Abstract Algorithms’ Post

While working on backend systems, we’ve traditionally used ExecutorService to handle concurrent transactions — especially for processing shipping data and storing it in the database. It works well… but managing threads at scale always needs careful tuning. Recently, I started evaluating virtual threads (Java 21) as an alternative. On paper, they look great: ✔️ Lightweight threads ✔️ Better scalability for concurrent tasks ✔️ Simpler code compared to complex thread pools But while exploring this, a few practical concerns came up: ❌ Not all operations benefit → CPU-heavy tasks don’t gain much ❌ Blocking calls still matter → If you block on I/O without proper handling, gains reduce ❌ Debugging & monitoring → Traditional tools may not give clear visibility ❌ Library compatibility → Some older libraries may not be optimized for virtual threads 🧠 What I realized: Virtual threads are powerful… but they need to be used in the right kind of workload. 💡 My takeaway: They are great for I/O-heavy, high-concurrency systems — but not a direct replacement for everything. Still exploring this space, especially how it fits into real production systems. Would love to hear if anyone has tried virtual threads in real-world use cases. #Java #Java21 #VirtualThreads #Backend #Concurrency #SystemDesign #SoftwareEngineering

I fixed a 4-second Spring Boot API without scaling anything. ⚠️the real problem? 47 database queries per request. 🧩we thought it was a performance issue. so we tried: • more RAM • indexes • monitoring CPU nothing changed. 🔍then I checked the SQL logs. and saw the real issue: → N+1 query problem → JPA lazy loading → 47 queries for a single API call 🛠️ the fix was simple: replace multiple queries with one optimized query: JOIN FETCH everything in a single DB call. 📉 result: • 4000ms → 190ms • 47 queries → 1 • DB CPU dropped massively 📌 lesson: don’t scale first. first understand what your code is doing to the database. #SpringBoot #Java #Backend #SystemDesign #APIs #SoftwareEngineering

🚨 Without connection pooling, your DB will struggle under load Spring Boot uses HikariCP by default. But default config isn’t always optimal. 💥 Issue I faced: Under load → DB connections exhausted Root cause: Pool size too small for concurrent traffic ✅ Fix: - Tuned max pool size - Monitored active connections 💡 Takeaway: Database is often the bottleneck. Connection pooling decides how well you scale. #Java #SpringBoot #BackendDevelopment #SoftwareEngineering  #Microservices #JPA #RESTAPI  #DeveloperLife #CareerGrowth

A small mistake in a Spring Boot API can quietly kill performance. I’ve seen endpoints that should respond in ~100ms end up taking 4+ seconds, even though CPU, memory, and database indexes all look fine. In many cases the issue turns out to be something simple like a findAll() call in a JPA repository that loads far more data than needed and triggers an N+1 query problem. Just replacing it with a targeted query or DTO projection can reduce the response time dramatically. Sometimes performance problems are not about infrastructure or scaling. They’re about how we write our queries. Curious to hear from other backend engineers — what’s the smallest code change that gave you the biggest performance improvement? #Java #SpringBoot #BackendDevelopment

Topic: Pagination in APIs Returning all data at once is one of the fastest ways to slow down your system. In real-world applications, datasets can be huge. Without pagination, APIs may: • Return massive payloads • Increase response time • Overload servers • Impact user experience Pagination helps by: • Limiting data per request • Improving performance • Reducing memory usage • Making APIs more scalable Common approaches: • Offset-based pagination • Cursor-based pagination Good API design always considers how data will grow over time. Because what works for 100 records won’t work for 1 million. How do you handle pagination in your APIs? #API #BackendDevelopment #Microservices #Java #SystemDesign

#Post6 In the previous posts, we built basic REST APIs step by step. But what happens when something goes wrong? 🤔 Example: User not found Invalid input Server error 👉 By default, Spring Boot returns a generic error response. But in real applications, we need proper and meaningful error handling. That’s where Exception Handling comes in 🔥 Instead of handling exceptions in every method, Spring provides a better approach using @ControllerAdvice 👉 It allows us to handle exceptions globally Example: @ControllerAdvice public class GlobalExceptionHandler { @ExceptionHandler(Exception.class) public String handleException(Exception ex) { return ex.getMessage(); } } 💡 Why use this? • Centralized error handling • Cleaner controller code • Better API response Key takeaway: Use global exception handling to manage errors in a clean and scalable way 🚀 In the next post, we will create custom exceptions for better control 🔥 #Java #SpringBoot #BackendDevelopment #RESTAPI #LearnInPublic

🚀 Spring Boot Tip for Faster Applications One simple improvement that can make a big difference in Spring Boot applications is database connection pooling. Instead of opening a new database connection for every request, Spring Boot uses HikariCP by default to manage connections efficiently. Why it matters: ⚡ Faster response times 📉 Reduced database overhead 🔁 Better handling of high traffic A few useful configurations: • maximumPoolSize – controls the number of connections • connectionTimeout – how long a request waits for a connection • idleTimeout – closes unused connections Optimizing database connections is a small change that can significantly improve application performance. Sometimes performance improvements don’t come from complex architecture, but from tuning the fundamentals. #SpringBoot #Java #BackendDevelopment #JavaDeveloper #Microservices #SoftwareEngineering #TechTips

Your API is slow… and it’s not always the database. 🚨 One mistake I see often: We blame the DB first. But the real bottleneck is somewhere else. Here’s what actually slows down APIs 👇 1️⃣ Thread pool exhaustion Too many requests, not enough threads → requests start waiting 2️⃣ Blocking calls External APIs, slow services → threads get stuck doing nothing 3️⃣ N+1 queries Looks fine in code, explodes in production 4️⃣ Connection pool limits DB is fast… but connections are not available 5️⃣ Serialization overhead Large responses = more CPU + slower network What I’ve learned: Performance issues are rarely in one place. They’re usually a chain of small inefficiencies. Fixing just the database won’t save you. You need to look at the system as a whole. When you debug a slow API… where do you start? 👇 #Java #SpringBoot #BackendDevelopment #Performance #SystemDesign #Microservices #SoftwareEngineer

Most developers think Spring handles 500 requests “all at once.” It doesn’t. Here’s what actually happens: Spring Boot (via embedded Tomcat in Spring Boot) uses a thread pool to handle incoming requests. Each request follows this path: → Request arrives at Tomcat → A free thread is assigned → DispatcherServlet routes it to your @RestController → Controller → Service → Database → Response is returned → Thread goes back to the pool That’s it. No magic. ━━━━━━━━━━━━━━━━━━━━ What happens when all threads are busy? → New requests are placed in a queue → If the queue is full, requests may be rejected (e.g., 503 errors depending on configuration) ━━━━━━━━━━━━━━━━━━━━ The real bottleneck isn’t traffic, it’s blocked threads. Consider this: A slow database call takes 3 seconds × 200 threads = Your system can stall under moderate load This is why backend engineers focus on: Thread pool tuning Reducing blocking operations Asynchronous processing (@Async) Efficient database access (connection pooling) Non-blocking architectures (Spring WebFlux) Key takeaway: Performance is not about handling more requests. It’s about how efficiently your threads are utilized. #Java #SpringBoot #Concurrency #BackendDevelopment #SoftwareEngineering #JavaDeveloper

How Spring Boot Handles Requests Internally (Deep Dive) Ever wondered what happens when you hit an API in Spring Boot? 🤔 Here’s the real flow 👇 🔹 DispatcherServlet Acts as the front controller receives all incoming requests 🔹 Handler Mapping Maps the request to the correct controller method 🔹 Controller Layer Handles request & sends response 🔹 Service Layer Contains business logic 🔹 Repository Layer Interacts with database using JPA/Hibernate 🔹 Response Handling Spring converts response into JSON using Jackson 🔹 Exception Handling Handled globally using @ControllerAdvice 💡 Understanding this flow helped me debug issues faster and design better APIs. #Java #SpringBoot #BackendDeveloper #Microservices #RESTAPI #FullStackDeveloper #LearningInPublic