2

SREcon 2016 Performance Checklists for SREs

 2 years ago
source link: https://www.slideshare.net/brendangregg/srecon-2016-performance-checklists-for-sres
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

SREcon 2016 Performance Checklists for SREs

  1. 1. Performance  Checklists   for  SREs   Brendan Gregg Senior Performance Architect
  2. 2. Performance  Checklists   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 per instance: cloud wide: 1.  RPS,  CPU   2.  Volume   6.  Load  Avg   3.  Instances   4.  Scaling   5.  CPU/RPS   7.  Java  Heap   8.  ParNew   9.  Latency   10.  99th  Qle  
  3. 3. Brendan  the  SRE   •  On the Perf Eng team & primary on-call rotation for Core: our central SRE team –  we get paged on SPS dips (starts per second) & more •  In this talk I'll condense some perf engineering into SRE timescales (minutes) using checklists
  4. 4. Performance  Engineering   !=   SRE  Performance   Incident  Response  
  5. 5. Performance  Engineering   •  Aim: best price/performance possible –  Can be endless: continual improvement •  Fixes can take hours, days, weeks, months –  Time to read docs & source code, experiment –  Can take on large projects no single team would staff •  Usually no prior "good" state –  No spot the difference. No starting point. –  Is now "good" or "bad"? Experience/instinct helps •  Solo/team work At Netflix: The Performance Engineering team, with help from developers +3
  6. 6. Performance  Engineering  
  7. 7. Performance  Engineering   stat tools tracers benchmarks documentation source code tuning PMCs profilers flame graphs monitoring dashboards
  8. 8. SRE  Perf  Incident  Response   •  Aim: resolve issue in minutes –  Quick resolution is king. Can scale up, roll back, redirect traffic. –  Must cope under pressure, and at 3am •  Previously was in a "good" state –  Spot the difference with historical graphs •  Get immediate help from all staff –  Must be social •  Reliability & perf issues often related At Netflix, the Core team (5 SREs), with immediate help from developers and performance engineers +1
  9. 9. SRE  Perf  Incident  Response  
  10. 10. SRE  Perf  Incident  Response   custom dashboards central event logs distributed system tracing chat rooms pager ticket system
  11. 11. NeSlix  Cloud  Analysis  Process   Atlas  Alerts   Atlas  Dashboards   Atlas  Metrics   Salp  Mogul   SSH,  instance  tools   ICE   4.  Check  Dependencies   Create   New  Alert   Plus some other tools not pictured Cost   3.  Drill  Down   5.  Root   Cause   Chronos   2.  Check  Events   In summary… Example SRE response path enumerated Redirected  to   a  new  Target   1.  Check  Issue  
  12. 12. The  Need  for  Checklists   •  Speed •  Completeness •  A Starting Point •  An Ending Point •  Reliability •  Training Perf checklists have historically been created for perf engineering (hours) not SRE response (minutes) More on checklists: Gawande, A., The Checklist Manifesto. Metropolitan Books, 2008 Boeing  707  Emergency  Checklist  (1969)  
  13. 13. SRE  Checklists  at  NeSlix   •  Some shared docs –  PRE Triage Methodology –  go/triage: a checklist of dashboards •  Most "checklists" are really custom dashboards –  Selected metrics for both reliability and performance •  I maintain my own per-service and per-device checklists
  14. 14. SRE  Performance  Checklists   The following are: •  Cloud performance checklists/dashboards •  SSH/Linux checklists (lowest common denominator) •  Methodologies for deriving cloud/instance checklists Ad Hoc Methodology Checklists Dashboards Including aspirational: what we want to do & build as dashboards
  15. 15. 1.  PRE  Triage  Checklist     Our  iniQal  checklist   NeSlix  specific  
  16. 16. PRE  Triage  Checklist   •  Performance and Reliability Engineering checklist –  Shared doc with a hierarchal checklist with 66 steps total 1.  Initial Impact 1.  record timestamp 2.  quantify: SPS, signups, support calls 3.  check impact: regional or global? 4.  check devices: device specific? 2.  Time Correlations 1.  pretriage dashboard 1.  check for suspect NIWS client: error rates 2.  check for source of error/request rate change 3.  […dashboard specifics…] Confirms, quantifies, & narrows problem. Helps you reason about the cause.
  17. 17. PRE  Triage  Checklist.  cont.   •  3. Evaluate Service Health –  perfvitals dashboard –  mogul dependency correlation –  by cluster/asg/node: •  latency: avg, 90 percentile •  request rate •  CPU: utilization, sys/user •  Java heap: GC rate, leaks •  memory •  load average •  thread contention (from Java) •  JVM crashes •  network: tput, sockets •  […] custom dashboards
  18. 18. 2.  predash     IniQal  dashboard   NeSlix  specific  
  19. 19. predash   Performance and Reliability Engineering dashboard A list of selected dashboards suited for incident response
  20. 20. predash   List of dashboards is its own checklist: 1.  Overview 2.  Client stats 3.  Client errors & retries 4.  NIWS HTTP errors 5.  NIWS Errors by code 6.  DRM request overview 7.  DoS attack metrics 8.  Push map 9.  Cluster status ...
  21. 21. 3.  perfvitals     Service  dashboard  
  22. 22. 1.  RPS,  CPU   2.  Volume   6.  Load  Avg   3.  Instances   4.  Scaling   5.  CPU/RPS   7.  Java  Heap   8.  ParNew   9.  Latency   10.  99th  Qle   perfvitals  
  23. 23. 4.  Cloud  ApplicaQon  Performance   Dashboard     A  generic  example  
  24. 24. Cloud  App  Perf  Dashboard   1.  Load 2.  Errors 3.  Latency 4.  Saturation 5.  Instances
  25. 25. Cloud  App  Perf  Dashboard   1.  Load 2.  Errors 3.  Latency 4.  Saturation 5.  Instances All time series, for every application, and dependencies. Draw a functional diagram with the entire data path. Same as Google's "Four Golden Signals" (Latency, Traffic, Errors, Saturation), with instances added due to cloud –  Beyer, B., Jones, C., Petoff, J., Murphy, N. Site Reliability Engineering. O'Reilly, Apr 2016 problem  of  load  applied?  req/sec,  by  type   errors,  Qmeouts,  retries   response  Qme  average,  99th  -­‐Qle,  distribuQon   CPU  load  averages,  queue  length/Qme   scale  up/down?  count,  state,  version  
  26. 26. 5.  Bad  Instance  Dashboard     An  An>-­‐Methodology  
  27. 27. Bad  Instance  Dashboard   1.  Plot request time per-instance 2.  Find the bad instance 3.  Terminate bad instance 4.  Someone else’s problem now! In SRE incident response, if it works, do it. 95th  percenQle  latency   (Atlas  Exploder)   Bad  instance   Terminate!  
  28. 28. Lots  More  Dashboards   We have countless more, mostly app specific and reliability focused •  Most reliability incidents involve time correlation with a central log system Sometimes, dashboards & monitoring aren't enough. Time for SSH. NIWS HTTP errors: Error  Types   Regions   Apps   Time  
  29. 29. 6.  Linux  Performance  Analysis   in   60,000  milliseconds  
  30. 30. Linux  Perf  Analysis  in  60s   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
  31. 31. Linux  Perf  Analysis  in  60s   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 load  averages   kernel  errors   overall  stats  by  Qme   CPU  balance   process  usage   disk  I/O   memory  usage   network  I/O   TCP  stats   check  overview   hap://techblog.neSlix.com/2015/11/linux-­‐performance-­‐analysis-­‐in-­‐60s.html  
  32. 32. 60s:  upQme,  dmesg,  vmstat   $ uptime 23:51:26 up 21:31, 1 user, load average: 30.02, 26.43, 19.02 $ dmesg | tail [1880957.563150] perl invoked oom-killer: gfp_mask=0x280da, order=0, oom_score_adj=0 [...] [1880957.563400] Out of memory: Kill process 18694 (perl) score 246 or sacrifice child [1880957.563408] Killed process 18694 (perl) total-vm:1972392kB, anon-rss:1953348kB, file-rss:0kB [2320864.954447] TCP: Possible SYN flooding on port 7001. Dropping request. Check SNMP counters. $ vmstat 1 procs ---------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 34 0 0 200889792 73708 591828 0 0 0 5 6 10 96 1 3 0 0 32 0 0 200889920 73708 591860 0 0 0 592 13284 4282 98 1 1 0 0 32 0 0 200890112 73708 591860 0 0 0 0 9501 2154 99 1 0 0 0 32 0 0 200889568 73712 591856 0 0 0 48 11900 2459 99 0 0 0 0 32 0 0 200890208 73712 591860 0 0 0 0 15898 4840 98 1 1 0 0 ^C
  33. 33. 60s:  mpstat   $ mpstat -P ALL 1 Linux 3.13.0-49-generic (titanclusters-xxxxx) 07/14/2015 _x86_64_ (32 CPU) 07:38:49 PM CPU %usr %nice %sys %iowait %irq %soft %steal %guest %gnice %idle 07:38:50 PM all 98.47 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.78 07:38:50 PM 0 96.04 0.00 2.97 0.00 0.00 0.00 0.00 0.00 0.00 0.99 07:38:50 PM 1 97.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 07:38:50 PM 2 98.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 07:38:50 PM 3 96.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.03 [...]
  34. 34. 60s:  pidstat   $ pidstat 1 Linux 3.13.0-49-generic (titanclusters-xxxxx) 07/14/2015 _x86_64_ (32 CPU) 07:41:02 PM UID PID %usr %system %guest %CPU CPU Command 07:41:03 PM 0 9 0.00 0.94 0.00 0.94 1 rcuos/0 07:41:03 PM 0 4214 5.66 5.66 0.00 11.32 15 mesos-slave 07:41:03 PM 0 4354 0.94 0.94 0.00 1.89 8 java 07:41:03 PM 0 6521 1596.23 1.89 0.00 1598.11 27 java 07:41:03 PM 0 6564 1571.70 7.55 0.00 1579.25 28 java 07:41:03 PM 60004 60154 0.94 4.72 0.00 5.66 9 pidstat 07:41:03 PM UID PID %usr %system %guest %CPU CPU Command 07:41:04 PM 0 4214 6.00 2.00 0.00 8.00 15 mesos-slave 07:41:04 PM 0 6521 1590.00 1.00 0.00 1591.00 27 java 07:41:04 PM 0 6564 1573.00 10.00 0.00 1583.00 28 java 07:41:04 PM 108 6718 1.00 0.00 0.00 1.00 0 snmp-pass 07:41:04 PM 60004 60154 1.00 4.00 0.00 5.00 9 pidstat ^C
  35. 35. 60s:  iostat   $ 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   ResulQng  Performance  
  36. 36. 60s:  free,  sar  –n  DEV   $ free -m total used free shared buffers cached Mem: 245998 24545 221453 83 59 541 -/+ buffers/cache: 23944 222053 Swap: 0 0 0 $ sar -n DEV 1 Linux 3.13.0-49-generic (titanclusters-xxxxx) 07/14/2015 _x86_64_ (32 CPU) 12:16:48 AM IFACE rxpck/s txpck/s rxkB/s txkB/s rxcmp/s txcmp/s rxmcst/s %ifutil 12:16:49 AM eth0 18763.00 5032.00 20686.42 478.30 0.00 0.00 0.00 0.00 12:16:49 AM lo 14.00 14.00 1.36 1.36 0.00 0.00 0.00 0.00 12:16:49 AM docker0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12:16:49 AM IFACE rxpck/s txpck/s rxkB/s txkB/s rxcmp/s txcmp/s rxmcst/s %ifutil 12:16:50 AM eth0 19763.00 5101.00 21999.10 482.56 0.00 0.00 0.00 0.00 12:16:50 AM lo 20.00 20.00 3.25 3.25 0.00 0.00 0.00 0.00 12:16:50 AM docker0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ^C
  37. 37. 60s:  sar  –n  TCP,ETCP   $ sar -n TCP,ETCP 1 Linux 3.13.0-49-generic (titanclusters-xxxxx) 07/14/2015 _x86_64_ (32 CPU) 12:17:19 AM active/s passive/s iseg/s oseg/s 12:17:20 AM 1.00 0.00 10233.00 18846.00 12:17:19 AM atmptf/s estres/s retrans/s isegerr/s orsts/s 12:17:20 AM 0.00 0.00 0.00 0.00 0.00 12:17:20 AM active/s passive/s iseg/s oseg/s 12:17:21 AM 1.00 0.00 8359.00 6039.00 12:17:20 AM atmptf/s estres/s retrans/s isegerr/s orsts/s 12:17:21 AM 0.00 0.00 0.00 0.00 0.00 ^C
  38. 38. 60s:  top   $ top top - 00:15:40 up 21:56, 1 user, load average: 31.09, 29.87, 29.92 Tasks: 871 total, 1 running, 868 sleeping, 0 stopped, 2 zombie %Cpu(s): 96.8 us, 0.4 sy, 0.0 ni, 2.7 id, 0.1 wa, 0.0 hi, 0.0 si, 0.0 st KiB Mem: 25190241+total, 24921688 used, 22698073+free, 60448 buffers KiB Swap: 0 total, 0 used, 0 free. 554208 cached Mem PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 20248 root 20 0 0.227t 0.012t 18748 S 3090 5.2 29812:58 java 4213 root 20 0 2722544 64640 44232 S 23.5 0.0 233:35.37 mesos-slave 66128 titancl+ 20 0 24344 2332 1172 R 1.0 0.0 0:00.07 top 5235 root 20 0 38.227g 547004 49996 S 0.7 0.2 2:02.74 java 4299 root 20 0 20.015g 2.682g 16836 S 0.3 1.1 33:14.42 java 1 root 20 0 33620 2920 1496 S 0.0 0.0 0:03.82 init 2 root 20 0 0 0 0 S 0.0 0.0 0:00.02 kthreadd 3 root 20 0 0 0 0 S 0.0 0.0 0:05.35 ksoftirqd/0 5 root 0 -20 0 0 0 S 0.0 0.0 0:00.00 kworker/0:0H 6 root 20 0 0 0 0 S 0.0 0.0 0:06.94 kworker/u256:0 8 root 20 0 0 0 0 S 0.0 0.0 2:38.05 rcu_sched
  39. 39. Other  Analysis  in  60s   •  We need such checklists for: –  Java –  Cassandra –  MySQL –  Nginx –  etc… •  Can follow a methodology: –  Process of elimination –  Workload characterization –  Differential diagnosis –  Some summaries: http://www.brendangregg.com/methodology.html •  Turn checklists into dashboards (many do exist)
  40. 40. 7.  Linux  Disk  Checklist  
  41. 41. Linux  Disk  Checklist   1.  iostat –xnz 1 2.  vmstat 1 3.  df -h 4.  ext4slower 10 5.  bioslower 10 6.  ext4dist 1 7.  biolatency 1 8.  cat /sys/devices/…/ioerr_cnt 9.  smartctl -l error /dev/sda1
  42. 42. Linux  Disk  Checklist   1.  iostat –xnz 1 2.  vmstat 1 3.  df -h 4.  ext4slower 10 5.  bioslower 10 6.  ext4dist 1 7.  biolatency 1 8.  cat /sys/devices/…/ioerr_cnt 9.  smartctl -l error /dev/sda1 any  disk  I/O?  if  not,  stop  looking   is  this  swapping?  or,  high  sys  Qme?   are  file  systems  nearly  full?   (zfs*,  xfs*,  etc.)  slow  file  system  I/O?   if  so,  check  disks   check  distribuQon  and  rate   if  interesQng,  check  disks                                                              (if  available)  errors                                                              (if  available)  errors   Another short checklist. Won't solve everything. FS focused. ext4slower/dist, bioslower, are from bcc/BPF tools.
  43. 43. ext4slower   •  ext4 operations slower than the threshold: •  Better indicator of application pain than disk I/O •  Measures & filters in-kernel for efficiency using BPF –  From 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 […]
  44. 44. BPF  is  coming…   Free  your  mind  
  45. 45. BPF   •  That file system checklist should be a dashboard: –  FS & disk latency histograms, heatmaps, IOPS, outlier log •  Now possible with enhanced BPF (Berkeley Packet Filter) –  Built into Linux 4.x: dynamic tracing, filters, histograms System dashboards of 2017+ should look very different
  46. 46. 8.  Linux  Network  Checklist  
  47. 47. Linux  Network  Checklist   1.  sar -n DEV,EDEV 1 2.  sar -n TCP,ETCP 1 3.  cat /etc/resolv.conf 4.  mpstat -P ALL 1 5.  tcpretrans 6.  tcpconnect 7.  tcpaccept 8.  netstat -rnv 9.  check firewall config 10.  netstat -s
  48. 48. Linux  Network  Checklist   1.  sar -n DEV,EDEV 1 2.  sar -n TCP,ETCP 1 3.  cat /etc/resolv.conf 4.  mpstat -P ALL 1 5.  tcpretrans 6.  tcpconnect 7.  tcpaccept 8.  netstat -rnv 9.  check firewall config 10.  netstat -s at  interface  limits?  or  use  nicstat   acQve/passive  load,  retransmit  rate   it's  always  DNS   high  kernel  Qme?  single  hot  CPU?   what  are  the  retransmits?  state?   connecQng  to  anything  unexpected?   unexpected  workload?   any  inefficient  routes?   anything  blocking/throaling?   play  252  metric  pickup   tcp*, are from bcc/BPF tools
  49. 49. tcpretrans   •  Just trace kernel TCP retransmit functions for efficiency: •  From either bcc (BPF) or perf-tools (ftrace, older kernels) # ./tcpretrans TIME PID IP LADDR:LPORT T> RADDR:RPORT STATE 01:55:05 0 4 10.153.223.157:22 R> 69.53.245.40:34619 ESTABLISHED 01:55:05 0 4 10.153.223.157:22 R> 69.53.245.40:34619 ESTABLISHED 01:55:17 0 4 10.153.223.157:22 R> 69.53.245.40:22957 ESTABLISHED […]
  50. 50. 9.  Linux  CPU  Checklist  
  51. 51. (too many lines – should be a utilization heat map)
  52. 52. http://www.brendangregg.com/HeatMaps/subsecondoffset.html
  53. 53. $ perf script […] java 14327 [022] 252764.179741: cycles: 7f36570a4932 SpinPause (/usr/lib/jvm/java-8 java 14315 [014] 252764.183517: cycles: 7f36570a4932 SpinPause (/usr/lib/jvm/java-8 java 14310 [012] 252764.185317: cycles: 7f36570a4932 SpinPause (/usr/lib/jvm/java-8 java 14332 [015] 252764.188720: cycles: 7f3658078350 pthread_cond_wait@@GLIBC_2.3.2 java 14341 [019] 252764.191307: cycles: 7f3656d150c8 ClassLoaderDataGraph::do_unloa java 14341 [019] 252764.198825: cycles: 7f3656d140b8 ClassLoaderData::free_dealloca java 14341 [019] 252764.207057: cycles: 7f3657192400 nmethod::do_unloading(BoolObje java 14341 [019] 252764.215962: cycles: 7f3656ba807e Assembler::locate_operand(unsi java 14341 [019] 252764.225141: cycles: 7f36571922e8 nmethod::do_unloading(BoolObje java 14341 [019] 252764.234578: cycles: 7f3656ec4960 CodeHeap::block_start(void*) c […]
  54. 54. Linux  CPU  Checklist   1.  uptime 2.  vmstat 1 3.  mpstat -P ALL 1 4.  pidstat 1 5.  CPU flame graph 6.  CPU subsecond offset heat map 7.  perf stat -a -- sleep 10
  55. 55. Linux  CPU  Checklist   1.  uptime 2.  vmstat 1 3.  mpstat -P ALL 1 4.  pidstat 1 5.  CPU flame graph 6.  CPU subsecond offset heat map 7.  perf stat -a -- sleep 10 load  averages   system-­‐wide  uQlizaQon,  run  q  length   CPU  balance   per-­‐process  CPU   CPU  profiling                                                          look  for  gaps                                                          IPC,  LLC  hit  raQo   htop can do 1-4
  56. 56. htop  
  57. 57. CPU  Flame  Graph  
  58. 58. perf_events  CPU  Flame  Graphs   •  We have this automated in Netflix Vector: •  Flame graph interpretation: –  x-axis: alphabetical stack sort, to maximize merging –  y-axis: stack depth –  color: random, or hue can be a dimension (eg, diff) –  Top edge is on-CPU, beneath it is ancestry •  Can also do Java & Node.js. Differentials. •  We're working on a d3 version for Vector 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
  59. 59. 10.  Tools  Method     An  An>-­‐Methodology  
  60. 60. Tools  Method   1.  RUN EVERYTHING AND HOPE FOR THE BEST For SRE response: a mental checklist to see what might have been missed (no time to run them all)
  61. 61. Linux  Perf  Observability  Tools  
  62. 62. Linux  StaQc  Performance  Tools  
  63. 63. Linux  perf-­‐tools  (mrace,  perf)  
  64. 64. Linux  bcc  tools  (BPF)   Needs  Linux  4.x   CONFIG_BPF_SYSCALL=y  
  65. 65. 11.  USE  Method     A  Methodology  
  66. 66. 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) Used to generate checklists. Starts with the questions, then finds the tools. Resource   UQlizaQon   (%)  X  
  67. 67. USE  Method  for  Hardware   •  For every resource, check: 1.  Utilization 2.  Saturation 3.  Errors •  Including busses & interconnects
  68. 68. (hap://www.brendangregg.com/USEmethod/use-­‐linux.html)  
  69. 69. USE  Method  for  Distributed  Systems   •  Draw a service diagram, and for every service: 1.  Utilization: resource usage (CPU, network) 2.  Saturation: request queueing, timeouts 3.  Errors •  Turn into a dashboard
  70. 70. NeSlix  Vector   •  Real time instance analysis tool –  https://github.com/netflix/vector –  http://techblog.netflix.com/2015/04/introducing-vector-netflixs-on-host.html •  USE method-inspired metrics –  More in development, incl. flame graphs
  71. 71. NeSlix  Vector  
  72. 72. NeSlix  Vector   utilization saturationCPU: utilization saturationNetwork: load utilization saturationMemory: load saturationDisk: utilization
  73. 73. 12.  Bonus:  External  Factor  Checklist  
  74. 74. External  Factor  Checklist   1.  Sports ball? 2.  Power outage? 3.  Snow storm? 4.  Internet/ISP down? 5.  Vendor firmware update? 6.  Public holiday/celebration? 7.  Chaos Kong? Social media searches (Twitter) often useful –  Can also be NSFW
  75. 75. Take  Aways   •  Checklists are great –  Speed, Completeness, Starting/Ending Point, Training –  Can be ad hoc, or from a methodology (USE method) •  Service dashboards –  Serve as checklists –  Metrics: Load, Errors, Latency, Saturation, Instances •  System dashboards with Linux BPF –  Latency histograms & heatmaps, etc. Free your mind. Please create and share more checklists
  76. 76. References   •  Netflix Tech Blog: •  http://techblog.netflix.com/2015/11/linux-performance-analysis-in-60s.html •  http://techblog.netflix.com/2015/02/sps-pulse-of-netflix-streaming.html •  http://techblog.netflix.com/2015/04/introducing-vector-netflixs-on-host.html •  Linux Performance & BPF tools: •  http://www.brendangregg.com/linuxperf.html •  https://github.com/iovisor/bcc#tools •  USE Method Linux: •  http://www.brendangregg.com/USEmethod/use-linux.html •  Flame Graphs: •  http://www.brendangregg.com/FlameGraphs/cpuflamegraphs.html •  Heat maps: •  http://cacm.acm.org/magazines/2010/7/95062-visualizing-system-latency/fulltext •  http://www.brendangregg.com/heatmaps.html •  Books: •  Beyer, B., et al. Site Reliability Engineering. O'Reilly,Apr 2016 •  Gawande, A. The Checklist Manifesto. Metropolitan Books, 2008 •  Gregg, B. Systems Performance. Prentice Hall, 2013 (more checklists & methods!) •  Thanks: Netflix Perf & Core teams for predash, pretriage, Vector, etc
  77. 77. Thanks   http://slideshare.net/brendangregg http://www.brendangregg.com [email protected] @brendangregg Netflix is hiring SREs!

report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK