Linux Performance Analysis and Tools

Linux Performance
Analysis and Tools

Brendan Gregg
Lead Performance Engineer

brendan@joyent.com SCaLE11x
@brendangregg February, 2013

Sunday, February 24, 13

Find the Bottleneck
Operating System Hardware

Applications
DBs, all server types, ...
System Libraries CPU
System Call Interface Inter-
connect
VFS Sockets Scheduler CPU
Linux Kernel

ext3/... ZFS TCP/UDP 1
LVM IP Virtual Memory
Block Device Interface Ethernet Memory Bus

Device Drivers DRAM
I/O Bus
I/O Bridge
Expander Interconnect

I/O Controller Network Controller
Interface
Transports
Disk Disk Port Port


whoami
• Lead Performance Engineer
• Work/Research: tools, visualizations, methodologies
• Was Brendan@Sun Microsystems, Oracle, now Joyent


Joyent
• High-Performance Cloud Infrastructure
• Compete on cloud instance/OS performance
• Public/private cloud provider
• OS-Virtualization for bare metal performance (Zones)
• Core developers of SmartOS and node.js
• KVM for Linux guests


SCaLE10x: Cloud Performance Analysis
• Example perf issues, including new tools and visualizations:

http://dtrace.org/blogs/brendan/2012/01/30/performance-analysis-talk-at-scale10x/


SCaLE11x: Linux Performance Analysis
• The primary operating system for my next book:
(secondary is the OpenSolaris-illumos-based SmartOS)

Systems
Performance
ENTERPRISE
AND THE CLOUD

Brendan Gregg
Prentice Hall, 2013


Agenda
• Background
• Linux Analysis and Tools
• Basic
• Intermediate
• Advanced
• Methodologies
• Challenges


Performance
• Why do performance analysis?
• Reduce IT spend – ﬁnd and eliminate waste, ﬁnd areas to
tune, and do more with less

• Build scalable architectures – understand system limits
and develop around them

• Solve issues – locate bottlenecks and latency outliers


Systems Performance
• Why study the operating system?
• Find and ﬁx kernel-based perf issues
• 2-20% wins: I/O or buffer size tuning, NUMA conﬁg, etc
• 2-200x wins: bugs, disabled features, perturbations causing
latency outliers

• Kernels change, new devices are added, workloads scale, and
new perf issues are encountered.

• Analyze application perf from kernel/system context
• 2-2000x wins: identifying and eliminating unnecessary work


Perspectives
• System analysis can be top-down, or bottom-up:
Workload

Workload
Developers Application
Analysis

System Libraries
Operating
System
System Calls
Software
Stack
Kernel

Resource
Sysadmins Devices
Analysis


Kernel Internals
• Eventually you’ll need to know some kernel internals
Operating System

Applications
System Libraries
user-level
System Call Interface
kernel-level
VFS Sockets Scheduler
Linux Kernel

ext3/... ZFS TCP/UDP
LVM IP Virtual
Memory
Block Device Int. Ethernet
Device Drivers


Common System Metrics
$ iostat
Linux 3.2.6-3.fc16.x86_64 (node104) 02/20/2013 _x86_64_ (1 CPU)

avg-cpu: %user %nice %system %iowait %steal %idle
0.02 0.00 0.10 0.04 0.00 99.84

Device: tps kB_read/s kB_wrtn/s kB_read kB_wrtn
vda 0.24 7.37 2.15 80735422 23571828
vdb 0.06 5.51 7.79 60333940 85320072

• It’s also worth studying common system metrics (iostat, ...),
even if you intend to use a monitoring product. Monitoring
products often use the same metrics, read from /proc.


Analysis and Tools


Analysis and Tools
• A quick tour of tools, to show what can be done
• Then, some methodologies for applying them


Analysis and Tools

Applications
System Libraries
LVM IP Virtual
Block Device Interface Ethernet Memory

Device Drivers DRAM

I/O Bridge


Disk Disk Port Port


Analysis and Tools
strace Operating System netstat Hardware
perf
Applications top
perf dtrace stap

DBs, all server types, ... pidstat
System Libraries mpstat perf
System Call Interface dstat
ext3/... ZFS TCP/UDP vmstat 1
LVM IP Virtual slabtop
Block Device Interface Ethernet Memory dstat
Device Drivers
free DRAM
top
perf
iostat I/O Bridge tcpdump ip nicstat dtrace
iotop
blktrace
dtrace I/O Controller Network Controller
dstat Various:
sar
Disk Disk Port Port /proc
ping

Tools: Basic
• uptime
• top or htop
• mpstat
• iostat
• vmstat
• free
• ping
• nicstat
• dstat


uptime
• Shows load averages, which are also shown by other tools:
$ uptime
16:23:34 up 126 days, 1:03, 1 user, load average: 5.09, 2.12, 1.82

• This counts runnable threads (tasks), on-CPU, or, runnable
and waiting. Linux includes tasks blocked on disk I/O.
• These are exponentially-damped moving averages, with time
constants of 1, 5 and 15 minutes. With three values you can
see if load is increasing, steady, or decreasing.
• If the load is greater than the CPU count, it might mean the
CPUs are saturated (100% utilized), and threads are suffering
scheduler latency. Might. There’s that disk I/O factor too.
• This is only useful as a clue. Use other tools to investigate!


top
• System-wide and per-process summaries:
$ top
top - 01:38:11 up 63 days, 1:17, 2 users, load average: 1.57, 1.81, 1.77
Tasks: 256 total, 2 running, 254 sleeping, 0 stopped, 0 zombie
Cpu(s): 2.0%us, 3.6%sy, 0.0%ni, 94.2%id, 0.0%wa, 0.0%hi, 0.2%si, 0.0%st
Mem: 49548744k total, 16746572k used, 32802172k free, 182900k buffers
Swap: 100663292k total, 0k used, 100663292k free, 14925240k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
11721 web 20 0 623m 50m 4984 R 93 0.1 0:59.50 node
11715 web 20 0 619m 20m 4916 S 25 0.0 0:07.52 node
10 root 20 0 0 0 0 S 1 0.0 248:52.56 ksoftirqd/2
51 root 20 0 0 0 0 S 0 0.0 0:35.66 events/0
11724 admin 20 0 19412 1444 960 R 0 0.0 0:00.07 top
1 root 20 0 23772 1948 1296 S 0 0.0 0:04.35 init
[...]

• %CPU = interval sum for all CPUs (varies on other OSes)
• top can consume CPU (syscalls to read /proc)
• Straight-forward. Or is it?

top, cont.
• Interview questions:
• 1. Does it show all CPU consumers?
• 2. A process has high %CPU – next steps for analysis?


top, cont.
• 1. top can miss:
• short-lived processes
• kernel threads (tasks), unless included (see top options)
• 2. analyzing high CPU processes:
• identify why – proﬁle code path
• identify what – execution or stall cycles
• High %CPU time may be stall cycles on memory I/O –
upgrading to faster CPUs doesn’t help!


htop
• Super top. Super conﬁgurable. Eg, basic CPU visualization:


mpstat
• Check for hot threads, unbalanced workloads:
$ mpstat -P ALL 1
02:47:49 CPU %usr %nice %sys %iowait %irq %soft %steal %guest %idle
02:47:50 all 54.37 0.00 33.12 0.00 0.00 0.00 0.00 0.00 12.50
02:47:50 0 22.00 0.00 57.00 0.00 0.00 0.00 0.00 0.00 21.00
02:47:50 1 19.00 0.00 65.00 0.00 0.00 0.00 0.00 0.00 16.00
02:47:50 2 24.00 0.00 52.00 0.00 0.00 0.00 0.00 0.00 24.00
02:47:50 3 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
02:47:50 4 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
02:47:50 5 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
02:47:50 6 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
02:47:50 7 16.00 0.00 63.00 0.00 0.00 0.00 0.00 0.00 21.00
02:47:50 8 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
[...]

• Columns are summarized system-wide in top(1)’s header


iostat
• Disk I/O statistics. 1st output is summary since boot.
$ iostat -xkdz 1

Linux 2.6.35-32-server (prod21) 02/20/13 _x86_64_ (16 CPU)

Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s ...
sda 0.00 0.00 0.00 0.00 0.00 0.00 / ...
sdb 0.00 0.35 0.00 0.05 0.10 1.58 ...
/ ...
Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s ...
sdb 0.00 0.00 591.00 0.00 2364.00 0.00 / ...

workload input
... avgqu-sz await r_await w_await svctm %util
... / 0.00 0.84 0.84 0.00 0.84 0.00
... 0.00 3.82 3.47 3.86 0.30 0.00
... / 0.00 2.31 2.31 0.00 2.31 0.00
...
... / avgqu-sz await r_await w_await svctm %util
... 0.95 1.61 1.61 0.00 1.61 95.00

resulting performance


iostat, cont.
• %util: usefulness depends on target – virtual devices backed
by multiple disks may accept more work a 100% utilization
• Also calculate I/O controller stats by summing their devices
• One nit: would like to see disk errors too. Add a “-e”?


vmstat
• Virtual-Memory statistics, and other high-level summaries:
$ vmstat 1
procs -----------memory---------- ---swap-- -----io---- -system-- ----cpu----
r b swpd free buff cache si so bi bo in cs us sy id wa
15 0 2852 46686812 279456 1401196 0 0 0 0 0 0 0 0 100 0
16 0 2852 46685192 279456 1401196 0 0 0 0 2136 36607 56 33 11 0
15 0 2852 46685952 279456 1401196 0 0 0 56 2150 36905 54 35 11 0
15 0 2852 46685960 279456 1401196 0 0 0 0 2173 36645 54 33 13 0
[...]

• First line of output includes some summary-since-boot values
• “r” = total number of runnable threads, including those running
• Swapping (aka paging) allows over-subscription of main
memory by swapping pages to disk, but costs performance


free
• Memory usage summary (Kbytes default):
$ free
total used free shared buffers cached
Mem: 49548744 32787912 16760832 0 61588 342696
-/+ buffers/cache: 32383628 17165116
Swap: 100663292 0 100663292

• buffers: block device I/O cache
• cached: virtual page cache


ping
• Simple network test (ICMP):
$ ping www.hilton.com
PING a831.b.akamai.net (63.234.226.9): 56 data bytes
64 bytes from 63.234.226.9: icmp_seq=0 ttl=56 time=737.737 ms
Request timeout for icmp_seq 1
^C
--- a831.b.akamai.net ping statistics ---
6 packets transmitted, 5 packets received, 16.7% packet loss
round-trip min/avg/max/stddev = 669.052/784.803/897.835/77.226 ms

• Used to measure network latency. Actually kernel <-> kernel
IP stack latency, including how the network handles ICMP.
• Tells us some, but not a lot (above is an exception).
Lots of other/better tools for this (eg, hping). Try using TCP.


nicstat
• Network statistics tool, ver 1.92 on Linux:
# nicstat -z 1
Time Int rKB/s wKB/s rPk/s wPk/s rAvs wAvs %Util Sat
01:20:58 eth0 0.07 0.00 0.95 0.02 79.43 64.81 0.00 0.00
01:20:58 eth4 0.28 0.01 0.20 0.10 1451.3 80.11 0.00 0.00
01:20:58 vlan123 0.00 0.00 0.00 0.02 42.00 64.81 0.00 0.00
01:20:58 br0 0.00 0.00 0.00 0.00 42.00 42.07 0.00 0.00
01:20:59 eth4 42376.0 974.5 28589.4 14002.1 1517.8 71.27 35.5 0.00
01:21:00 eth0 0.05 0.00 1.00 0.00 56.00 0.00 0.00 0.00
01:21:00 eth4 41834.7 977.9 28221.5 14058.3 1517.9 71.23 35.1 0.00
01:21:01 eth4 42017.9 979.0 28345.0 14073.0 1517.9 71.24 35.2 0.00
[...]

• This was the tool I wanted, and ﬁnally wrote it out of frustration
(Tim Cook ported and enhanced it on Linux)
• Calculate network controller stats by summing interfaces


dstat
• A better vmstat-like tool. Does coloring (FWIW).


Tools: Basic, recap
• uptime
• top or htop
• mpstat
• iostat
• vmstat
• free
• ping
• nicstat
• dstat


Tools: Basic, recap

Applications
top
mpstat
System Libraries
dstat
ext3/... ZFS TCP/UDP vmstat 1
LVM IP Virtual
Memory dstat
Block Device Interface Ethernet
free DRAM
Device Drivers
top

iostat infer I/O Bridge infer nicstat

dstat
Disk Disk Port Port
ping

Tools: Intermediate
• sar
• netstat
• pidstat
• strace
• tcpdump
• blktrace
• iotop
• slabtop
• sysctl
• /proc

sar
• System Activity Reporter. Eg, paging statistics -B:
$ sar -B 1

05:24:34 PM pgpgin/s pgpgout/s fault/s majflt/s pgfree/s pgscank/s pgscand/s pgsteal/s %vmeff
05:24:35 PM 0.00 0.00 267.68 0.00 29.29 0.00 0.00 0.00 0.00
05:24:36 PM 19.80 0.00 265.35 0.99 28.71 0.00 0.00 0.00 0.00
05:24:37 PM 12.12 0.00 1339.39 1.01 2763.64 0.00 1035.35 1035.35 100.00
05:24:38 PM 0.00 0.00 534.00 0.00 28.00 0.00 0.00 0.00 0.00
05:24:39 PM 220.00 0.00 644.00 3.00 74.00 0.00 0.00 0.00 0.00
05:24:40 PM 2206.06 0.00 6188.89 17.17 5222.22 2919.19 0.00 2919.19 100.00
[...]

• Conﬁgure to archive statistics from cron
• Many, many statistics available:
• -d: block device statistics, -q: run queue statistics, ...
• Same statistics as shown by other tools (vmstat, iostat, ...)


netstat
• Various network protocol statistics using -s:
$ netstat -s
[...]
Tcp:
127116 active connections openings
165223 passive connection openings
12904 failed connection attempts
19873 connection resets received
20 connections established
662889209 segments received
354923419 segments send out
405146 segments retransmited
6 bad segments received.
26379 resets sent
[...]
TcpExt:
2142 invalid SYN cookies received
3350 resets received for embryonic SYN_RECV sockets
7460 packets pruned from receive queue because of socket buffer overrun
2932 ICMP packets dropped because they were out-of-window
96670 TCP sockets finished time wait in fast timer
86 time wait sockets recycled by time stamp
1007 packets rejects in established connections because of timestamp
[...many...]


pidstat
• Very useful process breakdowns:
# pidstat 1

05:55:18 PM PID %usr %system %guest %CPU CPU Command
05:55:19 PM 12642 0.00 1.01 0.00 1.01 0 pidstat
05:55:19 PM 12643 5.05 11.11 0.00 16.16 0 cksum

05:55:19 PM PID %usr %system %guest %CPU CPU Command
05:55:20 PM 12643 6.93 6.93 0.00 13.86 0 cksum
[...]

# pidstat -d 1

05:55:22 PM PID kB_rd/s kB_wr/s kB_ccwr/s Command
05:55:23 PM 279 0.00 61.90 0.00 jbd2/vda2-8
05:55:23 PM 12643 151985.71 0.00 0.00 cksum

05:55:23 PM PID kB_rd/s kB_wr/s kB_ccwr/s Command
05:55:24 PM 12643 96616.67 0.00 0.00 cksum
[...]
disk I/O (yay!)


strace
• System call tracer:
$ strace -tttT -p 12670
1361424797.229550 read(3, "REQUEST 1888 CID 2"..., 65536) = 959 <0.009214>
1361424797.239053 read(3, "", 61440) = 0 <0.000017>
1361424797.239406 close(3) = 0 <0.000016>
1361424797.239738 munmap(0x7f8b22684000, 4096) = 0 <0.000023>
1361424797.240145 fstat(1, {st_mode=S_IFCHR|0620, st_rdev=makedev(136, 0), ...}) = 0
<0.000017>
[...]

• -ttt: microsecond timestamp since epoch (left column)
• -T: time spent in syscall (<seconds>)
• -p: PID to trace (or provide a command)
• Useful – high application latency often caused by resource I/O,
and most resource I/O is performed by syscalls


strace, cont.
• -c: print summary:
# strace -c dd if=/dev/zero of=/dev/null bs=512 count=1024k
[...]
% time seconds usecs/call calls errors syscall
------ ----------- ----------- --------- --------- ----------------
51.32 0.028376 0 1048581 read
48.68 0.026911 0 1048579 write
0.00 0.000000 0 7 open
[...]

• This is also a (worst case) demo of the strace overhead:
# time dd if=/dev/zero of=/dev/null bs=512 count=1024k
[...]
536870912 bytes (537 MB) copied, 0.35226 s, 1.5 GB/s
real 0m0.355s
user 0m0.021s
sys 0m0.022s
# time strace -c dd if=/dev/zero of=/dev/null bs=512 count=1024k
[...]
536870912 bytes (537 MB) copied, 71.9565 s, 7.5 MB/s
real 1m11.969s
user 0m3.179s
sys 1m6.346s

200x slower


tcpdump
• Sniff network packets, dump to output ﬁles for post analysis:
# tcpdump -i eth4 -w /tmp/out.tcpdump
tcpdump: listening on eth4, link-type EN10MB (Ethernet), capture size 65535
bytes
^C33651 packets captured
34160 packets received by filter
508 packets dropped by kernel

# tcpdump -nr /tmp/out.tcpdump
reading from file /tmp/out.tcpdump, link-type EN10MB (Ethernet)
06:24:43.908732 IP 10.2.0.2.55502 > 10.2.203.2.22: Flags [.], ack ...
06:24:43.908922 IP 10.2.0.2.55502 > 10.2.203.2.22: Flags [.], ack ...
06:24:43.908943 IP 10.2.203.2.22 > 10.2.0.2.55502: Flags [.], seq ...
06:24:43.909061 IP 10.2.0.2.55502 > 10.2.203.2.22: Flags [.], ack ...

• Output has timestamps with microsecond resolution
• Study odd network latency packet-by-packet
• Import ﬁle into other tools (wireshark)


tcpdump, cont.
• Does have overhead in terms of CPU and storage; previous
example dropped packets
• Should be using socket ring buffers to reduce overhead
• Can use ﬁlter expressions to also reduce overhead
• Could still be problematic for busy interfaces


blktrace
• Block device I/O event tracing. Launch using btrace, eg:
# btrace /dev/sdb
8,16 3 1 0.429604145 20442 A R 184773879 + 8 <- (8,17) 184773816
8,16 3 2 0.429604569 20442 Q R 184773879 + 8 [cksum]
8,16 3 3 0.429606014 20442 G R 184773879 + 8 [cksum]
8,16 3 4 0.429607624 20442 P N [cksum]
8,16 3 5 0.429608804 20442 I R 184773879 + 8 [cksum]
8,16 3 6 0.429610501 20442 U N [cksum] 1
8,16 3 7 0.429611912 20442 D R 184773879 + 8 [cksum]
8,16 1 1 0.440227144 0 C R 184773879 + 8 [0]
[...]

• Above output shows a single disk I/O event. Action time is
highlighted (seconds).
• Use for investigating I/O latency outliers


iotop
• Disk I/O by process:
# iotop -bod5
Total DISK READ: 35.38 M/s | Total DISK WRITE: 39.50 K/s
TID PRIO USER DISK READ DISK WRITE SWAPIN IO COMMAND
12824 be/4 root 35.35 M/s 0.00 B/s 0.00 % 80.59 % cksum ...
279 be/3 root 0.00 B/s 27.65 K/s 0.00 % 2.21 % [jbd2/vda2-8]
12716 be/4 root 28.44 K/s 0.00 B/s 2.35 % 0.00 % sshd: root@pts/0
12816 be/4 root 6.32 K/s 0.00 B/s 0.89 % 0.00 % python /usr/bin/
iotop -bod5
[...]

• IO: time thread was waiting on I/O (this is even more useful
than pidstat’s Kbytes)
• Needs CONFIG_TASK_IO_ACCOUNTING or something
similar enabled to work.


slabtop
• Kernel slab allocator usage top:
# slabtop -sc
Active / Total Objects (% used) : 900356 / 1072416 (84.0%)
Active / Total Slabs (% used) : 29085 / 29085 (100.0%)
Active / Total Caches (% used) : 68 / 91 (74.7%)
Active / Total Size (% used) : 237067.98K / 260697.24K (90.9%)
Minimum / Average / Maximum Object : 0.01K / 0.24K / 10.09K

OBJS ACTIVE USE OBJ SIZE SLABS OBJ/SLAB CACHE SIZE NAME
112035 110974 99% 0.91K 3201 35 102432K ext4_inode_cache
726660 579946 79% 0.11K 20185 36 80740K buffer_head
4608 4463 96% 4.00K 576 8 18432K kmalloc-4096
83496 76878 92% 0.19K 1988 42 15904K dentry
23809 23693 99% 0.55K 821 29 13136K radix_tree_node
11016 9559 86% 0.62K 216 51 6912K proc_inode_cache
3488 2702 77% 1.00K 109 32 3488K kmalloc-1024
510 431 84% 5.73K 102 5 3264K task_struct
10948 9054 82% 0.17K 238 46 1904K vm_area_struct
2585 1930 74% 0.58K 47 55 1504K inode_cache
[...]

• Shows where kernel memory is consumed


sysctl
• System settings:
# sysctl -a
[...]
net.ipv4.tcp_fack = 1
net.ipv4.tcp_reordering = 3
net.ipv4.tcp_ecn = 2
net.ipv4.tcp_dsack = 1
net.ipv4.tcp_mem = 24180 32240 48360
net.ipv4.tcp_wmem = 4096 16384 1031680
net.ipv4.tcp_rmem = 4096 87380 1031680
[...]

• Static performance tuning: check the conﬁg of the sysetm


/proc
• Read statistic sources directly:
$ cat /proc/meminfo
MemTotal: 8181740 kB
MemFree: 71632 kB
Buffers: 163288 kB
Cached: 4518600 kB
SwapCached: 7036 kB
Active: 4765476 kB
Inactive: 2866016 kB
Active(anon): 2480336 kB
Inactive(anon): 478580 kB
Active(file): 2285140 kB
Inactive(file): 2387436 kB
Unevictable: 0 kB
Mlocked: 0 kB
SwapTotal: 2932728 kB
SwapFree: 2799568 kB
Dirty: 76 kB
Writeback: 0 kB
[...]

• Also see /proc/vmstat


Tools: Intermediate, recap.
• sar
• netstat
• pidstat
• strace
• tcpdump
• blktrace
• iotop
• slabtop
• sysctl
• /proc

Tools: Advanced
• perf
• DTrace
• SystemTap
• and more ...


perf
• Originally Performance Counters for Linux (PCL), focusing on
CPU performance counters (programmable registers)
• Now a collection of proﬁling and tracing tools, with numerous
subcommands, including:
kmem Trace/measure kernel memory (slab) properties
kvm Trace/measure KVM guest OS
list List available events (targets of instrumentation)
lock Analyze lock events
probe Create dynamic probe points (dynamic tracing!)
record Run a command and record proﬁle data (as perf.data)
report Read perf.data and summarize, has an interactive mode
sched Trace/measure kernel scheduler statistics
stat Run a command, gather, and report perf counter stats


perf: Performance Counters
• Key performance counter summary:
$ perf stat gzip file1

Performance counter stats for 'gzip file1':

2294.924314 task-clock-msecs # 0.901 CPUs
62 context-switches # 0.000 M/sec
0 CPU-migrations # 0.000 M/sec
265 page-faults # 0.000 M/sec
5496871381 cycles # 2395.230 M/sec
12210601948 instructions # 2.221 IPC yay
1263678628 branches # 550.641 M/sec
13037608 branch-misses # 1.032 %
4725467 cache-references # 2.059 M/sec
2779597 cache-misses # 1.211 M/sec

2.546444859 seconds time elapsed

• Low IPC (<0.2) means stall cycles (likely memory); look for
ways to reduce memory I/O, and improve locality (NUMA)


perf: Performance Counters, cont.
• Can choose different counters:
$ perf list | grep Hardware
cpu-cycles OR cycles [Hardware event]
stalled-cycles-frontend OR idle-cycles-frontend [Hardware event]
stalled-cycles-backend OR idle-cycles-backend [Hardware event]
instructions [Hardware event]
cache-references [Hardware event]
[...]
$ perf stat -e instructions,cycles,L1-dcache-load-misses,LLC-load-
misses,dTLB-load-misses gzip file1

Performance counter stats for 'gzip file1':

12278136571 instructions # 2.199 IPC
5582247352 cycles
90367344 L1-dcache-load-misses
1227085 LLC-load-misses
685149 dTLB-load-misses

2.332492555 seconds time elapsed

• Supports additional custom counters (in hex or a desc) for
whatever the processor supports. Examine bus events.


perf: Performance Counters, cont.

Applications
System Libraries CPU
System Call Interface Inter-
connect
LVM IP Virtual Memory
Block Device Interface Ethernet Memory Bus

Device Drivers DRAM
I/O Bus perf stat
advanced activity: I/O Bridge
refer to the
processor I/O Controller Network Controller
manuals
Disk Disk Port Port


perf: Profiling
• Profiling (sampling) CPU activity:
# perf record -a -g -F 997 sleep 10
[ perf record: Woken up 44 times to write data ]

• -a: all CPUs
• -g: call stacks
• -F: Hertz
• sleep 10: duration to sample (dummy command)
• Generates a perf.data file
• Can profile other hardware events too, with call stacks


perf: Proﬁling, cont.
• Reading perf.data, forcing non-interactive mode (--stdio):
# perf report --stdio
[...]
# Overhead Command Shared Object Symbol
# ........ ........... ................. ..............................
#
72.98% swapper [kernel.kallsyms] [k] native_safe_halt
|
--- native_safe_halt
default_idle
cpu_idle
rest_init
start_kernel
x86_64_start_reservations
x86_64_start_kernel

9.43% dd [kernel.kallsyms] [k] acpi_pm_read
|
--- acpi_pm_read
ktime_get_ts
|
|--87.75%-- __delayacct_blkio_start
| io_schedule_timeout
| balance_dirty_pages_ratelimited_nr
| generic_file_buffered_write
[...]


perf: Proﬁling, cont.
• Flame Graphs support perf proﬁling data:

• Interactive SVG. Navigate to quantify and compare code paths

perf: Static Tracing
• Listing static tracepoints for block I/O:
$ perf list | grep block:
block:block_rq_abort [Tracepoint event]
block:block_rq_requeue [Tracepoint event]
block:block_rq_complete [Tracepoint event]
block:block_rq_insert [Tracepoint event]
block:block_rq_issue [Tracepoint event]
block:block_bio_bounce [Tracepoint event]
block:block_bio_complete [Tracepoint event]
block:block_bio_backmerge [Tracepoint event]
block:block_bio_frontmerge [Tracepoint event]
block:block_bio_queue [Tracepoint event]
block:block_getrq [Tracepoint event]
block:block_sleeprq [Tracepoint event]
block:block_plug [Tracepoint event]
block:block_unplug [Tracepoint event]
block:block_split [Tracepoint event]
block:block_bio_remap [Tracepoint event]
block:block_rq_remap [Tracepoint event]

• Many useful probes already provided for kernel tracing:
$ perf list | grep Tracepoint | wc -l
840


perf: Static Tracepoints net:
sock: skb:

Applications
System Libraries
sched:
syscalls: System Call Interface
ext4: ext3/... ZFS TCP/UDP 1
LVM IP Virtual vmscan:
block: Block Device Interface Ethernet Memory kmem:
Device Drivers DRAM

scsi: ... more can be added as needed
irq: I/O Bridge

I/O Controller Network Controller device
stats
can be
Disk Disk Port Port
inferred


perf: Dynamic Tracing
• Deﬁne custom probes from kernel code; eg, tcp_sendmsg():
# perf probe --add='tcp_sendmsg'
Add new event:
probe:tcp_sendmsg (on tcp_sendmsg)
[...]

# perf record -e probe:tcp_sendmsg -aR -g sleep 5
[ perf record: Woken up 1 times to write data ]
[ perf record: Captured and wrote 0.091 MB perf.data (~3972 samples) ]

# perf report --stdio
[...]
# Overhead Command Shared Object Symbol
# ........ ....... ................. ...........
#
100.00% sshd [kernel.kallsyms] [k] tcp_sendmsg
|
--- tcp_sendmsg
sock_aio_write
do_sync_write
vfs_write active traced call stacks from
sys_write arbitrary kernel locations!
system_call
__GI___libc_write


perf: Dynamic Tracing, cont.

Applications
perf probe --add

System Libraries
LVM IP Virtual

Device Drivers DRAM

advanced activity: I/O Bridge
refer to the
kernel source I/O Controller Network Controller device
code stats
can be
Disk Disk Port Port
inferred


perf: Dynamic Tracing, cont.
• Fills in kernel observability gaps
• Awesome capability
• Takes some effort to use (waiting for the trace-dump-
analyze cycle, and using post-processors to rework the
output, or the post-scripting capability)

• Would be the awesomest tool ever, if it wasn’t for ...


DTrace


DTrace
• Programmable, real-time, dynamic and static tracing
• Perf analysis and troubleshooting, without restarting anything
• Used on Solaris, illumos/SmartOS, Mac OS X, FreeBSD, ...
• Two ports in development for Linux (that we know of):
• 1. dtrace4linux
• Mostly by Paul Fox
• 2. Oracle Enterprise Linux DTrace
• Steady progress

There are a couple of awesome books about DTrace too


DTrace: Installation
• dtrace4linux version:
1. https://github.com/dtrace4linux/dtrace
2. README:
tools/get-deps.pl # if using Ubuntu
tools/get-deps-fedora.sh # RedHat/Fedora
make all
make install
make load (need to be root or have sudo access)

# make load
tools/load.pl
13:40:14 Syncing...
13:40:14 Loading: build-3.2.6-3.fc16.x86_64/driver/dtracedrv.ko
13:40:15 Preparing symbols...
13:40:15 Probes available: 281887
13:40:18 Time: 4s

• WARNING: still a prototype, can panic/freeze kernels.
I’m using it the lab to solve replicated production perf issues


DTrace: Programming
• Programming capabilities allow for powerful, efﬁcient, one-
liners and scripts. In-kernel custom ﬁltering and aggregation.
# dtrace -n 'fbt::tcp_sendmsg:entry /execname == "sshd"/ {
@["bytes"] = quantize(arg3); }'
dtrace: description 'fbt::tcp_sendmsg:entry ' matched 1 probe
^C

bytes
value ------------- Distribution ------------- count
16 | 0
32 |@@@@@@@@@@@@@@@@ 1869
64 |@@@@@@@@@@@@@ 1490
128 |@@@ 355
256 |@@@@ 461
512 |@@@ 373
1024 |@ 95
2048 | 4
4096 | 1
8192 | 0

• Example shows tcp_sendmsg() size dist for “sshd” PIDs


DTrace: Programming
• Programming capabilities allow for powerful, efficient, one-
liners and scripts. In-kernel custom filtering and aggregation.
# dtrace -n 'fbt::tcp_sendmsg:entry /execname == "sshd"/ {
@["bytes"] = quantize(arg3); }' filter
dtrace: description 'fbt::tcp_sendmsg:entry ' matched 1 probe
^C
aggregation (summarizes)
bytes
16 | 0
32 |@@@@@@@@@@@@@@@@ 1869
64 |@@@@@@@@@@@@@ 1490
128 |@@@ 355
256 |@@@@ 461
512 |@@@ kernel -> user transfers 373
1024 |@ 95
2048 | these these numbers 4
4096 | 1
8192 | only (pre-summarized) 0

• Example shows tcp_sendmsg() size dist for “sshd” PIDs
these examples use dtrace4linux


DTrace: Real-Time
• Multiple GUIs use DTrace for real-time statistics. Eg, Joyent
Cloud Analytics, showing real-time cloud-wide syscall latency:


DTrace, cont.
• Has advanced capabilities, but not necessarily difﬁcult;
You may just:
• use one-liners (google “DTrace one-liners”)
• use scripts (DTraceToolkit; DTrace book; google)
• tweak one-liners or scripts a little
• ask someone else to write the scripts you need
• Ideally, you learn DTrace and write your own


DTrace: Scripts
#!/usr/sbin/dtrace -s

fbt::vfs_read:entry 13 line script to time
{
self->start = timestamp; VFS reads by process name
}

fbt::vfs_read:return
/self->start/
{
@[execname, "ns"] = quantize(timestamp - self->start);
self->start = 0;
}

# ./vfsread.d
dtrace: script './vfsread.d' matched 2 probes
cksum ns
[...]
262144 | 0
524288 |@@@@@@@@@@ 834
1048576 | 8
2097152 | 30
4194304 | read latency distribution, 40
8388608 |@
16777216 |
0.5ms -> 33ms (disks) 66
28
33554432 | 1


DTrace: Basics
• CLI syntax:
dtrace -n ‘provider:module:function:name /predicate/ { action }’
optional do this when
probe description
ﬁlter probe “ﬁres”

• provider – library of related probes
• module:function – shows where probe is located (for debug)
• name – name of probe

• Online reference and tutorial: http://dtrace.org/guide


DTrace: Providers

Applications
System Libraries
LVM IP Virtual

Device Drivers DRAM

I/O Bridge


Disk Disk Port Port


DTrace: Providers java javascript
node perl python
syscall plockstat tcp udp ip
php ruby erlang
Applications objc tcl ...
pid

DBs, all server types, ... mysql postgres ...
System Libraries
sched profile
profile

System Call Interface proc
fbt

LVM IP Virtual
Memory cpc
Block Device Interface Ethernet
Device Drivers DRAM

cpc vminfo
io I/O Bridge
ip
infer fbt
infer
fbt and pid
are dynamic Disk Disk Port Port


DTrace: Linux Examples
• Following examples use fbt – kernel dynamic tracing


DTrace: ext4slower.d
• Show me:
• ext4 reads and writes
• slower than a speciﬁed latency (milliseconds)
• with time, process, direction, size, latency, and ﬁle name
# ./ext4slower.d 10
Tracing ext4 read/write slower than 10 ms
TIME PROCESS D KB ms FILE
2013 Feb 22 17:17:02 cksum R 64 35 100m
2013 Feb 22 17:17:02 cksum R 64 16 1m
2013 Feb 22 17:17:03 cksum R 64 18 data1
2013 Feb 22 17:17:03 cksum R 64 23 data1

• I wrote this to answer: is ext4 to blame for latency outliers?
• Argument is latency you are looking for: here, 10+ ms


DTrace: ext4slower.d, cont.
• Extending vfs_read() example:

#pragma D option quiet
#pragma D option defaultargs
#pragma D option switchrate=5

dtrace:::BEGIN
{
min_ns = $1 * 1000000;
printf("Tracing ext4 read/write slower than %d msn", $1);
printf("%-20s %-16s %1s %4s %6s %sn", "TIME", "PROCESS",
"D", "KB", "ms", "FILE");
}

fbt::vfs_read:entry, fbt::vfs_write:entry
{
this->file = (struct file *)arg0;
this->fs = this->file->f_path.dentry->d_inode->i_sb->s_type->name;
}

• ... continued:


DTrace: ext4slower.d, cont.
fbt::vfs_read:entry, fbt::vfs_write:entry
/stringof(this->fs) == "ext4"/
{
self->start = timestamp;
self->name = this->file->f_path.dentry->d_name.name;
}

fbt::vfs_read:return, fbt::vfs_write:return
/self->start && (this->delta = timestamp - self->start) > min_ns/
{
this->dir = probefunc == "vfs_read" ? "R" : "W";
printf("%-20Y %-16s %1s %4d %6d %sn", walltimestamp,
execname, this->dir, arg1 / 1024, this->delta / 1000000,
stringof(self->name));
}

fbt::vfs_read:return, fbt::vfs_write:return
{
self->start = 0;
self->name = 0;
}

• Immediately exonerate or blame ext4.
... should add more vfs_*() calls; or trace ext4 funcs directly


DTrace: tcpretransmit.d
• Show me:
• TCP retransmits
• destination IP address
• kernel stack (shows why)
• in real-time
• Don’t sniff all packets – only trace retransmits, to minimize
overhead


DTrace: tcpretransmit.d, cont.
# ./tcpretransmit.d
Tracing TCP retransmits... Ctrl-C to end.
2013 Feb 23 18:24:11: retransmit to 10.2.124.2, by:
kernel`tcp_retransmit_timer+0x1bd
kernel`tcp_write_timer+0x188
kernel`run_timer_softirq+0x12b
kernel`tcp_write_timer
kernel`__do_softirq+0xb8
kernel`read_tsc+0x9
kernel`sched_clock+0x9
kernel`sched_clock_local+0x25
kernel`call_softirq+0x1c
kernel`do_softirq+0x65
kernel`irq_exit+0x9e
kernel`smp_apic_timer_interrupt+0x6e
kernel`apic_timer_interrupt+0x6e
[...]

... can trace those stack functions directly for more detail


DTrace: tcpretransmit.d, cont.
• Source:

#pragma D option quiet

dtrace:::BEGIN { trace("Tracing TCP retransmits... Ctrl-C to end.n"); }

fbt::tcp_retransmit_skb:entry {
this->so = (struct sock *)arg0;
this->d = (unsigned char *)&this->so->__sk_common.skc_daddr;
printf("%Y: retransmit to %d.%d.%d.%d, by:", walltimestamp,
this->d[0], this->d[1], this->d[2], this->d[3]);
stack(99);
}


DTrace: Current State
• This was demoed on a prototype DTrace for Linux
• Right now (Feb 2013) not stable – will panic/freeze
• Needs other handholding to work around nits/bugs
• AFAIK, both DTrace ports welcome help (that means you!)
• Those examples were also fbt-based:
• Will probably need tweaks to match different kernels, since
the API is dynamically built from the kernel code

• DTrace stable providers solve that problem – but many
aren’t there on Linux yet


DTrace: Trying it out
• All providers are available to try on illumos/SmartOS
• illumos is the on-going fork of the OpenSolaris kernel
• SmartOS is Joyent’s illumos-based cloud OS (distro)
• Rough translation guide:
kernel: linux == illumos
distros: {ubuntu|CentOS|Fedora} == {SmartOS|OmniOS|OpenIndiana}

• DTrace implementation mature
• Joyent uses SmartOS as a hypervisor for running KVM Linux
on ZFS


DTrace: Other Capabilities
• Trace short lived processes
• Proﬁle CPU usage
• Time any thread blocking event
• Investigate disk I/O latency
• Investigate network I/O latency
• Examine cache activity
• Investigate memory allocation: growth or leaks
• Investigate swapping (paging) in detail
• Follow network packets through the stack
• Examine lock contention
• ...

SystemTap


SystemTap
• Created when there
wasn’t DTrace for
Linux ports
• Static and dynamic
tracing, probes,
tapsets, scripts, ...
• I’ve used it a lot:
• panics/freezes
• slow startups
• for Linux only
• incompatible with D

Tools: Advanced, recap.

Applications
System Libraries
LVM IP Virtual

Device Drivers DRAM

Given the tools to I/O Bridge
see everything,
how do you I/O Controller Network Controller
use them?
Disk Disk Port Port


And More ...
• Other observability tools at all levels include:
• ps, pmap, traceroute, ntop, ss, lsof, oproﬁle, gprof,
kcachegrind, valgrind, google proﬁler, nfsiostat, cifsiostat,
latencytop, powertop, LLTng, ktap, ...

• And many experimental tools: micro-benchmarks
• So many tools it gets confusing – where do you start?


Methodologies
• Selected four:
• Streetlight Anti-Method
• Workload Characterization Method
• Drill-Down Analysis Method
• USE Method
• Methodologies give beginners a starting point, casual users a
checklist, and experts a reminder


Streetlight Anti-Method


Streetlight Anti-Method
• 1. Pick observability tools that are
• familiar
• found on the Internet
• found at random
• 2. Run tools
• 3. Look for obvious issues

• Included for comparison (don’t use this methodology)


Streetlight Anti-Method, cont.
• Named after an observational bias called the streetlight effect

A policeman sees a drunk looking under a streetlight,
and asks what he is looking for.
The drunk says he has lost his keys.
The policeman can't ﬁnd them either,
and asks if he lost them under the streetlight.
The drunk replies:
“No, but this is where the light is best.”


top - 15:09:38 up 255 days, 16:54, 10 users, load average: 0.00, 0.03, 0.00
Tasks: 274 total, 1 running, 273 sleeping, 0 stopped, 0 zombie
Cpu(s): 0.7%us, 0.0%sy, 0.0%ni, 99.1%id, 0.1%wa, 0.0%hi, 0.0%si, 0.0%st
Mem: 8181740k total, 7654228k used, 527512k free, 405616k buffers
Swap: 2932728k total, 125064k used, 2807664k free, 3826244k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
16876 root 20 0 57596 17m 1972 S 4 0.2 3:00.60 python
3947 brendan 20 0 19352 1552 1060 R 0 0.0 0:00.06 top
15841 joshw 20 0 67144 23m 908 S 0 0.3 218:21.70 mosh-server
16922 joshw 20 0 54924 11m 920 S 0 0.1 121:34.20 mosh-server
1 root 20 0 23788 1432 736 S 0 0.0 0:18.15 init
2 root 20 0 0 0 0 S 0 0.0 0:00.61 kthreadd
3 root RT 0 0 0 0 S 0 0.0 0:00.11 migration/0
4 root 20 0 0 0 0 S 0 0.0 18:43.09 ksoftirqd/0
5 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/0
[...]

• Why are you still running top?


• Tools-based approach
• Inefﬁcient:
• can take time before the right tool is found
• can be wasteful when investigating false positives
• Incomplete:
• don’t ﬁnd the right tool, or,
• the right tool doesn’t exist


Workload Characterization Method


• 1. Who
• 2. Why
• 3. What
• 4. How


• 1. Who is causing the load? PID, UID, IP addr, ...
• 2. Why is the load called? code path
• 3. What is the load? IOPS, tput, direction, type
• 4. How is the load changing over time?


Workload Characterization Method, cont.
• Identiﬁes issues of load
• Best performance wins are from eliminating unnecessary work
• Don’t assume you know what the workload is – characterize
• Many of the previous analysis tools included workload statistics


Workload Characterization Method, cont.
• Pros:
• Potentially largest wins
• Cons:
• Only solves a class of issues – load
• Time consuming, and can be discouraging – most
attributes examined will not be a problem


Drill-Down Analysis Method


Drill-Down Analysis Method
• 1. Start at highest level
• 2. Examine next-level details
• 3. Pick most interesting breakdown
• 4. If problem unsolved, go to 2


Drill-Down Analysis Method, cont.: Example
• For example, ext4 – identify latency origin top-down:
Drill-Down Analysis
Applications

User System Libraries
Kernel
VFS
ext4
Block Device Interface
Device Drivers

Dynamic Tracing / DTrace is well suited for this,
as it can dig through all layers with custom detail


Drill-Down Analysis: ext4
• eg, ext4_readpages() latency distribution (microseconds):
# dtrace -n 'fbt::ext4_readpages:entry { self->ts = timestamp; }
fbt::ext4_readpages:return /self->ts/ {
@["us"] = lquantize((timestamp - self->ts) / 1000, 0, 10000, 250);
self->ts = 0;
}'
dtrace: description 'fbt::ext4_readpages:entry ' matched 2 probes
^C

us
< 0 | 0
0 |@@@@@@@@@@@@ 303
250 | cache hits 0
500 | 0
750 |@@@@ 88
1000 |@@@@@@@@@@@@@@ disk I/O 335
1250 | 0
1500 | 0
1750 |@@@@ 107
2000 |@@@@@@ 144
2250 | 0
2500 | 0
[...]


Drill-Down Analysis: ext4
• ... can dig out more details as needed: ﬁle name, code path:
# dtrace -n 'fbt::ext4_readpages:entry {
this->file = (struct file *)arg0;
this->name = this->file->f_path.dentry->d_name.name;
@[stringof(this->name), stack()] = count();
}'
dtrace: description 'fbt::ext4_readpages:entry ' matched 1 probe
^C[...]
foo8
kernel`__do_page_cache_readahead+0x1c7
kernel`ra_submit+0x21
kernel`ondemand_readahead+0x115
kernel`page_cache_async_readahead+0x80
kernel`radix_tree_lookup_slot+0xe
kernel`find_get_page+0x1e
kernel`generic_file_aio_read+0x48b
kernel`vma_merge+0x121
kernel`do_sync_read+0xd2
kernel`__switch_to+0x132
kernel`security_file_permission+0x93
# of kernel`rw_verify_area+0x61
kernel`vfs_read+0xb0
occurrences kernel`sys_read+0x4a
kernel`system_call_fastpath+0x16
122


Drill-Down Analysis Method, cont.
• Moves from higher- to lower-level details based on ﬁndings:
environment-wide down to metal
• Pros:
• Will identify root cause(s)
• Cons:
• Time consuming – especially when drilling in the wrong
direction


USE Method


USE Method
• For every resource, check:
• 1. Utilization
• 2. Saturation
• 3. Errors


USE Method, cont.
• For every resource, check:
• 1. Utilization: time resource was busy, or degree used
• 2. Saturation: degree of queued extra work
• 3. Errors: any errors

Saturation

Utilization
X
Errors


USE Method, cont.
• Hardware Resources:
• CPUs
• Main Memory
• Network Interfaces
• Storage Devices
• Controllers
• Interconnects
• Find the functional diagram and examine every item in the
data path...


USE Method, cont.: Functional Diagram
Hardware

Memory CPU Memory
Bus CPU Interconnect CPU Bus
DRAM DRAM
1 1

For each check: I/O Bus
1. Utilization
I/O Bridge
2. Saturation
3. Errors
Interface
Transports
Disk Disk Port Port


USE Method, cont.: Example Linux Checklist
http://dtrace.org/blogs/brendan/2012/03/07/the-use-method-linux-performance-checklist

Resource Type Metric
per-cpu: mpstat -P ALL 1, “%idle”; sar -P ALL,
“%idle”; system-wide: vmstat 1, “id”; sar -u, “%idle”;
CPU Utilization dstat -c, “idl”; per-process:top, “%CPU”; htop, “CPU
%”; ps -o pcpu; pidstat 1, “%CPU”; per-kernel-
thread: top/htop (“K” to toggle), where VIRT == 0
(heuristic). [1]
system-wide: vmstat 1, “r” > CPU count [2]; sar -q,
“runq-sz” > CPU count; dstat -p, “run” > CPU count;
per-process: /proc/PID/schedstat 2nd field
CPU Saturation (sched_info.run_delay); perf sched latency (shows
“Average” and “Maximum” delay per-schedule); dynamic
tracing, eg, SystemTap schedtimes.stp “queued(us)” [3]
perf (LPE) if processor specific error events (CPC) are
CPU Errors available; eg, AMD64′s “04Ah Single-bit ECC Errors
Recorded by Scrubber” [4]

... etc for all combinations (would ﬁll a dozen slides)


USE Method, cont.
• Some software resources can also be studied:
• Mutex Locks
• Thread Pools
• Process/Thread Capacity
• File Descriptor Capacity
• Consider possible USE metrics for each


USE Method, cont.
• This process may reveal missing metrics – those not provided
by your current toolset
• They are your known unknowns
• Much better than unknown unknowns
• More tools can be installed and developed to help
• So many top(1)s, but where is the interconnect-top?
• Full USE Method checklist may, practically, only be used for
critical issues


USE Method, cont.
• Resource-based approach
• Quick system health check, early in an investigation
• Pros:
• Complete: all resource bottlenecks and errors
• Not limited in scope by your current toolset
• No unknown unknowns – at least known unknowns
• Efﬁcient: picks three metrics for each resource –
from what may be dozens available

• Cons:
• Limited to a class of issues

Other Methodologies
• Include:
• Blame-Someone-Else Anti-Method
• Tools Method
• Ad-Hoc Checklist Method
• Problem Statement Method
• Scientiﬁc Method
• Latency Analysis
• Stack Proﬁle Method

• http://dtrace.org/blogs/brendan/2012/12/13/usenix-lisa-2012-performance-analysis-methodology/


Challenges
• Performance counter analysis (eg, bus or interconnect port
analysis) is time consuming – would like tools for convenience
• How about a “bustop” subcommand for perf?
• DTrace for Linux ports still in progress – will be awesome
when complete


Cloud Computing
• Performance may be limited by cloud resource controls, rather
than physical limits
• Hardware Virtualization complicates things – as a guest you
can’t analyze down to metal directly
• Hopefully the cloud provider provides an API for accessing
physical statistics, or does the analysis on your behalf

• We do analysis at Joyent (and our hypervisors have DTrace!)
• Free trial for new customers: good for $125 of usage value (~
one Small 1GB SmartMachine for 60 days). All prices subject
to change. Limited time only. Sign up at joyent.com


References
• Linux man pages, source, /Documentation
• USE Method: http://queue.acm.org/detail.cfm?id=2413037
• http://dtrace.org/blogs/brendan/2012/03/07/the-use-method-
linux-performance-checklist/
• http://dtrace.org/blogs/brendan/2012/12/13/usenix-lisa-2012-
performance-analysis-methodology/
• https://github.com/dtrace4linux, http://www.dtracebook.com,
http://illumos.org, http://smartos.org
• Upcoming: “Systems Performance” (Prentice Hall)


Thank you!
• email: brendan@joyent.com
• twitter: @brendangregg
• blog: http://dtrace.org/blogs/brendan
• blog resources:
• http://dtrace.org/blogs/brendan/tag/linux-2/
• http://dtrace.org/blogs/brendan/2012/02/29/the-use-method/
• http://dtrace.org/blogs/brendan/2012/03/07/the-use-method-linux-performance-
checklist/
• http://dtrace.org/blogs/brendan/2011/12/16/ﬂame-graphs/
• http://dtrace.org/blogs/brendan/2012/03/17/linux-kernel-performance-ﬂame-
graphs/
• http://dtrace.org/blogs/brendan/2011/10/15/using-systemtap/


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