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Aliaksandr Valialkin
56b399d220
These functions allow detecting anomalies in series and samples using Interquartile range method. See Outliers section at https://en.wikipedia.org/wiki/Interquartile_range for more details.
62 lines
1.3 KiB
Go
62 lines
1.3 KiB
Go
package metricsql
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import (
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"strings"
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)
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var aggrFuncs = map[string]bool{
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"any": true,
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"avg": true,
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"bottomk": true,
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"bottomk_avg": true,
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"bottomk_max": true,
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"bottomk_median": true,
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"bottomk_last": true,
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"bottomk_min": true,
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"count": true,
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"count_values": true,
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"distinct": true,
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"geomean": true,
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"group": true,
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"histogram": true,
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"limitk": true,
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"mad": true,
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"max": true,
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"median": true,
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"min": true,
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"mode": true,
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"outliers_iqr": true,
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"outliers_mad": true,
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"outliersk": true,
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"quantile": true,
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"quantiles": true,
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"share": true,
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"stddev": true,
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"stdvar": true,
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"sum": true,
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"sum2": true,
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"topk": true,
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"topk_avg": true,
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"topk_max": true,
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"topk_median": true,
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"topk_last": true,
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"topk_min": true,
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"zscore": true,
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}
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// IsAggrFunc returns whether funcName is a known aggregate function.
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func IsAggrFunc(s string) bool {
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s = strings.ToLower(s)
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return aggrFuncs[s]
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}
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func isAggrFuncModifier(s string) bool {
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s = strings.ToLower(s)
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switch s {
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case "by", "without":
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return true
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default:
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return false
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}
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}
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