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Automatically Detecting
Anomalies and Outliers in
Real-Time
Homin Lee, Data Scientist
Outline
● Monitoring
● Alerting
● Outlier vs. Anomaly Detection
● Outlier Detection Algorithms
● Anomaly Detection Algorithms
Monitor Everything
Monitor Everything
Datadog gathers performance data from all your application components.
Monitor Everything
Monitor Everything
Monitor Everything?
Alerting
Alerting?
Alerting?
Outlier and Anomaly Detection
Outlier
Detection
Outlier Detection
Outlier Detection
Outlier Detection
Outlier Detection Algorithms
MAD
median absolute deviation
DBSCAN
density-based spatial clustering of applications with noise
Robust Outlier Detection Algorithms
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
median = 4
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
median = 4
deviations = { -3, -2, -1, 0, 1, 2, 96 }
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
median = 4
deviations = { -3, -2, -1, 0, 1, 2, 96 }
abs deviations = { 0, 1, 1, 2, 2, 3, 96 }
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
median = 4
deviations = { -3, -2, -1, 0, 1, 2, 96 }
abs deviations = { 0, 1, 1, 2, 2, 3, 96 }
MAD = 2
Median Absolute Deviation
MAD(D) = median( { |di
- median(D)| } )
D = { 1, 2, 3, 4, 5, 6, 100 }
median = 4
deviations = { -3, -2, -1, 0, 1, 2, 96 }
abs deviations = { 0, 1, 1, 2, 2, 3, 96 }
MAD = 2 (std dev = 33.8)
Median Absolute Deviation
Parameters: Tolerance, Pct
} tol. = 3.0
DBSCAN
DBSCAN
Parameters:
epsilon, min_samples
DBSCAN
1 dd/2d/4 3d/4
DBSCAN
1 dd/2d/4 3d/4
DBSCAN
1 dd/2d/4 3d/4
~ median(dist from median series) × tolerance
MAD or DBSCAN?
MAD or DBSCAN?
Some subtleties
Some subtleties
Some subtleties
Anomaly
Detection
An Investigation
past 30 minutes
An Investigation
past 30 minutes
past day
An Investigation
past day
past week
past 30 minutes
An Investigation
past day
past week
past 30 minutes
past 5 weeks
An Investigation
past day
ANOMALY
past 30 minutes
past week
past 5 weeks
Anomalies
A time series point is an anomaly if:
● Given the past points in the series ( ), the point
in question ( ) is unlikely given your model of the past;
Anomalies
A time series point is an anomaly if:
● Given the past points in the series ( ), the point
in question ( ) is unlikely given your model of the past;
and you should alert on a set of anomalies if:
● they are a symptom of an issue you care about ( ).
Our Approach
1. Extract as much signal as we can from the time series.
2. Use robust statistical measures when creating the model.
3. Give the user control over when they get alerted.
What’s Normal?
What’s Normal?
What’s Normal?
What’s Normal?
Past Performance...
past 30 minutes past day
past week past 5 weeks
Past Performance...
Decomposition
Decomposition
Decomposition
Decomposition
Decomposition
Autocorrelation
Signal vs. Noise
Signal vs. Noise
Signal vs. Noise vs. Signal
Real-time Anomaly Detection
Anomaly Detection
Robust Anomaly Detection
Robust Anomaly Detection
Robust Anomaly Detection
Robust Anomaly Detection
Alerting
Alerting
Recap
● Extract as much signal as you can.
● Use robust statistical measures.
● Alert judiciously.
● Don’t over-optimize.
Anomalies or Noise?
Thanks!
Appendix
DASHBOARDS
Build Real-Time Interactive Dashboards
CORRELATION
Search And Correlate Metrics And Events
See It All In One Place
Your Servers, Your Clouds, Your Metrics, Your Apps, Your team. Together.
COLLABORATION
Share What You Saw, Write What You Did
METRIC ALERTS
Get Alerted On Critical Issues
DEVELOPER API
Instrument Your Apps,
Write New Integrations
See It All In One Place
Your Servers, Your Clouds, Your Metrics, Your Apps, Your team. Together.
Flexible Pricing
To Match Your Dynamic Infrastructure.
Free
Up to 5 Hosts
1 Day retention
Custom metrics and events
Discussion group supported
Pro
Up to 500 Hosts
$15 Per Host / Month
13 Month retention
Custom metrics and events
Metric alerts*
Email supported
Enterprise
500+ Hosts
Contact us for pricing:
+1 866 329 4466
sales@datadoghq.com
Customized retention
Custom metrics and events
Metric alerts*
Email and phone supported

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