Summary
rawvariance(<tsExpression>[,metrics|sources|sourceTags|pointTags|<pointTagKey>])
Returns the variance across the set of time series described by the expression. The results are computed from real reported data values only.
Use variance() to include interpolated values.
Parameters
| Parameter | Description |
|---|---|
| tsExpression | Expression describing the set of time series to return raw variances for. |
| metrics|sources|sourceTags|pointTags|<pointTagKey> | Optional group by parameter for organizing the time series into subgroups and then returning the raw variance for each subgroup.
Use one or more parameters to group by metric names, source names, source tag names, point tag names, values for a particular point tag key, or any combination of these items. Specify point tag keys by name. |
Description
The rawvariance() aggregation function computes the raw variance among data values at each moment in time, across the time series that are represented by the expression.
By default, rawvariance() produces a single series of raw variances by aggregating data values across all time series. You can optionally group the time series based on one or more characteristics, and obtain a separate series of raw variances for each group.
A raw variance is computed only from those time series that actually report real values at a given moment in time.
No interpolation is performed to fill in data gaps in any time series.
Use variance() if you want to include time series with interpolated values wherever possible. Using rawvariance() instead of variance() can significantly improve query performance.
Grouping
Like all aggregation functions, rawvariance() returns a single series of results by default. You can include a group by parameter to obtain separate raw variances for groups of time series that share common metric names, source names, source tags, point tags, or values for a particular point tag key.
The function returns a separate series of results corresponding to each group.
You can specify multiple ‘group by’ parameters to group the time series based on multiple characteristics. For example, rawvariance(ts("cpu.cpu*"), metrics, Customer) first groups by metric names, and then groups by the values of the Customer point tag.
zone and ZONE, when you use an aggregation function and apply grouping, we consider zone and ZONE as separate tags. Example
In following example, we use rawvariance() to display the variance for sample request latency, grouped by environment (dev or production).
