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Better service monitoring through histograms

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Talk given to San Francisco Perl Mongers about service monitoring with histograms

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Better service monitoring through histograms

1. 1. Better service monitoring through histograms Fred Moyer - @phredmoyer San Francisco Perl Mongers, 07-26-2016
2. 2. Systems break while we sleep How often are you woken up for false alarms? Welcome
3. 3. Synthetics Easy to setup, but not a real user
4. 4. Synthetics Stephen Falken: Uh, uh, General, what you see on these screens up here is a fantasy; a computer-enhanced hallucination. Those blips are not real missiles. They're phantoms. (War Games, 1983)
5. 5. Real Users These are your users, right?
6. 6. Real data Real Users
7. 7. 500 ms is really 2,000 ms Spike Erosion
8. 8. What threshold do you choose? Threshold Alerting
9. 9. “Alert me if requests take longer than 200 ms” 10,10,10,10,10,10,10,10,10,5000 Alerts on one outlier in 10 Threshold Alerting
10. 10. “Alert if request average over one minute is longer than 200 ms” avg(10,10,210,210,210,210) = 143 (860/6) Does not alert on multiple high samples Threshold Alerting
11. 11. ‘average’ eq ‘arithmetic mean’ A=S/N A = average N = the number of terms S = the sum of the numbers in the set Math Refresher
12. 12. median = midpoint of data set The 50th percentile is 555 - q(0.5) Value 111 222 333 444 555 666 777 888 999 Sample # 1 2 3 4 5 6 7 8 9 Math Refresher
13. 13. 90th percentile - 90% of samples below it The 90th percentile is 1,000 - q(0.9) Value 111 222 333 444 555 666 777 888 999 1,000 1,111 Sample # 1 2 3 4 5 6 7 8 9 10 11 Math Refresher
14. 14. 100th Percentile - the maximum value The 100th percentile is 1,111 - q(1) Value 111 222 333 444 555 666 777 888 999 1,000 1,111 Sample # 1 2 3 4 5 6 7 8 9 10 11 Math Refresher
15. 15. Sample value Number of samples Histogram
16. 16. Sample value Number of samples Normal Distribution
17. 17. Sample value Number of samples Normal Distribution 34% within one sigma (σ)
18. 18. Sample value Number of samples Non-Normal Distribution
19. 19. Sample value Number of samples Non-Normal Distribution
20. 20. Non-Normal Distribution Operations data groups at different points
21. 21. Non-Normal Distribution Users to the right of the red line are gone
22. 22. Request latency “We keep hearing from people that the website is slow. But it is fine when we test it, and the request latency graph is constant” You are only looking at part of the picture.
23. 23. Heat Map Histograms over time windows
24. 24. Percentiles
25. 25. Practical Percentiles Bandwidth usage is often billed at 95th percentile usage Record 5 minute data usage intervals Sort samples by value of sample Throw out the highest 5% of samples Charge usage based on the remaining top sample, i.e. 300 MB transferred over 5 minutes = 1 MB/s rate billing
26. 26. Practical Percentiles If I measure 95th percentile per 5 minutes all month long, I CANNOT calculate 95th percentile over the month.
27. 27. Angry users How many users are you pissing off?
28. 28. Angry users
29. 29. “Alert me if request latency 90th percentile over one minute is exceeded” Percentile based alerting q(0.9)[10,10,10,10,10,10,10,10,5000] == 10 Alert IS NOT triggered Do you want to be woken up for this? NO!
30. 30. “Alert me if request latency 90th percentile over one minute is exceeded” Percentile based alerting q(0.9)[10,10,10,10,10,10,250,300] = ~270 Alert IS triggered Do you want to be woken up for this? YES!