go-torch

by uber-archive

uber-archive / go-torch

Stochastic flame graph profiler for Go programs

3.8K Stars 221 Forks Last release: Not found MIT License 125 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

go-torch Build Status Coverage Status GoDoc

go-torch is deprecated, use pprof instead

As of Go 1.11, flamegraph visualizations are available in

go tool pprof
directly!
# This will listen on :8081 and open a browser.
# Change :8081 to a port of your choice.
$ go tool pprof -http=":8081" [binary] [profile]

If you cannot use Go 1.11, you can get the latest

pprof
tool and use it instead:
# Get the pprof tool directly
$ go get -u github.com/google/pprof

$ pprof -http=":8081" [binary] [profile]

Synopsis

Tool for stochastically profiling Go programs. Collects stack traces and synthesizes them into a flame graph. Uses Go's built in pprof library.

Example Flame Graph

Inception

Basic Usage

$ go-torch -h
Usage:
  go-torch [options] [binary] 

pprof Options: -u, --url= Base URL of your Go program (default: http://localhost:8080) -s, --suffix= URL path of pprof profile (default: /debug/pprof/profile) -b, --binaryinput= File path of previously saved binary profile. (binary profile is anything accepted by https://golang.org/cmd/pprof) --binaryname= File path of the binary that the binaryinput is for, used for pprof inputs -t, --seconds= Number of seconds to profile for (default: 30) --pprofArgs= Extra arguments for pprof

Output Options: -f, --file= Output file name (must be .svg) (default: torch.svg) -p, --print Print the generated svg to stdout instead of writing to file -r, --raw Print the raw call graph output to stdout instead of creating a flame graph; use with Brendan Gregg's flame graph perl script (see https://github.com/brendangregg/FlameGraph) --title= Graph title to display in the output file (default: Flame Graph) --width= Generated graph width (default: 1200) --hash Colors are keyed by function name hash --colors= Set color palette. Valid choices are: hot (default), mem, io, wakeup, chain, java, js, perl, red, green, blue, aqua, yellow, purple, orange --hash Graph colors are keyed by function name hash --cp Graph use consistent palette (palette.map) --inverted Icicle graph Help Options: -h, --help Show this help message

Write flamegraph using /debug/pprof endpoint

The default options will hit

http://localhost:8080/debug/pprof/profile
for a 30 second CPU profile, and write it out to torch.svg
$ go-torch
INFO[19:10:58] Run pprof command: go tool pprof -raw -seconds 30 http://localhost:8080/debug/pprof/profile
INFO[19:11:03] Writing svg to torch.svg

You can customize the base URL by using

-u
$ go-torch -u http://my-service:8080/
INFO[19:10:58] Run pprof command: go tool pprof -raw -seconds 30 http://my-service:8080/debug/pprof/profile
INFO[19:11:03] Writing svg to torch.svg

Or change the number of seconds to profile using

--seconds
:
$ go-torch --seconds 5
INFO[19:10:58] Run pprof command: go tool pprof -raw -seconds 5 http://localhost:8080/debug/pprof/profile
INFO[19:11:03] Writing svg to torch.svg

Using pprof arguments

go-torch
will pass through arguments to
go tool pprof
, which lets you take existing pprof commands and easily make them work with
go-torch
.

For example, after creating a CPU profile from a benchmark: ``` $ go test -bench . -cpuprofile=cpu.prof

This creates a cpu.prof file, and the $PKG.test binary.

The same arguments that can be used with `go tool pprof` will also work
with `go-torch`:

$ go tool pprof main.test cpu.prof

Same arguments work with go-torch

$ go-torch main.test cpu.prof INFO[19:00:29] Run pprof command: go tool pprof -raw -seconds 30 main.test cpu.prof INFO[19:00:29] Writing svg to torch.svg ```

Flags that are not handled by

go-torch
are passed through as well:
$ go-torch --alloc_objects main.test mem.prof
INFO[19:00:29] Run pprof command: go tool pprof -raw -seconds 30 --alloc_objects main.test mem.prof
INFO[19:00:29] Writing svg to torch.svg

Integrating With Your Application

To add profiling endpoints in your application, follow the official Go docs here. If your application is already running a server on the DefaultServeMux, just add this import to your application.

import _ "net/http/pprof"

If your application is not using the DefaultServeMux, you can still easily expose pprof endpoints by manually registering the net/http/pprof handlers or by using a library like this one.

Installation

$ go get github.com/uber/go-torch

You can also use go-torch using docker:

$ docker run uber/go-torch -u http://[address-of-host] -p > torch.svg

Using

-p
will print the SVG to standard out, which can then be redirected to a file. This avoids mounting volumes to a container.

Get the flame graph script:

When using the

go-torch
binary locally, you will need the Flamegraph scripts in your
PATH
:
$ cd $GOPATH/src/github.com/uber/go-torch
$ git clone https://github.com/brendangregg/FlameGraph.git

Development and Testing

Install the Go dependencies:

$ go get github.com/Masterminds/glide
$ cd $GOPATH/src/github.com/uber/go-torch
$ glide install

Run the Tests

$ go test ./...
ok    github.com/uber/go-torch   0.012s
ok    github.com/uber/go-torch/graph   0.017s
ok    github.com/uber/go-torch/visualization 0.052s

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.