![]() ![]() For example, a Counter meter maintains a single metric, namely a count. This is how Micrometer differentiates meters and metrics. The Micrometer library implements various Meter types each collecting one or more metrics. With Spring Boot it is common to add the actuator dependency rather than micrometer directly. In addition to adding metrics, the actuator dependency also adds useful management features to the application, such as health-check, heap-dump and thread-dump endpoints. We do this by adding the Spring Boot Actuator module which uses Micrometer at its core to collect metrics that are important to monitor JVM applications. In order to instrument it we include the Micrometer library. We start with the Spring Boot application. Project code to accompany this article is available on GitHub. We'll focus mainly on Prometheus but will also show how to send metrics to CloudWatch Metrics in order to present a more heterogeneous example. To demonstrate the concepts we'll build a local monitoring stack using Docker Compose as shown in the following diagram. Other aspects of monitoring such as log collection and distributed tracing are also very important but are not the focus of this article. Metrics could help in analysing whether application code and configuration need to be updated. When a failure does occur, we often need to review historical monitoring data to help find out the root cause.īefore going to production, it is important to stress-test an application to find and fix potential issues. It might also be desired to be notified when the application changes state, such as during a scaling event. If service availability is affected we want to be notified automatically so that we could respond to operational issues quickly. Monitoring is particularly useful when things go wrong. Metrics also help in deriving a performance baseline which could be used to define alerts, scaling rules, or to understand the performance effects of a recent change. Collecting and analyzing metrics helps us understand whether an application is healthy as well as how it behaves under load. It is difficult to know whether an application is working as expected without monitoring data. ![]() One of the most important aspects of an application is observability. The bearer token should go into anĪuthorization header ( Authorization: Bearer ).Application Monitoring with Micrometer, Prometheus, Grafana, and CloudWatch You can just use the bearer token and path from that configuration toĪccess your metrics. Prometheus.yml as follows: scrape_configs: The command will generate the scrape_configs section of the To generate a Prometheus config forĪn alias, use mc as follows mc admin prometheus generate. Prometheus scrape requests, override the default Prometheus config Prometheus supports a bearer token approach to authenticate The Prometheus endpoint in MinIO requires authentication by default. If MinIO is configured to expose metrics without authentication, youĭon't need to use mc to generate prometheus config. The appropriate bearer token to use for authentication: Sections tells you how to generate a configuration that will show you If you're running it in jwt mode (the default), the following ![]() export MINIO_PROMETHEUS_AUTH_TYPE="public" minio server ~/test Without authentication for prometheus metrics set environment asįollows. Public, by default MinIO runs in jwt mode. MinIO supports two authentication modes for Prometheus either jwt or
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