Summary
rawcount(<tsExpression>[,metrics|sources|sourceTags|pointTags|<pointTagKey>])
Returns the number of reporting time series described by the expression at each moment in time. A time series is counted as reporting at a given moment only if it has a real data value, instead of an interpolated value.
Use count() to include time series with interpolated values.
Parameters
| Parameter | Description |
|---|---|
| tsExpression | Expression describing the set of time series to be counted. |
| metrics|sources|sourceTags|pointTags|<pointTagKey> | Optional group by parameter for organizing the time series into subgroups and then returning a raw count 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 rawcount() aggregation function adds together the number of actually reporting time series represented by the expression, at each moment in time.
By default, rawcount() produces a single raw count across across all time series. You can optionally group the time series based on one or more characteristics, and obtain a separate raw count for each group.
A raw count 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 count() if you want the counts to include time series with interpolated values wherever possible. Using rawcount() instead of count() can significantly improve query performance.
Grouping
Like all aggregation functions, rawcount() returns a single series of results by default.
You can include a group by parameter to obtain separate raw counts 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, rawcount(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 will consider zone and ZONE as separate tags. Example
The following example shows the raw count grouped by the values of the env point tag. There’s an area for each environment.
