12/20/2023 0 Comments Monit vs prometheus![]() Here are some examples of how our metrics will look: http_requests_total > 0 example. For the purposes of this blog post let’s assume we’re working with http_requests_total metric, which is used on the examples page. To create alerts we first need to have some metrics collected. Since we’re talking about improving our alerting we’ll be focusing on alerting rules. A rule is basically a query that Prometheus will run for us in a loop, and when that query returns any results it will either be recorded as new metrics (with recording rules) or trigger alerts (with alerting rules). ![]() We can then query these metrics using Prometheus query language called PromQL using ad-hoc queries (for example to power Grafana dashboards) or via alerting or recording rules. ![]() Prometheus works by collecting metrics from our services and storing those metrics inside its database, called TSDB. If you’re not familiar with Prometheus you might want to start by watching this video to better understand the topic we’ll be covering here. One of the key responsibilities of Prometheus is to alert us when something goes wrong and in this blog post we’ll talk about how we make those alerts more reliable - and we’ll introduce an open source tool we’ve developed to help us with that, and share how you can use it too. Despite growing our infrastructure a lot, adding tons of new products and learning some hard lessons about operating Prometheus at scale, our original architecture of Prometheus (see Monitoring Cloudflare's Planet-Scale Edge Network with Prometheus for an in depth walk through) remains virtually unchanged, proving that Prometheus is a solid foundation for building observability into your services. We’ve been heavy Prometheus users since 2017 when we migrated off our previous monitoring system which used a customized Nagios setup. We use Prometheus as our core monitoring system.
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