Anomaly Detection for Monitoring

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About the Authors Baron Schwartz is the founder and CEO of VividCortex, a nextgeneration database monitoring solution. He speaks widely on the topics of database performance, scalability, and open source. He is the author of O’Reilly’s bestselling book High Performance MySQL, and many open-source tools for MySQL administration. He’s also an Oracle ACE and frequent participant in the PostgreSQL community. Preetam Jinka is an engineer at VividCortex and an undergraduate student at the University of Virginia, where he studies statistics and time series.
Acknowledgments We’d like to thank George Michie, who contributed some content to this book as well as helping us to clarify and keep things at an appropriate level of detail.
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Anomaly Detection for Monitoring A
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Table of Contents Foreword. . . . .
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Foreword Monitoring is currently un
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tion” to anomaly detection is imp
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Why do we assume these things? Are
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Conclusions If you are like most of
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CHAPTER 2 A Crash Course in Anomaly
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How can you achieve similar results
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cause most anomaly detection techni
Page 23 and 24: CHAPTER 3 Modeling and Predicting A
Page 25 and 26: (say, the size of the drill bit), a
Page 27 and 28: lies. To fix this problem, the cont
Page 29 and 30: ally decaying window. This is made
Page 31 and 32: ack again, meaning that they smooth
Page 33 and 34: emains consistent across all of the
Page 35 and 36: Evaluating Predictions One of the m
Page 37 and 38: No. You’ve stumbled into statisti
Page 39: As an aside, there’s a rumor goin
Page 42 and 43: Dealing with Trend Trends break mod
Page 44 and 45: pletely out of phase with the seaso
Page 46 and 47: mon situations. You can probably gu
Page 49 and 50: CHAPTER 5 Practical Anomaly Detecti
Page 51 and 52: If you can get close to that, you m
Page 53 and 54: We previously discussed how frequen
Page 55 and 56: First, observe how odd this metric
Page 57 and 58: After differencing, it looks like w
Page 59 and 60: Perhaps, instead of differencing, w
Page 61 and 62: CHAPTER 6 The Broader Landscape As
Page 63 and 64: We could apply a EWMA control chart
Page 65 and 66: figure out how unlikely it was for
Page 67 and 68: predict or find structure in data w
Page 69 and 70: Graphite and RRDTool Graphite and R
Page 71 and 72: APPENDIX A Appendix Code Control Ch
Page 73: var ma = new movingAverage(0.5); ma
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Anomaly Detection for Monitoring

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