Java Web Development (JSP/Servlets) Services

The message left the first service at 2:14 PM and never reached the last one. Between those facts: four pipeline stages, six topics, a pub/sub API moving every record. The engineer on call had Kafdrop open, the offsets looked fine, and nobody could say which stage had eaten the record.

That gap is why we built message search for Kafdrop at a hackathon. The setup was familiar from my day job: a platform moving data between pipeline stages over a pub/sub API, with no way to ask where a message went when something breaks. Kafdrop showed partitions and offsets. It couldn't search inside the messages.

The Core Problem

In Part 1, we built a Quarkus MCP tool server. In Part 2, we secured it with agentgateway. In Part 3, we added distributed tracing. The architecture handles single-request tool calls well — but enterprise processes are rarely single-step.

Consider what happens when a developer tells Goose: "Migrate the users table to add an email column, then deploy the updated service to production." This triggers a chain of operations:

The Java compiler’s job is to check whether code follows the rules of the language. Trying to remove an element from a collection that can never contain it, or using the wrong letter in a date pattern, does not break those rules, so nothing stops the code from compiling. In short, any code that follows the rules compiles, even when it does not do what its author intended.

What Is Error Prone?

Error Prone is an open-source static analysis tool that Google built to fill this gap. Unlike most static analysis tools, it does not run as a separate step; it plugs into javac as a plugin. Using the information the compiler produces while analyzing the code, it looks for patterns that follow the rules of the language but are most likely wrong, and reports them as compiler warnings or errors.

Modern enterprise applications must process increasing data volumes while guaranteeing low latency, high availability, and predictable performance. While relational databases are essential for transactional workloads and complex queries, some applications do not fit the relational model. Systems that manage millions of user engagements, financial events, or IoT data often need a persistence solution built for distributed environments and horizontal scalability. In these cases, ScyllaDB affords a compelling option for enterprise architects.

ScyllaDB is a high-performance, distributed NoSQL database based on the wide-column data model. Inspired by Apache Cassandra and compatible with its query language (CQL), ScyllaDB uses a shard-per-core architecture to maximize hardware efficiency and preserve consistent performance under intensive workloads. When should ScyllaDB be chosen over traditional relational databases or other NoSQL options? This article examines its column-family model, compares it with Cassandra, and shows how to integrate ScyllaDB into a Java enterprise application.

What does it take to build and sustain an open-source project for more than a decade? For Shai Almog, creator of Codename One, it takes a willingness to challenge conventional approaches to software development and a continued commitment to the developers who rely on the work.

I recently caught up with Shai to discuss the inspiration behind Codename One, the realities of maintaining an open-source project, and his perspective on how the Java ecosystem has evolved over the years. Check it out below!