bcc VS flamegraph

But wait, there's more! Enter the BCC toolkit and library, your trusty sidekick in simplifying the arcane art of writing eBPF applications. With BCC by your side, you'll be wielding eBPF like a seasoned pro in no time.

Nice - I normally use [bash-readline](https://github.com/iovisor/bcc/blob/master/tools/bashreadlin...) when coworking/co-inhabiting a server or training someone.

One of the big wins is not so much “build and run your own stuff” but there are very nice low-cost (in terms of compute) performance utilities built on eBPF

https://github.com/iovisor/bcc

There are so many utilities in that list; there’s a diagram midway down the readme which tries to help show their uses. bcc-tools should be available in any distro.

Also, Brendan Gregg does a ton of performance stuff that is worth knowing about if you check out his other work. Not eBPF only. Flame graphs are useful.

Reference: https://github.com/iovisor/bcc/blob/master/libbpf-tools/memleak.c

In this article, we'll build a basic version of an HTTPS sniffer, inspired by bcc-sslsniff.py, but we'll use Rust and Aya. We're going to demonstrate the capabilities of uprobes by employing uprobe and uretprobe along with familiar maps like PerCpuArray, HashMap, and PerEventArray. This will be a straightforward example to help us explore how uprobes function.

As xpd program I am using the BCC example xdp_redirect_map.py in skb mode as my NIC does not support native mode, attaching the program to veth2 and a dummy function to veth3

https://github.com/iovisor/bcc/blob/master/docs/reference_gu...

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: