brendangregg / FlameGraph

# Flame Graphs visualize profiled code
Main Website: http://www.brendangregg.com/flamegraphs.html
Example (click to zoom):
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:
 [code] 
Now only capturing PID 181:
 [code] 
### DTrace
Using DTrace to capture 60 seconds of kernel stacks at 997 Hertz:
 [code] 
Using DTrace to capture 60 seconds of user-level stacks for PID 12345 at 97 Hertz:
 [code] 
60 seconds of user-level stacks, including time spent in-kernel, for PID 12345 at 97 Hertz:
 [code] 
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 s