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https://github.com/uber-archive/go-torch
Stochastic flame graph profiler for Go programs
https://github.com/uber-archive/go-torch
Last synced: about 2 months ago
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Stochastic flame graph profiler for Go programs
- Host: GitHub
- URL: https://github.com/uber-archive/go-torch
- Owner: uber-archive
- License: mit
- Archived: true
- Created: 2015-07-21T22:49:42.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2018-11-07T07:13:54.000Z (about 6 years ago)
- Last Synced: 2024-04-15T07:14:39.807Z (7 months ago)
- Language: Go
- Homepage:
- Size: 319 KB
- Stars: 3,958
- Watchers: 2,078
- Forks: 244
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# go-torch [](https://travis-ci.org/uber/go-torch) [](http://coveralls.io/github/uber/go-torch?branch=master) [](https://godoc.org/github.com/uber/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.
[pprof]: https://golang.org/pkg/net/http/pprof/
## Example Flame Graph
[](http://uber.github.io/go-torch/meta.svg)
## 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.
[here]: https://golang.org/pkg/net/http/pprof/
```go
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](https://github.com/e-dard/netbug).
## 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
```