Matirx 自动生成火焰图小工具

# Flame Graphs visualize profiled code Main Website: http://www.brendangregg.com/flamegraphs.html Example (click to zoom): [](http://www.brendangregg.com/FlameGraphs/cpu-bash-flamegraph.svg) Click a box to zoom the Flame Graph to this stack frame only. To search and highlight all stack frames matching a regular expression, click the _search_ button in the upper right corner or press Ctrl-F. By default, search is case sensitive, but this can be toggled by pressing Ctrl-I or by clicking the _ic_ button in the upper right corner. 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 - `stackcollapse-wcp.pl`: for wallClockProfiler output 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: [](http://www.brendangregg.com/FlameGraphs/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: [](http://www.brendangregg.com/FlameGraphs/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 Gr