j-vogel

Good catches from several of you. The original fix dropped the try-catch entirely which was a regression for overflow and edge cases like a bare "-". Updated the post to keep a try-catch around the final parseInt as a safety net. The pre-validation still avoids the expensive path for the common cases, which is the core point. Appreciate the feedback.

Author here. DB and external service calls are often the biggest wins, thanks for calling that out.

In my demo app, the CPU hotspots were entirely in application code, not I/O wait. And across a fleet, even "smaller" gains in CPU and heap compound into real cost and throughput differences. They're different problems, but your point is valid. Goal here is to get more folks thinking about other aspects of performance especially when the software is running at scale.

Author here. Great callout. That's the -XX:+OmitStackTraceInFastThrow optimization, been around since JDK 5. The C2 compiler detects exceptions thrown repeatedly from the same site and starts reusing a preallocated instance without filling the stack trace. Good for performance, but it makes debugging harder in production since you lose the trace. You can disable it with -XX:-OmitStackTraceInFastThrow if you need the traces back.

Fair point on ecosystem decisions, that's basically the thesis of the post. These patterns aren't Java being slow, they're developers (myself included) writing code that looks fine but works against the JVM. Enterprise Java gets a bad rap partly because these patterns compound silently across large codebases and nobody profiles until something breaks.

I'm a fan of Rust too. But there are millions of Java applications running in production right now, and some of them are running these anti-patterns today. Not everyone has the option to rewrite in a different language. For those teams, knowing what to look for in a profiler can make a real difference without changing a single dependency.