min
Min aggregation operator. There are two variants of the :min operator.
Aggregation
| Input Stack: |
⇨ |
Output Stack: |
Select the minimum value for corresponding times across all matching time series.
Parameters
- query: A query expression that selects the time series to aggregate
Examples
Find the minimum CPU usage across all matching nodes:
name,ssCpuUser,:eq,
:min
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. The minimum of these non-NaN
values is selected. This leads to a final result of:
| Name | Data |
| ssCpuUser |
[1.0, 2.0, 2.0] |
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.
Math
| Input Stack: |
⇨ |
Output Stack: |
Select the minimum value for corresponding times across the time series resulting from the
input expression. This variant is typically used when you need to apply a different aggregation
function for grouping first, then find the minimum across the groups.
Parameters
- expr: A time series expression that may contain multiple series to find the minimum across
Examples
First group by cluster using sum aggregation, then find the minimum across all groups:
| Before | After |
 |  |
name,sps,:eq,
:sum,
(,nf.cluster,),:by
| name,sps,:eq,
:sum,
(,nf.cluster,),:by,
:min
|
- :max - Maximum aggregation function
- :sum - Sum aggregation function
- :avg - Average aggregation function
- :count - Count aggregation function
- :by - Group time series by tag values before aggregating