I think what's far more common is prototyping something in Python or R, and then realizing that 99% of its CPU time is already spent in wrapped C code. And then you call it a day and move on to other things :)

This is why Julia hasn't taken off nearly as quickly as I initially expected - Python/R/etc are highly optimized for their common use-cases.

Someone has to write those libraries.

> This is why Julia hasn't taken off nearly as quickly as I initially expected

Not sure what you were expecting, but consider:

- the most conservative userbase estimate I would believe is 20k users (based on mailing list subscriptions, website stats, and download numbers).

- sustained package ecosystem growth: http://pkg.julialang.org/pulse.html

Yes, but the discussion is about the "extremely common" case of prototyping, which definitely should not require library building, and usually should not require veering much from established libraries.

>Not sure what you were expecting

The data community can coalesce around a tool extremely quickly, in the matter of a year or two. Spark is about the same age as Julia, and has a thriving ecosystem around it. In 2004 R was a fairly esoteric analysis tool, but 2008-2010 it was the de facto data science language. Python made similar advancements in just a few years in the data science community.

Julia? I don't know a single person who uses it day-to-day, but I know a lot of people who tried very hard (myself included). The critical mass simply is not there: people aren't building packages because the users don't exist, and they don't exist because the packages don't exist. Your chart shows a linear growth in packages, which implies a constant amount of development work. This means it's not a growing language. This is what a growing language looks like: http://blog.revolutionanalytics.com/2010/01/r-package-growth...

This could be early exponential growth.

Or the constant growth may hit a tipping point as a critical mass of tools and infrastructure is developed.