- How AI Is Rewriting Full-Stack Java Systems: Practical Patterns with Spring Boot, Kafka and WebSockets
Building real-time applications means balancing user responsiveness with heavy backend processing. A proven solution is to decouple heavy workloads using events and asynchronous processing. In this approach, a Spring Boot application quickly publishes events to Kafka instead of processing requests inline. Then Kafka consumers (with AI/ML logic) handle the data in the background, and the results are pushed to clients in real time via WebSockets. This article highlights three key patterns enabling this architecture:
- Event Production with Spring Boot and Kafka
- AI-Driven Processing in Kafka Consumers
- Real-Time WebSocket Delivery to the Frontend
Event Production with Spring Boot and Kafka
The first step is capturing an event and publishing it to Kafka. By offloading work to Kafka the application can respond immediately to the user without waiting for processing. Spring Boot’s integration with Apache Kafka provides a KafkaTemplate to send messages to topics.
- How to Test PUT API Request Using REST-Assured Java
PUT requests are typically used for updating an existing resource. This means replacing the current data for the target resource with the data sent in the API request body.
Just like POST requests, the content-type header is important because it tells the server how to interpret the data we’re sending.
- Comparing Top Gen AI Frameworks for Java in 2026
Java has always been a serious language for production systems, and in 2026, the Generative AI ecosystem has finally caught up. For years, Java developers watched from the sidelines as Python and TypeScript accumulated framework after framework for building LLM-powered applications. Today, the picture is very different. Java has multiple mature, actively maintained AI frameworks, each with its own philosophy and trade-offs.
This article covers the four frameworks I have personally used to ship Java AI applications: Genkit Java, Spring AI, LangChain4j, and Google ADK Java. Each one represents a meaningfully different bet on what a Java AI framework should be, and understanding those differences will save you from picking the wrong tool.
- Java ProcessBuilder: Deadlocks, Zombies, and the 64 KB Wall
Recently at IBM Software Labs, I worked on a task that forced me to understand something many Java developers rarely think about — how Java interacts with the operating system.
Most of our daily work happens safely inside the JVM. Memory management, threads, and file handling — the JVM abstracts these away nicely.
- Java Backend Development in the Era of Kubernetes and Docker
We moved our monolithic Java application to Kubernetes last year. The promise was scalability and resilience. The reality was a series of silent failures during deployments. Users reported dropped connections every time we pushed a new version. Our monitoring showed zero downtime, but the customer experience told a different story. Requests vanished into the void during rolling updates. We spent weeks chasing network ghosts before finding the root cause. The issue was not the network. It was how our Java application handled termination signals.
In this article, I will share how we adapted our Java backend for container orchestration. I will explain the specific lifecycle issues we encountered. I will detail the configuration changes that solved the dropout problem. This is not a guide on writing Dockerfiles. It is a record of the operational friction we faced when Java met Kubernetes. Building cloud-native Java apps requires more than just packaging a JAR. It requires understanding how the orchestration layer interacts with the JVM.