speed

Bloop’s compiler performance is ~29% faster than SBT (on at least one project)

I had read that Bloop was faster than Scala compiler tools like scalac and fsc, so I wondered if it was faster than SBT, and if so, how much faster. So I downloaded Eric Torreborre’s specs2 source code, which has 880 source code files, and compiled the entire project with both SBT and Bloop.

SBT performance

To test SBT’s speed, I ran all the commands from inside the SBT command prompt, which I usually do anyway to avoid the SBT/JVM startup lag time. I also ran clean and compile several times before recording SBT’s speed, because I thought that would be a better reflection of real-world usage and performance. I ran the tests four times, and the average time with SBT was 49 seconds, and that was very consistent, always coming in between 48 and 50 seconds.

Bloop performance

Measuring Scrum team productivity/speed with Function Point Analysis

I bought my first copy of Agile Software Development with Scrum, by Schwarber and Beedle back around 2002, I think. I was just thumbing through it last night when I saw that they use Function Points as a metric to demonstrate the velocity that agile software teams achieve, and more specifically use it to show that some teams develop software much faster using Scrum.

I didn’t know about Function Point Analysis back in 2002 — I didn’t become a Certified Function Point Specialist until about two years later — so I probably just skimmed over that line then, but when I saw it last night I thought it was cool that they used function points as a metric for software team development speed.

A note about Scala/Java startup time alvin January 15, 2019 - 4:04pm

I was reading this post by Martin Odersky (Make the Scala runtime independent of the standard library) and came across this comment by Li Haoyi: “This would also make it more feasible to use Scala for tiny bootstrap scripts; current Mill’s launcher is written in Java because the added classloading needed to use scala.Predef (even just println) easily adds a 200-400ms of initialization overhead.” I haven’t written anything where the startup time of a Scala application was a huge problem, but that was interesting to read.

(Though I should say that I wish all Scala/Java command-line apps started faster. It’s one reason I occasionally think about using Haskell for small scripts, so I can compile them to an executable.)

Looking at what makes a baseball move alvin November 29, 2018 - 7:33am

Back in my day, aerospace engineering undergrad students had very little time to work in the wind tunnels at Texas A&M, but in the limited time I had I tried to look at what makes a knuckleball move erratically. Barton Smith at Utah State University did the same thing (presumably with much more wind tunnel time) looking at a baseball’s spin rate, spin axis, and orientation of the ball.