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#Download prometheus ios tool code

#Download prometheus ios tool code

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. Each metric can be drilled down by an arbitrary number of key=value pairs (labels). Prometheus data is stored in the form of metrics, with each metric having a name that is used for referencing and querying it.

PromQL is the query language used to create dashboards and alerts.

Alertmanager to trigger alerts based on those metrics.

Prometheus to centralize and store the metrics.

Multiple exporters that typically run on the monitored host to export local metrics.

Architecture Ī typical monitoring platform with Prometheus is composed of multiple tools: In August 2018, the Cloud Native Computing Foundation announced that the Prometheus project had graduated. Subsequent versions were released through 20, leading to Prometheus 2.0 in November 2017. Prometheus 1.0 was released in July 2016. The blog post announcing this stated that the tool was in use at many companies including DigitalOcean, Ericsson, CoreOS, Weaveworks, Red Hat, and Google. In May 2016, the Cloud Native Computing Foundation accepted Prometheus as its second incubated project, after Kubernetes. The official public announcement was made in January 2015. īy 2013, Prometheus was introduced for production monitoring at SoundCloud. Prometheus was inspired by the monitoring tool Borgmon used at Google.

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.

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. 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.