go-torch

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

Basic Usage

$ go-torch -h
Usage:
go-torch [options] [binary] <profile source>
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