Metric types đź”—
In Splunk Observability Cloud, there are four types of metrics: gauge, counters, cumulative counters, and histograms.
The following table lists the types of supported metrics and their default rollups in Splunk Observability Cloud:
Metric 
Description 
Rollup 

Represent data that has a specific value at each point in time. Gauge metrics can increase or decrease. 
Average 

Represent a count of occurrences in a time interval. Counter metrics can only increase during the time interval. 
Sum 

Represent a running count of occurrences, and measure the change in the value of the metric from the previous data point. 
Delta 

Represent a distribution of measurements or metrics, with complete percentile data available. Data is distributed into equally sized intervals or â€śbucketsâ€ť. 
Histogram 
The type of the metric determines which default rollup function Splunk Observability Cloud applies to summarize individual incoming data points to match a specified data resolution. A rollup is a statistical function that takes all the data points in a metric time series (MTS) over a time period and outputs a single data point. Splunk Observability Cloud applies rollups after it retrieves the data points from storage but before it applies analytics functions. To learn more about rollups and data resolution, see Rollups in Data resolution and rollups in charts.
Note
Splunk Observability Cloud applies the SignalFlow average()
function to data points for gauge metrics. When you specify a 10second resolution for a line graph plot, and Splunk Observability Cloud is receiving data for the metric every second, each point in the line represents the average of 10 data points.
Gauges đź”—
Fan speed, CPU utilization, memory usage, and time spent processing a request are examples of gauge metric data.
Splunk Observability Cloud applies the SignalFlow average()
function to data points for gauge metrics.
When you specify a ten second resolution for a line graph plot, and Splunk Observability Cloud is receiving data for the metric every second, each point on the line represents the average of 10 data points.
Counters đź”—
Number of requests handled, emails sent, and errors encountered are examples of counter metric data. The machine or app that generates the counter increments its value every time something happens and resets the value at the end of each reporting interval.
Splunk Observability Cloud applies the SignalFlow sum()
function to data points for counter metrics. When you specify a ten second resolution for a line graph plot, and Splunk Observability Cloud is receiving data for the metric every second, each point on the line represents the sum of 10 data points.
Cumulative counters đź”—
Number of successful jobs, number of loggedin users, and number of warnings are examples of cumulative counter metric data. Cumulative counter metrics differ from counter metrics in the following ways:
Cumulative counters only reset to 0 when the monitored machine or application restarts or when the counter value reaches the maximum value representable (2 ^{32} or 2 ^{64} ).
In most cases, youâ€™re interested in how much the metric value changed between measurements.
Splunk Observability Cloud applies the SignalFlow delta()
function to data points for cumulative counter metrics. When you specify a ten second resolution for a line graph plot, and Splunk Observability Cloud is receiving data for the metric every second, each point on the line represents the change between the first data point received and
the 10th data point received. As a result, you donâ€™t have to create custom SignalFlow to apply the delta()
function, and the plot line represents variations.
Histograms đź”—
Histograms can summarize data in ways that are difficult to reproduce with other metrics. Thanks to the buckets, the distribution of your continuous data over time is easier to explore, as you donâ€™t have to analyze the entire dataset to see where all the data points are. At the same time, histogram helps reduce usage of your subscription.
Splunk Observability Cloud applies the SignalFlow histogram()
function to data points for histogram metrics, with a default percentile value of 90. You can apply several other functions to histograms, like min
, max
, count
, sum
, percentile
, and cumulative_distribution_function
.
For more information, see Histogram metrics in Splunk Observability Cloud.