Stateless APIs: Externalizing State for Scalability

Revolutionizing Low-Latency Caching 🚀 Prototyping for FastCache is moving fast, and I’m excited to share some major milestones in our journey toward a high-performance, multi-tenant caching solution: ✅ Cross-Language Support: We’ve officially launched native client libraries for both Java and C++, making integration seamless for enterprise environments. ✅ High Availability: Cache replication is now live! Replicas are fully configurable to ensure your data stays safe and available. ✅ Docker Ready: Want to test it? You can pull the latest image right now: docker pull alexaborisov/fastcache:latest ✅ Open Source Clients: I’ve opened the public repository for our cache clients. Contributors join here: https://lnkd.in/emZwi-2A Join the Build! 🛠️ We are looking for contributors! Whether you are a C++ enthusiast, Java expert, come help us shape the future of distributed caching. Want to add other language - welcome Some details are available at https://lnkd.in/eQGju8MQ #DistributedSystems #Cpp #Java #CloudInfrastructure #OpenSource #Latency #Database #Docker

🚀 Discovering Spring AIO: Revolutionizing Asynchronous I/O in Java Applications In the world of backend development, efficient handling of input/output operations is key to scaling high-performance applications. Spring AIO emerges as an innovative solution that integrates native asynchronous capabilities into the Spring ecosystem, allowing concurrent request processing without blocking main threads. 🔍 What is Spring AIO and why does it matter? Spring AIO, or Asynchronous Input/Output in Spring, leverages Java NIO.2 APIs for non-blocking operations. This reduces latency in scenarios like web servers with thousands of simultaneous connections, optimizing resources and improving throughput compared to traditional synchronous models. 💡 Key benefits in practice: - Greater scalability: Handles more connections with fewer threads, ideal for microservices in containers like Docker. ⚡ - Reduction in overhead: Avoids costly context switching, freeing up CPU for business logic. 📈 - Seamless integration: Easily configured with Spring Boot, using annotations like @Async and Reactive Streams. 🔗 - Support for WebFlux: Combines with reactive programming for real-time data streams. 🌊 🛠️ Step-by-step implementation The article details how to enable AIO in a Spring project: start by configuring the event loop with Netty or similar, then adapt controllers for asynchronous operations. A simple example involves a service that reads large files without pausing the main thread, demonstrating gains of up to 50% in performance benchmarks. This approach not only accelerates development but also prepares your apps for future demands like edge computing and 5G. For more information, visit: https://enigmasecurity.cl #SpringBoot #JavaDevelopment #AsynchronousIO #BackendEngineering #TechInnovation If this summary has been useful to you, consider donating to the Enigma Security community to continue supporting more news: https://lnkd.in/er_qUAQh Connect with me on LinkedIn to discuss more about development trends: https://lnkd.in/eXXHi_Rr 📅 Thu, 05 Mar 2026 16:07:12 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

Documentation Is Not a Failure of Good Code The limits of self-documenting code. I used to believe clean code needs no comments. If you had to explain it, you'd failed. Now I know better. What Code Can't Tell You Why we chose PostgreSQL over MySQL. Why this field is optional but shouldn't be. Why this ugly workaround exists. What business rule lives here. Code tells you what. It rarely tells you why. The Balance Self-documenting code is the goal. Clear names. Small functions. Obvious structure. But when the why matters, write it down. Future you will be grateful. What I Do Comment the decisions, not the mechanics. Document the trade-offs. Explain the historical accidents. Good code + good documentation = something someone else can maintain. Do you document the why or just the what? #Documentation #CleanCode #SoftwareEngineering #KnowledgeSharing #Maintainability #Java

6+ years into backend development, one thing I’ve learned — performance is where real engineering begins. In one of my recent projects, we were facing slow API response times under high load. Here’s what I did to improve performance by ~40%: • Identified bottlenecks using logs and query analysis • Optimized database queries and indexing • Introduced Redis caching for frequently accessed data • Reduced unnecessary data processing in service layer • Improved API response structure to avoid over-fetching Result: ✔ Faster response time ✔ Reduced DB load ✔ Better user experience during peak traffic Key takeaway: Most performance issues are not solved by scaling infra, but by writing efficient code and optimizing data access. What’s one performance issue you’ve solved recently? #Java #SpringBoot #Microservices #BackendDevelopment #PerformanceOptimization #SoftwareEngineering

I have been recently working on spring-web-query, an open-source library to make filtering, sorting and pagination in Spring Boot based APIs easy to implement. The idea came from seeing the same query parsing, validation, and mapping logic repeated across projects. I wanted a cleaner way to define query behaviour up front while keeping APIs flexible and safe. The result is a library that supports declarative querying with RSQL (RESTful Service Query Language, a URI-friendly query language), DTO-aware contracts, nested field paths, and Spring Boot auto-configuration. If that sounds useful, I'd love for you to check it out and share feedback: https://lnkd.in/g5NMs2mV I’ve been actively working on this, with 12 releases already shipped in the last month. It’s evolving quickly, and I’m continuing to improve it based on real usage and feedback. Contributions, ideas, and feedback are all welcome. #Java #SpringBoot #Spring #SpringDataJPA #OpenSource #BackendDevelopment #APIDesign #SoftwareEngineering

Not every performance issue is caused by bad code. Sometimes, it’s caused by good code running in the wrong design. In modern Java-based microservices, system design plays a bigger role than ever. Even well-written services can struggle if architecture decisions aren’t aligned with scale and usage patterns. Key areas that make a real difference: • API design – Clear contracts and efficient payloads reduce unnecessary load • Database interaction – Poor queries or excessive calls can quickly become bottlenecks • Service communication – Choosing the right patterns (sync vs async) impacts latency and reliability • Concurrency handling – Using modern features like Virtual Threads effectively • Caching strategies – Reducing repeated computations and database hits • Error handling – Designing graceful fallbacks instead of hard failures • Monitoring & tracing – Identifying issues before they impact users The biggest learning? Optimization isn’t something you do at the end—it’s something you design for from the beginning. Java provides the tools, but it’s how we use them that defines system performance and reliability. Strong design + clean code = systems that scale. #Java #Microservices #BackendDevelopment #SoftwareEngineering #SystemDesign #Performance #TechTrends

#Post3 In the previous post, we understood the role of @RestController in building APIs. Now the next step is 👇 How do we map HTTP requests to methods? That’s where mapping annotations come in 🔥 In Spring Boot, we use: • @GetMapping → for GET requests • @PostMapping → for POST requests • @PutMapping → for UPDATE • @DeleteMapping → for DELETE • @PatchMapping → for partial updates Example: @GetMapping("/users") → fetch all users @PostMapping("/users") → create a new user 💡 What about @RequestMapping? @RequestMapping is a generic annotation that can handle all HTTP methods. Example: @RequestMapping(value="/users", method=RequestMethod.GET) 👉 But in modern Spring Boot, we prefer specific annotations like @GetMapping for cleaner and readable code Key takeaway: Use specific mapping annotations for better clarity and maintainability 👍 In the next post, we will understand how @RequestBody works in handling request data 🔥 #Java #SpringBoot #BackendDevelopment #RESTAPI #LearnInPublic

I built a distributed rate limiter in Java to explore a scaling problem I’ve seen multiple times: naive rate limiting doesn’t scale. The usual approach: request → datastore → decision works fine… until your datastore becomes the bottleneck. So I tried a different design: - local counters - batched updates - async flush to datastore This reduces distributed operations dramatically while keeping latency low. Of course, this comes with trade-offs: you lose strict accuracy in favor of throughput. I documented the architecture, trade-offs, concurrency model, and benchmarks in my GitHub repository. If you're dealing with high-throughput systems or rate limiting challenges, I’d be happy to exchange ideas.

@Transactional is the most used — and most misunderstood — annotation in Spring Boot. Here's what's actually happening when you add it. 🧵 Save this. It will save you from a 2am production incident. ─── WHAT MOST DEVELOPERS THINK ─── "It wraps my method in a database transaction." ─── WHAT IT ACTUALLY DOES ─── Spring creates a proxy around your class at startup. When you call a @Transactional method, you're not calling your class directly you're calling Spring's proxy, which: 1. Opens a database connection 2. Begins a transaction 3. Calls your actual method 4. Commits on success 5. Rolls back on unchecked exception (RuntimeException) 6. Does NOT rollback on checked exceptions by default ─── THE TRAPS NOBODY WARNS YOU ABOUT ─── ⚠ Self-invocation doesn't work If method A() calls @Transactional method B() in the SAME class — the proxy is bypassed. No transaction. Silence. Bugs. ⚠ Default isolation level is READ_COMMITTED Dirty reads are prevented. Phantom reads are not. For financial data, you need SERIALIZABLE. ⚠ Checked exceptions don't trigger rollback new IOException() will NOT rollback your transaction unless you add: @Transactional(rollbackFor = Exception.class) ⚠ Transaction scope matters @Transactional on a public method called from another class = works. @Transactional on a private method = silently ignored. Always. ─── THE RIGHT MENTAL MODEL ─── @Transactional is not magic. It's a proxy. Understand the proxy. Which of these caught you off guard? 👇 #SpringBoot #Java #Transactional #BackendDevelopment #SoftwareEngineering #JavaDeveloper #SpringFramework #Database

For months I debated with myself: should the FIXT engine for Beam run on Node.js or Java? Beam is the multi-tenancy infrastructure-as-a-service platform for stock brokers that I’ve been building. At the core of it sits a FIX engine that connects to the exchange, processes market data, and distributes events to the rest of the platform. I initially built it with Node.js. And to be fair, Node worked. I had a full session manager, FIX message parser, orchestration layer, and the surrounding services all running. But something kept bothering me. The socket connection would occasionally drop, and keeping everything stable required a lot of wiring and careful tuning. At the same time, NGX was pushing gigabytes of market data daily. The service had to parse FIX messages, normalize the data, run computations, and publish events over RabbitMQ so downstream services could analyze data, build candles, update ledgers, and feed the frontend. Some days it felt like I could wake up and the service might just stop working. My main reason for choosing Node.js at the beginning was simple: I didn’t want to introduce a new technology into the team. But eventually I had to admit something — performance and reliability matter more for infrastructure like this. So over the weekend I dusted off my Java sleeves and rebuilt the entire FIX engine. The new architecture is straightforward and resilient: Trading services send commands through an exchange → The FIX engine processes them and communicates with NGX → Responses from the exchange are streamed back → Events are published to RabbitMQ → Other services consume them instantly. The result is a fast, reliable, battle-tested system capable of processing gigabytes of data daily without breaking a sweat. Sometimes engineering is about building things. But often it’s about choosing the right tool for the job.