Grafana and time series databases

More than a Thousand Words

Admins are rumored to feel more at ease working with text-based terminals than with graphical tools, and the command line often is better suited to classic admin tasks than a GUI because it allows scripting and direct input. For other admin tasks, though, this assessment is typically inverted: When it comes to processing measurement data and statistics, visual tools are clearly superior. In particular, data for monitoring, alerting, and trending (MAT) that comes from several sources requires visualization for meaningful analysis.

Virtually every large environment, whether a container platform or a public cloud environment, is strongly dependent on MAT. Only MAT provides reliable clues on the health and usage of systems that let you know when you need to add new hardware because your platform is currently fully utilized. Grafana [1] targets admins who need MAT analysis.

Match Winner

Grafana cooperates with various back ends and can pull the data you want to display from many sources. Its developers refer to this configuration as data-driven architecture. In place of classic event monitoring, in which monitoring is a spin-off of the need to collect various metrics continuously, Grafana uses a time-series-based principle. For example, if you operate a multinode cluster for MySQL based on Galera, you will want the load on all database back ends to be equally high. If system load on one of the back ends suddenly drops off, it is a certain indicator that something is wrong.

Unlike typical incident monitoring in the style of Nagios, Grafana bases its conclusions on performance data. Although Grafana is not primarily about monitoring, it does help you prepare the corresponding time series from monitoring systems in an easily interpretable way. In this article, I first highlight the key features of Grafana and then present the most

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