Blazing Performance with Flame Graphs
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Blazing Performance with Flame Graphs
1. Blazing Performance with Flame Graphs Brendan Gregg
2. An Interactive Visualization for Stack Traces
3. My Previous Visualizations Include • Latency Heat Maps (and other heat map types), including:
• Quotes from LISA'13 yesterday: •
"Heat maps are a wonderful thing, use them" – Caskey Dickson
•
"If you do distributed systems, you need this" – Theo Schlossnagle
• I did heat maps and visualizations in my LISA'10 talk
4. Audience • This is for developers, sysadmins, support staff, and performance engineers
• This is a skill-up for everyone: beginners to experts • This helps analyze all software: kernels and applications
5. whoami • G’Day, I’m Brendan • Recipient of the LISA 2013 Award for Outstanding Achievement in System Administration! (Thank you!)
• Work/Research: tools, methodologies, visualizations
• Author of Systems Performance, primary author of DTrace (Prentice Hall, 2011)
• Lead Performance Engineer @joyent; also teach classes: Cloud Perf coming up: http://www.joyent.com/developers/training-services
6. Joyent • High-Performance Cloud Infrastructure • Public/private cloud provider • OS-Virtualization for bare metal performance • KVM for Linux guests • Core developers of SmartOS and node.js
• Office walls decorated with Flame Graphs:
7. Agenda: Two Talks in One • 1. CPU Flame Graphs • Example • Background • Flame Graphs • Generation • Types: CPU • 2. Advanced Flame Graphs • Types: Memory, I/O, Off-CPU, Hot/Cold, Wakeup • Developments • SVG demos: https://github.com/brendangregg/FlameGraph/demos
8. CPU Flame Graphs
9. Example
10. Example • As a short example, I’ll describe the real world performance issue that led me to create flame graphs
• Then I’ll explain them in detail
11. Example: The Problem • A production MySQL database had poor performance • It was a heavy CPU consumer, so I used a CPU profiler to see why. It sampled stack traces at timed intervals
• The profiler condensed its output by only printing unique stacks along with their occurrence counts, sorted by count
• The following shows the profiler command and the two most frequently sampled stacks...
12. Example: CPU Profiling # dtrace -x ustackframes=100 -n 'profile-997 /execname == "mysqld"/ { @[ustack()] = count(); } tick-60s { exit(0); }' dtrace: description 'profile-997 ' matched 2 probes CPU ID FUNCTION:NAME 1 75195 :tick-60s [...] libc.so.1`__priocntlset+0xa libc.so.1`getparam+0x83 libc.so.1`pthread_getschedparam+0x3c libc.so.1`pthread_setschedprio+0x1f mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x9ab mysqld`_Z10do_commandP3THD+0x198 mysqld`handle_one_connection+0x1a6 libc.so.1`_thrp_setup+0x8d libc.so.1`_lwp_start 4884 mysqld`_Z13add_to_statusP17system_status_varS0_+0x47 mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67 mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222 mysqld`_Z10do_commandP3THD+0x198 mysqld`handle_one_connection+0x1a6 libc.so.1`_thrp_setup+0x8d libc.so.1`_lwp_start 5530
13. Example: CPU Profiling # dtrace -x ustackframes=100 -n 'profile-997 /execname == "mysqld"/ { @[ustack()] = count(); } tick-60s { exit(0); }' dtrace: description 'profile-997 ' matched 2 probes CPU ID FUNCTION:NAME Profiling 1 75195 :tick-60s Command [...] libc.so.1`__priocntlset+0xa (DTrace) libc.so.1`getparam+0x83 libc.so.1`pthread_getschedparam+0x3c libc.so.1`pthread_setschedprio+0x1f mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x9ab mysqld`_Z10do_commandP3THD+0x198 mysqld`handle_one_connection+0x1a6 libc.so.1`_thrp_setup+0x8d libc.so.1`_lwp_start 4884
Stack Trace
mysqld`_Z13add_to_statusP17system_status_varS0_+0x47 mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67 mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222 mysqld`_Z10do_commandP3THD+0x198 mysqld`handle_one_connection+0x1a6 libc.so.1`_thrp_setup+0x8d libc.so.1`_lwp_start 5530 # of occurrences
14. Example: Profile Data • Over 500,000 lines were elided from that output (“[...]”) • Full output looks like this...
15. Example: Profile Data
60 seconds of on-CPU MySQL
16. Example: Profile Data First Stack
Size of One Stack
Last Stack
27,053 Unique Stacks MySQL 60 seconds of on-CPU
17. Example: Profile Data • The most frequent stack, printed last, shows CPU usage in add_to_status(), which is from the “show status” command. Is that to blame?
• Hard to tell – it only accounts for < 2% of the samples • I wanted a way to quickly understand stack trace profile data, without browsing 500,000+ lines of output
18. Example:Visualizations • To understand this profile data quickly, I created visualization that worked very well, named “Flame Graph” for its resemblance to fire (also as it was showing a “hot” CPU issue)
Profile Data.txt
Flame Graph.svg
some Perl
19. Example: Flame Graph Same profile data
20. Example: Flame Graph Same profile data
Where CPU is really consumed
The "show status" Stack
All Stack Samples
One Stack Sample
21. Example: Flame Graph • All data in one picture • Interactive using JavaScript and a browser: mouse overs • Stack elements that are frequent can be seen, read, and compared visually. Frame width is relative to sample count
• CPU usage was now understood properly and quickly, leading to a 40% performance win
22. Background
23. Background: Stack Frame • A stack frame shows a location in code • Profilers usually show them on a single line. Eg: libc.so.1`mutex_trylock_adaptive+0x112
module
function
instruction offset
24. Background: Stack Trace • A stack trace is a list of frames. Their index is the stack depth: current
libc.so.1`mutex_trylock_adaptive+0x112 24
parent parent
libc.so.1`mutex_lock_impl+0x165
23
parent grand parent
libc.so.1`mutex_lock+0xc
22 Stack Depth
[...] libc.so.1`_lwp_start
0
25. Background: Stack Trace • One full stack: libc.so.1`mutex_trylock_adaptive+0x112 libc.so.1`mutex_lock_impl+0x165 libc.so.1`mutex_lock+0xc mysqld`key_cache_read+0x741 mysqld`_mi_fetch_keypage+0x48 mysqld`w_search+0x84 mysqld`_mi_ck_write_btree+0xa5 mysqld`mi_write+0x344 mysqld`ha_myisam::write_row+0x43 mysqld`handler::ha_write_row+0x8d mysqld`end_write+0x1a3 mysqld`evaluate_join_record+0x11e mysqld`sub_select+0x86 mysqld`do_select+0xd9 mysqld`JOIN::exec+0x482 mysqld`mysql_select+0x30e mysqld`handle_select+0x17d mysqld`execute_sqlcom_select+0xa6 mysqld`mysql_execute_command+0x124b mysqld`mysql_parse+0x3e1 mysqld`dispatch_command+0x1619 mysqld`do_handle_one_connection+0x1e5 mysqld`handle_one_connection+0x4c libc.so.1`_thrp_setup+0xbc libc.so.1`_lwp_start
26. Background: Stack Trace • Read top-down or bottom-up, and look for key functions libc.so.1`mutex_trylock_adaptive+0x112 libc.so.1`mutex_lock_impl+0x165 libc.so.1`mutex_lock+0xc mysqld`key_cache_read+0x741 mysqld`_mi_fetch_keypage+0x48 mysqld`w_search+0x84 mysqld`_mi_ck_write_btree+0xa5 mysqld`mi_write+0x344 mysqld`ha_myisam::write_row+0x43 mysqld`handler::ha_write_row+0x8d mysqld`end_write+0x1a3 mysqld`evaluate_join_record+0x11e mysqld`sub_select+0x86 mysqld`do_select+0xd9 mysqld`JOIN::exec+0x482 mysqld`mysql_select+0x30e mysqld`handle_select+0x17d mysqld`execute_sqlcom_select+0xa6 mysqld`mysql_execute_command+0x124b mysqld`mysql_parse+0x3e1 mysqld`dispatch_command+0x1619 mysqld`do_handle_one_connection+0x1e5 mysqld`handle_one_connection+0x4c libc.so.1`_thrp_setup+0xbc libc.so.1`_lwp_start
Ancestry
Code Path
27. Background: Stack Modes • Two types of stacks can be profiled: • user-level for applications (user mode) • kernel-level for the kernel (kernel mode) • During a system call, an application may have both
28. Background: Software Internals • You don’t need to be a programmer to understand stacks. • Some function names are self explanatory, others require source code browsing (if available). Not as bad as it sounds:
• MySQL has ~15,000 functions in > 0.5 million lines of code • The earlier stack has 20 MySQL functions. To understand them, you may need to browse only 0.13% (20 / 15000) of the code. Might take hours, but it is doable.
• If you have C++ signatures, you can use a demangler first: mysqld`_ZN4JOIN4execEv+0x482 gc++filt, demangler.com mysqld`JOIN::exec()+0x482
29. Background: Stack Visualization • Stack frames can be visualized as rectangles (boxes) • Function names can be truncated to fit • In this case, color is chosen randomly (from a warm palette) to differentiate adjacent frames libc.so.1`mutex_trylock_adaptive+0x112
libc.so.1`mutex_trylock_...
libc.so.1`mutex_lock_impl+0x165
libc.so.1`mutex_lock_imp...
libc.so.1`mutex_lock+0xc
libc.so.1`mutex_lock+0xc
mysqld`key_cache_read+0x741
mysqld`key_cache_read+0x741
• A stack trace becomes a column of colored rectangles
30. Background: Time Series Stacks • Time series ordering allows time-based pattern identification • However, stacks can change thousands of times per second
Stack Depth
Time (seconds)
31. Background: Time Series Stacks • Time series ordering allows time-based pattern identification • However, stacks can change thousands of times per second One Stack Sample
Stack Depth
Time (seconds)
32. Background: Frame Merging • When zoomed out, stacks appear as narrow stripes • Adjacent identical functions can be merged to improve readability, eg: mu...
mu...
ge...
muex_tryl...
ge...
mu...
mu...
mu...
mutex_lock_impl()
mu...
mu...
mu...
mutex_lock()
ke...
ke...
ke...
key_cache_read()
• This sometimes works: eg, a repetitive single threaded app • Often does not (previous slide already did this), due to code execution between samples or parallel thread execution
33. Background: Frame Merging • Time-series ordering isn’t necessary for the primary use case: identify the most common (“hottest”) code path or paths
• By using a different x-axis sort order, frame merging can be greatly improved...
34. Flame Graphs
35. Flame Graphs • Flame Graphs sort stacks alphabetically. This sort is applied from the bottom frame upwards. This increases merging and visualizes code paths.
Stack Depth
Alphabet
36. Flame Graphs: Definition • Each box represents a function (a merged stack frame) • y-axis shows stack depth • top function led directly to the profiling event • everything beneath it is ancestry (explains why) • x-axis spans the sample population, sorted alphabetically • Box width is proportional to the total time a function was profiled directly or its children were profiled
• All threads can be shown in the same Flame Graph (the default), or as separate per-thread Flame Graphs
• Flame Graphs can be interactive: mouse over for details
37. Flame Graphs:Variations • Profile data can be anything: CPU, I/O, memory, ... • Naming suggestion: [event] [units] Flame Graph • Eg: "FS Latency Flame Graph" • By default, Flame Graphs == CPU Sample Flame Graphs • Colors can be used for another dimension • by default, random colors are used to differentiate boxes • --hash for hash-based on function name • Distribution applications can be shown in the same Flame Graph (merge samples from multiple systems)
38. Flame Graphs: A Simple Example • A CPU Sample Flame Graph: f() d()
e()
c()
h()
b()
g()
a()
• I’ll illustrate how these are read by posing various questions
39. Flame Graphs: How to Read • A CPU Sample Flame Graph: f() d()
e()
c()
h()
b()
g()
a()
• Q: which function is on-CPU the most?
40. Flame Graphs: How to Read • A CPU Sample Flame Graph: f() d()
top edge shows who is on-CPU directly
e()
c()
h()
b()
g()
a()
• Q: which function is on-CPU the most? • A: f() e() is on-CPU a little, but its runtime is mostly spent in f(), which is on-CPU directly
41. Flame Graphs: How to Read • A CPU Sample Flame Graph: f() d()
e()
c()
h()
b()
g()
a()
• Q: why is f() on-CPU?
42. Flame Graphs: How to Read • A CPU Sample Flame Graph:
f() was called by e() e() was called by c() ...
f() d()
e()
ancestry
c()
h()
b()
g()
a()
• Q: why is f() on-CPU? • A: a() → b() → c() → e() → f()
43. Flame Graphs: How to Read • A CPU Sample Flame Graph: f() d()
e()
c()
h()
b()
g()
a()
• Q: how does b() compare to g()?
44. Flame Graphs: How to Read • A CPU Sample Flame Graph:
visually compare lengths
f() d()
e()
c()
h()
b()
g()
a()
• Q: how does b() compare to g()? • A: b() looks like it is running (present) about 10 times more often than g()
45. Flame Graphs: How to Read • A CPU Sample Flame Graph:
... or mouse over
f() d()
e()
c()
h()
b()
g()
a()
status line or tool tip: b() is 90%
• Q: how does b() compare to g()? • A: for interactive Flame Graphs, mouse over shows b() is 90%, g() is 10%
46. Flame Graphs: How to Read • A CPU Sample Flame Graph:
... or mouse over
f() d()
e()
c()
h()
b()
g()
a()
status line or tool tip: g() is 10%
• Q: how does b() compare to g()? • A: for interactive Flame Graphs, mouse over shows b() is 90%, g() is 10%
47. Flame Graphs: How to Read • A CPU Sample Flame Graph: f() d()
e()
c()
h()
b()
g()
a()
• Q: why are we running f()?
48. Flame Graphs: How to Read • A CPU Sample Flame Graph:
look for branches
f() d()
e()
c()
h()
b()
g()
a()
• Q: why are we running f()? • A: code path branches can reveal key functions: • a() choose the b() path • c() choose the e() path
49. Flame Graphs: Example 1 • Customer alerting software periodically checks a log, however, it is taking too long (minutes).
• It includes grep(1) of an ~18 Mbyte log file, which takes around 10 minutes!
• grep(1) appears to be on-CPU for this time. Why?
50. Flame Graphs: Example 1 • CPU Sample Flame Graph for grep(1) user-level stacks:
51. Flame Graphs: Example 1 • CPU Sample Flame Graph for grep(1) user-level stacks: UTF8?
• 82% of samples are in check_multibyte_string() or its children. This seems odd as the log file is plain ASCII.
• And why is UTF8 on the scene? ... Oh, LANG=en_US.UTF-8
52. Flame Graphs: Example 1 • CPU Sample Flame Graph for grep(1) user-level stacks: UTF8?
• Switching to LANG=C improved performance by 2000x • A simple example, but I did spot this from the raw profiler text before the Flame Graph. You really need Flame Graphs when the text gets too long and unwieldy.
53. Flame Graphs: Example 2 • A potential customer benchmarks disk I/O on a cloud instance. The performance is not as fast as hoped.
• The host has new hardware and software. Issues with the new type of disks is suspected.
54. Flame Graphs: Example 2 • A potential customer benchmarks disk I/O on a cloud instance. The performance is not as fast as hoped.
• The host has new hardware and software. Issues with the new type of disks is suspected.
• I take a look, and notice CPU time in the kernel is modest. • I’d normally assume this was I/O overheads and not profile it yet, instead beginning with I/O latency analysis.
• But Flame Graphs make it easy, and it may be useful to see what code paths (illumos kernel) are on the table.
55. Flame Graphs: Example 2
56. Flame Graphs: Example 2
• 24% in tsc_read()? Time Stamp Counter? Checking ancestry...
57. Flame Graphs: Example 2
• 62% in zfs_zone_io_throttle? Oh, we had forgotten that this new platform had ZFS I/O throttles turned on by default!
58. Flame Graphs: Example 3 • Application performance is about half that of a competitor • Everything is believed identical (H/W, application, config, workload) except for the OS and kernel
• Application is CPU busy, nearly 100% in user-mode. How can the kernel cause a 2x delta when the app isn't in kernel-mode?
• Flame graphs on both platforms for user-mode were created: • Linux, using perf • SmartOS, using DTrace • Added flamegraph.pl --hash option for consistent function colors (not random), aiding comparisons
59. Flame Graphs: Example 3 Extra Function: UnzipDocid()
Linux
SmartOS
• Function label formats are different, but that's just due to different profilers/stackcollapse.pl's (should fix this)
• Widths slighly different, but we already know perf differs • Extra function? This is executing different application software! SphDocID_t
UnzipDocid ()
{ return UnzipOffset(); }
• Actually, a different compiler option was eliding this function
60. Flame Graphs: More Examples • Flame Graphs are typically more detailed, like the earlier MySQL example
• Next, how to generate them, then more examples
61. Generation
62. Generation • I’ll describe the original Perl version I wrote and shared on github:
•
https://github.com/brendangregg/FlameGraph
• There are other great Flame Graph implementations with different features and usage, which I’ll cover in the last section
63. Generation: Steps • 1. Profile event of interest • 2. stackcollapse.pl • 3. flamegraph.pl
64. Generation: Overview • Full command line example. This uses DTrace for CPU profiling of the kernel: # dtrace -x stackframes=100 -n 'profile-997 /arg0/ { @[stack()] = count(); } tick-60s { exit(0); }' -o out.stacks # stackcollapse.pl < out.stacks > out.folded # flamegraph.pl < out.folded > out.svg
• Then, open out.svg in a browser • Intermediate files could be avoided (piping), but they can be handy for some manual processing if needed (eg, using vi)
65. Generation: Profiling Data • The profile data, at a minimum, is a series of stack traces • These can also include stack trace counts. Eg: mysqld`_Z13add_to_statusP17system_status_varS0_+0x47 mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67 mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222 mysqld`_Z10do_commandP3THD+0x198 mysqld`handle_one_connection+0x1a6 libc.so.1`_thrp_setup+0x8d libc.so.1`_lwp_start 5530 # of occurrences for this stack
• This example is from DTrace, which prints a series of these. The format of each group is: stack, count, newline
• Your profiler needs to print full (not truncated) stacks, with symbols. This may be step 0: get the profiler to work!
66. Generation: Profiling Tools • Solaris/FreeBSD/SmartOS/...: • DTrace • Linux: • perf, SystemTap • OS X: • Instruments • Windows: • Xperf.exe
67. Generation: Profiling Examples: DTrace • CPU profile kernel stacks at 997 Hertz, for 60 secs: # dtrace -x stackframes=100 -n 'profile-997 /arg0/ { @[stack()] = count(); } tick-60s { exit(0); }' -o out.kern_stacks
• CPU profile user-level stacks for PID 12345 at 99 Hertz, 60s: # dtrace -x ustackframes=100 -n 'profile-97 /PID == 12345 && arg1/ { @[ustack()] = count(); } tick-60s { exit(0); }' -o out.user_stacks
• Should also work on Mac OS X, but is pending some fixes preventing stack walking (use Instruments instead)
• Should work for Linux one day with the DTrace ports
68. Generation: Profiling Examples: perf • CPU profile full stacks at 97 Hertz, for 60 secs: # perf record -a -g -F 97 sleep 60 # perf script > out.stacks
• Need debug symbol packages installed (*dbgsym), otherwise stack frames may show as hexidecimal
• May need compilers to cooperate (-fno-omit-frame-pointer) • Has both user and kernel stacks, and the kernel idle thread. Can filter the idle thread after stackcollapse-perf.pl using: # stackcollapse-perf.pl < out.stacks | grep -v cpu_idle | ...
69. Generation: Profiling Examples: SystemTap • CPU profile kernel stacks at 100 Hertz, for 60 secs: # stap -s 32 -D MAXTRACE=100 -D MAXSTRINGLEN=4096 -D MAXMAPENTRIES=10240 -D MAXACTION=10000 -D STP_OVERLOAD_THRESHOLD=5000000000 --all-modules -ve 'global s; probe timer.profile { s[backtrace()] <<< 1; } probe end { foreach (i in s+) { print_stack(i); printf("t%dn", @count(s[i])); } } probe timer.s(60) { exit(); }' > out.kern_stacks
• Need debug symbol packages installed (*dbgsym), otherwise stack frames may show as hexidecimal
• May need compilers to cooperate (-fno-omit-frame-pointer)
70. Generation: Dynamic Languages • C or C++ are usually easy to profile, runtime environments (JVM, node.js, ...) are usually not, typically a way to show program stacks and not just runtime internals.
• Eg, DTrace’s ustack helper for node.js: 0xfc618bc0 0xfc61bd62 0xfe870841 0xfc61c1f3 0xfc617685 0xfe870841 0xfc6154d7 0xfe870e1a [...]
libc.so.1`gettimeofday+0x7 Date at position << adaptor >> << constructor >> (anon) as exports.active at timers.js position 7590 (anon) as Socket._write at net.js position 21336 (anon) as Socket.write at net.js position 19714 << adaptor >> (anon) as OutgoingMessage._writeRaw at http.js p... (anon) as OutgoingMessage._send at http.js posit... << adaptor >> (anon) as OutgoingMessage.end at http.js pos... [...]
http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spend-its-time/
71. Generation: stackcollapse.pl • Converts profile data into a single line records • Variants exist for DTrace, perf, SystemTap, Instruments, Xperf • Eg, DTrace: unix`i86_mwait+0xd unix`cpu_idle_mwait+0xf1 unix`idle+0x114 unix`thread_start+0x8 19486
# stackcollapse.pl < out.stacks > out.folded
unix`thread_start;unix`idle;unix`cpu_idle_mwait;unix`i86_mwait 19486
72. Generation: stackcollapse.pl • Converts profile data into a single line records • Variants exist for DTrace, perf, SystemTap, Instruments, Xperf • Eg, DTrace: unix`i86_mwait+0xd unix`cpu_idle_mwait+0xf1 unix`idle+0x114 unix`thread_start+0x8 19486
# stackcollapse.pl < out.stacks > out.folded
unix`thread_start;unix`idle;unix`cpu_idle_mwait;unix`i86_mwait 19486
stack trace, frames are ‘;’ delimited
count
73. Generation: stackcollapse.pl • Full output is many lines, one line per stack • Bonus: can be grepped # ./stackcollapse-stap.pl out.stacks | grep ext4fs_dirhash system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_ tree;htree_dirblock_to_tree;ext4fs_dirhash 100 system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_ tree;htree_dirblock_to_tree;ext4fs_dirhash;half_md4_transform 505 system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_ tree;htree_dirblock_to_tree;ext4fs_dirhash;str2hashbuf_signed 353 [...]
• That shows all stacks containing ext4fs_dirhash(); useful debug aid by itself
• grep can also be used to filter stacks before Flame Graphs • eg: grep -v cpu_idle
74. Generation: Final Output • Desires: • Full control of output • High density detail • Portable: easily viewable • Interactive
75. Generation: Final Output • Desires: • Full control of output • High density detail • Portable: easily viewable
PNG SVG+JS
• Interactive • SVG+JS: Scalable Vector Graphics with embedded JavaScript • Common standards, and supported by web browsers • Can print poster size (scalable); but loses interactivity! • Can be emitted by a simple Perl program...
76. Generation: flamegraph.pl • Converts folded stacks into an interactive SVG. Eg: # flamegraph.pl --titletext="Flame Graph: MySQL" out.folded > graph.svg
• Options: --titletext
change the title text (default is “Flame Graph”)
--width
width of image (default is 1200)
--height
height of each frame (default is 16)
--minwidth
omit functions smaller than this width (default is 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
color palette: "hot", "mem", "io"
--hash
colors are keyed by function name hash
77. Types
78. Types • CPU • Memory • Off-CPU • More
79. CPU
80. CPU • Measure code paths that consume CPU • Helps us understand and optimize CPU usage, improving performance and scalability
• Commonly performed by sampling CPU stack traces at a timed interval (eg, 100 Hertz for every 10 ms), on all CPUs
• DTrace/perf/SystemTap examples shown earlier • Can also be performed by tracing function execution
81. CPU: Sampling CPU stack sampling:
A
A
A
B A
-
-
-
-
B A
A
A
A
A(
) B(
) syscall
On-CPU
X Off-CPU block . . . . . . . . . interrupt
user-level kernel
82. CPU: Tracing CPU function tracing:
A(
B(
B)
A)
A(
) B(
) syscall
On-CPU
X Off-CPU block . . . . . . . . . interrupt
user-level kernel
83. CPU: Profiling • Sampling: • Coarse but usually effective • Can also be low overhead, depending on the stack type and sample rate, which is fixed (eg, 100 Hz x CPU count)
• Tracing: • Overheads can be too high, distorting results and hurting the target (eg, millions of trace events per second)
• Most Flame Graphs are generated using stack sampling
84. CPU: Profiling Results • Example results. Could you do this? As an experiment to investigate the performance of the resulting TCP/IP implementation ... the 11/750 is CPU saturated, but the 11/780 has about 30% idle time. The time spent in the system processing the data is spread out among handling for the Ethernet (20%), IP packet processing (10%), TCP processing (30%), checksumming (25%), and user system call handling (15%), with no single part of the handling dominating the time in the system.
85. CPU: Profiling Results • Example results. Could you do this? As an experiment to investigate the performance of the resulting TCP/IP implementation ... the 11/750 is CPU saturated, but the 11/780 has about 30% idle time. The time spent in the system processing the data is spread out among handling for the Ethernet (20%), IP packet processing (10%), TCP processing (30%), checksumming (25%), and user system call handling (15%), with no single part of the handling dominating the time in the system.
– Bill Joy, 1981, TCP-IP Digest, Vol 1 #6
• An impressive report, that even today would be difficult to do • Flame Graphs make this a lot easier
86. CPU: Another Example • A file system is archived using tar(1). • The files and directories are cached, and the run time is mostly on-CPU in the kernel (Linux). Where exactly?
87. CPU: Another Example
88. CPU: Another Example
• 20% for reading directories
89. CPU: Another Example
• 54% for file statistics
90. CPU: Another Example
• Also good for learning kernel internals: browse the active code
91. CPU: Recognition • Once you start profiling a target, you begin to recognize the common stacks and patterns
• Linux getdents() ext4 path: • The next slides show similar example kernel-mode CPU Sample Flame Graphs
92. CPU: Recognition: illumos localhost TCP • From a TCP localhost latency issue (illumos kernel):
illumos fused-TCP receive
illumos fused-TCP send
93. CPU: Recognition: illumos IP DCE issue DCE lookup
DCE lookup DCE lookup
94. CPU: Recognition: Linux TCP send • Profiled from a KVM guest: Linux TCP sendmsg
95. CPU: Recognition: Syscall Towers
96. CPU: Recognition: Syscall Towers lstat() open() close()
writev()
pollsys() read()
write()
stat() stat64()
bnx xmit
sendfile() bnx recv
ip fanout receive
97. CPU: Both Stacks • Apart from showing either user- or kernel-level stacks, both can be included by stacking kernel on top of user
• Linux perf does this by default • DTrace can by aggregating @[stack(), ustack()] • The different stacks can be highlighted in different ways: • different colors or hues • separator: flamegraph.pl will color gray any functions called "-", which can be inserted as stack separators
• Kernel stacks are only present during syscalls or interrupts
98. CPU: Both Stacks Example: KVM/qemu user only kernel stack
user stack
99. Advanced Flame Graphs
100. Other Targets • Apart from CPU samples, stack traces can be collected for any event; eg:
• disk, network, or FS I/O • CPU events, including cache misses • lock contention and holds • memory allocation • Other values, instead of sample counts, can also be used: • latency • bytes • The next sections demonstrate memory allocation, I/O tracing, and then all blocking types via off-CPU tracing
101. Memory
102. Memory • Analyze memory growth or leaks by tracing one of the following memory events:
• 1. Allocator functions: malloc(), free() • 2. brk() syscall • 3. mmap() syscall • 4. Page faults • Instead of stacks and sample counts, measure stacks with byte counts
• Merging shows show total bytes by code path
103. Memory: Four Targets
104. Memory: Allocator • Trace malloc(), free(), realloc(), calloc(), ... • These operate on virtual memory • *alloc() stacks show why memory was first allocated (as opposed to populated): Memory Allocation Flame Graphs
• With free()/realloc()/..., suspected memory leaks during tracing can be identified: Memory Leak Flame Graphs!
• Down side: allocator functions are frequent, so tracing can slow the target somewhat (eg, 25%)
• For comparison: Valgrind memcheck is more thorough, but its CPU simulation can slow the target 20 - 30x
105. Memory: Allocator: malloc() • As a simple example, just tracing malloc() calls with user-level stacks and bytes requested, using DTrace: # dtrace -x ustackframes=100 -n 'pid$target::malloc:entry { @[ustack()] = sum(arg0); } tick-60s { exit(0); }' -p 529 -o out.malloc
• malloc() Bytes Flame Graph: # stackcollapse.pl out.malloc | flamegraph.pl --title="malloc() bytes" --countname="bytes" --colors=mem > out.malloc.svg
• The options customize the title, countname, and color palette
106. Memory: Allocator: malloc()
107. Memory: Allocator: Leaks • Yichun Zhang developed Memory Leak Flame Graphs using SystemTap to trace allocator functions, and applied them to leaks in Nginx (web server):
108. Memory: brk() • Many apps grow their virtual memory size using brk(), which sets the heap pointer
• A stack trace on brk() shows what triggered growth • Eg, this script (brkbytes.d) traces brk() growth for “mysqld”: #!/usr/sbin/dtrace -s inline string target = "mysqld"; uint brk[int]; syscall::brk:entry /execname == target/ { self->p = arg0; } syscall::brk:return /arg0 == 0 && self->p && brk[pid]/ { @[ustack()] = sum(self->p - brk[pid]); } syscall::brk:return /arg0 == 0 && self->p/ { brk[pid] = self->p; } syscall::brk:return /self->p/ { self->p = 0; }
109. Memory: brk(): Heap Expansion # ./brkbytes.d -n 'tick-60s { exit(0); }' > out.brk # stackcollapse.pl out.brk | flamegraph.pl --countname="bytes" --title="Heap Expansion Flame Graph" --colors=mem > out.brk.svg
110. Memory: brk() • brk() tracing has low overhead: these calls are typically infrequent
• Reasons for brk(): • A memory growth code path • A memory leak code path • An innocent application code path, that happened to spillover the current heap size
• Asynchronous allocator code path, that grew the application in response to diminishing free space
111. Memory: mmap() • mmap() may be used by the application or it’s user-level allocator to map in large regions of virtual memory
• It may be followed by munmap() to free the area, which can also be traced
• Eg, mmap() tracing, similar to brk tracing, to show bytes and the stacks responsible: # dtrace -n 'syscall::mmap:entry /execname == "mysqld"/ { @[ustack()] = sum(arg1); }' -o out.mmap # stackcollapse.pl out.mmap | flamegraph.pl --countname="bytes" --title="mmap() bytes Flame Graph" --colors=mem > out.mmap.svg
• This should be low overhead – depends on the frequency
112. Memory: Page Faults • brk() and mmap() expand virtual memory • Page faults expand physical memory (RSS). This is demandbased allocation, deferring mapping to the actual write
• Tracing page faults show the stack responsible for consuming (writing to) memory: # dtrace -x ustackframes=100 -n 'vminfo:::as_fault /execname == "mysqld"/ { @[ustack()] = count(); } tick-60s { exit(0); }' > out.fault # stackcollapse.pl out.mysqld_fault01 | flamegraph.pl --countname=pages --title="Page Fault Flame Graph" --colors=mem > mysqld_fault.svg
113. Memory: Page Faults
114. I/O
115. I/O • Show time spent in I/O, eg, storage I/O • Measure I/O completion events with stacks and their latency; merging to show total time waiting by code path Application system calls VFS
Logical I/O: Measure here for user stacks, and real application latency
FS Block Device Interface Disks
Physical I/O: Measure here for kernel stacks, and disk I/O latency
116. I/O: Logical I/O Laency • For example, ZFS call latency using DTrace (zfsustack.d): #!/usr/sbin/dtrace -s #pragma D option quiet #pragma D option ustackframes=100 fbt::zfs_read:entry, fbt::zfs_write:entry, fbt::zfs_readdir:entry, fbt::zfs_getattr:entry, fbt::zfs_setattr:entry { self->start = timestamp; } fbt::zfs_read:return, fbt::zfs_write:return, fbt::zfs_readdir:return, fbt::zfs_getattr:return, fbt::zfs_setattr:return /self->start/ { this->time = timestamp - self->start; @[ustack(), execname] = sum(this->time); self->start = 0; } dtrace:::END { printa("%k%sn%@dn", @); }
Timestamp from function start (entry)
... to function end (return)
117. I/O: Logical I/O Laency • Making an I/O Time Flame Graph: # ./zfsustacks.d -n 'tick-10s { exit(0); }' -o out.iostacks # stackcollapse.pl out.iostacks | awk '{ print $1, $2 / 1000000 }' | flamegraph.pl --title="FS I/O Time Flame Graph" --color=io --countname=ms --width=500 > out.iostacks.svg
• DTrace script measures all processes, for 10 seconds • awk to covert ns to ms
118. I/O: Time Flame Graph: gzip • gzip(1) waits more time in write()s than read()s
119. I/O: Time Flame Graph: MySQL
120. I/O: Flame Graphs • I/O latency tracing: hugely useful • But once you pick an I/O type, there usually isn't that many different code paths calling it
• Flame Graphs are nice, but often not necessary
121. Off-CPU
122. Off-CPU Off-CPU tracing: off-CPU
on-CPU X A
A(
) syscall
On-CPU
X Off-CPU X block . . . . . . . . . interrupt
user-level kernel
123. Off-CPU: Performance Analysis • Generic approach for all blocking events, including I/O • An advanced performance analysis methodology: •
http://dtrace.org/blogs/brendan/2011/07/08/off-cpu-performance-analysis/
• Counterpart to (on-)CPU profiling • Measure time a thread spent off-CPU, along with stacks • Off-CPU reasons: • Waiting (sleeping) on I/O, locks, timers • Runnable waiting for CPU • Runnable waiting for page/swap-ins • The stack trace will explain which
124. Off-CPU: Time Flame Graphs • Off-CPU profiling data (durations and stacks) can be rendered as Off-CPU Time Flame Graphs
• As this involves many more code paths, Flame Graphs are usually really useful
• Yichun Zhang created these, and has been using them on Linux with SystemTap to collect the profile data. See:
•
http://agentzh.org/misc/slides/off-cpu-flame-graphs.pdf
• Which describes their uses for Nginx performance analysis
125. Off-CPU: Profiling • Example of off-CPU profiling for the bash shell: # dtrace -x ustackframes=100 -n ' sched:::off-cpu /execname == "bash"/ { self->ts = timestamp; } sched:::on-cpu /self->ts/ { @[ustack()] = sum(timestamp - self->ts); self->ts = 0; } tick-30s { exit(0); }' -o out.offcpu
• Traces time from when a thread switches off-CPU to when it returns on-CPU, with user-level stacks. ie, time blocked or sleeping
• Off-CPU Time Flame Graph: # stackcollapse.pl < out.offcpu | awk '{ print $1, $2 / 1000000 }' | flamegraph.pl --title="Off-CPU Time Flame Graph" --color=io --countname=ms --width=600 > out.offcpu.svg
• This uses awk to convert nanoseconds into milliseconds
126. Off-CPU: Bash Shell
127. Off-CPU: Bash Shell waiting for child processes
waiting for keystrokes
128. Off-CPU: Bash Shell • For that simple example, the trace data was so short it could have just been read (54 lines, 4 unique stacks):
• For multithreaded applications, idle thread time can dominate
• For example, an idle MySQL server...
libc.so.1`__forkx+0xb libc.so.1`fork+0x1d bash`make_child+0xb5 bash`execute_simple_command+0xb02 bash`execute_command_internal+0xae6 bash`execute_command+0x45 bash`reader_loop+0x240 bash`main+0xaff bash`_start+0x83 19052 libc.so.1`syscall+0x13 bash`file_status+0x19 bash`find_in_path_element+0x3e bash`find_user_command_in_path+0x114 bash`find_user_command_internal+0x6f bash`search_for_command+0x109 bash`execute_simple_command+0xa97 bash`execute_command_internal+0xae6 bash`execute_command+0x45 bash`reader_loop+0x240 bash`main+0xaff bash`_start+0x83 7557782 libc.so.1`__waitid+0x15 libc.so.1`waitpid+0x65 bash`waitchld+0x87 bash`wait_for+0x2ce bash`execute_command_internal+0x1758 bash`execute_command+0x45 bash`reader_loop+0x240 bash`main+0xaff bash`_start+0x83 1193160644 libc.so.1`__read+0x15 bash`rl_getc+0x2b bash`rl_read_key+0x22d bash`readline_internal_char+0x113 bash`readline+0x49 bash`yy_readline_get+0x52 bash`shell_getc+0xe1 bash`read_token+0x6f bash`yyparse+0x4b9 bash`parse_command+0x67 bash`read_command+0x52 bash`reader_loop+0xa5 bash`main+0xaff bash`_start+0x83 12588900307
129. Off-CPU: MySQL Idle
130. Off-CPU: MySQL Idle
Columns from _thrp_setup are threads or thread groups
MySQL gives thread routines descriptive names (thanks!) Mouse over each to identify
(profiling time was 30s)
131. Off-CPU: MySQL Idle buf_flush_page_cleaner_thread mysqld_main dict_stats_thread srv_monitor_thread fts_optimize_thread srv_master_thread io_handler_thread srv_error_monitor_thread lock_wait_timeout_thread pfs_spawn_thread
mysqld Threads
132. Off-CPU: MySQL Idle • Some thread columns are wider than the measurement time: evidence of multiple threads
• This can be shown a number of ways. Eg, adding process name, PID, and TID to the top of each user stack: #!/usr/sbin/dtrace -s #pragma D option ustackframes=100 sched:::off-cpu /execname == "mysqld"/ { self->ts = timestamp; } sched:::on-cpu /self->ts/ { @[execname, pid, curlwpsinfo->pr_lwpid, ustack()] = sum(timestamp - self->ts); self->ts = 0; } dtrace:::END { printa("n%s-%d/%d%k%@dn", @); }
133. Off-CPU: MySQL Idle
1 thread
many threads
2 threads
4 threads doing work (less idle)
thread ID (TID)
134. Off-CPU: Challenges • Including multiple threads in one Flame Graph might still be confusing. Separate Flame Graphs for each can be created
• Off-CPU stacks often don't explain themselves:
• This is blocked on a conditional variable. The real reason it is blocked and taking time isn't visible here
• Now lets look at a busy MySQL server, which presents another challenge...
135. Off-CPU: MySQL Busy
net_read_packet() -> pollsys() idle threads
136. Off-CPU: MySQL Busy random narrow stacks during work, with no reason to sleep?
137. Off-CPU: MySQL Busy • Those were user-level stacks only. The kernel-level stack, which can be included, will usually explain what happened
• eg, involuntary context switch due to time slice expired • Those paths are likely hot in the CPU Sample Flame Graph
138. Hot/Cold
139. Hot/Cold: Profiling
On-CPU Profiling Off-CPU Profiling (everything else) Thread State Transition Diagram
140. Hot/Cold: Profiling • Profiling both on-CPU and off-CPU stacks shows everything • In my LISA'12 talk I called this the Stack Profile Method: profile all stacks
• Both on-CPU ("hot") and off-CPU ("cold") stacks can be included in the same Flame Graph, colored differently: Hot Cold Flame Graphs!
• Merging multiple threads gets even weirder. Creating a separate graph per-thread makes much more sense, as comparisons to see how a thread's time is divided between on- and off-CPU activity
• For example, a single web server thread with kernel stacks...
141. Hot/Cold: Flame Graphs
142. Hot/Cold: Flame Graphs
On-CPU (!?) Off-CPU
143. Hot/Cold: Challenges • Sadly, this often doesn't work well for two reasons: • 1. On-CPU time columns get compressed by off-CPU time • Previous example dominated by the idle path – waiting for a new connection – which is not very interesting!
• Works better with zoomable Flame Graphs, but then we've lost the ability to see key details on first glance
• Pairs of on-CPU and off-CPU Flame Graphs may be the best approach, giving both the full width
• 2. Has the same challenge from off-CPU Flame Graphs: real reason for blocking may not be visible
144. State of the Art • That was the end of Flame Graphs, but I can't stop here – we're so close
• On + Off-CPU Flame Graphs can attack any issue • 1. The compressed problem is solvable via one or more of: • zoomable Flame Graphs • separate on- and off-CPU Flame Graphs • per-thread Flame Graphs • 2. How do we show the real reason for blocking?
145. Wakeup Tracing Wakeup tracing: sleep
wakeup
)
A(
user-level kernel
On-CPU X Off-CPU X block . . . . . . . . . . . . . wakeup B(
146. Tracing Wakeups • The systems knows who woke up who • Tracing who performed the wakeup – and their stack – can show the real reason for waiting
• Wakeup Latency Flame Graph • Advanced activity • Consider overheads – might trace too much • Eg, consider ssh, starting with the Off CPU Time Flame Graph
147. Off-CPU Time Flame Graph: ssh Waiting on a conditional variable But why did we wait this long?
Object sleeping on
148. Wakeup Latency Flame Graph: ssh
149. Wakeup Latency Flame Graph: ssh
These code paths, ... woke up these objects
150. Tracing Wakeup, Example (DTrace) #!/usr/sbin/dtrace -s #pragma D option quiet #pragma D option ustackframes=100 #pragma D option stackframes=100 int related[uint64_t];
This example targets sshd (previous example also matched vmstat, after discovering that sshd was blocked on vmstat, which it was: "vmstat 1")
sched:::sleep /execname == "sshd"/ { ts[curlwpsinfo->pr_addr] = timestamp; }
Time from sleep to wakeup
sched:::wakeup /ts[args[0]->pr_addr]/ { this->d = timestamp - ts[args[0]->pr_addr]; @[args[1]->pr_fname, args[1]->pr_pid, args[0]->pr_lwpid, args[0]->pr_wchan, stack(), ustack(), execname, pid, curlwpsinfo->pr_lwpid] = sum(this->d); ts[args[0]->pr_addr] = 0; }
Stack traces of who is doing the waking
dtrace:::END { printa("n%s-%d/%d-%x%k-%k%s-%d/%dn%@dn", @); }
Aggregate if possible instead of dumping, to minimize overheads
151. Following Stack Chains • 1st level of wakeups often not enough • Would like to programmatically follow multiple chains of wakeup stacks, and visualize them
• I've discussed this with others before – it's a hard problem • The following is in development!: Chain Graph
152. Chain Graph
153. Chain Graph ... Wakeup Thread 2 I wokeup Wakeup Thread 1 I wokeup Wakeup Stacks why I waited
Off CPU Stacks: why I blocked
154. Chain Graph Visualization • New, experimental; check for later improvements • Stacks associated based on sleeping object address • Retains the value of relative widths equals latency • Wakeup stacks frames can be listed in reverse (may be less confusing when following towers bottom-up)
• Towers can get very tall, tracing wakeups through different software threads, back to metal
155. Following Wakeup Chains, Example (DTrace) #!/usr/sbin/dtrace -s #pragma D option quiet #pragma D option ustackframes=100 #pragma D option stackframes=100 int related[uint64_t]; sched:::sleep /execname == "sshd" || related[curlwpsinfo->pr_addr]/ { ts[curlwpsinfo->pr_addr] = timestamp; } sched:::wakeup /ts[args[0]->pr_addr]/ { this->d = timestamp - ts[args[0]->pr_addr]; @[args[1]->pr_fname, args[1]->pr_pid, args[0]->pr_lwpid, args[0]->pr_wchan, stack(), ustack(), execname, pid, curlwpsinfo->pr_lwpid] = sum(this->d); ts[args[0]->pr_addr] = 0; related[curlwpsinfo->pr_addr] = 1; } dtrace:::END { printa("n%s-%d/%d-%x%k-%k%s-%d/%dn%@dn", @); }
Also follow who wakes up the waker
156. Developments
157. Developments • There have been many other great developments in the world of Flame Graphs. The following is a short tour.
158. node.js Flame Graphs • Dave Pacheco developed the DTrace ustack helper for v8, and created Flame Graphs with node.js functions
http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spend-its-time/
159. OS X Instruments Flame Graphs • Mark Probst developed a way to produce Flame Graphs from Instruments
1. Use the Time Profile instrument 2. Instrument -> Export Track 3. stackcollapse-instruments.pl 4. flamegraphs.pl
http://schani.wordpress.com/2012/11/16/flame-graphs-for-instruments/
160. Ruby Flame Graphs • Sam Saffron developed Flame Graphs with the Ruby MiniProfiler
• These stacks are very deep (many frames), so the function names have been dropped and only the rectangles are drawn
• This preserves the value of seeing the big picture at first glance! http://samsaffron.com/archive/2013/03/19/flame-graphs-in-ruby-miniprofiler
161. Windows Xperf Flame Graphs • Bruce Dawson developed Flame Graphs from Xperf data, and an xperf_to_collapsedstacks.py script Visual Studio CPU Usage
http://randomascii.wordpress.com/2013/03/26/summarizing-xperf-cpu-usage-with-flame-graphs/
162. WebKit Web Inspector Flame Charts • Available in Google Chrome developer tools, these show JavaScript CPU stacks as colored rectangles
• Inspired by Flame Graphs but
not the same: they show the passage of time on the x-axis!
• This generally works here as: • the target is single threaded apps often with repetitive code paths
• ability to zoom • Can a "Flame Graph" mode be provided for the same data?
https://bugs.webkit.org/show_bug.cgi?id=111162
163. Perl Devel::NYTProf Flame Graphs • Tim Bunce has been adding Flame Graph features, and included them in the Perl profiler: Devel::NYTProf
http://blog.timbunce.org/2013/04/08/nytprof-v5-flaming-precision/
164. Leak and Off-CPU Time Flame Graphs • Yichun Zhang (agentzh) has created Memory Leak and OffCPU Time Flame Graphs, and has given good talks to explain how Flame Graphs work
http://agentzh.org/#Presentations http://agentzh.org/misc/slides/yapc-na-2013-flame-graphs.pdf http://www.youtube.com/watch?v=rxn7HoNrv9A http://agentzh.org/misc/slides/off-cpu-flame-graphs.pdf http://agentzh.org/misc/flamegraph/nginx-leaks-2013-10-08.svg https://github.com/agentzh/nginx-systemtap-toolkit
... these also provide examples of using SystemTap on Linux
165. Color Schemes • Colors can be used to convey data, instead of the default random color scheme. This example from Dave Pacheco colors each function by its degree of direct on-CPU execution
• A Flame Graph tool could let you select different color schemes
• Another can be: color by a hash on the function name, so colors are consistent https://npmjs.org/package/stackvis
166. Zoomable Flame Graphs • Dave Pacheco has also used d3 to provide click to zoom!
Zoom
https://npmjs.org/package/stackvis
167. Flame Graph Differentials • Robert Mustacchi has been experimenting with showing the difference between two Flame Graphs, as a Flame Graph. Great potential for non-regression testing, and comparisons!
168. Flame Graphs as a Service • Pedro Teixeira has a project for node.js Flame Graphs as a service: automatically generated for each github push!
http://www.youtube.com/watch?v=sMohaWP5YqA
169. References & Acknowledgements • Neelakanth Nadgir (realneel): developed SVGs using Ruby and JavaScript of time-series function trace data with stack levels, inspired by Roch's work
• Roch Bourbonnais: developed Call Stack Analyzer, which produced similar time-series visualizations
• Edward Tufte: inspired me to explore visualizations that show all the data at once, as Flame Graphs do
• Thanks to all who have developed Flame Graphs further!
realneel's function_call_graph.rb visualization
170. Thank you! • Questions? • Homepage: http://www.brendangregg.com (links to everything) • Resources and further reading: •
http://dtrace.org/blogs/brendan/2011/12/16/flame-graphs/: see "Updates"
•
http://dtrace.org/blogs/brendan/2012/03/17/linux-kernel-performance-flamegraphs/
•
http://dtrace.org/blogs/brendan/2013/08/16/memory-leak-growth-flame-graphs/
•
http://dtrace.org/blogs/brendan/2011/07/08/off-cpu-performance-analysis/
•
http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spendits-time/
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