Signal-to-noise ratio in automated log anomaly detection

Question

We are drowning in false positives from our ML-based log anomaly detector. It flags every deployment spike as an incident. Has anyone found a way to tune the baseline dynamically based on deployment schedules, or are we better off with a rule-based filter in front? The current precision is abysmal.

Nia · Answer

Out-of-order Kafka is a classic problem. We use a combination of: (1) monotonic sequence numbers per event source, (2) a small in-memory window (last 100 events per key) for reordering, and (3) idempotent writes with upserts keyed on (source_id, sequence). For clock skew, use Lamport timestamps instead of wall clock time.

Signal-to-noise ratio in automated log anomaly detection

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