A helper method that computes the average or mean from one or more time series using the
count aggregate to determine how many time series have data at an interval and
dividing the sum of the values by the count. This avoids issues where one or time series
are missing data at a specific time resulting in an artificially low average. E.g. the
expression:
when matching against the sample data in the table below, the highlighted time series would be
included in the aggregate result:
Name
nf.app
nf.node
Data
ssCpuUser
alerttest
i-0123
[1.0, 2.0, NaN]
ssCpuSystem
alerttest
i-0123
[3.0, 4.0, 5.0]
ssCpuUser
nccp
i-0abc
[8.0, 7.0, 6.0]
ssCpuSystem
nccp
i-0abc
[6.0, 7.0, 8.0]
numRequests
nccp
i-0abc
[1.0, 2.0, 4.0]
ssCpuUser
api
i-0456
[1.0, 2.0, 2.0]
The values from the corresponding intervals will be aggregated. For the first interval using
the sample data above the values are 1.0, 8.0, and 1.0. Each value other than NaN
contributes one to the average. This leads to a final result of:
Name
Data
ssCpuUser
[3.33, 3.66, 4.0]
The only tags for the aggregated result are those that are matched exactly (:eq clause)
as part of the choosing criteria or are included in a group by.
Compute the average of all the time series from the input expression. This is typically used when
there is a need to use some other aggregation for the grouping. Example:
