What is anomaly detection in observability and what are its limitations?

Anomaly detection in observability is the automated identification of data points or patterns in metrics, logs, or traces that deviate significantly from historical baselines or expected behavior. Instead of manually setting static thresholds like "alert if CPU > 80%", anomaly detection learns seasonal patterns (traffic spikes every Monday at 9 AM), trends (gradual memory leak over days), and correlated multi-metric behaviors, then alerts only when observed values fall outside learned normal ranges.

Common approaches include:

Statistical methods: Z-score, moving averages, exponential smoothing (Holt-Winters) detect deviations from a rolling baseline. Simple and interpretable.

Machine learning models: Isolation Forest, LSTM neural networks, and Prophet (Facebook's time-series library) can capture complex seasonal patterns. Datadog, New Relic, and Dynatrace all offer ML-based anomaly detection for metrics.

Limitations are significant. First, anomaly detection requires training data — new services or after major refactors, there is no baseline. Second, it generates alert noise during legitimate events (product launches, holiday traffic spikes) which look like anomalies but are expected. Third, it operates on known metrics: it can only flag what it can see, not unknown-unknowns that were never instrumented. Fourth, understanding why a metric is anomalous still requires human investigation — anomaly detection replaces threshold-setting, not debugging. Fifth, precision-recall trade-offs mean reducing false positives often increases false negatives, and vice versa.

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