Metric types

The Prometheus client libraries offer four core metric types. These are currently only differentiated in the client libraries (to enable APIs tailored to the usage of the specific types) and in the wire protocol. The Prometheus server does not yet make use of the type information and flattens all data into untyped time series. This may change in the future.

Counter

A counter is a cumulative metric that represents a single numerical value that only ever goes up. A counter is typically used to count requests served, tasks completed, errors occurred, etc. Counters should not be used to expose current counts of items whose number can also go down, e.g. the number of currently running goroutines. Use gauges for this use case.

Client library usage documentation for counters:

Gauge

A gauge is a metric that represents a single numerical value that can arbitrarily go up and down.

Gauges are typically used for measured values like temperatures or current memory usage, but also "counts" that can go up and down, like the number of running goroutines.

Client library usage documentation for gauges:

Histogram

A histogram samples observations (usually things like request durations or response sizes) and counts them in configurable buckets. It also provides a sum of all observed values.

A histogram with a base metric name of <basename> exposes multiple time series during a scrape:

  • cumulative counters for the observation buckets, exposed as <basename>_bucket{le="<upper inclusive bound>"}
  • the total sum of all observed values, exposed as <basename>_sum
  • the count of events that have been observed, exposed as <basename>_count (identical to <basename>_bucket{le="+Inf"} above)

Use the histogram_quantile() function to calculate quantiles from histograms or even aggregations of histograms. A histogram is also suitable to calculate an Apdex score. When operating on buckets, remember that the histogram is cumulative. See histograms and summaries for details of histogram usage and differences to summaries.

Client library usage documentation for histograms:

Summary

Similar to a histogram, a summary samples observations (usually things like request durations and response sizes). While it also provides a total count of observations and a sum of all observed values, it calculates configurable quantiles over a sliding time window.

A summary with a base metric name of <basename> exposes multiple time series during a scrape:

  • streaming φ-quantiles (0 ≤ φ ≤ 1) of observed events, exposed as <basename>{quantile="<φ>"}
  • the total sum of all observed values, exposed as <basename>_sum
  • the count of events that have been observed, exposed as <basename>_count

See histograms and summaries for detailed explanations of φ-quantiles, summary usage, and differences to histograms.

Client library usage documentation for summaries: