An open-source monitoring system with a dimensional data model, flexible query language, efficient time series database and modern alerting approach.

What is Prometheus?

Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. It is now a standalone open source project and maintained independently of any company. To emphasize this, and to clarify the project’s governance structure, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.


Prometheus’s main features are:

  • a multi-dimensional data model with time series data identified by metric name and key/value pairs
  • PromQL, a flexible query language to leverage this dimensionality
  • no reliance on distributed storage; single server nodes are autonomous
  • time series collection happens via a pull model over HTTP
  • pushing time series is supported via an intermediary gateway
  • targets are discovered via service discovery or static configuration
  • multiple modes of graphing and dashboarding support


The Prometheus ecosystem consists of multiple components, many of which are optional:

  • the main Prometheus server which scrapes and stores time series data
  • client libraries for instrumenting application code
  • a push gateway for supporting short-lived jobs
  • special-purpose exporters for services like HAProxy, StatsD, Graphite, etc.
  • an alertmanager to handle alerts
  • various support tools

Most Prometheus components are written in Go, making them easy to build and Deploy as static binaries.

When does it fit?

Prometheus works well for recording any purely numeric time series. It fits both machine-centric monitoring as well as monitoring of highly dynamic service-oriented architectures. In a world of microservices, its support for multi-dimensional data collection and querying is a particular strength.

Prometheus is designed for reliability, to be the system you Go to during an outage to allow you to quickly diagnose problems. Each Prometheus server is standalone, not depending on network storage or other remote services. You can rely on it when other parts of your infrastructure are broken, and you do not need to setup extensive infrastructure to use it.

When does it not fit?

Prometheus values reliability. You can always view what statistics are available about your system, even under failure conditions. If you need 100% accuracy, such as for per-request billing, Prometheus is not a good choice as the collected data will likely not be detailed and complete enough. In such a case you would be best off using some other system to collect and analyze the data for billing, and Prometheus for the rest of your monitoring.

Data model

Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Besides stored time series, Prometheus may generate temporary derived time series as the result of queries.

Metric names and labels

Every time series is uniquely identified by its metric name and optional key-value pairs called 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). It may contain ASCII letters and digits, as well as underscores and colons. It must match the regex [a-zA-Z_:][a-zA-Z0-9_:]*.

Note: The colons are reserved for user defined recording rules. They should not be used by exporters or direct instrumentation.

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.

Label names may contain ASCII letters, numbers, as well as underscores. They must match the regex [a-zA-Z_][a-zA-Z0-9_]*. Label names beginning with __ are reserved for internal use.

Label values may contain any Unicode characters. A label with an empty label value is considered equivalent to a label that does not exist. See also the best practices for naming metrics and labels.