Elixir saves Pinterest $2M a year in server costs

The key VM feature you're missing is process isolation. Without it, supervisors are not really possible - you can implement something that vaguely looks like them, but it won't provide the fault tolerance guarantees.

Imagine for example an Erlang process that leaks files. At some point it hits an EMFILE, gets killed, and the supervisor restarts it. The system will then go back to operating normally.

Now imagine a Goroutine doing the same. It hits EMFILE, exits, and the supervisor restarts it. This doesn't help anything: it just hits the same error and the system is unusable. There's no way for the VM to guarantee cleanup when a Goroutine exits, because it doesn't isolate Goroutines and track which one owns which resources.

Links and monitors are tools to extend the same behavior to user-managed resources like DB connections and so on. The responsibility for cleanup if a process crashes while holding a DB connection falls on the DB connection library, not on the error handling inside the crashing process.

The first reference for this assertion is anecdotal. I've been a professional dev for 13 years and grew up with family members and friends in the same line of work. I've seen and heard of many, many projects built in different languages and anecdote after anecdote has been in the direction of people getting more done more quickly in higher-level languages. Writing similar things in Elixir/Clojure/etc goes faster than it does in Ruby/Python/JavaScript, which goes faster than using Java/.Net, which goes faster than using C/Fortran/Pascal. You haven't been around for those anecdotes I've heard, but a public one you may be familiar with is Discord. They scaled to over 5 million concurrent users on just 7 server engineers IIRC. Two years later, at a much larger scale, they still only had 4 engineers focused on infrastructure and 40 total: https://news.ycombinator.com/item?id=19238221. How many cases can you think of where a team of 7 Java or Python devs built something equivalent?

Secondly, there's research. Google "Function Point Metrics". Most research on programming productivity is paywalled but I shared one paper that isn't on one of my first Elixir-focused YT videos 5 years ago: https://www.youtube.com/watch?v=1e2_NXLxi-E&t=412s. Obviously this isn't perfect since it doesn't include what is appropriate for what domains or how well they scale with project size. Still it's a useful data point, as are pure measures of expressiveness: https://redmonk.com/dberkholz/2013/03/25/programming-languag...

Finally, thinking from first principles, why would anyone be adopting newer languages if they didn't offer some advantages over older ones? Why would certain features, such as pattern-matching and macros regularly be adopted by languages of the past 15 years, despite being very rare in languages created in the 90s? Furthermore, why would startups—for which productivity is a matter of survival—be early adopters? The simplest explanation, in my opinion, is that there really are some productivity advantages and sometimes those advantages are enough to overwhelm the difficulty in learning something new and working in a smaller ecosystem.

I feel like statically typed langs, (I'm looking at you Java), are a bit more stubborn to work with, especially around API design, prototyping, greenfield stuff.

I do like langs like Haskell and Standard ML where they are statically typed, the the type system is mathematically sounds, and the types are inferred.

I want my type system to be inferred and bomb proof or to just get out of the way. Its the best of both world.

Yahoo used C++ for instance (it would not be my choice, even though it's weakly statically-typed).

But, yea, like you said--there are tradeoffs.

I think dynamically-typed languages has the allure of letting you build quickly initially, but the downsides of dynamic typing start hitting pretty soon afterwrd.

I write a lot of small scripts in Python. It certainly is easier to throw something quickly together, especially when the data model is amorphous, with dynamic typing. But that doesn't mean I'd use Python for a large project.