Docs » Identify metric types

Identify metric types πŸ”—

In Splunk Observability Cloud, there are three types of metrics: gauge metrics, counter metrics, and cumulative counter metrics. The type of the metric determines how Splunk Observability Cloud summarizes individual incoming data points to match a specified data resolution.

Splunk Observability Cloud summarizes incoming data points by applying the default rollup function for the metrics. For example, 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 Observability Cloud is receiving data for the metric every second, each point in the line represents the average of 10 data points. To learn more about rollups and data resolution, see the Resolution and Data Retention topic.

Types of metrics in Splunk Observability Cloud:

Gauges πŸ”—

Fan speed, CPU utilization, memory usage, and time spent processing a request are examples of gauge metric data.

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 Observability Cloud is receiving data for the metric every second, each point on the line represents the average of 10 data points.

Counters πŸ”—

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.

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 Observability Cloud is receiving data for the metric every second, each point on the line represents the sum of 10 data points.

Cumulative counters πŸ”—

Number of successful jobs, number of logged-in 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, you’re interested in how much the metric value changed between measurements.

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 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 don’t have to create custom SignalFlow to apply the delta() function, and the plot line represents variations.