Monitoring Metrics (Statistical Collector)


Dimensional Data Modeling - Metrics - Key Performance indicators (KPI) in Monitoring.

Monitoring Metrics are also known as:

  • Statistical Collector
  • Perfcounter (generally on Windows)
  • Performance metrics

A monitoring metrics basically get data from a collector and produce statistic(s) (See also the page Counter - Statistics (for Engineers on operational data))

They are event metrics.

For the business related view of metrics, see Dimensional Data Modeling - Metrics - Key Performance indicators (KPI)

The monitoring metrics are calculated from time serie data.

They are known as statistical data collectors because they gather data and compute statistics. They are used to collect, evaluate and report results, e.g.

  • mean residence times of customers
  • or the average utilisation of a server.


  • Timer, a Timer measures both the count of timed events and the total time of all events timed.
  • Counter is used to record occurrences of events like the number of customer arrivals. Representing a counter without rate aggregation over some time window is rarely useful, as the representation is a function of both the rapidity with which the counter is incremented and the longevity of the service.
  • Gauge, - a single metric (gauge values are not rates)
  • histogram - An histogram ranks the measured values into predefined classes.
  • Regressions: A Regression perform a regressional analysis of two different collected value series (x and y). It reports the number of observations, mean (for x and y), regression and correlation coefficients, standard deviations and interception.

Time serie:

  • Tallies. A tally record the mean and standard deviation of a (time) series of values. Example: Computing the average time customers spent on a system
  • Accumulates: An Accumulate record the mean and standard deviation of a time series of values against different time interval. Example: System load

See also: Prometheus Metrics Type


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