I occasionally compile some statistics on programming language popularity by running a bunch of Google searches to rank programming languages according to the number of results. I wouldn’t read too much into these stats, but they are not without value.

This time, I’ve included the code I use (written in Racket) and the raw data.

I made the following Google searches and summed the results:

"implemented in <lang>"
"written in <lang>"
"developed in <lang>"
"programmed in <lang>"

I made a number of Google searches of the forms below and summed the results:

"implemented in <lang>"
"written in <lang>"
"developed in <lang>"
"programmed in <lang>"

I made a number of Google searches of the forms below and summed the results:

"implemented in <lang>"
"written in <lang>"
"developed in <lang>"
"programmed in <lang>"

I made a number of Google searches of the forms below and summed the results:

"implemented in <lang>"
"written in <lang>"
"developed in <lang>"
"programmed in <lang>"

The End of Moore’s Law

For the last few years (since 2009), I’ve been pitching the idea to my peers that language speed & concurrency/parallel capabilities will become more important as CPU clock speeds plateau and manufacturers add more CPU cores instead of advancing clock rates. My 2+ year old Macbook Pro has 4 cores and 8 hyperthreads.

Update 11/23/2020: The ultimate winner for my primary programming language was Racket.

Background

The 2008 Programming Language Plan didn’t go as well as I hoped, so I’m regrouping for another go at it. I did make progress learning some Logo and teaching it to my daughters, and I worked through seven chapters of “Programming in Haskell” which was very enjoyable, but I also spent way too much time trying to decide which language(s) to learn without actually learning them.