Java Web Development (JSP/Servlets) Services

Running Apache Flink on a mainframe sounds odd at first. A modern stream processing engine on a platform most people call legacy? But take a closer look. It is not only possible. It might be a smart move for some of the largest financial institutions in the world. This post explores why some enterprises want Apache Flink on the mainframe, how it could work, and whether it is a brilliant innovation or a technical detour.

Disclaimer: The views and opinions expressed in this blog are strictly my own and do not necessarily reflect the official policy or position of my employer.

If you ask Java developers about the concept of ‘Sneaky Throws,’ I am almost sure there will be a couple of opinions that are quite differently expressed, but similar in their meaning. Some will sum it up as being able to throw checked exceptions without declaring them explicitly; others will amend that it means writing functional-style code (lambdas) and being allowed to call methods that throw checked exceptions. 

Most probably, it will be surely mentioned that there’s a Lombok annotation called exactly @SneakyThrows that solves the problem immediately when put on a method. Last but not least, to outline it in a more pragmatic manner, the concept allows tricking the Java compiler into treating checked exceptions as runtime exceptions.

In a previous article, Working with Spreadsheets in Java: A Practical Overview, we walked through the common scenarios where Java applications need to interact with spreadsheets and the categories of tools available for the job. One of the factors mentioned there was support for modern Excel formulas — a topic that deserves more space than a single bullet point.

Java applications interact with Excel more often than most teams plan for: file uploads from finance, calculation logic authored in a workbook, reporting exports back to business users. The files these users produce today are not the same as the files they produced five years ago. Excel 365 and Excel 2021 introduced a new formula model, and workbooks authored in those versions routinely use it. Depending on which library you use, those formulas may evaluate correctly, fail silently with stale cached values, or throw exceptions at recalculation time.

A pull request arrives. A few hundred lines of Java implementing the new discount rule: tiered thresholds, a regional exception, something about loyalty tiers that nobody can quite explain. It compiles. The tests pass. An LLM wrote it in about forty seconds.

Now: who reviews it?

Every Java developer who runs services on Kubernetes has watched this scene play out. Traffic spikes, the autoscaler adds a pod, and then everyone waits. The container is running in two seconds. The application is not ready for another twelve seconds. During those ten seconds, your existing pods absorb the extra load, latency climbs, and if things are bad enough, the autoscaler panics and adds even more pods that are also not ready.

I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work