Metrics Collection using Prometheus

In production systems, issues can happen at various levels. Based on metrics collection over a period of time, a good alerting/monitoring…

In production systems, issues can happen at various levels. Based on metrics collection over a period of time, a good alerting/monitoring solution can help to avoid some of the issues or can help to take action before the issue affects users. Prometheus is one such solution that can be used independently for metrics collection and alerts or in conjunction with something like Grafana for better dashboards and alerts. Prometheus is a CNCF graduated project, so it has wide enterprise adoption and community support.

Prometheus mainly is a pull-based system that scrapes/collects from source systems/applications on set schedules. This part is taken care of by the scheduler component. Then metrics are stored in a time series database and exposed using API. Prometheus does have UI to run queries using its PromQL language. We can set alerts in Prometheus. Prometheus can get these metrics from windows and Linux servers using exporters or from defined metrics endpoints where applications publish their metrics to.

Scheduler scrapes metrics from servers, and applications

For batch processes, a push gateway can be used.

Exporters

Linux servers, windows servers, Kubernetes already collect a lot of metrics by default. By using exporters they can make those metrics available at standard endpoint like /metrics in Prometheus format. Prometheus can then collect these metrics at set schedules. These are in Key/value format but keys can have labels. For example, for Memory Available, it can be at just node level but for disk_io, it can be per disk by using label per disk.

node-exporter prometheus

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