Flame Graphs visualize profiled code
Main Website: http://www.brendangregg.com/flamegraphs.html
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:
-
Capture stacks
-
Fold stacks
-
flamegraph.pl
-
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.
- Fold stacks ============== Use the stackcollapse programs to fold stack samples into single lines. The programs provided are:
stackcollapse.pl
: for DTrace stacksstackcollapse-perf.pl
: for Linux perf_events "perf script" outputstackcollapse-pmc.pl
: for FreeBSD pmcstat -G stacksstackcollapse-stap.pl
: for SystemTap stacksstackcollapse-instruments.pl
: for XCode Instrumentsstackcollapse-vtune.pl
: for Intel VTune profilesstackcollapse-ljp.awk
: for Lightweight Java Profilerstackcollapse-jstack.pl
: for Java jstack(1) outputstackcollapse-gdb.pl
: for gdb(1) stacksstackcollapse-go.pl
: for Golang pprof 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
[...]
- 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:
You can create this using:
$ gunzip -c example-perf-stacks.txt.gz | ./stackcollapse-perf.pl --kernel | ./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 --kernel, which allows a separate color to be used for kernel 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:
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 # 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 # set color palette. choices are: hot (default), mem, io,
# wakeup, chain, java, js, perl, red, green, blue, aqua,
# yellow, purple, orange
--hash # colors are keyed by function name hash
--cp # use consistent palette (palette.map)
--reverse # generate stack-reversed flame graph
--inverted # icicle graph
--negate # switch differential hues (blue<->red)
--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