Reference to the count() function

Summary

count(<expression>[,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 even if it has interpolated values. Use rawcount() if you don’t want to consider interpolated values.

Parameters

ParameterDescription
expression 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 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 count() aggregate function adds together the number of reporting time series represented by the expression, at each moment in time.

By default, count() produces a single count across all time series. You can optionally group the time series based on one or more characteristics, and obtain a separate count for each group.

If a time series has data gaps, count() fills them in by interpolation whenever possible. A time series with an interpolated value is considered to be reporting and is included in the current count. When a value cannot be interpolated into a time series (or if the series stops reporting altogether), the series is excluded from the count.

Grouping

Like all aggregation functions, count() returns a single series of results by default.

You can include a ‘group by’ parameter to obtain separate 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, count(ts("cpu.cpu*"), metrics, Customer) first groups by metric names, and then groups by the values of the Customer point tag.

Interpolation

If any time series has gaps in its data, Wavefront attempts to fill these gaps with interpolated values before applying the function. A value can be interpolated into a time series only if at least one other time series reports a real data value at the same moment in time.

Within a given time series, an interpolated value is calculated from two real reported values on either side of it. Sometimes interpolation is not possible–for example, when a new value has not been reported yet in a live-view chart. In this case, Wavefront finds the last known reported value in the series, and assigns it to any subsequent moment in time for which a real reported data value is present in some other time series. We use the last known reported value only if interpolation can’t occur and if the last known reported value has been reported within the last 15% of the query time in the chart window.

You can use rawcount() to suppress interpolation. See Standard Versus Raw Aggregation Functions.

Examples

The following examples contrast the result you get for two different types of servers. We’re using a Single Stat View chart to first get a count of all servers that have a sample requests latency and the source app-1* (11 servers). Then we get a count of all servers that have the source app-2* While the example is a bit contrived, it illustrates how to use the function.

count 1

count 2

The following example groups all sources whose name starts with "app-1*" by the env point tag. The Stacked Area chart shows that 1 server is tagged with dev and 10 servers are tagged with production.

count grouped

Caveats

Count is not a static number, it will change if the number of hosts reporting the metric changes. For some types of analysis, it is better to use avg() instead of count().