BEYOND AVERAGES
Dan Kuebrich / appneta.com
A few of my favorite abstractions

• Abstraction lets us trade information
for actionability

•
That’s a great trade!
•
• ...
Averages: average at best
Averages: average at best
Averages: average at best
Averages: average at best
Percentiles: 1 of 100 slices
95%
Percentiles: 2 of 100 slices
95%

10%
Percentiles: 2 of 100 slices
95%

10%
Percentiles: 2 of 100 slices
95%

10%
Percentiles: 2 of 100 slices
95%

10%
Computers are hard

• Rarely do we have a single normal distribution
underlying the data

• Different users, different req...
Is there a place between Averageland
and “A Beautiful Mind”?

http://now-here-this.timeout.com/2012/10/07/crazy-walls-of-c...
(eg. # of calls)

Frequency

Histograms

Value

(eg. latency)
Populations revisited
95%

10%
(eg. # of calls)

Frequency

Histograms

Value

(eg. latency)
Populations re-revisited
95%

?

10%
(eg. # of calls)

Frequency

3d Histograms?

Value

(eg. latency)
(eg. # of calls)

Frequency

3d Histograms?

Time

Value

(eg. latency)
(eg. # of calls)

Frequency

Heatmaps

Value

(eg. latency)
(eg. # of calls)

Frequency

Heatmaps

Value

(eg. latency)
(eg. # of calls)

Frequency

Heatmaps

Value

(eg. latency)
(eg. latency)

Value

Heatmaps

Time
OK, but what about the real
world?

http://www.justincarmony.com/blog/2012/06/05/customizing-graphite-charts-for-clearer-r...
Mystery #1
Mystery #1
Mystery #1
Mystery #1
Mystery #1
Mystery #2
Mystery #2
bottom 98%
Mystery #2
all of it
Mystery #3
Mystery #3: UNSOLVED
Thanks!
Dan Kuebrich
dan@appneta.com
@dankosaur
Beyond Averages
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Beyond Averages

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When raw data becomes overwhelming, we turn to abstraction to understand our world. In our systems, the data is always overwhelming. Solutions like summary statistics have come to our rescue, and they are good—up to a point. In order to truly understand our systems, we need to know when and how to sidestep those abstractions,to get deep, detailed performance insight. In this brief diatribe inspired by John Rauser’s 2011 Velocity keynote “Look at Your Data”, I’ll explore techniques for visualizing the underlying structure of performance data and how this empowers drilling down to populations and individual samples in the data set.

Video: http://www.youtube.com/watch?v=InyHBnd_chw

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Beyond Averages

  1. 1. BEYOND AVERAGES Dan Kuebrich / appneta.com
  2. 2. A few of my favorite abstractions • Abstraction lets us trade information for actionability • That’s a great trade! • • ... right? Min, max, average, quantiles, stdev
  3. 3. Averages: average at best
  4. 4. Averages: average at best
  5. 5. Averages: average at best
  6. 6. Averages: average at best
  7. 7. Percentiles: 1 of 100 slices 95%
  8. 8. Percentiles: 2 of 100 slices 95% 10%
  9. 9. Percentiles: 2 of 100 slices 95% 10%
  10. 10. Percentiles: 2 of 100 slices 95% 10%
  11. 11. Percentiles: 2 of 100 slices 95% 10%
  12. 12. Computers are hard • Rarely do we have a single normal distribution underlying the data • Different users, different requests, different resources, different instances, different times
  13. 13. Is there a place between Averageland and “A Beautiful Mind”? http://now-here-this.timeout.com/2012/10/07/crazy-walls-of-clues-from-tv-film-reviewed-by-carrie-from-homeland/
  14. 14. (eg. # of calls) Frequency Histograms Value (eg. latency)
  15. 15. Populations revisited 95% 10%
  16. 16. (eg. # of calls) Frequency Histograms Value (eg. latency)
  17. 17. Populations re-revisited 95% ? 10%
  18. 18. (eg. # of calls) Frequency 3d Histograms? Value (eg. latency)
  19. 19. (eg. # of calls) Frequency 3d Histograms? Time Value (eg. latency)
  20. 20. (eg. # of calls) Frequency Heatmaps Value (eg. latency)
  21. 21. (eg. # of calls) Frequency Heatmaps Value (eg. latency)
  22. 22. (eg. # of calls) Frequency Heatmaps Value (eg. latency)
  23. 23. (eg. latency) Value Heatmaps Time
  24. 24. OK, but what about the real world? http://www.justincarmony.com/blog/2012/06/05/customizing-graphite-charts-for-clearer-results/
  25. 25. Mystery #1
  26. 26. Mystery #1
  27. 27. Mystery #1
  28. 28. Mystery #1
  29. 29. Mystery #1
  30. 30. Mystery #2
  31. 31. Mystery #2 bottom 98%
  32. 32. Mystery #2 all of it
  33. 33. Mystery #3
  34. 34. Mystery #3: UNSOLVED
  35. 35. Thanks! Dan Kuebrich dan@appneta.com @dankosaur

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