docs: add dev instructions on generating/analyzing pprof samples

Signed-off-by: David Karlsson <35727626+dvdksn@users.noreply.github.com>
This commit is contained in:
David Karlsson 2024-10-28 17:12:49 +01:00
parent 2bdf451b68
commit ca502cc9a5
1 changed files with 84 additions and 1 deletions

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@ -188,6 +188,89 @@ To generate new vendored files with go modules run:
$ make vendor
```
### Generate profiling data
You can configure Buildx to generate [`pprof`](https://github.com/google/pprof)
memory and CPU profiles to analyze and optimize your builds. These profiles are
useful for identifying performance bottlenecks, detecting memory
inefficiencies, and ensuring the program (Buildx) runs efficiently.
The following environment variables control whether Buildx generates profiling
data for builds:
```console
$ export BUILDX_CPU_PROFILE=buildx_cpu.prof
$ export BUILDX_MEM_PROFILE=buildx_mem.prof
```
When set, Buildx emits profiling samples for the builds to the location
specified by the environment variable.
To analyze and visualize profiling samples, you need `pprof` from the Go
toolchain, and (optionally) GraphViz for visualization in a graphical format.
To inspect profiling data with `pprof`:
1. Build a local binary of Buildx from source.
```console
$ docker buildx bake
```
The binary gets exported to `./bin/build/buildx`.
2. Run a build and with the environment variables set to generate profiling data.
```console
$ export BUILDX_CPU_PROFILE=buildx_cpu.prof
$ export BUILDX_MEM_PROFILE=buildx_mem.prof
$ ./bin/build/buildx bake
```
This creates `buildx_cpu.prof` and `buildx_mem.prof` for the build.
3. Start `pprof` and specify the filename of the profile that you want to
analyze.
```console
$ go tool pprof buildx_cpu.prof
```
This opens the `pprof` interactive console. From here, you can inspect the
profiling sample using various commands. For example, use `top 10` command
to view the top 10 most time-consuming entries.
```plaintext
(pprof) top 10
Showing nodes accounting for 3.04s, 91.02% of 3.34s total
Dropped 123 nodes (cum <= 0.02s)
Showing top 10 nodes out of 159
flat flat% sum% cum cum%
1.14s 34.13% 34.13% 1.14s 34.13% syscall.syscall
0.91s 27.25% 61.38% 0.91s 27.25% runtime.kevent
0.35s 10.48% 71.86% 0.35s 10.48% runtime.pthread_cond_wait
0.22s 6.59% 78.44% 0.22s 6.59% runtime.pthread_cond_signal
0.15s 4.49% 82.93% 0.15s 4.49% runtime.usleep
0.10s 2.99% 85.93% 0.10s 2.99% runtime.memclrNoHeapPointers
0.10s 2.99% 88.92% 0.10s 2.99% runtime.memmove
0.03s 0.9% 89.82% 0.03s 0.9% runtime.madvise
0.02s 0.6% 90.42% 0.02s 0.6% runtime.(*mspan).typePointersOfUnchecked
0.02s 0.6% 91.02% 0.02s 0.6% runtime.pcvalue
```
To view the call graph in a GUI, run `go tool pprof -http=:8081 <sample>`.
> [!NOTE]
> Requires [GraphViz](https://www.graphviz.org/) to be installed.
```console
$ go tool pprof -http=:8081 buildx_cpu.prof
Serving web UI on http://127.0.0.1:8081
http://127.0.0.1:8081
```
For more information about using `pprof` and how to interpret the call graph,
refer to the [`pprof` README](https://github.com/google/pprof/blob/main/doc/README.md).
### Conventions
@ -343,4 +426,4 @@ The rules:
If you are having trouble getting into the mood of idiomatic Go, we recommend
reading through [Effective Go](https://golang.org/doc/effective_go.html). The
[Go Blog](https://blog.golang.org) is also a great resource.
[Go Blog](https://blog.golang.org) is also a great resource.