Linux Systems Performance 2016

1. Linux Systems Performance Brendan Gregg Senior Performance Architect Apr, 2016

2. Systems Performance in 50 mins

3. Agenda A brief discussion of 6 facets of Linux performance: 1.  Observability 2.  Methodologies 3.  Benchmarking 4.  Profiling 5.  Tracing 6.  Tuning Audience: Everyone (DBAs, developers, operations, …)

4. 1. Observability

5. How do you measure these?

6. Linux Observability Tools

7. Observability Tools •  Tools showcase common metrics –  Learning Linux tools is useful even if you never use them: the same metrics are in GUIs •  We usually use these metrics via: –  Netflix Atlas: cloud-wide monitoring –  Netflix Vector: instance analysis •  Linux has many tools –  Plus many extra kernel sources of data that lack tools, are harder to use, and are practically undocumented •  Some tool examples…

8. upGme •  One way to print load averages: •  A measure of resource demand: CPUs + disks –  Other OSes only show CPUs: easier to interpret •  Exponentially-damped moving averages •  Time constants of 1, 5, and 15 minutes –  Historic trend without the line graph •  Load > # of CPUs, may mean CPU saturation –  Don’t spend more than 5 seconds studying these $ uptime 07:42:06 up 8:16, 1 user, load average: 2.27, 2.84, 2.91

9. top (or htop) •  System and per-process interval summary: •  %CPU is summed across all CPUs •  Can miss short-lived processes (atop won’t) •  Can consume noticeable CPU to read /proc $ top - 18:50:26 up 7:43, 1 user, load average: 4.11, 4.91, 5.22 Tasks: 209 total, 1 running, 206 sleeping, 0 stopped, 2 zombie Cpu(s): 47.1%us, 4.0%sy, 0.0%ni, 48.4%id, 0.0%wa, 0.0%hi, 0.3%si, 0.2%st Mem: 70197156k total, 44831072k used, 25366084k free, 36360k buffers Swap: 0k total, 0k used, 0k free, 11873356k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 5738 apiprod 20 0 62.6g 29g 352m S 417 44.2 2144:15 java 1386 apiprod 20 0 17452 1388 964 R 0 0.0 0:00.02 top 1 root 20 0 24340 2272 1340 S 0 0.0 0:01.51 init 2 root 20 0 0 0 0 S 0 0.0 0:00.00 kthreadd […]

10. htop

11. vmstat •  Virtual memory statistics and more: •  USAGE: vmstat [interval [count]] •  First output line has some summary since boot values –  Should be all; partial is confusing •  High level CPU summary –  “r” is runnable tasks $ vmstat –Sm 1 procs -----------memory---------- ---swap-- -----io---- -system-- ----cpu---- r b swpd free buff cache si so bi bo in cs us sy id wa 8 0 0 1620 149 552 0 0 1 179 77 12 25 34 0 0 7 0 0 1598 149 552 0 0 0 0 205 186 46 13 0 0 8 0 0 1617 149 552 0 0 0 8 210 435 39 21 0 0 8 0 0 1589 149 552 0 0 0 0 218 219 42 17 0 0 […]

12. iostat •  Block I/O (disk) stats. 1st output is since boot. •  Very useful set of stats $ iostat -xmdz 1 Linux 3.13.0-29 (db001-eb883efa) 08/18/2014 _x86_64_ (16 CPU) Device: rrqm/s wrqm/s r/s w/s rMB/s wMB/s ... xvda 0.00 0.00 0.00 0.00 0.00 0.00 / ... xvdb 213.00 0.00 15299.00 0.00 338.17 0.00 ... xvdc 129.00 0.00 15271.00 3.00 336.65 0.01 / ... md0 0.00 0.00 31082.00 3.00 678.45 0.01 ... ... avgqu-sz await r_await w_await svctm %util ... / 0.00 0.00 0.00 0.00 0.00 0.00 ... 126.09 8.22 8.22 0.00 0.06 86.40 ... / 99.31 6.47 6.47 0.00 0.06 86.00 ... 0.00 0.00 0.00 0.00 0.00 0.00 Workload ResulGng Performance

13. free •  Main memory usage: •  buffers: block device I/O cache •  cached: virtual page cache $ free -m total used free shared buffers cached Mem: 3750 1111 2639 0 147 527 -/+ buffers/cache: 436 3313 Swap: 0 0 0

14. strace •  System call tracer: •  Eg, -ttt: time (us) since epoch; -T: syscall time (s) •  Translates syscall args –  Very helpful for solving system usage issues •  Currently has massive overhead (ptrace based) –  Can slow the target by > 100x. Use extreme caution. $ strace –tttT –p 313 1408393285.779746 getgroups(0, NULL) = 1 <0.000016> 1408393285.779873 getgroups(1, [0]) = 1 <0.000015> 1408393285.780797 close(3) = 0 <0.000016> 1408393285.781338 write(1, "LinuxCon 2014!n", 15LinuxCon 2014! ) = 15 <0.000048>

15. tcpdump •  Sniff network packets for post analysis: •  Study packet sequences with timestamps (us) •  CPU overhead optimized (socket ring buffers), but can still be significant. Use caution. $ tcpdump -i eth0 -w /tmp/out.tcpdump tcpdump: listening on eth0, link-type EN10MB (Ethernet), capture size 65535 bytes ^C7985 packets captured 8996 packets received by filter 1010 packets dropped by kernel # tcpdump -nr /tmp/out.tcpdump | head reading from file /tmp/out.tcpdump, link-type EN10MB (Ethernet) 20:41:05.038437 IP 10.44.107.151.22 > 10.53.237.72.46425: Flags [P.], seq 18... 20:41:05.038533 IP 10.44.107.151.22 > 10.53.237.72.46425: Flags [P.], seq 48... 20:41:05.038584 IP 10.44.107.151.22 > 10.53.237.72.46425: Flags [P.], seq 96... […]

16. netstat •  Various network protocol statistics using -s: •  A multi-tool: -i: interface stats -r: route table default: list conns •  netstat -p: shows process details! •  Per-second interval with -c $ netstat –s […] Tcp: 736455 active connections openings 176887 passive connection openings 33 failed connection attempts 1466 connection resets received 3311 connections established 91975192 segments received 180415763 segments send out 223685 segments retransmited 2 bad segments received. 39481 resets sent […] TcpExt: 12377 invalid SYN cookies received 2982 delayed acks sent […]

17. slabtop •  Kernel slab allocator memory usage: $ slabtop Active / Total Objects (% used) : 4692768 / 4751161 (98.8%) Active / Total Slabs (% used) : 129083 / 129083 (100.0%) Active / Total Caches (% used) : 71 / 109 (65.1%) Active / Total Size (% used) : 729966.22K / 738277.47K (98.9%) Minimum / Average / Maximum Object : 0.01K / 0.16K / 8.00K OBJS ACTIVE USE OBJ SIZE SLABS OBJ/SLAB CACHE SIZE NAME 3565575 3565575 100% 0.10K 91425 39 365700K buffer_head 314916 314066 99% 0.19K 14996 21 59984K dentry 184192 183751 99% 0.06K 2878 64 11512K kmalloc-64 138618 138618 100% 0.94K 4077 34 130464K xfs_inode 138602 138602 100% 0.21K 3746 37 29968K xfs_ili 102116 99012 96% 0.55K 3647 28 58352K radix_tree_node 97482 49093 50% 0.09K 2321 42 9284K kmalloc-96 22695 20777 91% 0.05K 267 85 1068K shared_policy_node 21312 21312 100% 0.86K 576 37 18432K ext4_inode_cache 16288 14601 89% 0.25K 509 32 4072K kmalloc-256 […]

18. pcstat •  Show page cache residency by file: •  Uses the mincore(2) syscall. Useful for database performance analysis. # ./pcstat data0* |----------+----------------+------------+-----------+---------| | Name | Size | Pages | Cached | Percent | |----------+----------------+------------+-----------+---------| | data00 | 104857600 | 25600 | 25600 | 100.000 | | data01 | 104857600 | 25600 | 25600 | 100.000 | | data02 | 104857600 | 25600 | 4080 | 015.938 | | data03 | 104857600 | 25600 | 25600 | 100.000 | | data04 | 104857600 | 25600 | 16010 | 062.539 | | data05 | 104857600 | 25600 | 0 | 000.000 | |----------+----------------+------------+-----------+---------|

19. perf_events •  Provides the "perf" command •  In Linux source code: tools/perf –  Usually pkg added by linux-tools-common, etc. •  Multi-tool with many capabilities –  CPU profiling –  PMC profiling –  Static & dynamic tracing •  Covered later in Profiling & Tracing

20. Where do you start?...and stop?

21. 2. Methodologies

22. An#-‐Methodologies •  The lack of a deliberate methodology… •  Street Light Anti-Method: –  1. Pick observability tools that are •  Familiar •  Found on the Internet •  Found at random –  2. Run tools –  3. Look for obvious issues •  Drunk Man Anti-Method: –  Tune things at random until the problem goes away

23. Methodologies •  Linux Performance Analysis in 60 seconds •  The USE method •  CPU Profile Method •  Resource Analysis •  Workload Analysis •  Others include: –  Workload characterization –  Drill-down analysis –  Off-CPU analysis –  Static performance tuning –  5 whys –  …

24. Linux Perf Analysis in 60s 1.  uptime 2.  dmesg | tail 3.  vmstat 1 4.  mpstat -P ALL 1 5.  pidstat 1 6.  iostat -xz 1 7.  free -m 8.  sar -n DEV 1 9.  sar -n TCP,ETCP 1 10.  top

25. Linux Perf Analysis in 60s 1.  uptime 2.  dmesg | tail 3.  vmstat 1 4.  mpstat -P ALL 1 5.  pidstat 1 6.  iostat -xz 1 7.  free -m 8.  sar -n DEV 1 9.  sar -n TCP,ETCP 1 10.  top load averages kernel errors overall stats by Gme CPU balance process usage disk I/O memory usage network I/O TCP stats check overview hTp://techblog.neVlix.com/2015/11/linux-‐performance-‐analysis-‐in-‐60s.html

26. The USE Method •  For every resource, check: 1.  Utilization 2.  Saturation 3.  Errors •  Definitions: –  Utilization: busy time –  Saturation: queue length or queued time –  Errors: easy to interpret (objective) •  Helps if you have a functional (block) diagram of your system / software / environment, showing all resources Start with the questions, then find the tools Resource UGlizaGon (%) X

27. USE Method for Hardware •  For every resource, check: 1.  Utilization 2.  Saturation 3.  Errors •  Including busses & interconnects

28. (hTp://www.brendangregg.com/USEmethod/use-‐linux.html)

29. CPU Proﬁle Method 1.  Take a CPU profile 2.  Understand all software in profile > 1% •  Discovers a wide range of issues by their CPU usage –  Directly: CPU consumers –  Indirectly: initialization of I/O, locks, times, ... •  Narrows target of study to only its running components Flame Graph

30. Resource Analysis •  Typical approach for system performance analysis: begin with system tools & metrics •  Pros: –  Generic –  Aids resource perf tuning •  Cons: –  Uneven coverage –  False positives ApplicaGon System Libraries System Calls Kernel Hardware Workload Analysis

31. Workload Analysis •  Begin with application metrics & context •  Pros: –  Accurate, proportional metrics –  App context •  Cons: –  App specific –  Difficult to dig from app to resource ApplicaGon System Libraries System Calls Kernel Hardware Workload Analysis

32. 3. Benchmarking

33. ~100% of benchmarks are wrong

34. Benchmarking •  Apart from observational analysis, benchmarking is a useful form of experimental analysis –  Try observational first; benchmarks can perturb •  However, benchmarking is error prone: –  Testing the wrong target: eg, FS cache instead of disk –  Choosing the wrong target: eg, disk instead of FS cache … doesn’t resemble real world usage –  Invalid results: eg, bugs –  Misleading results: you benchmark A, but actually measure B, and conclude you measured C •  The energy needed to refute benchmarks is multiple orders of magnitude bigger than to run them

35. Benchmark Examples •  Micro benchmarks: –  File system maximum cached read operations/sec –  Network maximum throughput •  Macro (application) benchmarks: –  Simulated application maximum request rate •  Bad benchmarks: –  gitpid() in a tight loop –  Context switch timing

36. The Benchmark Paradox •  Benchmarking is used for product evaluations –  Eg, evaluating cloud vendors •  The Benchmark Paradox: –  If your product’s chances of winning a benchmark are 50/50, you’ll usually lose –  http://www.brendangregg.com/blog/2014-05-03/the-benchmark-paradox.html •  Solving this seeming paradox (and benchmarking in general)…

37. For any given benchmark result, ask: why isn’t it 10x?

38. AcGve Benchmarking •  Root cause performance analysis while the benchmark is still running –  Use the earlier observability tools –  Identify the limiter (or suspected limiter) and include it with the benchmark results –  Answer: why not 10x? •  This takes time, but uncovers most mistakes

39. 4. Proﬁling

40. Proﬁling •  Can 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.”

41. Proﬁling •  Can 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 hTps://www.rfc-‐editor.org/rfc/museum/tcp-‐ip-‐digest/tcp-‐ip-‐digest.v1n6.1

42. perf_events •  Introduced earlier: multi-tool, profiler. Provides "perf". usage: perf [--version] [--help] [OPTIONS] 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 file bench General framework for benchmark suites buildid-cache Manage build-id cache. buildid-list List the buildids in a perf.data file data Data file related processing diff Read 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 mem Profile memory accesses 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. trace strace inspired tool probe Define new dynamic tracepoints See 'perf help COMMAND' for more information on a specific command.

43. perf_events: CPU proﬁling •  Sampling full stack traces at 99 Hertz, for 30 secs: # perf record -F 99 -ag -- sleep 30 [ perf record: Woken up 9 times to write data ] [ perf record: Captured and wrote 2.745 MB perf.data (~119930 samples) ] # perf report -n --stdio 1.40% 162 java [kernel.kallsyms] [k] _raw_spin_lock | --- _raw_spin_lock | |--63.21%-- try_to_wake_up | | | |--63.91%-- default_wake_function | | | | | |--56.11%-- __wake_up_common | | | __wake_up_locked | | | ep_poll_callback | | | __wake_up_common | | | __wake_up_sync_key | | | | | | | |--59.19%-- sock_def_readable […78,000 lines truncated…]

44. perf_events: Full "report" Output

45. … as a Flame Graph

46. perf_events: Flame Graphs •  Flame Graphs: –  x-axis: alphabetical stack sort, to maximize merging –  y-axis: stack depth –  color: random, or hue can be a dimension (eg, diff) •  Interpretation: –  top edge is on-CPU, beneath it is ancestry •  Easy to get working –  http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html •  Also in Mar/Apr issue of ACMQ 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

47. Size of one stack •  The first ever flame graph was generated for MySQL •  This background is the output of a DTrace CPU profile, which only printed unique stacks with counts

48. 1st Flame Graph: MySQL … same data

49. perf_events: 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 visualizaGon perf list list events Typical Workflow

50. perf_events: Counters •  Performance Monitoring Counters (PMCs): •  Identify CPU cycle breakdowns, esp. stall types •  PMCs not enabled by-default in clouds (yet) •  Can be time-consuming to use (CPU manuals) $ perf list | grep –i 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] […] branch-misses [Hardware event] bus-cycles [Hardware event] L1-dcache-loads [Hardware cache event] L1-dcache-load-misses [Hardware cache event] […] rNNN (see 'perf list --help' on how to encode it) [Raw hardware event … mem:<addr>[:access] [Hardware breakpoint]

51. 5. Tracing

52. Linux Event Sources

53. Tracing Stack tracepoints, kprobes, uprobes jrace, perf_events, BPF perf front-‐end tools: tracing frameworks: back-‐end instrumentaGon: trace-cmd, perf-‐tools, bcc, … add-‐on tools: in Linux

54. jrace •  Added by Steven Rostedt and others since 2.6.27 –  CONFIG_FTRACE, CONFIG_FUNCTION_PROFILER, … –  See Linux source: Documentation/trace/ftrace.txt –  A collection of powerful features, good for hacking •  Use directly via /sys/kernel/debug/tracing (not easy): •  Or via front-ends: –  Steven's trace-cmd –  my perf-tools: iosnoop, iolatency, funccount, kprobe, … linux-4.0.0+# ls /sys/kernel/debug/tracing/ available_events max_graph_depth stack_max_size available_filter_functions options stack_trace available_tracers per_cpu stack_trace_filter buffer_size_kb printk_formats trace […]

55. jrace: perf-‐tools iosnoop •  Block I/O (disk) events with latency: # ./iosnoop –ts Tracing block I/O. Ctrl-C to end. STARTs ENDs COMM PID TYPE DEV BLOCK BYTES LATms 5982800.302061 5982800.302679 supervise 1809 W 202,1 17039600 4096 0.62 5982800.302423 5982800.302842 supervise 1809 W 202,1 17039608 4096 0.42 5982800.304962 5982800.305446 supervise 1801 W 202,1 17039616 4096 0.48 5982800.305250 5982800.305676 supervise 1801 W 202,1 17039624 4096 0.43 […] # ./iosnoop –h USAGE: iosnoop [-hQst] [-d device] [-i iotype] [-p PID] [-n name] [duration] -d device # device string (eg, "202,1) -i iotype # match type (eg, '*R*' for all reads) -n name # process name to match on I/O issue -p PID # PID to match on I/O issue -Q # include queueing time in LATms -s # include start time of I/O (s) -t # include completion time of I/O (s) -h # this usage message duration # duration seconds, and use buffers […]

56. jrace: perf-‐tools iolatency •  Block I/O (disk) latency distributions: # ./iolatency Tracing block I/O. Output every 1 seconds. Ctrl-C to end. >=(ms) .. <(ms) : I/O |Distribution | 0 -> 1 : 2104 |######################################| 1 -> 2 : 280 |###### | 2 -> 4 : 2 |# | 4 -> 8 : 0 | | 8 -> 16 : 202 |#### | >=(ms) .. <(ms) : I/O |Distribution | 0 -> 1 : 1144 |######################################| 1 -> 2 : 267 |######### | 2 -> 4 : 10 |# | 4 -> 8 : 5 |# | 8 -> 16 : 248 |######### | 16 -> 32 : 601 |#################### | 32 -> 64 : 117 |#### | […]

57. jrace: perf-‐tools funccount •  Count a kernel function call rate: –  -i: set an output interval (seconds), otherwise until Ctrl-C # ./funccount -i 1 'bio_*' Tracing "bio_*"... Ctrl-C to end. FUNC COUNT bio_attempt_back_merge 26 bio_get_nr_vecs 361 bio_alloc 536 bio_alloc_bioset 536 bio_endio 536 bio_free 536 bio_fs_destructor 536 bio_init 536 bio_integrity_enabled 536 bio_put 729 bio_add_page 1004 [...] Counts are in-‐kernel, for low overhead

58. jrace: perf-‐tools uprobe •  Dynamically trace user-level functions; eg, MySQL: –  Filter on string match; eg, "SELECT": •  Ok for hacking, but not friendly; need perf_events/BPF # ./uprobe 'p:dispatch_command /opt/mysql/bin/mysqld:_Z16dispatch_command19enum_server_commandP3THDPcj +0(%dx):string' Tracing uprobe dispatch_command (p:dispatch_command /opt/mysql/bin/mysqld:0x2dbd40 +0(%dx):string). Ctrl-C to end. mysqld-2855 [001] d... 19956674.509085: dispatch_command: (0x6dbd40) arg1="show tables" mysqld-2855 [001] d... 19956675.541155: dispatch_command: (0x6dbd40) arg1="SELECT * FROM numbers where number > 32000" # ./uprobe 'p:dispatch_command /opt/mysql/bin/mysqld:_Z16dispatch_command19enum_server_commandP3THDPcj cmd=+0(%dx):string' 'cmd ~ "SELECT*"' Tracing uprobe dispatch_command (p:dispatch_command /opt/mysql/bin/mysqld:0x2dbd40 cmd= +0(%dx):string). Ctrl-C to end. mysqld-2855 [001] d... 19956754.619958: dispatch_command: (0x6dbd40) cmd="SELECT * FROM numbers where number > 32000" mysqld-2855 [001] d... 19956755.060125: dispatch_command: (0x6dbd40) cmd="SELECT * FROM numbers where number > 32000"

59. perf_events •  Powerful profiler (covered earlier) and tracer: –  User-level and kernel dynamic tracing –  Kernel line tracing and local variables (debuginfo) –  Kernel filtering expressions –  Efficient in-kernel counts (perf stat) •  Intended as the official Linux tracer/profiler •  Becoming more programmable with BPF support (2016) –  Search lkml for "perf" and "BPF"

60. perf_events: LisGng Events # perf list cpu-cycles OR cycles [Hardware event] instructions [Hardware event] cache-references [Hardware event] cache-misses [Hardware event] branch-instructions OR branches [Hardware 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] […] 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] […]

61. perf_events: Tracing Tracepoints •  If -g is used in "perf record", stack traces are included •  If "perf script" output is too verbose, try "perf report", or making a flame graph # perf record -e block:block_rq_complete -a sleep 10 [ perf record: Woken up 1 times to write data ] [ perf record: Captured and wrote 0.428 MB perf.data (~18687 samples) ] # perf script run 30339 [000] 2083345.722767: block:block_rq_complete: 202,1 W () 12984648 + 8 [0] run 30339 [000] 2083345.722857: block:block_rq_complete: 202,1 W () 12986336 + 8 [0] run 30339 [000] 2083345.723180: block:block_rq_complete: 202,1 W () 12986528 + 8 [0] swapper 0 [000] 2083345.723489: block:block_rq_complete: 202,1 W () 12986496 + 8 [0] swapper 0 [000] 2083346.745840: block:block_rq_complete: 202,1 WS () 1052984 + 144 [0] supervise 30342 [000] 2083346.746571: block:block_rq_complete: 202,1 WS () 1053128 + 8 [0] supervise 30342 [000] 2083346.746663: block:block_rq_complete: 202,1 W () 12986608 + 8 [0] run 30342 [000] 2083346.747003: block:block_rq_complete: 202,1 W () 12986832 + 8 [0] [...]

62. BPF •  Enhanced Berkeley Packet Filter, now just "BPF" –  Enhancements added in Linux 3.15, 3.19, 4.1, 4.5, … •  Provides programmatic tracing –  measure latency, custom histograms, … System dashboards of 2017+ should look very different

63. BPF: bcc ext4slower •  ext4 operations slower than the threshold: •  Better indicator of application pain than disk I/O •  Measures & filters in-kernel for efficiency using BPF –  https://github.com/iovisor/bcc # ./ext4slower 1 Tracing ext4 operations slower than 1 ms TIME COMM PID T BYTES OFF_KB LAT(ms) FILENAME 06:49:17 bash 3616 R 128 0 7.75 cksum 06:49:17 cksum 3616 R 39552 0 1.34 [ 06:49:17 cksum 3616 R 96 0 5.36 2to3-2.7 06:49:17 cksum 3616 R 96 0 14.94 2to3-3.4 06:49:17 cksum 3616 R 10320 0 6.82 411toppm 06:49:17 cksum 3616 R 65536 0 4.01 a2p 06:49:17 cksum 3616 R 55400 0 8.77 ab 06:49:17 cksum 3616 R 36792 0 16.34 aclocal-1.14 06:49:17 cksum 3616 R 15008 0 19.31 acpi_listen […]

64. BPF: bcc tools (early 2016)

65. 6. Tuning

66. Ubuntu Trusty Tuning: Early 2016 (1/2) •  CPU schedtool –B PID disable Ubuntu apport (crash reporter) also upgrade to Xenial (better HT scheduling) •  Virtual Memory vm.swappiness = 0 # from 60 kernel.numa_balancing = 0 # temp workaround •  Huge Pages echo never > /sys/kernel/mm/transparent_hugepage/enabled •  File System vm.dirty_ratio = 80 # from 40 vm.dirty_background_ratio = 5 # from 10 vm.dirty_expire_centisecs = 12000 # from 3000 mount -o defaults,noatime,discard,nobarrier … •  Storage I/O /sys/block/*/queue/rq_affinity 2 /sys/block/*/queue/scheduler noop /sys/block/*/queue/nr_requests 256

67. Ubuntu Trusty Tuning: Early 2016 (2/2) •  Storage (continued) /sys/block/*/queue/read_ahead_kb 256 mdadm –chunk=64 … •  Networking net.core.somaxconn = 1000 net.core.netdev_max_backlog = 5000 net.core.rmem_max = 16777216 net.core.wmem_max = 16777216 net.ipv4.tcp_wmem = 4096 12582912 16777216 net.ipv4.tcp_rmem = 4096 12582912 16777216 net.ipv4.tcp_max_syn_backlog = 8096 net.ipv4.tcp_slow_start_after_idle = 0 net.ipv4.tcp_tw_reuse = 1 net.ipv4.ip_local_port_range = 10240 65535 net.ipv4.tcp_abort_on_overflow = 1 # maybe •  Hypervisor (Xen) echo tsc > /sys/devices/…/current_clocksource Plus PVHVM (HVM), SR-IOV, …

68. Summary A brief discussion of 6 facets of Linux performance: 1.  Observability 2.  Methodologies 3.  Benchmarking 4.  Profiling 5.  Tracing 6.  Tuning

69. Takeaways Some things to print out for your office wall: 1.  uptime 2.  dmesg -T | tail 3.  vmstat 1 4.  mpstat -P ALL 1 5.  pidstat 1 6.  iostat -xz 1 7.  free -m 8.  sar -n DEV 1 9.  sar -n TCP,ETCP 1 10.  top

70. More Links •  Netflix Tech Blog on Linux: •  http://techblog.netflix.com/2015/11/linux-performance-analysis-in-60s.html •  http://techblog.netflix.com/2015/08/netflix-at-velocity-2015-linux.html •  Linux Performance: •  http://www.brendangregg.com/linuxperf.html •  Linux perf_events: •  https://perf.wiki.kernel.org/index.php/Main_Page •  http://www.brendangregg.com/perf.html •  Linux ftrace: •  https://www.kernel.org/doc/Documentation/trace/ftrace.txt •  https://github.com/brendangregg/perf-tools •  USE Method Linux: •  http://www.brendangregg.com/USEmethod/use-linux.html •  Flame Graphs: •  http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html •  http://queue.acm.org/detail.cfm?id=2927301

71. Thanks •  Questions? •  http://slideshare.net/brendangregg •  http://www.brendangregg.com •  bgregg@netflix.com •  @brendangregg