Summary
rawsum(<tsExpression>[,metrics|sources|sourceTags|pointTags|<pointTagKey>])
Returns the raw sum of the set of time series described by the expression.
The results are computed from real reported data values only.
Use sum() to include interpolated values.
Parameters
| Parameter | Description |
|---|---|
| tsExpression | Expression describing the set of time series to be summed. |
| metrics|sources|sourceTags|pointTags|<pointTagKey> | Optional group by parameter for organizing the time series into subgroups and then returning a raw sum 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 rawsum() aggregation function adds together the data values reported at each moment in time, across the time series that are represented by the expression.
By default, rawsum() returns a single series of sums 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 sums for each group.
A raw sum is computed only from real values reported at a given moment in time.
No interpolation is performed to fill in data gaps in any time series.
Use sum() if you want the sums to include interpolated values wherever possible. Using rawsum() instead of sum() can significantly improve query performance.
Grouping
Like all aggregation functions, rawsum() returns a single series of results by default. You can include a group by parameter to obtain separate raw sums 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, rawsum(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. Examples
The following chart for returns a single line that sums the load average for all time series.
You can group by point tag for each line. The following example groups the query above by environment.
