lisa VS parca-agent

You might be interested in that: https://github.com/ARM-software/lisa/blob/master/lisa/monad.py

This is to replace the current flow purely based on pandas dataframe and offline trace.dat parsing used in LISA: https://github.com/ARM-software/lisa (collecting a trace.dat is nice for debugging but limits to small durations, and pandas does not allow running computations in constant memory, which is an issue for very big traces)

The module added by this PR seems to be a pretty good fit: https://github.com/ARM-software/lisa/pull/1722

I made that thing taking some vague inspiration from SML module system: https://github.com/ARM-software/lisa/pull/1722/files

import builtins from operator import add import functools # These functions can be found at: # https://github.com/ARM-software/lisa/blob/master/lisa/utils.py#L147 # Note: my implementation of curry() seems to be broken wrt named parameters (or for parameters with defaults, haven't looked at the details) for some reason but for this example it does not matter from lisa.utils import compose, curry def even(x): return x % 2 == 0 # The builtin functions don't have a signature, which will upset curry() so we # redefine it here def map(f, iterable): return builtins.map(f, iterable) def filter(f, iterable): return builtins.filter(f, iterable) # Swapped init and iterable to be curry-friendly def reduce(f, init, iterable): return functools.reduce(f, iterable, init) def pipeline(*items): # Add a currying layer so that we spare the user the need to do it return compose(*(curry(f)(*args) for (f, *args) in items)) # x = filter(even, list) |> map(lambda x: x+1) |> reduce(+) f = pipeline( (filter, even), (map, lambda x: x+1), (reduce, add, 0), ) l = [1,2,3,4] x = f(l) print(x)

If that's true, you should probably update the docs. Everything I could find implied dotnet, jvm, python were still unsupported. For example, the roadmap section of the readme mentions most of these but nothing mentions dotnet. However I did find your tickets and a demo being merged in which makes it seem maybe supported?

Ticket: https://github.com/parca-dev/parca-agent/issues/161

Demo: https://github.com/parca-dev/parca-demo/pull/18

Couldn't see any advantages to this over https://github.com/parca-dev/parca-agent. Which uses eBPF so it can be used with non-instrumented apps and code paths.

The pervasive lack of frame pointers is the reason why we've developed a custom format derived from DWARF unwind information thanks to some insights: DWARF unwind information is incredible flexible, it supports many arches and allows restoring any arbitrary register. But we only need 3: the frame pointer, the stack pointer, and in non-x86 the return address.

In addition, this encoding doesn't use that many bytes, but unfortunately reading and parsing that information is quite expensive.

For that reason I've developed a new unwinder that uses custom unwind information derived from DWARF (https://www.polarsignals.com/blog/posts/2022/11/29/profiling..., previously discussed in https://news.ycombinator.com/item?id=33788794) that runs in BPF. This new compact representation can be binary searched easily and each unwind row has a size of 16 bytes. I are currently working on reducing it down to ~10 bytes.

All the code is fully OSS (Apache 2.0 for userspace and GPL for BPF), and part of the Parca project (https://github.com/parca-dev/parca-agent).

We've also given some talks in FOSDEM going deeper into how we made it scale for many big processes.

I find this surprising! Was this for off the shelf applications or some custom binaries?

As mentioned above, we see DWARF expressions such as `DW_CFA_def_cfa_expression` on the regular. See the "Test Plan" section and commit messages of the PR that introduced support for this particular opcode [0]

[0]: https://github.com/parca-dev/parca-agent/pull/1058

Let's see an example perf map file for NodeJS. The runtimes out there output this file with more or less the same format, more or less!

The good news is we got you covered. If you are using Parca Agent, we already do the heavy lifting for you to symbolize captured stack traces. And we keep extending our support for the different languages and runtimes.