Metrics and Entities When a monitoring tool collects performance metrics, it usually records the measurement as well as its metadata information. For example, a container CPU usage metric also reports the container name, the node name, instance type, and perhaps the application service to which the container belongs. This context
Introduction Springboot [https://spring.io/projects/spring-boot] is a popular Java framework for building modern applications. With the help of the actuator [https://docs.spring.io/spring-boot/docs/current/reference/html/actuator.html] module and the micrometer [https://micrometer.io/] library, we can configure a Springboot application to expose performance
We use both average and quantiles to measure latencies, but monitoring tools often have limitations with percentiles, which sometimes produce counterintuitive results. While investigating a latency problem, we noticed that Prometheus -- the system we use for monitoring -- reported the P99 latency for our app was less than the
Compared to the old term “monitoring”, the new buzzword “observability [https://en.wikipedia.org/wiki/Observability]” is not merely a marketing slogan. It does convey a new set of challenges with the cloud-native paradigm shift. This blog provides an engineer’s perspective on these challenges and why Asserts thinks Knowledge
Introduction Prometheus has four metric types [https://prometheus.io/docs/concepts/metric_types/], among which the two basic ones are counter and gauge. A gauge is intuitive as it matches our expectation for a metric, i.e., it represents a numeric value that measures something like temperature, memory usage, etc.