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|Initial release||24 November 2012|
v2.24.1 / 20 January 2021
|Type||Time series database|
|License||Apache License 2.0|
Prometheus is a free software application used for event monitoring and alerting. It records real-time metrics in a time series database (allowing for high dimensionality) built using a HTTP pull model, with flexible queries and real-time alerting. The project is written in Go and licensed under the Apache 2 License, with source code available on GitHub, and is a graduated project of the Cloud Native Computing Foundation, along with Kubernetes and Envoy.
Prometheus was developed at SoundCloud starting in 2012, when the company discovered that their existing metrics and monitoring solutions (using StatsD and Graphite) were not sufficient for their needs. Specifically, they identified needs that Prometheus was built to meet including: a multi-dimensional data model, operational simplicity, scalable data collection, and a powerful query language, all in a single tool. The project was open-source from the beginning, and began to be used by Boxever and Docker users as well, despite not being explicitly announced. Prometheus was inspired by the monitoring tool Borgmon used at Google.
In May 2016, the Cloud Native Computing Foundation accepted Prometheus as its second incubated project, after Kubernetes. The blog post announcing this stated that the tool was in use at many companies including Digital Ocean, Ericsson, CoreOS, Weaveworks, Red Hat, and Google.
In August 2018, the Cloud Native Computing Foundation announced that the Prometheus project had graduated.
A typical monitoring platform with Prometheus is composed of multiple tools:
- Multiple exporters that typically run on the monitored host to export local metrics.
- Prometheus to centralize and store the metrics.
- Alertmanager to trigger alerts based on those metrics.
- Grafana to produce dashboards.
- PromQL is the query language used to create dashboards and alerts.
Data storage format
Prometheus data is stored in the form of metrics, with each metric having a name that is used for referencing and querying it. Each metric can be drilled down by an arbitrary number of key=value pairs (labels). Labels can include information on the data source (which server the data is coming from) and other application-specific breakdown information such as the HTTP status code (for metrics related to HTTP responses), query method (GET versus POST), endpoint, etc. The ability to specify an arbitrary list of labels and to query based on these in real time is why Prometheus' data model is called multi-dimensional.
Prometheus collects data in the form of time series. The time series are built through a pull model: the Prometheus server queries a list of data sources (sometimes called exporters) at a specific polling frequency. Each of the data sources serves the current values of the metrics for that data source at the endpoint queried by Prometheus. The Prometheus server then aggregates data across the data sources. Prometheus has a number of mechanisms to automatically discover resources that it should be using as data sources.
Prometheus provides its own query language PromQL (Prometheus Query Language) that lets users select and aggregate data. PromQL is specifically adjusted to work in convention with a Time-Series Database and therefore provides time-related query functionalities. Examples include the rate() function, the instant vector and the range vector which can provide many samples for each queried time series. Prometheus has four clearly defined metric types around which the PromQL components revolve. The four types are
Alerts and monitoring
Configuration for alerts can be specified in Prometheus that specifies a condition that needs to be maintained for a specific duration in order for an alert to trigger. When alerts trigger, they are forwarded to Alertmanager service. Alertmanager can include logic to silence alerts and also to forward them to email, Slack, or notification services such as PagerDuty. Some other messaging systems like Microsoft Teams could be configured using the Alertmanager Webhook Receiver as mechanism for external integrations.
Prometheus is not intended as a dashboarding solution. Although it can be used to graph specific queries, it is not a full-fledged dashboarding solution and needs to be hooked up with Grafana to generate dashboards; this has been cited as a disadvantage due to the additional setup complexity.
Prometheus favors white-box monitoring. Applications are encouraged to publish (export) internal metrics to be collected periodically by Prometheus. Some exporters and agents for various applications are available to provide metrics. Prometheus supports some monitoring and administration protocols to allow interoperability for transitioning: Graphite, StatsD, SNMP, JMX, and CollectD.
Prometheus focuses on the availability of the platform and basic operations. The metrics are typically stored for few weeks. For long term storage, the metrics can be streamed to remote storage solutions. 
Standardization into OpenMetrics
There is an effort to promote Prometheus exposition format into a standard known as OpenMetrics. Some products adopted the format: InfluxData's TICK suite, InfluxDB, Google Cloud Platform, and DataDog.
Prometheus was first used in-house at SoundCloud, where it was developed, for monitoring their systems. The Cloud Native Computing Foundation has a number of case studies of other companies using Prometheus. These include digital hosting service Digital Ocean, digital festival DreamHack, and email and contact migration service ShuttleCloud. Separately, Pandora Radio has mentioned using Prometheus to monitor its data pipeline.
- Check MK
- Ganglia (software)
- Sensu Core
- Comparison of network monitoring systems
- List of systems management systems
- Releases at Github
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Even though Borgmon remains internal to Google, the idea of treating time-series data as a data source for generating alerts is now accessible to everyone through those open source tools like Prometheus ...
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I joined SoundCloud back in 2012 coming from Google...we didn't yet have any monitoring tools that that works with this kind of dynamic environment. We were kind of missing the way Google did its monitoring for its own internal cluster scheduler and we were very inspired by that and finally decided to build our own open-source solution.
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