In this chapter, we came to understand what time series data is, and looked at an overview of how a modern time series database such as Prometheus works, not only logically, but physically as well. We went through the Prometheus metrics notation and how metric names and labels relate to each other, and also covered what defines a sample. Prometheus metrics have four types, and we had the chance to go through every one of them and provide some useful examples. Finally, we dived into how longitudinal and cross-sectional aggregations work, which is essential to fully take advantage of Prometheus' query language.
In the next chapter, we'll return to a more hands-on approach and go into Prometheus server configuration, and how to manage it on both virtual machines and Kubernetes.
