Analysis methods for t-digest compressed sample data.
Unlike metrics-samples which stores raw sample values, digest-samples uses t-digest compression, which preserves quantile accuracy but loses individual sample identity. This affects outlier detection: medcouple cannot be computed from digest centroids, so this module uses standard symmetric boxplot thresholds instead of the adjusted boxplot method. For skewed distributions, consider using full sample collection if accurate outlier classification is important.
Analysis methods for t-digest compressed sample data. Unlike metrics-samples which stores raw sample values, digest-samples uses t-digest compression, which preserves quantile accuracy but loses individual sample identity. This affects outlier detection: medcouple cannot be computed from digest centroids, so this module uses standard symmetric boxplot thresholds instead of the adjusted boxplot method. For skewed distributions, consider using full sample collection if accurate outlier classification is important.
(digest-outliers digest quantiles)Compute outliers for a digest using Tukey's boxplot thresholds.
Uses symmetric 1.5×IQR whiskers rather than the medcouple-adjusted method used by metrics-samples. The medcouple method requires computing the medcouple (a robust skewness measure) from individual sample values, which are not available in a t-digest structure—only weighted centroids are preserved. This may result in more false-positive outlier detection for skewed distributions when using digest collection.
Compute outliers for a digest using Tukey's boxplot thresholds. Uses symmetric 1.5×IQR whiskers rather than the medcouple-adjusted method used by metrics-samples. The medcouple method requires computing the medcouple (a robust skewness measure) from individual sample values, which are not available in a t-digest structure—only weighted centroids are preserved. This may result in more false-positive outlier detection for skewed distributions when using digest collection.
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