The Code Hut

Building event-driven microservices is only half the battle. To run them in production, you need observability, monitoring, and reliability practices that ensure your system behaves as expected under load, failures, and unexpected events. This post covers logging, metrics, tracing, and fault tolerance strategies for Java microservices using Kafka and Spring Boot. 1. 🌐 Observability Basics Observability lets you understand what’s happening inside your microservices by collecting: Metrics: Numeric indicators of system health (latency, throughput, error rates) Logs: Event records that help diagnose issues Tracing: Tracks requests across distributed services Popular tools: Prometheus + Grafana for metrics, ELK/EFK stack for logs, Jaeger/OpenTelemetry for tracing. 2. 📝 Structured Logging Use structured logging to make logs machine-readable and easier to analyze: @Slf4j @Service public class OrderConsumer { @KafkaListener(topics = "orders-topic...

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...