If you want to see real C++ scripting: the (slightly insane) people of CERN use a 99% ANSI compliant, self-written C++ interpreter (!) for online data analytics.
The cost of compiling at -O3 is going to affect interactive use of course though.
What part of "interpreted" did you not understand?
I don‘t understand your comment, do you care to enlighten me?
Python is an interpreted language.
Yes, dynamic recompiling, JIT-ing, etc etc.
But you used a interpreter (for C++) with some expectation it would be faster than a language that is itself interpreted? I mean, I guess you could assume that the stronger typing of C++ would provide an advantage over Python...
But if you hear "interpreted", you should assume 10x slowdown at minimum, unless there has been human-centuries invested in performance improvement like the JVM.
RE: "nothing in the original message about expecting faster that interpreted code" He literally said "it's not any faster than Python" which is an interepreted language. He was expecting interpreted C++ to be substantially/perciptibly faster than Python ... an interpreted language. In case you didn't know a "scripting" language is 99% of the time interpreted.
This is because D has one of the fastest compilers. You can use rdmd as the REPL and also invoking the compiler for scripting [1].
[1]Why I use the D programming language for scripting:
Of course, very few people outside of CERN need all of this. Even within CERN, many projects don't. But for those who do, there are very few - if any - alternatives.
From what I have seen in R and Python, the main reason for speed issues are incompetent programmers. Certainly, bad C++ code is much faster than bad Python code, but there is also the effort to build/maintain/document/teach Root to noobs.
Hot take: It's really about preferences, not features.
root is many many decades old. And I understand that the scripts developed under it are often directly incorporated into C++ applications. CERN is a big C++ user (I have a little experience with their GEANT4 framework), and being able to do everything in one language is a big productivity boost (see for example the rise of node.js for web-related work).
Oh, one more thing: There's very few professional developers dedicated to this. A lot of it is built and maintained by grad students and researchers in-between writing papers. They're smart people, and they can code, but they have neither time nor interest in learning a new language or framework every other year. They move around. A lot. It wouldn't work to have different tech stacks for different projects - you need to pick one solution, not just for one area but for the entire field. So people can spend less time learning and more time doing. There's no one available to migrate legacy code because some new cool language appeared or yesterday's cool library isn't maintained anymore. These projects run for decades. Whatever tech you pick you must be certain that it will still be around and supported 10, 20, 30 years later. That the code still runs and the data that you paid billions for can still be read.
I was certainly unaware of the size of the data coming from the detectors. If speed is the argument that beats all others, I rest my case. From what I read on the root.cern website, root is a data analysis and simulation environment, so I was not aware of the aspect of prototyping for online use.
Because I spent a lot of time thinking how software development can work in an analysis heavy research environment, I still would like to comment on some of your points. To distribute binaries and source code, packages work very well for us. Especially if you want to reuse software components in unseen contexts, packages and a package registry makes the most sense.
The use case "re-run online algorithms in offline analysis" is a very familiar one. In my line of work, we do that daily: Switching between online and offline to test + deploy algorithms. Vastly smaller scale, of course. But to us, packages are the first part of the solution. All you do is change the data source. For offline, it's local data or a remote DB, for online, it's an interface such as a websocket.
The second part of the solution are unit- and integration tests. Other users will immediately see what you did (or didn't) test. Again, packages are the distribution system of choice. This has nothing to do with Matlab/Python/R/Julia. Rust has crates.io, JS has npmjs, even Java has something like Maven Central.
Regarding the funded-by-taxpayers argument: The issue I see here is that the cool ML, simulation, data analysis stuff which the CERN people do remains in the root ecosystem. If they used something like PyPI, I could use their stuff too. I have a lot of clustering problems, especially on time series. With a more or less proprietary system like root, I can't use any of CERN's implementations.
Regarding "researchers don't have time to learn new languages": If you look on github.com/root-project/root/issues and root-forum.cern.ch, there are suspiciously many questions regarding "how can I make use of root and Python libraries", and "X doesn't work in root, what do". Newbs have to learn root as well, and they seem to like using Python at least as an enhancement.