Prometheus Concepts

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Prometheus is a time series database plus tools to collect metrics to be stored in the database as aggregated metrics. Prometheus is a solution for system monitoring. It is not designed to catch individual events in time (such as a service outage) but it is designed to gather aggregated metrics about services. Prometheus is not designed to store raw information, such as text. Prometheus provides alerting capabilities with Alertmanager. Prometheus has some built-in visualization capabilities, but Grafana can also be used.


Prometheus will monitor targets. They are also sometimes referred to as "resources". A target can be a server, database, virtual machine, etc. Different targets can have different pull rates. Targets can be discovered dynamically with service discovery.

Pull vs. Push

Prometheus will actively pull (scrap) metrics, by connecting to targets. The retrieval is done by invoking HTTP into endpoints, which are defined in the configuration.

Advantages of pull vs. push:

  • Centralized control - the configuration is done on Prometheus and not on individual targets.
  • Adjustable pull rate - the system has control over pull rate, and avoids being overloaded.

Pull Rate

Sources of Metrics

Instrumented Applications

Prometheus pulls metrics from instrumented applications. An instrumented application exposes Prometheus metrics on a given URL. Such an application will be identified by Prometheus as a target and scrapped at regular periods.


An alternative are prebuilt exporters. Examples: node exporter, SQL exporter, HAProxy exporter. The exporters are usually exposed as Docker images, and can be configured to monitor existing targets. Available exporters:

Node Exporter

Node Exporter


Push gateways are used in case of applications or short-lived jobs that do not export metrics directly.



By default, the prometheus container stores metrics on an emtpyDir volume named "prometheus-prometheus-kube-prometheus-prometheus-db" mounted in /prometheus in the container.


Prometheus pushes alerts to Alertmanager via custom rules defined in its configuration files.

Service Discovery

Prometheus can discover targets dynamically.


A Prometheus metric is a key/value pair plus a set of labels, commonly referred to as metadata. The key is the name of the metric. The value is a float numerical value.

cpu_usage{core="1",ip="127.0.01"} 14.04

Also see:

Monitoring Concepts | Metrics


Metric Name


Also see instant vector


Series can be queried at the /api/v1/series endpoint.

Data Types used in Queries

Instant Vector

A set of time series containing a simple sample for each time series, all sharing the same timestamp. More:

PromQL | Instant Vector

Range Vector

A set of time series containing a range of data points over time for each series. More:

PromQL | Range Vector


A simple numeric floating point value. More:

PromQL | Scalar


A simple string value. More:

PromQL | String



Prometheus server will attach "job" and "instance" labels to each metric:



Prometheus server will attach "job" and "instance" labels to each metric:


Metric Types


A counter counts elements over time. A counter is a monotonically increasing positive value. A counter can only go up or can be reset. It must never decrease.


A gauge measures values that can go up and down.



Prometheus Format

The Prometheus format is structured plain text.





Prometheus Console

Prometheus comes with a built-in console that allows performing metric queries and inspecting configuration. When Prometheus is deployed in Kubernetes and on some Kubernetes instances, such as Docker Desktop Kubernetes, access to console must be enabled explicitly by deploying a LoadBalancer service.

Server configuration can be viewed with Status → Configuration.

Prometheus Operator

Prometheus Operator Concepts

Prometheus Adapter for Kubernetes Metrics APIs

Prometheus Adapter for Kubernetes Metrics APIs