Prometheus Architecture

Prometheus Architecture

Data Model (TSDB)

  • A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.
  • Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions.
  • Every time series is uniquely identified by its metric name and optional key-value pairs called labels.

Metric Names and Labels

  • The metric name specifies the general feature of a system that is measured (e.g. http_requests_total - the total number of HTTP requests received).
  • Labels enable Prometheus’s dimensional data model: any given combination of labels for the same metric name identifies a particular dimensional instantiation of that metric (for example: all HTTP requests that used the method POST to the /api/tracks handler).
  • The query language allows filtering and aggregation based on these dimensions.
  • Changing any label value, including adding or removing a label, will create a new time series.

Notation: Time Series

  • Given a metric name and a set of labels, time series are frequently identified using this notation:

    <metric name>{<label name>=<label value>, ...}

  • For example, a time series with the metric name api_http_requests_total and the labels method=”POST” and handler=”/messages” could be written like this:

    api_http_requests_total{method="POST", handler="/messages"}

Querying Prometheus: PromQL

  • Prometheus provides a functional query language called PromQL (Prometheus Query Language) that lets the user select and aggregate time series data in real time.
  • The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus’s expression browser, or consumed by external systems via the HTTP API.


  • Prometheus is configured via command-line flags and a configuration file. While the command-line flags configure immutable system parameters (such as storage locations, amount of data to keep on disk and in memory, etc.),
  • the configuration file defines everything related to scraping jobs and their instances, as well as which rule files to load.


  scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.

  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'
    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.
      - targets: ['localhost:9090']

Exporters and Integrations

Kubernetes Monitoring Architecture

  • System metrics (core metrics & non-metrics)

    System metrics are generic metrics that are generally available from every entity that is monitored (e.g. usage of CPU and memory by container and node).

  • Service metrics

    Service metrics are explicitly defined in application code and exported (e.g. number of 500s served by the API server).

  • Core (system) metrics

    which are metrics that Kubernetes understands and uses for operation of its internal components and core utilities – for example, metrics used for scheduling (including the inputs to the algorithms for resource estimation, initial resources/vertical autoscaling, cluster autoscaling, and horizontal pod autoscaling excluding custom metrics), the kube dashboard, and “kubectl top.”



  • Expression browser
  • Grafana

The USE and RED Methods

  • USE: Utilization, saturation, and errors

  • RED: Requests, Errors, and Duration