Micrometer provides a simple facade over the instrumentation clients for the most popular monitoring systems, allowing you to instrument your JVM-based application code without vendor lock-in. Think SLF4J, but for application metrics! Application metrics recorded by Micrometer are intended to be used to observe, alert, and react to the current/recent operational state of your environment. https://micrometer.io/docs

Few concepts in micro meter

Meter is collection of measurements about application .

Meter registry is to held(store) collected measurements

Micrometer packs with a supported set of Meter primitives including: Timer, Counter, Gauge, DistributionSummary, LongTaskTimer, FunctionCounter, FunctionTimer, and TimeGauge. Different meter types result in a different number of time series metrics. Find details for few of it on


Spring boot provides dependency management and auto configuration through actuator . Adding following dependency add required dependencies and auto configuration of beans


Sprint boot auto configure the micro meter registry based on the registry available on class path .If we add below , spring will auto configure prometheus meter registry


Create simple spring boot application to monitor

Create simple web application using https://start.spring.io/

Add simple rest controller ex:

public String sayHello(@PathVariable("name") String name) {
return "Hello " + name + " Greetings from micro meter application.";

public String sayHi() {
return "Micro monitoring applcation ";

Create image using docker file using any of the steps mentioned in below links

FROM adoptopenjdk:11-jre-hotspot as builder
WORKDIR application
ARG JAR_FILE=target/*.jar
COPY ${JAR_FILE} application.jar
RUN java -Djarmode=layertools -jar application.jar extract

FROM adoptopenjdk:11-jre-hotspot
WORKDIR application
COPY --from=builder application/dependencies/ ./
COPY --from=builder application/snapshot-dependencies/ ./
COPY --from=builder application/resources/ ./
COPY --from=builder application/application/ ./
ENTRYPOINT ["java", "org.springframework.boot.loader.JarLauncher"]

Prometheus yaml to defining that pull data from application using actuator url

# my global config
scrape_interval: 5s
evaluation_interval: 5s

# timeseries scraped from this config.
- job_name: 'app_metrics'
metrics_path: '/actuator/prometheus'
- targets: ['web:8080']

Create docker compose file with prometheus(store data) and Grafana (visualise)

version: "3"
image: web:0.0.2-SNAPSHOT
- "8080:8080"
hostname: web

image: prom/prometheus
- "9090:9090"
- ./prometheus.yml:/etc/prometheus/prometheus.yml
hostname: prometheus

image: grafana/grafana
- prometheus
- "3000:3000"
hostname: grafana

Run docker-compose up -d in the same folder having above docker compose file

Once all three apps are up and running you can access promotheus and grafana and app

Prometheus : http://localhost:9090/targets

Grafana : http://localhost:3000/login

Application : http://localhost:8080/actuator/prometheus

Configure grafana data source and create dashboard

Configure Datasource

Use admin and admin as credentials to log in (change default password is optional .. you can skip )

Click on gear icon → choose data source → Add datasource → choose prometheus

only add URL details (should be same as application name defined in docker compose) and use remaining default details .

If url working , you will see below when you click on save

Configure Dashboard

Click on plus symbol ,choose import option and input as 9568 0r 4701 and add datasource as prometheus .

Time to observe metrics

We can hit application rest url using browser or use postman and run in loop .Other easy way to create load and change the metrics and observe the same is using ab — Apache HTTP server benchmarking tool (Available on mac so no need to install but for other operating system we have to install ab tool)

ab -n 500000 -c 50 http://localhost:8080/monitoring/test

Observe the dashboard on grafana

Check below values from the response of http://localhost:8080/actuator/prometheus


Metrics about Outbound http calls

All outbound calls can be monitored if we instantiate rest template client using the rest template builder. Rest template builder uses rest template customiser to register interceptor to collect metrics. Find details in below classes


Actuator supports following metrics

Supported Metrics

Spring Boot registers the following core metrics when applicable:

  • JVM metrics, report utilization of:
  • Various memory and buffer pools
  • Statistics related to garbage collection
  • Threads utilization
  • Number of classes loaded/unloaded
  • CPU metrics
  • File descriptor metrics
  • Kafka consumer, producer, and streams metrics
  • Log4j2 metrics: record the number of events logged to Log4j2 at each level
  • Logback metrics: record the number of events logged to Logback at each level
  • Uptime metrics: report a gauge for uptime and a fixed gauge representing the application’s absolute start time
  • Tomcat metrics (server.tomcat.mbeanregistry.enabled must be set to true for all Tomcat metrics to be registered)
  • Spring integration metrics



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