Bring the Noise: Continuously Deploying Under a Hailstorm of Metrics

This talk was given at Velocity '13 in Santa Clara, and an abbreviated version was given at BACON '13 in London. It offers an overview of Etsy's Kale stack.

BACON Video: devslovebacon.com/conferences/bacon-2013/talks/bring-the-noise-continuously-deploying-under-a-hailstorm-of-metrics

Velocity video forthcoming.

Transcript

1. Abe Stanway @jonlives BRING THE NOISE! MAKING SENSE OF A HAILSTORM OF METRICS Jon Cowie @abestanway
2. Ninety minutes is a long time. - motivations - skyline - oculus - demo! - questions This talk: ~10 ~25 ~30 ~10 ~15
3. Ninety minutes is a long time. - motivations - skyline - oculus - demo! - questions This talk: ~10 ~25 ~30 ~10 ~15 But we have some sweet stuff to show you.
4. Background and Motivations
6. 1.5 billion page views $117 million of goods sold 950 thousand users
7. 1.5 billion page views $117 million of goods sold 950 thousand users (in december ‘12)
8. We practice continuous deployment.
9. de • ploy /diˈploi/ Verb To release your code for the world to see, hopefully without breaking the Internet
10. Everyone deploys. 250+ committers.
11. Day one: DEPLOY
13. 30+ DEPLOYS A DAY (~8 commits per deploy!)
14. “30 deploys a day? Is that safe?”
15. We optimize for quick recovery by anticipating problems...
16. ...instead of fearing human error.
17. Can’t fix what you don’t measure! - W. Edwards Deming
18. StatsD graphite Skyline Oculus Supergrep homemade! not homemade Nagios Ganglia
19. Text Real time error logging
20. “Not all things that break throw errors.” - Oscar Wilde
21. StatsD
22. StatsD::increment(“foo.bar”)
23. If it moves, graph it!
24. If it moves, graph it! we would graph them ➞
25. If it doesn’t move, graph it anyway (it might make a run for it)
26. DASHBOARDS!
27. [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 20] [1358731200, 60] [1358731200, 20] [1358731200, 20]
28. DASHBOARDS! x 250,000
30. lol nagios
31. “...but there are also unknown unknowns - there are things we do not know we don’t know.”
32. Unknown anomalies
33. Unknown correlations
34. Kale.
35. Kale: - leaves - green stuff
36. Kale: - leaves - green stuffOCULUS SKYLINE
37. Q). How do you analyze a timeseries for anomalies in real time?
38. A). Lots of HTTP requests to Graphite’s API!
39. Q). How do you analyze a quarter million timeseries for anomalies in real time?
40. SKYLINE
41. SKYLINE
42. A real time anomaly detection system
43. Real time?
44. Kinda.
45. StatsD Ten second resolution
46. Ganglia One minute resolution
47. ~ 10s ( ~ 1min Best case:
48. ( Takes about 90 seconds with our throughput.
49. ( Still faster than you would have discovered it otherwise.
50. Memory > Disk
52. Q). How do you get a quarter million timeseries into Redis on time?
53. STREAM IT!
54. Graphite’s relay agent original graphite backup graphite
55. Graphite’s relay agent original graphite backup graphite [statsd.numStats, [1365603422, 82345]] pickles [statsd.numStats, [1365603432, 80611]] [statsd.numStats, [1365603412, 73421]]
56. Graphite’s relay agent original graphite skyline [statsd.numStats, [1365603422, 82345]] pickles [statsd.numStats, [1365603432, 80611]] [statsd.numStats, [1365603412, 73421]]
57. We import from Ganglia too.
58. Storing timeseries
59. Minimize I/O Minimize memory
60. redis.append() - Strings - Constant time - One operation per update
61. JSON?
62. “[1358711400, 51],” => get statsD.numStats ----------------------------
63. “[1358711400, 51], => get statsD.numStats ---------------------------- [1358711410, 23],”
64. “[1358711400, 51], => get statsD.numStats ---------------------------- [1358711410, 23], [1358711420, 45],”
65. OVER HALF CPU time spent decoding JSON
66. [1,2]
67. [ 1 , 2 ] Stuff we care about Extra junk
68. MESSAGEPACK
69. MESSAGEPACK A binary-based serialization protocol
70. \x93\x01\x02 Array size (16 or 32 bit big endian integer) Things we care about
71. \x93\x01\x02 Array size (16 or 32 bit big endian integer) Things we care about \x93\x02\x03
72. CUT IN HALF Run Time + Memory Used
73. ROOMBA.PY CLEANS THE DATA
74. “Wait...you wrote this in Python?”
75. Great statistics libraries Not fun for parallelism
76. Assign Redis keys to each process Process decodes and analyzes The Analyzer
77. Anomalous metrics written as JSON setInterval() retrieves from front end The Analyzer
79. What does it mean to be anomalous?
80. Consensus model
81. Implement everything you can get your hands on
82. Basic algorithm: “A metric is anomalous if its latest datapoint is over three standard deviations above its moving average.”
83. Grubb’s test, ordinary least squares
84. Histogram binning
85. Four horsemen of the modelpocalypse
86. 1. Seasonality 2. Spike influence 3. Normality 4. Parameters
87. Anomaly?
88. Nope.
89. Text Spikes artificially raise the moving average Anomaly detected (yay!) Anomaly missed :( Bigger moving average
90. Real world data doesn’t necessarily follow a perfect normal distribution.
91. Too many metrics to fit parameters for them all!
92. A robust set of algorithms is the current focus of this project.
93. Q). How do you analyze a quarter million timeseries for correlations?
94. OCULUS
95. Image comparison is expensive and slow
96. “[[975, 1365528530], [643, 1365528540], [750, 1365528550], [992, 1365528560], [580, 1365528570], [586, 1365528580], [649, 1365528590], [548, 1365528600], [901, 1365528610], [633, 1365528620]]” Use raw timeseries instead of raw graphs
97. Naming Things Cache Invalidation Numerical Comparison? HARD PROBLEMS
98. Naming Things Cache Invalidation Numerical Comparison? HARD PROBLEMS
99. Euclidian Distance
100. Dynamic Time Warping (helps with phase shifts)
101. We’ve solved it!
102. O(N2)
103. O(N2) x 250k
104. Too slow!
105. doesn’t
106. No need to run DTW on all 250k.
107. Discard obviously dissimilar metrics.
108. “975 643 643 750 992 992 992 580” “sharpdecrement flat increment sharpincrement flat flat shapdecrement” Shape Description Alphabet
109. “975 643 643 750 992 992 992 580” “sharpdecrement flat increment sharpincrement flat flat shapdecrement” Shape Description Alphabet “24 4 4 11 25 25 25 0 1” (normalization step)
111. Search for shape description fingerprint in Elasticsearch
112. Run DTW on results as final polish
113. O(N2) on ~10k metrics
114. Still too slow.
115. Fast DTW - O(N) coarsen project refine
116. Elasticsearch Details Phrase search for first pass scores across shape description fingerprints
117. Elasticsearch Details Phrase search for first pass scores across shape description fingerprints Custom FastDTW and euclidian distance plugins to score across the remaining filtered timeseries
118. Elasticsearch Structure { :id => “statsd.numStats”, :fingerprint => “sdec inc sinc sdec”, :values => "10 1 2 15 4" }
119. Mappings Specify tokenizers “Untouched” fields
120. First pass query :match => { :fingerprint => { :query => “sdec inc sinc sdec inc”, :type => "phrase", :slop => 20 } } shape description fingerprint
121. Refinement query {:custom_score => { :query => <first_pass_query>, :script => "oculus_dtw", :params => { :query_value => “10 20 20 10 30”, :query_field => "values.untouched", }, } raw timeseries
122. Skyline Elasticsearch Resque Sinatra Ganglia Graphite StatsD KALE Flask
123. Populating Elasticsearch
124. ES Index resque workers
125. Too slow to update and search
126. New Index Last Index Webapp
127. Sinatra frontend Queries ES Renders results
128. Collections
129. devops <3
131. Special thanks to: Dr. Neil Gunther, PerfDynamics Dr. Brian Whitman, Echonest Burc Arpat, Facebook Seth Walker, Etsy Rafe Colburn, Etsy Mike Rembetsy, Etsy John Allspaw, Etsy
132. @abestanway @jonlives Thanks! github.com/etsy/skyline github.com/etsy/oculus