How to use Prometheus for anomaly detection in GitLab

Andrew Newdigate from GitLab explores how Prometheus's query language can be utilized for anomaly detection in time series data, highlighting its importance for diagnosing incidents, detecting performance regressions, resolving abuse, and enhancing security. By using the http_requests_total metric, he demonstrates the significance of choosing the correct level of data aggregation to effectively identify anomalies without missing genuine issues or generating false positives. Anomaly detection is achieved through z-scores, assuming a normal distribution, or by leveraging seasonal patterns in the data for more precise predictions. The blog post provides detailed methods, including statistical techniques and queries, to set up anomaly detection and alerting systems using Prometheus. Andrew emphasizes the importance of testing data for normal distribution and discusses how seasonal metrics can enhance anomaly detection accuracy, allowing GitLab to respond more effectively to irregularities in their systems.

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