Stochastic flame graph profiler for Go programs
Go Shell
Latest commit 1e8874b Jan 12, 2017 @prashantv prashantv committed on GitHub Fix pprof parsing when there's more than 2 columns (#53)
Previously, the pprof parser assumed there were 2 columns, and stored
them as "samples" and "total". Instead parse all values and store
columns which we can use in future.

README.md

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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] <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