I have, and mentioned one lower down in the comments. Unix philosophy was great but does not scale well in terms of maintainability or efficiency. Invoking processes over and over again loops is godawful slow. And the horror of complicated shell scripts is legendary.
This results in weird behavior, such as writing a groovy (Java?) script for Jenkins to execute bazel in order to build a go binary that runs the very same commands in an exec.Command() construct. Or people who download and import pandas to grab the third field in a csv file.
During the course of learning, I've naturally written code in bash that should have been written in another language. I replaced if statements with case because they turned out to be more performant. It's a great learning experience and why I got into python and go.
IMO we should use the right tool for the job. Sometimes that tool is a combination of unix utilities that you can put in a shell script for easier maintenance. It's just procedural execution of (usually very efficient) binaries, akin to a jenkins script or gitlab pipeline. Just mind the exceptions and use exit codes.
* often times, it's not just the third column I want. Sometimes it becomes "third column unless the first column is 'b' then instead grab the fourth column". Having a good data representation makes sure that I'm not mixing logic code with representation parsing code
* I don't have to care about CSV parsing edge cases. Escaped comma? Quotes? I don't care, the library will either handle it or throw an explicit error. With custom parsing code, instead of an error, I'll get some mangled result in the middle of the file that I won't even catch / notice until later down the line
* when working with CSVs, in my area (ML / scientific compute), Python is often the right context to be in.
It's not a lack of being "taught" shell scripts. It's the fact that shell programming constructs aren't well documented, your "standard library" is basically dependent on whatever binaries happen to be available on the filesystem, error handling is almost non-existent, etc.
It's very easy to write a bad shell script that "solves" a problem as long as a bunch of assumptions aren't violated. In my experience, senior software engineers are extremely averse to hidden assumptions and very concerned with reliability of the systems they build.
Of course this can happen with any language, especially as it ages and adds complexity.
But this doesn't make this approach inherently wrong or obsolete. The programmer is wrong for trying to use the tools beyond their capabilities. Where that line is drawn is subjective, as is the concept of maintainability, but if you feel that you're struggling to accomplish something, and that it's becoming a chore to maintain, the path forward is choosing a more capable tool, like a programming language.
Perl was invented because the gap from shell to more capable languages was (and is) really big. Languages like Python and Ruby didn’t exist yet, and Perl had a really, really strong sweet spot in text processing.
Still does.
Ubiquity, speed, and conciseness.
Perl is usually installed by default on Linux and Unix systems. Ruby might be there, it depends.
Perl is faster than Ruby. Ruby has been one of the slower scripting languages. But Ruby has been working on performance improvements in the past few releases. I have not seen any benchmarks of the current Perl versus the current Ruby, so this may have changed.
Perl is more concise than Ruby allowing more functionality for less code.
The JVM would like a word. (It has slow startup, but can be very fast at runtime due to JIT optimization and cacheing.)
Scripts should start and run quickly.
Ruby has historically been fairly slow which is why Ruby 3 focused heavily on performance. It has been improving a lot, but I have not seen any benchmarks against other programming languages.
Everything was needlessly hard because these tools were not built for that. Easy to talk about philosophy and the "classic Unix approach" if you don't have to build modern applications this way.
You hit it on the head with the slowness of loops when the body comprises a series of program invocations. The horror really seeps in when you realize the original author wasn't stopped by the lack of data structures: they could get around that with some creative variable names.