How do you do anomaly detection in Wavefront? [Advanced]
Answer
Anomaly detection in Wavefront is done by comparing current metric behavior against historical or statistical baselines, then alerting when deviation is significant and sustained. It is useful when static thresholds are hard to set.
Technical explanation
Anomaly detection works best on metrics with stable seasonality or predictable patterns.
It should be combined with impact signals so normal business spikes do not page humans.
Validate anomalies against SLOs, deployments, and incidents before trusting them for paging.
Hands-on example
Example: monitor payment authorization latency, which normally rises during business hours. Use an anomaly/baseline query to compare current p95 to the expected band for that time. Page only if anomaly is sustained and checkout error-budget burn is also elevated.
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