It's more useful to think of Julia's latency as a fundamental constraint of the compilation model. People don't complain that Python can't deliver static binaries, or that C++ is unsuitable for scripting. Julia's compiler can do both (although the "static binaries" haven't materialized, because noone seriously works on it), but the tradeoff is that you suffer from latency. That's fundamental. Future versions of Julia may cut the compilation time down further, but it will never be instant or even close to instant.
So, like other languages, it means you have to look at the strengths and weaknesses of the language for your use case. For myself, I can't imagine a situation where I want to plot something right now and not in 15 seconds. Whenever I plot something, it's always after minutes or (usually) hours of data analysis. Most of my Julia use is either long, interactive sessions where ten seconds of startup doesn't matter much, or long-running pipelines where it doesn't, either.
If you have N scripts that produce N plots for a paper, you can't just drop them in a makefile or you will pay the 15 second penalty over and over and over every time you build. You can't reset the interactive session as a matter of habit to aggressively control state build-up. Even iterating on a dashboard becomes painful with a long startup time. You have to treat your Julia session like a child, not like cattle. That's limiting and not everyone wants to it, especially now that they are used to treating python sessions like cattle and reaping the benefits.
It seems like every time julia is mentioned, someone has to clarify this.
I think Julia needs to throw away some of the unnecessary obsession for just-ahead-of-time (JAOT) compilation. Compiling everything beforehand to LLVM machine code seems good for raw runtime speed, but it doesn't matter when it takes so much setup time to even just glue some simple libraries. Julia should seriously consider running most of the less-computationally intensive codepaths in a custom-made interpreter without LLVM, while precompiling the numerically intensive parts of library code in raw machine code with the current LLVM backend. Maybe there should be a flag you can use to mark certain functions to be unconditionally compiled AOT-style, and leave the rest to the interpreter. Interpreters can be made surprisingly fast with some effort - stuff like the interpreter in LuaJIT (https://luajit.org/) and HashLink (https://hashlink.haxe.org/) is what comes into my mind.
Anyways, this is just my experience and my job involves a lot of waiting and stuff anyways so a couple seconds here and there doesn't bother me.
:/
In an ideal world Julia would have two backends:
- A fast, hand-made JIT backend (something like LuaJIT) which is mostly interpreted and only JIT-compiled in frequently run code paths.
- The current slow, almost-AOT-like LLVM backend which is only used for precompilation of packages
It's just like Debug/Release bulids in C++, except that the Debug builds are lightning fast to compile (but slower at runtime) and Release builds are slower to compile (but hyper-optimized for runtime speed). I guess there's going to be an immense engineering effort if something like this happens, given that Julia today is immensely coupled with the LLVM compiler/runtime. But that's just my pipe dream.