# Flame Graphs visualize profiled code

Main Website: http://www.brendangregg.com/flamegraphs.html

Example (click to zoom):
[![Example](http://www.brendangregg.com/FlameGraphs/cpu-bash-flamegraph.svg)](https://jing.yandex-team.ru/files/yazevnul/contrib-tools-flame-graph-cpu-bash-flamegraph.svg)

Other sites:
- The Flame Graph article in ACMQ and CACM: http://queue.acm.org/detail.cfm?id=2927301 http://cacm.acm.org/magazines/2016/6/202665-the-flame-graph/abstract
- CPU profiling using Linux perf\_events, DTrace, SystemTap, or ktap: http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html
- CPU profiling using XCode Instruments: http://schani.wordpress.com/2012/11/16/flame-graphs-for-instruments/
- CPU profiling using Xperf.exe: http://randomascii.wordpress.com/2013/03/26/summarizing-xperf-cpu-usage-with-flame-graphs/
- Memory profiling: http://www.brendangregg.com/FlameGraphs/memoryflamegraphs.html
- Other examples, updates, and news: http://www.brendangregg.com/flamegraphs.html#Updates

Flame graphs can be created in three steps:

1. Capture stacks
2. Fold stacks
3. flamegraph.pl

1\. Capture stacks
=================
Stack samples can be captured using Linux perf\_events, FreeBSD pmcstat (hwpmc), DTrace, SystemTap, and many other profilers. See the stackcollapse-\* converters.

### Linux perf\_events

Using Linux perf\_events (aka "perf") to capture 60 seconds of 99 Hertz stack samples, both user- and kernel-level stacks, all processes:

```
# perf record -F 99 -a -g -- sleep 60
# perf script > out.perf
```

Now only capturing PID 181:

```
# perf record -F 99 -p 181 -g -- sleep 60
# perf script > out.perf
```

### DTrace

Using DTrace to capture 60 seconds of kernel stacks at 997 Hertz:

```
# dtrace -x stackframes=100 -n 'profile-997 /arg0/ { @[stack()] = count(); } tick-60s { exit(0); }' -o out.kern_stacks
```

Using DTrace to capture 60 seconds of user-level stacks for PID 12345 at 97 Hertz:

```
# dtrace -x ustackframes=100 -n 'profile-97 /pid == 12345 && arg1/ { @[ustack()] = count(); } tick-60s { exit(0); }' -o out.user_stacks
```

60 seconds of user-level stacks, including time spent in-kernel, for PID 12345 at 97 Hertz:

```
# dtrace -x ustackframes=100 -n 'profile-97 /pid == 12345/ { @[ustack()] = count(); } tick-60s { exit(0); }' -o out.user_stacks
```

Switch `ustack()` for `jstack()` if the application has a ustack helper to include translated frames (eg, node.js frames; see: http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spend-its-time/).  The rate for user-level stack collection is deliberately slower than kernel, which is especially important when using `jstack()` as it performs additional work to translate frames.

2\. Fold stacks
==============
Use the stackcollapse programs to fold stack samples into single lines.  The programs provided are:

- `stackcollapse.pl`: for DTrace stacks
- `stackcollapse-perf.pl`: for Linux perf_events "perf script" output
- `stackcollapse-pmc.pl`: for FreeBSD pmcstat -G stacks
- `stackcollapse-stap.pl`: for SystemTap stacks
- `stackcollapse-instruments.pl`: for XCode Instruments
- `stackcollapse-vtune.pl`: for Intel VTune profiles
- `stackcollapse-ljp.awk`: for Lightweight Java Profiler
- `stackcollapse-jstack.pl`: for Java jstack(1) output
- `stackcollapse-gdb.pl`: for gdb(1) stacks
- `stackcollapse-go.pl`: for Golang pprof stacks
- `stackcollapse-vsprof.pl`: for Microsoft Visual Studio profiles

Usage example:

```
For perf_events:
$ ./stackcollapse-perf.pl out.perf > out.folded

For DTrace:
$ ./stackcollapse.pl out.kern_stacks > out.kern_folded
```

The output looks like this:

```
unix`_sys_sysenter_post_swapgs 1401
unix`_sys_sysenter_post_swapgs;genunix`close 5
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf 85
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf;c2audit`audit_closef 26
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf;c2audit`audit_setf 5
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf;genunix`audit_getstate 6
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf;genunix`audit_unfalloc 2
unix`_sys_sysenter_post_swapgs;genunix`close;genunix`closeandsetf;genunix`closef 48
[...]
```

3\. flamegraph.pl
================
Use flamegraph.pl to render a SVG.

```
$ ./flamegraph.pl out.kern_folded > kernel.svg
```

An advantage of having the folded input file (and why this is separate to flamegraph.pl) is that you can use grep for functions of interest. Eg:

```
$ grep cpuid out.kern_folded | ./flamegraph.pl > cpuid.svg
```

Provided Examples
=================

### Linux perf\_events

An example output from Linux "perf script" is included, gzip'd, as example-perf-stacks.txt.gz. The resulting flame graph is example-perf.svg:

[![Example](http://www.brendangregg.com/FlameGraphs/example-perf.svg)](https://jing.yandex-team.ru/files/yazevnul/contrib-tools-flame-graph-example-perf.svg)

You can create this using:

```
$ gunzip -c example-perf-stacks.txt.gz | ./stackcollapse-perf.pl --all | ./flamegraph.pl --color=java --hash > example-perf.svg
```

This shows my typical workflow: I'll gzip profiles on the target, then copy them to my laptop for analysis. Since I have hundreds of profiles, I leave them gzip'd!

Since this profile included Java, I used the flamegraph.pl --color=java palette. I've also used stackcollapse-perf.pl --all, which includes all annotations that help flamegraph.pl use separate colors for kernel and user level code. The resulting flame graph uses: green == Java, yellow == C++, red == user-mode native, orange == kernel.

This profile was from an analysis of vert.x performance. The benchmark client, wrk, is also visible in the flame graph.

### DTrace

An example output from DTrace is also included, example-dtrace-stacks.txt, and the resulting flame graph, example-dtrace.svg:

[![Example](http://www.brendangregg.com/FlameGraphs/example-dtrace.svg)](https://jing.yandex-team.ru/files/yazevnul/contrib-tools-flame-graph-example-dtrace.svg)

You can generate this using:

```
$ ./stackcollapse.pl example-stacks.txt | ./flamegraph.pl > example.svg
```

This was from a particular performance investigation: the Flame Graph identified that CPU time was spent in the lofs module, and quantified that time.


Options
=======
See the USAGE message (--help) for options:

USAGE: ./flamegraph.pl [options] infile > outfile.svg

	--title TEXT     # change title text
	--subtitle TEXT  # second level title (optional)
	--width NUM      # width of image (default 1200)
	--height NUM     # height of each frame (default 16)
	--minwidth NUM   # omit smaller functions (default 0.1 pixels)
	--fonttype FONT  # font type (default "Verdana")
	--fontsize NUM   # font size (default 12)
	--countname TEXT # count type label (default "samples")
	--nametype TEXT  # name type label (default "Function:")
	--colors PALETTE # set color palette. choices are: hot (default), mem,
	                 # io, wakeup, chain, java, js, perl, red, green, blue,
	                 # aqua, yellow, purple, orange
	--bgcolors COLOR # set background colors. gradient choices are yellow
	                 # (default), blue, green, grey; flat colors use "#rrggbb"
	--hash           # colors are keyed by function name hash
	--cp             # use consistent palette (palette.map)
	--reverse        # generate stack-reversed flame graph
	--inverted       # icicle graph
	--flamechart     # produce a flame chart (sort by time, do not merge stacks)
	--negate         # switch differential hues (blue<->red)
	--notes TEXT     # add notes comment in SVG (for debugging)
	--help           # this message

	eg,
	./flamegraph.pl --title="Flame Graph: malloc()" trace.txt > graph.svg

As suggested in the example, flame graphs can process traces of any event,
such as malloc()s, provided stack traces are gathered.


Consistent Palette
==================
If you use the `--cp` option, it will use the $colors selection and randomly
generate the palette like normal. Any future flamegraphs created using the `--cp`
option will use the same palette map. Any new symbols from future flamegraphs
will have their colors randomly generated using the $colors selection.

If you don't like the palette, just delete the palette.map file.

This allows your to change your colorscheme between flamegraphs to make the
differences REALLY stand out.

Example:

Say we have 2 captures, one with a problem, and one when it was working
(whatever "it" is):

```
cat working.folded | ./flamegraph.pl --cp > working.svg
# this generates a palette.map, as per the normal random generated look.

cat broken.folded | ./flamegraph.pl --cp --colors mem > broken.svg
# this svg will use the same palette.map for the same events, but a very
# different colorscheme for any new events.
```

Take a look at the demo directory for an example:

palette-example-working.svg
palette-example-broken.svg