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.
A counter is a cumulative metric that represents a single monotonically increasing counter whose value can only increase or be reset to zero on restart. For example, you can use a counter to represent the number of requests served, tasks completed, or errors.
Do not use a counter to expose a value that can decrease. For example, do not use a counter for the number of currently running processes; instead use a gauge.
Client library usage documentation for counters:
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 concurrent requests.
Client library usage documentation for gauges:
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:
<basename>_bucket{le="<upper inclusive bound>"}
<basename>_sum
<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:
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:
<basename>{quantile="<φ>"}
<basename>_sum
<basename>_count
See histograms and summaries for detailed explanations of φ-quantiles, summary usage, and differences to histograms.
Client library usage documentation for summaries:
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