Java at 30: Interview with James Gosling

Java performance isn't the fastest, that's ok, a close 3rd place behind C/CPP ain't bad. And you're still ahead of Go, and 10x or more ahead of Python and Ruby.

Java syntax isn't perfect, but it is consistent, and predictable. And hey, if you're using an Idea or Eclipse (and not notepad, atom, etc), it's just pressing control-space all day and you're fine.

Java memory management seems weird from a Unix Philosophy POV, till you understand whats happening. Again, not perfect, but a good tradeoff.

What do you get for all of these tradeoffs? Speed, memory safety. But with that you still still have dynamic invocation capabilities (making things like interception possible) and hotswap/live redefinition (things that C/CPP cannot do).

Perfect? No, but very practical for the real world use case.

> And hey, if you're using an Idea or Eclipse (and not notepad, atom, etc),

Java's tools are really top notch. Using IntelliJ for Java feels a whole new different world from using IDEs for other languages.

Speaking of Go, does anyone know why Go community is not hot on developing containers for concurrent data structures? I see Mutex this and lock that scattering in Go code, while in Java community the #1 advice on writing concurrency code is to use Java's amazing containers. Sometimes, I do miss the java.util.concurrent and JCTools.

It's a core principle.

    Don't communicate by sharing memory; share memory by communicating.

The overuse of Mutex and Lock are from developers bringing along patterns from other language where they are used to communicating via shared memory. So this aspect of the language just doesn't click as well for many people at first. How long it takes you to get it depends on your experience.

Thanks! What about data structure shared by multiple goroutines? Say, an in-memory cache object? Or do we always have multiple goroutines talk to a dedicated goroutine for the shared value? Would the performance be okay for all use cases? A reason that people use JCTools is that it can easily support hundreds of millions of concurrent reads/writes to its data structures on a 10-year old laptop.