Brief reviews of the primary types of Prometheus metrics

Prometheus is the Cloud Native Computing Foundation’s free and open-source metric-monitoring tool. To simplify the user experience (UX), it is designed to be utilised as a single binary for data ingestion, storage, and querying. 

The Prometheus monitoring system includes the native query language Prometheus Query Language (PromQL), which is frequently used. Also popular are text exposition formats, efficient metric storage, and the Prometheus Query Language (PromQL). When it comes to your Prometheus metric storage, PromQL is your go-to query language for finding the information you need. 

PromQL queries may be made on four primary categories of Prometheus metrics. Metrics, indicators, histograms, and abstracts are all examples. The official Prometheus client libraries (in Go, Java, Ruby, and Python) all have these metric types. They are adequate for the vast majority of applications and purposes. Please note that the documentation for each client library provides information on how to use each metric type with the corresponding API for that client library. 

This blog covers the four primary types of Prometheus metrics, gives guidance on when to utilise each kind, and links to useful resources for doing so. You will also need to know about the types of prometheus metrics there.

To keep track of the frequency of a certain occurrence, we utilise counters.

Counters are crucial to any software or service that has to keep track of how often something happens. They are used for monitoring and analysing Prometheus metrics whose values are continually (or monotonically) increasing and are shown as time series. 

In the context of metric client libraries, counters are running tally records. Over the course of an application’s lifetime, they accumulate the total number of events that have taken place. Running counts are another name for counters. The metrics endpoint exposed by a client library may be scraped by instructing Prometheus to take periodic measurements of these events.

Sum Value Estimation 

When it comes to estimating the sum of values at a given time, running counts are particularly precise since they allow for the interpolation of any missing sample sets. As a result, running counts are quite reliable. However, if you want to keep a running total of many counts, you must first use the rate() method to each count to see the changes that occur on that count every second before you can aggregate the counts. After that, you may add up the totals using the sum() function. 

Metric deviations may be measured with the use of gauges.

Gauges are tools that are routinely used to instantaneously measure a metric of interest. The difference between a counter and a gauge is that the latter’s value might fluctuate erratically over time (as in CPU usage and temperature, for example). 

Gauges are useful when you want to query a number that may move up or down, but you don’t care how quickly it’s changing. It’s important to remember that gauges can’t be used with the rate() function since rates only work on metrics that always increase (like counters). 

Histograms are helpful for showing how numbers are distributed. 

Histograms collect samples of data and organise the values of those observations into predetermined buckets depending on the frequency or count of the observations. In the absence of providing your own buckets, the Prometheus client library will make use of a default set. These bins may be used to keep tabs on how often certain instances of a given attribute occur.