From the course: Prometheus and Grafana: Visualizing Application Performance

Prometheus overview

From the course: Prometheus and Grafana: Visualizing Application Performance

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Prometheus overview

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- [Instructor] Prometheus is a popular open-source solution for system monitoring and alerting. Like Grafana, it has gained widespread adoption, because of its powerful features. Since we'll be visualizing Prometheus metrics in this course, it is worthwhile to get acquainted. We will discuss the basics in this video, but I highly recommend taking the "Prometheus Essential Training" course on the LinkedIn Learning platform first. In that course, I go into greater detail about how to understand Prometheus like a pro. Prometheus was created by engineers at SoundCloud in 2012 and has since become a Cloud Native Foundation project. This status came, because it has become a valuable asset in the community for monitoring cloud-native applications. The core functionality it provides involves recording metrics, including a timestamp of when they were recorded. It also contains optional key-value pairs known as labels, which provide crucial context for querying and analysis. Metrics, labels, and time series, these are fundamental concepts in Prometheus. All collected data is stored as a time series, which is a continuous stream of time-stamped values. The metric is a numerical measurement, and the labels are identifiers that help with querying. For example, a metric named httprequesttotal might obtain the total number of HTTP requests received by a server. We can then have labels that allow the identification of specific servers or methods. Prometheus supports several metric types like counters, gauges, histograms, and summaries. Each of these is designed to capture different kinds of measurements. But how do you write queries in Prometheus? PromQL is a Prometheus query language and boast a wide array of features for analysis. You can match on labels, create complex mathematical and logical operators, perform aggregation, and so on. PromQL is fundamental to how we'll create dashboard panels, so I encourage you to take some time to familiarize yourselves with the basics. But don't worry if you're not great with PromQL, it's a common problem as it can be daunting. In the practical videos, I'll provide a lot of tips and tricks. In Chapter 3, we'll even discuss how to use native Grafana features to make writing PromQL easier. And in Chapter 7, you'll see how generative AI can help you here as well. But before all the exciting stuff, let's get you acquainted with the Grafana user interface.

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