The Code Hut

Now that we’ve covered the design principles of event-driven microservices, it’s time to implement them in Java using Spring Boot and Apache Kafka . This post walks through creating producers, consumers, handling retries, and ensuring messages are processed reliably. 1. 🌐 Setting Up Kafka with Spring Boot Spring Boot provides excellent support for Kafka via spring-kafka . First, add the dependency: org.springframework.kafka spring-kafka 3.0.0 Then configure the producer and consumer in application.yml : spring: kafka: bootstrap-servers: localhost:9092 consumer: group-id: orders-group auto-offset-reset: earliest key-deserializer: org.apache.kafka.common.serialization.StringDeserializer value-deserializer: org.springframework.kafka.support.serializer.JsonDeserializer producer: key-serializer: org.apache.kafka.common.serialization.StringSerializer value-serializer: org.springframework.kafka.support.serializ...

Event-driven microservices allow distributed systems to communicate asynchronously, decoupling services and improving scalability, reliability, and responsiveness. In this post, we explore how to design event-driven microservices in Java, focusing on architecture patterns, messaging strategies, and best practices for production-ready systems. 1. 🌐 Understanding Event-Driven Microservices In event-driven microservices, services communicate by producing and consuming events rather than direct API calls. This decouples service dependencies and allows systems to react to changes asynchronously. Key Concepts Event: A record of something that happened, e.g., OrderCreated . Producer: The service that emits events. Consumer: The service that reacts to events. Event Broker: Middleware (Kafka, RabbitMQ, etc.) that transports events reliably. Event Schema: Defines the structure of an event (JSON, Avro, Protobuf). 2. ⚡ Common Design Patterns Publish-Subscrib...

SQL performance issues often come from common anti-patterns. Knowing these mistakes helps you write faster, cleaner, and maintainable queries. 1. 🚫 SELECT * (Lazy Column Fetching) -- BAD SELECT * FROM users; -- GOOD SELECT id, username FROM users; ✅ Always select only the columns you need. 2. 📦 Storing Comma-Separated Values -- BAD: hobbies column "reading,swimming,gaming" SELECT * FROM users WHERE hobbies LIKE '%swimming%'; -- GOOD: normalized table CREATE TABLE user_hobbies ( user_id INT, hobby VARCHAR(50) ); ✅ Normalize data for better performance and maintainability. 3. 🔍 LIKE with Leading Wildcards -- BAD SELECT * FROM products WHERE name LIKE '%phone'; -- GOOD SELECT * FROM products WHERE name LIKE 'phone%'; ✅ Avoid leading wildcards; consider full-text search if needed. 4. 🧱 Ignoring Indexes CREATE INDEX idx_user_id ON orders(user_id); ✅ Index columns used in WHERE, JOIN, and ORDER BY clauses. 5. 🔄 Subque...

When building distributed systems in Java, services need to communicate efficiently and reliably. Two of the most common approaches are REST over HTTP and gRPC . While both enable service-to-service communication, they differ significantly in protocol, performance, tooling, and design philosophy. This post compares REST (using Spring Web / WebClient) and gRPC from a Java developer’s perspective, with concrete examples, trade-offs, and guidance on when to choose each. 1. 🌐 REST (HTTP + JSON) REST (Representational State Transfer) is an architectural style built on HTTP semantics. It exposes resources via URLs and uses HTTP verbs such as GET , POST , PUT , and DELETE . Payloads are typically JSON. Key Characteristics Resource-oriented (URLs represent entities) Uses HTTP semantics (status codes, headers, caching) Human-readable JSON payloads Broad ecosystem support (browsers, curl, Postman) Spring REST Controller — Example @RestController @RequestMapping(...

Keeping up with the latest in Spring and testing frameworks is essential for building scalable, reactive, and maintainable applications . Here’s a complete and updated overview of what’s new in Spring Boot 4 , Spring Framework 7 , and JUnit 6 — including real examples, architectural insights, and practical features for enterprise microservices. 🚀 1️⃣ Spring Boot 4 — Key Enhancements 💨 Faster startup and lower memory footprint using improved AOT ☁️ Better cloud-native support and Spring Cloud integration 📊 Observability by default (Micrometer 2 + OpenTelemetry) 🧩 Complete modularization — giant auto-config JAR split into dozens of small modules 🔒 Built-in null-safety using JSpecify annotations 🛠️ New @ConfigurationPropertiesSource for modular configuration metadata 📉 Improved SSL health reporting (e.g., expiringChains ) 📝 Fully optimized for GraalVM native images ⚡ Virtual threads support (Java 21/25) for 10,000+ concurrent requests 🗂️ First-...

Software development involves a lot of terminology and acronyms — many reused across domains. This reference sheet summarizes the most common ones every modern developer should know. 📘 🔧 Core Development Concepts SDLC — Software Development Life Cycle: process from planning to maintenance. CI/CD — Continuous Integration / Continuous Delivery: automated build, test, and deployment. API — Application Programming Interface: contract for communication between systems. SDK — Software Development Kit: tools for building apps on a platform. CLI — Command-Line Interface: text-based control interface for developers. ☁️ Cloud & Infrastructure IaaS — Infrastructure as a Service (e.g., Azure VM, AWS EC2). PaaS — Platform as a Service (e.g., Azure App Service, Heroku). SaaS — Software as a Service (e.g., Office 365, Salesforce). IaC — Infrastructure as Code: provisioning infra via code (Terraform, ARM, CloudFormation). GitOps — Managing infra and...