hsluv VS pprof

Here's a potential solution to having consistent accessible color palettes in Koala: https://www.hsluv.org

If you want to take a step further, take a look into perceptually uniform color spaces, like HSL(uv) or LCh(uv).

Maxima enabled me to make my color space [HSLuv](https://www.hsluv.org/). I encoded CIELUV <-> RGB transformation functions into Maxima, ran `solve` and converted the output back into code. It's great to be able to commit [Maxima code](https://github.com/hsluv/hsluv/tree/master/math) into your repository and not leave the math as an "exercise to the reader".

If you want to make it more useful than a browser's built-in color picker, perhaps support other color spaces? Maybe HSLuv or CIE L*a*b*?

Chroma could be included, but as a minor criterion. When I look at color pickers that try to balance human perception against geometric simplicity like HSLuv and Okhsl, chroma is the property that gets distorted the most. Perceptual brightness and hue seem to be more important.

> Now, HSL isn't necessarily the best color mode to use in every situation; it tends to produce gradients that can be overly bright and vivid, because it doesn't take into account human perception.

Shout-out to to [HSLUV](https://www.hsluv.org/) which does exactly that.

I'm a little bit of a color freak. Allow me to leave some suggestions :)

- Picking from the 256 color pallete will likely give you colors with different brightness. This may hurt readability of darker colors on a dark background, and may make some color stand out unintentionally. Consider using something like HSLuv [1] to pick colors with the same lightness, then convert to the closest Xterm color [2].

- To make it obvious there is a gradient, I'd pick one lightness (assuming HSLuv) and one saturation (I usually stick to 100%), then pick a distance in hue for each step. For example if I expect to see a maximum of 7 steps on the screen at once, one way is to start at 0, then 30, then 60, etc. You may choose to go over 180, but keep in mind 360 will be the same as 0 so maybe stop at 240. Note how by picking adjacent colors from the table you are still picking a distance, but the distance is too small so it's hard to see.

- You may want to choose a different starting point than 0, and maybe different direction for the steps, depending on whether you want the colors to "mean" anything. For example red is commonly associated with warning, so you can arrange to have the top of the range aligned with red. Or arrange to avoid the red region if you don't want that association.

[1] https://www.hsluv.org/

Check out HSLuv as an alternative for UI design: https://www.hsluv.org/

HSLuv is a HSL alternative, which aims to maintain the perceived lightness of colors across the hue spektrum. It also includes a HPLuv variant, which additionally maintains saturation, at the cost of color coverage. Both are very useful for procedually generating or modifying colors. More Info: https://www.hsluv.org/comparison/

The pprof format is not tied to Go. From my understanding, it's used within Google across multiple languages. The format is defined in the pprof repository[0], and the visualization tool is source-language agnostic. I've seen libraries in numerous languages (e.g. Python, Java) to publish profiles in pprof format. This is an indicator the pprof format has become de-facto. Grafana Pyroscope[1] is a tool that's capable of parsing the pprof format, agnostic to the source programming language, and has instructions for Go, Java, Python, Ruby, node.js, Rust, and .NET.

My understanding is that you're searching for a combination of the profiles, metrics, and tracing. Caddy supports all 3.

[0] https://github.com/google/pprof/blob/main/doc/README.md

[1] https://grafana.com/docs/pyroscope/latest/

metrics and tracing need to be manually enabled (for now, perhaps)

Because we couldn't identify the issue using the results we got from Callgrind, we reached for another profiler, gperftools. It's a sampling profiler and therefor it has a smaller impact on the application's performance in exchange for less accurate call statistics. After filtering out the unimportant parts and visualizing the rest with pprof, it was evident that something strange was happening with the send function. It took only 71 milliseconds with the previous implementation and more than 900 milliseconds with the new implementation of our Bolt server. It was very suspicious, but based on Callgrind, its cost was almost the same as before. We were confused as the two results seemed to conflict with each other.

github.com - google/pprof

The application performances in a specific code path (e.g. gdb, pprof, …).

pprof is https://github.com/google/pprof, it's a very useful tool in golang , and really really really convenient

Go also has pretty good out of the box profiling (pprof[0]) and third-party runtime debugging (delv[1]) that can be used both remotely and local.

These tools also have decent editor integration and can be use hand in hand:

https://blog.jetbrains.com/go/2019/04/03/profiling-go-applic...

https://blog.jetbrains.com/go/2020/03/03/how-to-find-gorouti...

[0] https://github.com/google/pprof

[1] https://github.com/go-delve/delve

And what we do in Google Cloud is that we still use the pprof. But it's a kind of forked version of the pprof because the visualization part is totally different. So we give that tool as the Cloud Profiler. So that is the product name. And then, the difference between the pprof and a Cloud Profiler is that Cloud Profiler provides the agent library for each famous programming language such as Java, Python, Node.js, and Go. And then what you need to do is to just write 5 to 10 lines of code in a new application. That launches the profile agent in your application as a subsidiary thread of the main thread. And then, that thread periodically collects the profile data of the application and then sends that data back to Google Cloud and the Cloud Profiler.

gprftools (https://github.com/gperftools/gperftools) can be easily plugged in using LD_PRELOAD and signal, and has nice go implemented visualization tool https://github.com/google/pprof.