Thank you Thomas for the link to the vips library. I didn't know about it and now I want to read more about its design and internals.
The objective of the article was to set a baseline using the Go image library and play with several factors to see how it affects performance. In this first article, I wasn't really trying to come up with the fastest possible image server but to point a few basic techniques that can improve access speed and reduce memory consumption. These techniques should be applicable to any image library, so similar relative performance gains can be achieved with any language or library. The next part, which I'm currently writing, proposes the snappy compression as a way of improving access speed to the data. Cheers, Pablo On Tuesday, December 19, 2017 at 10:28:48 AM UTC+1, Thomas Bruyelle wrote: > > Interesting and nice pieces of code. I wonder if the performances can be > compared to something like `vips` (https://jcupitt.github.io/libvips). > > Le lundi 18 décembre 2017 22:51:49 UTC+1, Pablo Rozas Larraondo a écrit : >> >> Hi, >> >> For those interested on serving or using satellite imagery, I've just >> published the first of a three part series on this subject using Go: >> >> >> https://medium.com/@p.rozas.larraondo/divide-compress-and-conquer-building-an-earth-data-server-in-go-part-1-d82eee2eceb1 >> >> Any feedback or comment that you might have would be greatly appreciated! >> >> Thanks, >> Pablo >> > -- You received this message because you are subscribed to the Google Groups "golang-nuts" group. To unsubscribe from this group and stop receiving emails from it, send an email to golang-nuts+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
