Linux Profiling at Netflix

Linux Profiling at Netflix
using perf_events (aka "perf")
Brendan Gregg
Senior Performance Architect
Performance Engineering Team
bgregg@netflix.com @brendangregg
Feb
2015

This Talk
•  This talk is about Linux profiling using perf_events
–  How to get CPU profiling to work, and overcome gotchas
–  A tour of perf_events and its features
•  This is based on our use of perf_events at Netflix

•  Massive Amazon EC2 Linux cloud
–  Tens of thousands of instances
–  Autoscale by ~3k each day
–  CentOS and Ubuntu, Java and Node.js
•  FreeBSD for content delivery
–  Approx 33% of the US Internet traffic at night
•  Performance is critical
–  Customer satisfaction: >50M subscribers
–  $$$ price/performance
–  Develop tools for cloud-wide analysis, and
make them open source: NetflixOSS
–  Use server tools as needed

Agenda
1.  Why We Need Linux Profiling
2.  Crash Course
3.  CPU Profiling
4.  Gotchas
–  Stacks (gcc, Java)
–  Symbols (Node.js, Java)
–  Guest PMCs
–  PEBS
–  Overheads
5.  Tracing

1. Why We Need Linux Profiling

Why We Need Linux Profiling
•  Our primary motivation is simple:
Understand CPU usage quickly and completely

Netflix Vector
Quickly:
1.  Observe high CPU usage
2.  Generate a perf_events-based flame graph

Java

Kernel

(C)

JVM

(C++)

Completely:
Flame Graph

Value for Netflix
•  Uses for CPU profiling:
–  Help with incident response
–  Non-regression testing
–  Software evaluations
–  Identify performance tuning targets
–  Part of CPU workload characterization
•  Built into Netflix Vector
–  A near real-time instance analysis tool (will be NetflixOSS)

Workload Characterization
•  For CPUs:
1.  Who
2.  Why
3.  What
4.  How

Workload Characterization
•  For CPUs:
1.  Who: which PIDs, programs, users
2.  Why: code paths, context
3.  What: CPU instructions, cycles
4.  How: changing over time
•  Can you currently answer them? How?

CPU Tools
Who
How What
Why
top,
htop!
perf record -g!
ﬂame
graphs

monitoring
perf stat -a -d!

Most companies & monitoring products today
Who
How What
Why
top,
htop!
perf record -g!
ﬂame
Graphs

monitoring
perf stat -a -d!

Re-setting Expectations
•  That was pretty good… 20 years ago.
•  Today you should easily understand why CPUs are used:
–  A profile of all CPU consumers and code paths
–  Visualized effectively
–  This should be easy to do
•  Best done using:
–  A perf_events CPU profile of stack traces
–  Visualized as a flame graph
–  This will usually mean some sysadmin/devops work, to get
perf_events working, and to automate profiling

Recent Example
1.  Poor performance, and 1
CPU at 100%
2.  perf_events flame graph
shows JVM stuck
compiling

perf_events
•  The main Linux profiler, used via the "perf" command
•  Add from linux-tools-common, etc.
•  Source code & docs in Linux: tools/perf
•  Supports many profiling/tracing features:
–  CPU Performance Monitoring Counters (PMCs)
–  Statically defined tracepoints
–  User and kernel dynamic tracing
–  Kernel line and local variable tracing
–  Efficient in-kernel counts and filters
–  Stack tracing, libunwind
–  Code annotation
•  Some bugs in the past; has been stable for us
perf_events

ponycorn

A Multitool of Subcommands
# perf!
!
usage: perf [--version] [--help] COMMAND [ARGS]!
!
The most commonly used perf commands are:!
annotate Read perf.data (created by perf record) and display annotated code!
archive Create archive with object files with build-ids found in perf.data!
bench General framework for benchmark suites!
buildid-cache Manage build-id cache.!
buildid-list List the buildids in a perf.data file!
diff Read two perf.data files and display the differential profile!
evlist List the event names in a perf.data file!
inject Filter to augment the events stream with additional information!
kmem Tool to trace/measure kernel memory(slab) properties!
kvm Tool to trace/measure kvm guest os!
list List all symbolic event types!
lock Analyze lock events!
probe Define new dynamic tracepoints!
record Run a command and record its profile into perf.data!
report Read perf.data (created by perf record) and display the profile!
sched Tool to trace/measure scheduler properties (latencies)!
script Read perf.data (created by perf record) and display trace output!
stat Run a command and gather performance counter statistics!
test Runs sanity tests.!
timechart Tool to visualize total system behavior during a workload!
top System profiling tool.!
!
See 'perf help COMMAND' for more information on a specific command.!

perf Command Format
•  perf instruments using stat or record. This has three
main parts: action, event, scope.
•  e.g., profiling on-CPU stack traces:
perf record -F 99 -a -g -- sleep 10!
Event:
99
Hertz

Scope:
all
CPUs

Ac+on:
record
stack
traces

Note:
sleep
10
is
a
dummy
command
to
set
the
duraOon

perf Actions
•  Count events (perf stat …)
–  Uses an efficient in-kernel counter, and prints the results!
•  Sample events (perf record …)
–  Records details of every event to a dump file (perf.data)
•  Timestamp, CPU, PID, instruction pointer, …
–  This incurs higher overhead, relative to the rate of events!
–  Include the call graph (stack trace) using -g!
•  Other actions include:
–  List events (perf list)
–  Report from a perf.data file (perf report)
–  Dump a perf.data file as text (perf script)
–  top style profiling (perf top)

perf Actions: Workflow
perf stat! perf record!
perf report! perf script!
count
events
capture
stacks

text
UI
dump
profile

stackcollapse-perf.pl!
flamegraph.pl!
perf.data

flame
graph

visualizaOon

perf list!
list
events

Typical

Workflow

perf Events
•  Custom timers
–  e.g., 99 Hertz (samples per second)
•  Hardware events
–  CPU Performance Monitoring Counters (PMCs)
•  Tracepoints
–  Statically defined in software
•  Dynamic tracing
–  Created using uprobes (user) or kprobes (kernel)
–  Can do kernel line tracing with local variables (needs kernel
debuginfo)

perf Events: List
# perf list!
List of pre-defined events (to be used in -e):!
cpu-cycles OR cycles [Hardware event]!
instructions [Hardware event]!
cache-references [Hardware event]!
cache-misses [Hardware event]!
branch-instructions OR branches [Hardware event]!
branch-misses [Hardware event]!
bus-cycles [Hardware event]!
stalled-cycles-frontend OR idle-cycles-frontend [Hardware event]!
stalled-cycles-backend OR idle-cycles-backend [Hardware event]!
[…]!
cpu-clock [Software event]!
task-clock [Software event]!
page-faults OR faults [Software event]!
context-switches OR cs [Software event]!
cpu-migrations OR migrations [Software event]!
[…]!
L1-dcache-loads [Hardware cache event]!
L1-dcache-load-misses [Hardware cache event]!
L1-dcache-stores [Hardware cache event]!
[…] !
skb:kfree_skb [Tracepoint event]!
skb:consume_skb [Tracepoint event]!
skb:skb_copy_datagram_iovec [Tracepoint event]!
net:net_dev_xmit [Tracepoint event]!
net:net_dev_queue [Tracepoint event]!
net:netif_receive_skb [Tracepoint event]!
net:netif_rx [Tracepoint event]!
[…]!

perf Scope
•  System-wide: all CPUs (-a)
•  Target PID (-p PID)
•  Target command (…)
•  Specific CPUs (-c …)
•  User-level only (<event>:u)
•  Kernel-level only (<event>:k)
•  A custom filter to match variables (--filter …)
The following one-liner tour includes some complex action,
event, and scope combinations.

One-Liners: Listing Events
# Listing all currently known events:!
perf list!
!
# Searching for "sched" tracepoints:!
perf list | grep sched!
!
# Listing sched tracepoints:!
perf list 'sched:*'!

One-Liners: Counting Events
# CPU counter statistics for the specified command:!
perf stat command!
!
# Detailed CPU counter statistics (includes extras) for the specified command:!
perf stat -d command!
!
# CPU counter statistics for the specified PID, until Ctrl-C:!
perf stat -p PID!
!
# CPU counter statistics for the entire system, for 5 seconds:!
perf stat -a sleep 5!
!
# Various CPU last level cache statistics for the specified command:!
perf stat -e LLC-loads,LLC-load-misses,LLC-stores,LLC-prefetches command!
!
# Count system calls for the specified PID, until Ctrl-C:!
perf stat -e 'syscalls:sys_enter_*' -p PID!
!
# Count scheduler events for the specified PID, for 10 seconds:!
perf stat -e 'sched:*' -p PID sleep 10!
!
# Count block device I/O events for the entire system, for 10 seconds:!
perf stat -e 'block:*' -a sleep 10!
!
# Show system calls by process, refreshing every 2 seconds:!
perf top -e raw_syscalls:sys_enter -ns comm!

One-Liners: Profiling Events
# Sample on-CPU functions for the specified command, at 99 Hertz:!
perf record -F 99 command!
!
# Sample on-CPU functions for the specified PID, at 99 Hertz, until Ctrl-C:!
perf record -F 99 -p PID!
!
# Sample CPU stack traces for the specified PID, at 99 Hertz, for 10 seconds:!
perf record -F 99 -p PID -g -- sleep 10!
!
# Sample CPU stack traces for the entire system, at 99 Hertz, for 10 seconds:!
perf record -F 99 -ag -- sleep 10!
!
# Sample CPU stack traces, once every 10,000 Level 1 data cache misses, for 5 s:!
perf record -e L1-dcache-load-misses -c 10000 -ag -- sleep 5!
!
# Sample CPU stack traces, once every 100 last level cache misses, for 5 seconds:!
perf record -e LLC-load-misses -c 100 -ag -- sleep 5 !
!
# Sample on-CPU kernel instructions, for 5 seconds:!
perf record -e cycles:k -a -- sleep 5 !
!
# Sample on-CPU user instructions, for 5 seconds:!
perf record -e cycles:u -a -- sleep 5!

One-Liners: Reporting
# Show perf.data in an ncurses browser (TUI) if possible:!
perf report!
!
# Show perf.data with a column for sample count:!
perf report -n!
!
# Show perf.data as a text report, with data coalesced and percentages:!
perf report --stdio!
!
# List all raw events from perf.data:!
perf script!
!
# List all raw events from perf.data, with customized fields:!
perf script -f comm,tid,pid,time,cpu,event,ip,sym,dso!
!
# Dump raw contents from perf.data as hex (for debugging):!
perf script -D!
!
# Disassemble and annotate instructions with percentages (needs some debuginfo):!
perf annotate --stdio!

And More
•  perf can also probe and record dynamic tracepoints, and
custom CPU PMCs
•  This can get a little advanced
•  I'll start with CPU profiling, then gotchas

CPU Profiling
A!
B!
block

interrupt

on-‐CPU
oﬀ-‐CPU

A!
B!
A! A!
B!
A!
syscall

Ome

•  Record stacks at a timed interval: simple and effective
–  Pros: Low (deterministic) overhead
–  Cons: Coarse accuracy, but usually sufficient
stack

samples:
A!

perf Reporting
•  perf report summarizes by combining common paths
•  Previous output truncated 78,000 lines of summary
•  The following is what a mere 8,000 lines looks like…

Flame Graphs
•  Flame Graphs:
–  x-axis: alphabetical stack sort, to maximize merging
–  y-axis: stack depth
–  color: random, or hue can be a dimension
•  e.g., software type, or difference between two profiles for
non-regression testing ("differential flame graphs")
–  interpretation: top edge is on-CPU, beneath it is ancestry
•  Just a Perl program to convert perf stacks into SVG
–  Includes JavaScript: open in a browser for interactivity
•  Easy to get working
http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html
git clone --depth 1 https://github.com/brendangregg/FlameGraph!
cd FlameGraph!
perf record -F 99 -a –g -- sleep 30!
perf script | ./stackcollapse-perf.pl |./flamegraph.pl > perf.svg!

When you try to use perf
•  Stacks don't work (missing)
•  Symbols don't work (hex numbers)
•  Can't profile Java
•  Can't profile Node.js/io.js
•  PMCs aren't available
•  Dynamic tracing function arguments don't work
•  perf locks up

How to really get started
1.  Get "perf" to work
2.  Get stack walking to work
3.  Fix symbol translation
4.  Get IPC to work
5.  Test perf under load
This is my actual checklist.

How to really get started
1.  Get "perf" to work
2.  Get stack walking to work
3.  Fix symbol translation
4.  Get IPC to work
5.  Test perf under load
This is my actual checklist.
Install
perf-‐tools-‐common

and
perf-‐tools-‐`uname
-‐r`

packages;
Or
compile
in
the

Linux
source:
tools/perf

The
"gotchas"…

Gotcha #1 Broken Stacks
Start by testing stacks:
1.  Take a CPU profile
2.  Run perf report
3.  If stacks are often < 3 frames, or don't reach "thread
start" or "main", they are probably broken. Fix them.
perf record -F 99 -a –g -- sleep 30!
perf report -n --stdio!

Identifying Broken Stacks
71.79% 334 sed sed [.] 0x000000000001afc1!
| !
|--11.65%-- 0x40a447!
| 0x40659a!
| 0x408dd8!
| 0x408ed1!
| 0x402689!
| 0x7fa1cd08aec5!
| !
|--1.33%-- 0x40a4a1!
| | !
| |--60.01%-- 0x40659a!
| | 0x408dd8!
| | 0x408ed1!
| | 0x402689!
| | 0x7fa1cd08aec5!
78.50% 409 sed libc-2.19.so [.] 0x00000000000dd7d4!
| !
|--3.65%-- 0x7f2516d5d10d!
| !
|--2.19%-- 0x7f2516d0332f!
| !
|--1.22%-- 0x7f2516cffbd2!
| !
|--1.22%-- 0x7f2516d5d5ad!
broken

probably
not
broken

missing
symbols,
but

that's
another
problem

Broken Stacks Flame Graph
Broken
Java
stacks

(missing
frame
pointer)

Java
==
green

system
==
red

C++
==
yellow

Fixing Broken Stacks
Either:
A.  Fix frame pointer-based stack walking (the default)
–  Pros: simple, supports any system stack walker
–  Cons: might cost a little extra CPU to make available
B.  Use libunwind and DWARF: perf record -g dwarf!
–  Pros: more debug info
–  Cons: not on older kernels, and inflates instance size
C.  Use a custom walker (probably needs kernel support)
–  Pros: full stack walking (e.g., unwind inlined frames) & args
–  Cons: custom kernel code, can cost more CPU when in use
Our current preference is (A)
–  So how do we fix the frame pointer…

gcc -fno-omit-frame-pointer
•  Once upon a time, x86 had fewer registers, and the frame
pointer register was reused for general purpose to improve
performance. This breaks system stack walking.
•  gcc provides -fno-omit-frame-pointer to fix this
–  Please make this the default in gcc!

JDK-8068945
•  Java's compilers also reuse the frame pointer, and
unfortunately there's no -XX:no-omit-frame-pointer (yet)
•  I hacked hotspot to fix the frame pointer, and published the
patch as a prototype suggestion (JDK-8068945)
--- openjdk8clean/hotspot/src/cpu/x86/vm/x86_64.ad 2014-03-04 …!
+++ openjdk8/hotspot/src/cpu/x86/vm/x86_64.ad 2014-11-08 …!
@@ -166,10 +166,9 @@!
// 3) reg_class stack_slots( /* one chunk of stack-based "registers" */ )!
//!
!
-// Class for all pointer registers (including RSP)!
+// Class for all pointer registers (including RSP, excluding RBP)!
reg_class any_reg(RAX, RAX_H,!
RDX, RDX_H,!
- RBP, RBP_H,!
RDI, RDI_H,!
RSI, RSI_H,!
RCX, RCX_H,!
[...]!
Remove
RBP
from

register
pools

JDK-8068945
•  We've been using our patched OpenJDK for profiling
•  To do: make this an option (-XX:MoreFramePointer), and (at
some point) fix for invokedynamic
–  See "A hotspot patch for stack profiling (frame pointer)" on the
hotspot compiler dev mailing list, being discussed now
--- openjdk8clean/hotspot/src/cpu/x86/vm/macroAssembler_x86.cpp 2014-03-04…!
+++ openjdk8/hotspot/src/cpu/x86/vm/macroAssembler_x86.cpp 2014-11-07 …!
@@ -5236,6 +5236,7 @@!
// We always push rbp, so that on return to interpreter rbp, will be!
// restored correctly and we can correct the stack.!
push(rbp);!
+ mov(rbp, rsp);!
// Remove word for ebp!
framesize -= wordSize;!
!
--- openjdk8clean/hotspot/src/cpu/x86/vm/c1_MacroAssembler_x86.cpp …!
+++ openjdk8/hotspot/src/cpu/x86/vm/c1_MacroAssembler_x86.cpp …!
[...]!
Fix
x86-‐64
funcOon

prologues

Broken Java Stacks
•  Check with "perf script"
to see stack samples
•  These are 1 or 2 levels
deep (junk values)
# perf script!
[…]!
java 4579 cpu-clock: !
ffffffff8172adff tracesys ([kernel.kallsyms])!
7f4183bad7ce pthread_cond_timedwait@@GLIBC_2…!
!
7f417908c10b [unknown] (/tmp/perf-4458.map)!
!
7f4179101c97 [unknown] (/tmp/perf-4458.map)!
!
7f41792fc65f [unknown] (/tmp/perf-4458.map)!
a2d53351ff7da603 [unknown] ([unknown])!
!
7f4179349aec [unknown] (/tmp/perf-4458.map)!
!
7f4179101d0f [unknown] (/tmp/perf-4458.map)!
!
7f417908c194 [unknown] (/tmp/perf-4458.map)!
[…]!

Fixed Java Stacks
•  With JDK-8068945
stacks are full, and go
all the way to
start_thread()
•  This is what the CPUs
are really running:
inlined frames are not
present
# perf script!
[…]!
7fff76f2dce1 [unknown] ([vdso])!
7fd3173f7a93 os::javaTimeMillis() (/usr/lib/jvm…!
7fd301861e46 [unknown] (/tmp/perf-8131.map)!
7fd30184def8 [unknown] (/tmp/perf-8131.map)!
7fd30174f544 [unknown] (/tmp/perf-8131.map)!
7fd30175d3a8 [unknown] (/tmp/perf-8131.map)!
7fd30166d51c [unknown] (/tmp/perf-8131.map)!
7fd3016c2280 [unknown] (/tmp/perf-8131.map)!
7fd301b02ec0 [unknown] (/tmp/perf-8131.map)!
7fd3016ece04 [unknown] (/tmp/perf-8131.map)!
7fd30177783c [unknown] (/tmp/perf-8131.map)!
7fd301600aa8 [unknown] (/tmp/perf-8131.map)!
7fd301a4484c [unknown] (/tmp/perf-8131.map)!
7fd301007325 [unknown] (/tmp/perf-8131.map)!
7fd301007325 [unknown] (/tmp/perf-8131.map)!
7fd3171df76a JavaCalls::call_helper(JavaValue*,…!
7fd3171dce44 JavaCalls::call_virtual(JavaValue*…!
7fd3171dd43a JavaCalls::call_virtual(JavaValue*…!
7fd31721b6ce thread_entry(JavaThread*, Thread*)…!
7fd3175389e0 JavaThread::thread_main_inner() (/…!
7fd317538cb2 JavaThread::run() (/usr/lib/jvm/nf…!
7fd3173f6f52 java_start(Thread*) (/usr/lib/jvm/…!
7fd317a7e182 start_thread (/lib/x86_64-linux-gn…!

Fixed Stacks Flame Graph
Java

(no
symbols)

Gotcha #2 Missing Symbols
12.06% 62 sed sed [.] re_search_internal !
|!
| !
|--96.78%-- re_search_stub!
| rpl_re_search!
| match_regex!
| do_subst!
| execute_program!
| process_files!
| main!
| __libc_start_main!
71.79% 334 sed sed [.] 0x000000000001afc1!
| !
|--11.65%-- 0x40a447!
| 0x40659a!
| 0x408dd8!
| 0x408ed1!
| 0x402689!
| 0x7fa1cd08aec5!
broken

not
broken

•  Missing symbols should be obvious in perf report/script:

Fixing Symbols
•  For installed packages:
A.  Add a -dbgsym package, if available
B.  Recompile from source
•  For JIT (Java, Node.js, …):
A.  Create a /tmp/perf-PID.map file. perf already looks for this
B.  Or use one of the new symbol loggers (see lkml)
# perf script!
Failed to open /tmp/perf-8131.map, continuing without symbols!
[…]!
7fff76f2dce1 [unknown] ([vdso])!
7fd3173f7a93 os::javaTimeMillis() (/usr/lib/jvm…!
[…]!

perf JIT symbols: Java, Node.js
•  Using the /tmp map file for symbol translation:
–  Pros: simple, can be low overhead (snapshot on demand)
–  Cons: stale symbols
–  Map format is "START SIZE symbolname"
–  Another gotcha: If perf is run as root, chown root <mapfile>
•  Java
–  https://github.com/jrudolph/perf-map-agent
–  Agent attaches and writes the map file on demand (previous
versions attached on Java start, and wrote continually)
•  Node.js
–  node --perf_basic_prof writes the map file continually
–  Available from 0.11.13+
–  Currently has a file growth issue; see my patch in
https://code.google.com/p/v8/issues/detail?id=3453

Java

Kernel

JVM

Java: Stacks & Symbols
flamegraph.pl --color=java!

Java: Inlining
•  Disabling inlining:
–  -XX:-Inline
–  Many more Java frames
–  80% slower (in this case)
•  Not really necessary
–  Inlined flame graphs often
make enough sense
–  Or tune -XX:MaxInlineSize
and -XX:InlineSmallCode a
little to reveal more frames,
without costing much perf
–  Can even go faster!
No
inlining

Node.js: Stacks & Symbols
•  Covered previously on the Netflix Tech Blog
http://techblog.netflix.com/2014/11/nodejs-in-flames.html

Gotcha #3 Guest PMCs
•  Using PMCs from a Xen guest (currently):
# perf stat -a -d sleep 5!
!
Performance counter stats for 'system wide':!
!
10003.718595 task-clock (msec) # 2.000 CPUs utilized [100.00%]!
323 context-switches # 0.032 K/sec [100.00%]!
17 cpu-migrations # 0.002 K/sec [100.00%]!
233 page-faults # 0.023 K/sec !
<not supported> cycles !
<not supported> stalled-cycles-frontend !
<not supported> stalled-cycles-backend !
<not supported> instructions !
<not supported> branches !
<not supported> branch-misses !
<not supported> L1-dcache-loads !
<not supported> L1-dcache-load-misses !
<not supported> LLC-loads !
<not supported> LLC-load-misses !
!
5.001607197 seconds time elapsed!

Guest PMCs
•  Without PMCs, %CPU is ambiguous. We can't measure:
–  Instructions Per Cycle (IPC)
–  CPU cache hits/misses
–  MMU TLB hits/misses
–  Branch misprediction
–  Stall cycles
•  Should be fixable: hypervisors can expose PMCs
–  At the very least, enough PMCs for IPC to work:
INST_RETIRED.ANY_P & CPU_CLK_UNHALTED.THREAD_P
•  In the meantime
–  I'm using a physical server for PMC analysis
–  Also some MSRs on the cloud

MSRs
•  Model Specific Registers (MSRs) may be exposed when
PMCs are not
•  Better than nothing. Can solve some issues.
–  showboost is from my msr-cloud-tools collection (on github)
# ./showboost!
CPU MHz : 2500!
Turbo MHz : 2900 (10 active)!
Turbo Ratio : 116% (10 active)!
CPU 0 summary every 5 seconds...!
!
TIME C0_MCYC C0_ACYC UTIL RATIO MHz!
17:28:03 4226511637 4902783333 33% 116% 2900!
17:28:08 4397892841 5101713941 35% 116% 2900!
17:28:13 4550831380 5279462058 36% 116% 2900!
17:28:18 4680962051 5429605341 37% 115% 2899!
17:28:23 4782942155 5547813280 38% 115% 2899!
[...]!

Gotcha #4 Instruction Profiling
# perf annotate -i perf.data.noplooper --stdio!
Percent | Source code & Disassembly of noplooper!
--------------------------------------------------------!
: Disassembly of section .text:!
:!
: 00000000004004ed <main>:!
0.00 : 4004ed: push %rbp!
0.00 : 4004ee: mov %rsp,%rbp!
20.86 : 4004f1: nop!
0.00 : 4004f2: nop!
0.00 : 4004f3: nop!
0.00 : 4004f4: nop!
19.84 : 4004f5: nop!
0.00 : 4004f6: nop!
0.00 : 4004f7: nop!
0.00 : 4004f8: nop!
18.73 : 4004f9: nop!
0.00 : 4004fa: nop!
0.00 : 4004fb: nop!
0.00 : 4004fc: nop!
19.08 : 4004fd: nop!
0.00 : 4004fe: nop!
0.00 : 4004ff: nop!
0.00 : 400500: nop!
21.49 : 400501: jmp 4004f1 <main+0x4>!
16
NOPs
in
a
loop

Let's
proﬁle
instrucOons

to
see
which
are
hot!

(have
I
lost
my
mind?)

Instruction Profiling
•  Even distribution (A)? Or something else?
(A)
(B)

(C)
(D)

Instruction Profiling
# perf annotate -i perf.data.noplooper --stdio!
Percent | Source code & Disassembly of noplooper!
--------------------------------------------------------!
: Disassembly of section .text:!
:!
: 00000000004004ed <main>:!
0.00 : 4004ed: push %rbp!
0.00 : 4004ee: mov %rsp,%rbp!
20.86 : 4004f1: nop!
0.00 : 4004f2: nop!
0.00 : 4004f3: nop!
0.00 : 4004f4: nop!
19.84 : 4004f5: nop!
0.00 : 4004f6: nop!
0.00 : 4004f7: nop!
0.00 : 4004f8: nop!
18.73 : 4004f9: nop!
0.00 : 4004fa: nop!
0.00 : 4004fb: nop!
0.00 : 4004fc: nop!
19.08 : 4004fd: nop!
0.00 : 4004fe: nop!
0.00 : 4004ff: nop!
0.00 : 400500: nop!
21.49 : 400501: jmp 4004f1 <main+0x4>!
Go
home
instrucOon

pointer,
you're
drunk

PEBS
•  I believe this is sample "skid", plus parallel and out-of-
order execution of micro-ops: the sampled IP is the
resumption instruction, not what is currently executing.
•  PEBS may help: Intel's Precise Event Based Sampling
•  perf_events has support:
–  perf record -e cycles:pp!
–  The 'p' can be specified multiple times:
•  0 - SAMPLE_IP can have arbitrary skid
•  1 - SAMPLE_IP must have constant skid
•  2 - SAMPLE_IP requested to have 0 skid
•  3 - SAMPLE_IP must have 0 skid
–  … from tools/perf/Documentation/perf-list.txt

Gotcha #5 Overhead
•  Overhead is relative to the rate of events instrumented
•  perf stat does in-kernel counts, with relatively low
CPU overhead
•  perf record writes perf.data, which has slightly
higher CPU overhead, plus file system and disk I/O
•  Test before use
–  In the lab
–  Run perf stat to understand rate, before perf record
•  Also consider --filter, to filter events in-kernel

Profiling vs Tracing
•  Profiling takes samples. Tracing records every event.
•  There are many tracers for Linux (SystemTap, ktap, etc),
but only two in mainline: perf_events and ftrace
tracepoints,
kprobes,
uprobes

perf_events,
krace,
eBPF,
…

perf,
perf-‐tools

many
one-‐liners:

front-‐end
tools:

tracing
frameworks:

tracing
instrumentaOon:

Linux
Tracing
Stack

# perf record -e block:block_rq_insert -a!
^C[ perf record: Woken up 1 times to write data ]!
!
# perf script!
[…]!
java 9940 [015] 1199510.044783: block_rq_insert: 202,1 R 0 () 4783360 + 88 [java]!
[…]!
Tracing Example

# perf record -e block:block_rq_insert -a!
^C[ perf record: Woken up 1 times to write data ]!
!
# perf script!
[…]!
[…]!
Tracing Example
include/trace/events/block.h: java 9940 [015] 1199510.044783: block_rq_insert: 202,1 R 0 ()
4783360 + 88 [java]!
DECLARE_EVENT_CLASS(block_rq,!
[...]!
TP_printk("%d,%d %s %u (%s) %llu + %u [%s]",!
MAJOR(__entry->dev), MINOR(__entry->dev),!
__entry->rwbs, __entry->bytes, __get_str(cmd),!
(unsigned long long)__entry->sector,!
__entry->nr_sector, __entry->comm)!
format
string
process
PID
[CPU]
Omestamp:
eventname:

kernel
source

may
be
the

only
docs

perf Block I/O Latency Heat Map
•  Trace data may only make sense when visualized
•  e.g., block I/O latency from perf_events trace data:
hop://www.brendangregg.com/blog/2014-‐07-‐01/perf-‐heat-‐maps.html

SSD
I/O

(fast,
with
queueing)

HDD
I/O

(random,
modes)

One-Liners: Static Tracing
# Trace new processes, until Ctrl-C:!
perf record -e sched:sched_process_exec -a!
!
# Trace all context-switches with stack traces, for 1 second:!
perf record -e context-switches –ag -- sleep 1!
!
# Trace CPU migrations, for 10 seconds:!
perf record -e migrations -a -- sleep 10!
!
# Trace all connect()s with stack traces (outbound connections), until Ctrl-C:!
perf record -e syscalls:sys_enter_connect –ag!
!
# Trace all block device (disk I/O) requests with stack traces, until Ctrl-C:!
perf record -e block:block_rq_insert -ag!
!
# Trace all block device issues and completions (has timestamps), until Ctrl-C:!
perf record -e block:block_rq_issue -e block:block_rq_complete -a!
!
# Trace all block completions, of size at least 100 Kbytes, until Ctrl-C:!
perf record -e block:block_rq_complete --filter 'nr_sector > 200'!
!
# Trace all block completions, synchronous writes only, until Ctrl-C:!
perf record -e block:block_rq_complete --filter 'rwbs == "WS"'!
!
# Trace all block completions, all types of writes, until Ctrl-C:!
perf record -e block:block_rq_complete --filter 'rwbs ~ "*W*"'!
!
# Trace all ext4 calls, and write to a non-ext4 location, until Ctrl-C:!
perf record -e 'ext4:*' -o /tmp/perf.data -a!

Tracepoint Variables
•  Some previous one-liners used variables with --filter
•  The ftrace interface has a way to print them:
# cat /sys/kernel/debug/tracing/events/block/block_rq_insert/format !
name: block_rq_insert!
ID: 884!
format:!
!field:unsigned short common_type; !offset:0;!size:2; !signed:0;!
!field:unsigned char common_flags; !offset:2;!size:1; !signed:0;!
!field:unsigned char common_preempt_count; !offset:3;!size:1; !signed:0;!
!field:int common_pid; !offset:4;!size:4; !signed:1;!
!
!field:dev_t dev; !offset:8;!size:4; !signed:0;!
!field:sector_t sector; !offset:16;!size:8; !signed:0;!
!field:unsigned int nr_sector; !offset:24;!size:4; !signed:0;!
!field:unsigned int bytes;!offset:28;!size:4; !signed:0;!
!field:char rwbs[8];!offset:32;!size:8; !signed:1;!
!field:char comm[16];!offset:40;!size:16; !signed:1;!
!field:__data_loc char[] cmd; !offset:56;!size:4; !signed:1;!
!
print fmt: "%d,%d %s %u (%s) %llu + %u [%s]", ((unsigned int) ((REC->dev) >> 20)),
((unsigned int) ((REC->dev) & ((1U << 20) - 1))), REC->rwbs, REC->bytes, __get_str(cmd),
(unsigned long long)REC->sector, REC->nr_sector, REC->comm!
(format
string

internals)

variables

One-Liners: Dynamic Tracing
# Add a tracepoint for the kernel tcp_sendmsg() function entry (--add optional):!
perf probe --add tcp_sendmsg!
!
# Remove the tcp_sendmsg() tracepoint (or use --del):!
perf probe -d tcp_sendmsg!
!
# Add a tracepoint for the kernel tcp_sendmsg() function return:!
perf probe 'tcp_sendmsg%return'!
!
# Show avail vars for the tcp_sendmsg(), plus external vars (needs debuginfo):!
perf probe -V tcp_sendmsg --externs!
!
# Show available line probes for tcp_sendmsg() (needs debuginfo):!
perf probe -L tcp_sendmsg!
!
# Add a tracepoint for tcp_sendmsg() line 81 with local var seglen (debuginfo):!
perf probe 'tcp_sendmsg:81 seglen'!
!
# Add a tracepoint for do_sys_open() with the filename as a string (debuginfo):!
perf probe 'do_sys_open filename:string'!
!
# Add a tracepoint for myfunc() return, and include the retval as a string:!
perf probe 'myfunc%return +0($retval):string'!
!
# Add a tracepoint for the user-level malloc() function from libc:!
perf probe -x /lib64/libc.so.6 malloc!
!
# List currently available dynamic probes:!
perf probe -l!

One-Liners: Advanced Dynamic Tracing
# Add a tracepoint for tcp_sendmsg(), with three entry regs (platform specific):!
perf probe 'tcp_sendmsg %ax %dx %cx'!
!
# Add a tracepoint for tcp_sendmsg(), with an alias ("bytes") for %cx register:!
perf probe 'tcp_sendmsg bytes=%cx'!
!
# Trace previously created probe when the bytes (alias) var is greater than 100:!
perf record -e probe:tcp_sendmsg --filter 'bytes > 100'!
!
# Add a tracepoint for tcp_sendmsg() return, and capture the return value:!
perf probe 'tcp_sendmsg%return $retval'!
!
# Add a tracepoint for tcp_sendmsg(), and "size" entry argument (debuginfo):!
perf probe 'tcp_sendmsg size'!
!
# Add a tracepoint for tcp_sendmsg(), with size and socket state (debuginfo):!
perf probe 'tcp_sendmsg size sk->__sk_common.skc_state'!
!
# Trace previous probe when size > 0, and state != TCP_ESTABLISHED(1) (debuginfo):!
perf record -e probe:tcp_sendmsg --filter 'size > 0 && skc_state != 1' -a!
•  Kernel debuginfo is an onerous requirement for the Netflix cloud
•  We can use registers instead (as above). But which registers?

The Rosetta Stone of Registers
# perf probe -nv 'tcp_sendmsg size sk->__sk_common.skc_state'!
[…]!
Added new event:!
Writing event: p:probe/tcp_sendmsg tcp_sendmsg+0 size=%cx:u64 skc_state=+18(%si):u8!
probe:tcp_sendmsg (on tcp_sendmsg with size skc_state=sk->__sk_common.skc_state)!
!
You can now use it in all perf tools, such as:!
!
!perf record -e probe:tcp_sendmsg -aR sleep 1!
One server instance with kernel debuginfo, and -nv (dry run, verbose):
# perf probe 'tcp_sendmsg+0 size=%cx:u64 skc_state=+18(%si):u8'!
Failed to find path of kernel module.!
Added new event:!
probe:tcp_sendmsg (on tcp_sendmsg with size=%cx:u64 skc_state=+18(%si):u8)!
!
You can now use it in all perf tools, such as:!
!
!perf record -e probe:tcp_sendmsg -aR sleep 1!
All other instances (of the same kernel version):
Copy-‐n-‐paste!

Masami Hiramatsu was investigating a way to better automate this

perf_events Scripting
•  perf also has a scripting interface (Perl or Python)
–  These run perf and post-process trace data in user-space
–  Data path has some optimizations
•  Kernel buffering, and dynamic (optimal) number of buffer reads
•  But may still not be enough for high volume events
•  Andi Kleen has scripted perf for his PMC tools
–  https://github.com/andikleen/pmu-tools
–  Includes toplev for applying "Top Down" methodology
•  I've developed my own tools for perf & ftrace
–  https://github.com/brendangregg/perf-tools
–  Each tool has a man page and examples file
–  These are unsupported temporary hacks; their internals
should be rewritten when kernel features arrive (e.g., eBPF)

perf-tools: bitesize
•  Block (disk) I/O size distributions:
•  This automates perf with a set of in-kernel filters and
counts for each bucket, to reduce overhead
•  Will be much easier with eBPF
# ./bitesize !
Tracing block I/O size (bytes), until Ctrl-C...!
^C!
Kbytes : I/O Distribution!
-> 0.9 : 0 | |!
1.0 -> 7.9 : 38 |# |!
8.0 -> 63.9 : 10108 |######################################|!
64.0 -> 127.9 : 13 |# |!
128.0 -> : 1 |# |
[…]!

eBPF
•  Extended BPF: programs on tracepoints
–  High-performance filtering: JIT
–  In-kernel summaries: maps
•  e.g., in-kernel latency heat map (showing bimodal):
Low

latency

cache

hits

High

latency

device

I/O

Time

Linux Profiling Future
•  eBPF is integrating, and provides the final missing piece
of tracing infrastructure: efficient kernel programming
–  perf_events + eBPF?
–  ftrace + eBPF?
–  Other tracers + eBPF?
•  At Netflix, the future is Vector, and more self-service
automation of perf_events

Summary & Your Action Items
•  Short term: get full CPU profiling to work
A.  Automate perf CPU profiles with flame graphs. See this talk!
B.  … or use Netflix Vector when it is open sourced
C.  … or ask performance monitoring vendors for this
–  Most importantly, you should expect that full CPU profiles are
available at your company. The ROI is worth it.
•  Long term: PMCs & tracing
–  Use perf_events to profile
other targets: CPU cycles,
file system I/O, disk I/O,
memory usage, …
•  Go forth and profile!
The
"real"
checklist
reminder:

1.  Get
"perf"
to
work

2.  Get
stack
walking
to
work

3.  Fix
symbol
translaOon

4.  Get
IPC
to
work

5.  Test
perf
under
load

Links & References
•  perf_events
•  Kernel source: tools/perf/Documentation
•  https://perf.wiki.kernel.org/index.php/Main_Page
•  http://www.brendangregg.com/perf.html
•  http://web.eece.maine.edu/~vweaver/projects/perf_events/
•  Mailing list http://vger.kernel.org/vger-lists.html#linux-perf-users
•  perf-tools: https://github.com/brendangregg/perf-tools
•  PMU tools: https://github.com/andikleen/pmu-tools
•  perf, ftrace, and more: http://www.brendangregg.com/linuxperf.html
•  Java frame pointer patch
•  http://mail.openjdk.java.net/pipermail/hotspot-compiler-dev/2014-December/016477.html
•  https://bugs.openjdk.java.net/browse/JDK-8068945
•  Node.js: http://techblog.netflix.com/2014/11/nodejs-in-flames.html
•  Methodology: http://www.brendangregg.com/methodology.html
•  Flame graphs: http://www.brendangregg.com/flamegraphs.html
•  Heat maps: http://www.brendangregg.com/heatmaps.html
•  eBPF: http://lwn.net/Articles/603983/

Thanks
•  Questions?
•  http://slideshare.net/brendangregg
•  http://www.brendangregg.com
•  bgregg@netflix.com
–  Performance and Reliability Engineering
•  @brendangregg

Linux Profiling at Netflix

More Related Content

Linux Profiling at Netflix