rhysh / FlameGraph

stack trace visualizer

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Flame Graphs visualize profiled code

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

Example (click to zoom): Example

Other sites:

Flame graphs can be created in three steps:

  1. Capture stacks

  2. Fold stacks

  3. flamegraph.pl

  4. 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.

  1. 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

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
[...]
  1. 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 Example

An example output from DTrace is included, both the captured stacks and the resulting Flame Graph. You can generate it yourself 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

    --titletext             # change title text
    --width                 # width of image (default 1200)
    --height                # height of each frame (default 16)
    --minwidth              # omit smaller functions (default 0.1 pixels)
    --fonttype              # font type (default "Verdana")
    --fontsize              # font size (default 12)
    --countname             # count type label (default "samples")
    --nametype              # name type label (default "Function:")
    --colors                # "hot", "mem", "io" palette (default "hot")
    --hash                  # colors are keyed by function name hash
    --cp                    # use consistent palette (palette.map)
eg,
    ./flamegraph.pl --titletext="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

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stack trace visualizer


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