flamegraph VS Chocolatey

You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.

I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason

having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)

I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).

Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.

You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph

Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph

I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!

From the readme of cargo flamegraph:

Chocolatey Windows software management solution, we use this for installing Python and Deno

Authenticating with Kyma is a (in my opinion) unnecessary challenge as it leverages the OIDC-login plugin for kubectl. You find a description of the setup here. This works fine when on a Mac but can give you some headaches on a Windows and on Linux machine especially when combined with restrictive setups in corporate environments. For Windows I can only recommend installing krew via chocolatey and then install the OIDC plugin via kubectl krew install oidc-login. At least for me that was the only way to get this working on Windows.

On a Windows machine, you can use Chocolatey by running the command.

I've used WSL2 and GHC/Nix--worked without any issues. However, there is Chocolatey: https://chocolatey.org/

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.

Type the following commands on the Windows terminal to install helm. You can use either Scoop a command-line installer for Windows or Chocolatey which is a Package Manager for Windows to install helm.

Für Software ninite.com und chocolatey.org

While you can install it in many ways, the easiest is using a package manager like Homebrew for macOS or chocolatey for Windows.