & widely used • Write Through → Strong consistency • Write Back → High performance but complex 🔹 Outcome: • Reduced database load • Faster API responses • Better system performance Caching is not just about storing data — it’s about choosing the right strategy. 🚀 #Java #SpringBoot #Redis #SystemDesign #BackendDevelopment #Caching #LearningInPublic

🚨 Most backend engineers think they’re good at debugging. Until production breaks. Anyone can write code. Anyone can build APIs. But when a microservice fails at 2 AM… ⌛ logs are messy… and nothing makes sense... That’s where real engineers are different. Here’s what actually helps: • Reading logs like a story, not just scanning errors • Understanding system flow across microservices • Knowing how APIs, databases, and services interact • Reproducing issues before writing a single fix • Staying calm under pressure (this is underrated) Whether it’s Spring Boot, distributed systems, or AWS, debugging exposes how deep your understanding really is. Good developers write code. Great engineers debug systems. 💡 #BackendEngineer #Java #SpringBoot #Microservices #SystemDesign #AWS #APIs #Debugging #SoftwareEngineering #dotnet #csharp

💻 Turning ideas into scalable backend systems… one API at a time 🚀 Over the past 2.5+ years, I’ve been building robust backend solutions using Python & Django — focusing on performance, security, and real-world impact. ✨ What I’ve been working on: 🔐 Secure authentication systems (JWT & RBAC) ⚡ High-performance REST APIs & PostgreSQL optimization 🧩 Scalable microservices architecture 🚀 Caching & performance tuning (Redis / Memcached) 🔗 Third-party API integrations 📈 Currently leveling up with CI/CD, system design, and deployment workflows to build more efficient and scalable systems. I enjoy solving challenging problems and collaborating with teams that build meaningful products. 🔥 Accepting new challenges and leveling up my skills in the backend/Python ecosystem. If you’re building something exciting — let’s connect 🤝 #PythonDeveloper #Django #BackendDevelopment #APIs #PostgreSQL #Microservices #TechCareers

A backend API can be super fast in development… but still fail badly in production. I’ve seen this happen in real projects where everything looked fine in lower environments, but once real traffic hit, the issues started: Slow response times Timeout failures Retry storms Database bottlenecks Kafka/event lag Service-to-service dependency failures The real problem usually isn’t just the code. It’s the system design around the API. What helped fix it: Redis caching for repeated reads Kafka for async event processing Better Spring Boot service optimization Reducing unnecessary downstream calls Stronger retry / timeout handling Better monitoring with logs and metrics Smarter Docker / Kubernetes scaling My biggest takeaway: A “fast API” is not the same as a production-ready API. Production performance is really about: stability scalability resilience observability That’s where real backend engineering starts. #Java #SpringBoot #Microservices #Kafka #BackendDevelopment #Redis #Kubernetes #APIDesign #SoftwareEngineering #SystemDesign #CloudComputing

Amazon's CodeWhisperer just released real-time vulnerability scanning that caught 73% more security flaws than static analysis tools in my latest Spring Boot project. This AI-powered code assistant now integrates directly with IntelliJ and VS Code, scanning your Java code as you type. It identifies SQL injection risks, dependency vulnerabilities, and authentication bypasses before they reach your repository. The tool costs $19 per developer monthly for the professional tier, with enterprise features starting at $39. For Java teams managing microservices architectures, this shifts security left in a meaningful way. Traditional security scans happen during CI/CD or periodic audits, often catching issues after significant development investment. CodeWhisperer's real-time feedback means architects can enforce security patterns during the actual coding process, not as an afterthought. As someone who has implemented security reviews across multiple enterprise Java platforms, I see this as a genuine productivity multiplier. The false positive rate sits around 15%, which beats most static analysis tools I have used. However, the real value lies in its understanding of Spring Security configurations and JPA query patterns. It caught three authentication bypass scenarios in legacy code that our existing SonarQube setup missed entirely. The pricing puts it in direct competition with GitHub Copilot Enterprise, but CodeWhisperer's security focus gives it a distinct advantage for regulated industries. Financial services and healthcare organizations will find the compliance reporting features particularly valuable. What security vulnerabilities are you discovering too late in your development cycle right now? #AI #Java #SpringBoot #SoftwareArchitecture #TechLeadership #AIStrategy #GenerativeAI #SystemDesign #JavaDeveloper #EngineeringManager #AIAdoption #APIs

Requirement: FIAS Data Sync Middleware Apply for this new project https://lnkd.in/dxYbZEpZ I need a compact middleware script—your choice of Python or Node.js—that pulls JSON payloads from a REST endpoint hosted on AWS and forwards them, in near real-time, to a local device that only understands the FIAS protocol over raw TCP/IP sockets. The flow is straightforward: authenticate to the AWS API, poll or subscribe for new records, validate and, when necessary, transform the JSON, store or stage it in the local PostgreSQL instance, then push each record down the wire using FIAS commands so the on-prem device stays perfectly in sync with the cloud source. Robust logging, reconnection logic, and graceful error handling are essential because the local connection can be unreliable. Configuration items such as AWS credentials, polling interval, socket host/port, and retry limits should live in a separate file or environment variables for quick tweaking without code edits. Deliverables • Clean, well-commented source code (Python or Node.js) • Sample config file with placeholders for secrets • Setup instructions and one-command launch script (systemd service file is a plus) • Short README that documents the data flow, FIAS message structure you implemented, and how to extend the field mapping • Proof-of-concept run that shows data fetched from AWS, written into PostgreSQL, and echoed by the local device via FIAS If you have prior experience with socket programming, FIAS, or AWS SDKs, let me know—otherwise I’m happy to share sample payloads and the device’s FIAS spec so you can start right away. right away. Skills Required Python NoSQL Couch & Mongo Amazon Web Services Node.js PostgreSQL JSON API Development REST API Mobisium → mkt@mobisium.com pratham.parab@mobisium.com Let’s build something impactful together at MOBISIUM #Hiring #BackendDevelopment #AWS #APIDevelopment #Python #NodeJS #PostgreSQL #SocketProgramming #SystemIntegration #Mobisium

Everything works perfectly… until concurrency hits your system. One request becomes hundreds. One thread becomes many. And suddenly… your “working code” starts breaking. Let’s be clear: Concurrency is NOT just about threads. It’s about how your system behaves under pressure. There are 3 layers where most systems fail 👇 1️⃣ Application Lock Using synchronized, ReentrantLock, Atomic classes ✔ Fast ❌ Works only inside a single JVM → Breaks in distributed systems 2️⃣ Database Lock Optimistic (@Version) & Pessimistic locking ✔ Ensures data consistency ❌ Adds latency and contention 3️⃣ Distributed Lock Redis, Zookeeper, Hazelcast ✔ Works across multiple services ✔ Prevents duplicate processing (payments, schedulers) ❌ Complex and needs careful design And then comes the most misunderstood concept: Isolation Level. Most developers think @Transactional = safe. It’s NOT. Isolation defines how transactions see each other: → READ_COMMITTED → REPEATABLE_READ → SERIALIZABLE (strongest) SERIALIZABLE sounds perfect… But in reality? ❌ Slower ❌ Higher lock contention ❌ Possible deadlocks Real systems don’t rely on one solution. They combine: Application control + DB consistency + Distributed coordination That’s how scalable systems are built. Because in production… It’s not your logic that fails. It’s your concurrency design. ## #SystemDesign #SpringBoot #BackendEngineering #Concurrency #Microservices #Java