Outlier detection in Datadog: A look at the algorithms

Outlier detection is a crucial aspect of maintaining healthy hosts within an infrastructure, minimizing service degradation and disruption. Datadog offers two algorithms for this purpose: DBSCAN (density-based spatial clustering of applications with noise) and MAD (median absolute deviation). DBSCAN works by greedily agglomerating points that are close to each other, while MAD is a robust measure of variability. Both algorithms have their strengths in detecting outliers based on the specific behavior of hosts. When setting up alerts, it's essential to consider the size of the time window analyzed and whether banding behavior requires separate monitoring for different groups.

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