The Dark of Building an Production Incident Syste
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The document discusses building an effective production incident system using statistics. It explains that using the median and percentiles to define a baseline range captures normal system behavior better than trying to fit a specific distribution model. Two examples are provided: 1) Using the binomial distribution to determine if an error rate exceeds expectations. 2) Using percentiles to check if response times have drifted above the median without knowing the underlying distribution. The key is applying statistical methods to objectively determine what constitutes a normal range of values versus a problem requiring alerting.
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The Dark of Building an Production Incident Syste
- 1. The Dark Art of Building a Production Incident System @Alois Reitbauer Tech. Evangelist & Product Mgr., Compuware
- 4. Other things can happen as well Continuous deployments Infrastructure changes other “everyday” stuff
- 5. Scaling an incident system
- 6. How it feels to do what we do
- 7. Do you alert? Typical error rate of 3 percent at 10.000 transactions/min During the night we now have 5 errors in 100 requests.
- 8. Do you alert? Typical response time has been around 300 ms. Now we see response times up to 600 ms.
- 9. We a r e g o o d a t f i x i n g problems, but not really good at detecting them.
- 10. How can we get better? .
- 11. It is all about statistics I t ’s a l l a b o u t s t a t i s t i c s
- 12. Statistics is about objectively lying to yourself in a meaningful way.
- 13. How to design an incident
- 14. It looks really simple How to calculate this value? Which metric to pick? How to get this baseline? How to define that this happened?
- 15. Which metrics to pick?
- 16. Three types of metrics Capacity Metrics Define how much of resource is used. Discrete Metrics Simple countable things, like errors or users. Continuous Metrics Metrics represented by a range of values at any given time.
- 17. Capacity Metrics Good for capacity planning, not so good for production alerting
- 18. Connection Pools
- 19. b e tte r u s e Connection acquisition time Tells you, whether anyone needed a connection and did not get it.
- 20. CPU Usage
- 21. b e tte r u s e Combination of Load Average and CPU usage even better correlate the with response times of applications
- 22. D i s c re te M e t r i c s Pretty easy to track and analyze.
- 23. C o nt i n u o u s M e t r i c s Require some extra work as they are not that easy to track.
- 24. Continuous Metrics – The hope 42
- 25. Continuous Metrics – The reality
- 26. What the average tells us
- 27. What the median tells us
- 28. How to get a baseline?
- 29. A baseline is not a number Baselines define the range of a value combined with a probability
- 30. Normal distribution as baseline Mean: 500 ms Std. Dev.: 100 ms 0 100 200 300 400 500 600 68 % 400ms – 500 ms 95 % 300ms – 700 ms 99 % 200ms – 800 ms 700 800 900
- 31. This can go really wrong “Why alerts suck and monitoring solutions need to become better”
- 32. How this leads to false alerts
- 33. Many false alerts Aggressive Baseline
- 34. No alerts at all Moderate Baseline
- 35. Find the right distribution model However, this can be really hard to impossible
- 36. Your distribution might look like this
- 37. … or like this
- 38. or completely different you never know …
- 39. How can we solve this problem?
- 40. Normal distribution - again 50 Percent slower than μ Median 97.6 Percent slower than μ + 2σ 97th Percentile
- 41. The 50 th and 90 th percentile define normal behavior without needing to know anything about the distribution model
- 42. Median shows the real problem
- 43. How to define nonnormal behavior?
- 44. Fortunately this is not the problem we need to solve We are only talking about missed expectations
- 45. Let’s look at two scenarios Errors Is a certain error rate likely to happen or not? Response Times Is a certain increase in response time significant enough to trigger an incident?
- 46. The error rate scenario We have a typical error rate of 3 percent at 10.000 transactions/minute During the night we now have 5 errors in 100 requests. Should we alert – or not?
- 47. What can we learn
- 49. B i n o m i a l D i st r i b u t i o n Tells us how likely it is to see n successes in a certain number of trials
- 50. How many errors are ok? Likeliness of at least n errors 120.0% 18 % probability to see 5 or more errors. Which is within 2 times Std. Deviation. We do not alert. 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
- 51. Response Time Example Our median response time is 300 ms and we measure 200 ms 500 ms 400 ms 150 ms 350 ms 350 ms 200 ms 400 ms 600 ms 600 ms
- 53. Did the median drift significantly? Check all values above 300 ms 200 ms 500 ms 400 ms 150 ms 350 ms 350 ms 200 ms 400 ms 600 ms 600 ms 7 values are higher than the median. Is this normal? We can again use the Binomial Distribution
- 54. Applying the Binomial Distribution We have a 50 percent likeliness to see values above the median. How likely is is that 7 out of 10 samples are higher? The probability is 17 percent, so we should not alert.
- 55. … and we are done! How to calculate this value? Which metric to pick? How to get this baseline? How to define that this happened?
- 56. This was just the beginning There are many more use things about statistics, probabilities, testing, ….