The following metrics are exposed for each supported DataSource defined in your application:

All data source metrics share the datasource. prefix. The prefix is further qualified for each data source:

It is possible to override part or all of those defaults by registering a bean with a customized version of DataSourcePublicMetrics. By default, Spring Boot provides metadata for all supported datasources; you can add additional DataSourcePoolMetadataProvider beans if your favorite data source isn’t supported out of the box. See DataSourcePoolMetadataProvidersConfiguration for examples.

To record your own metrics inject a CounterService and/or GaugeService into your bean. The CounterService exposes increment, decrement and reset methods; the GaugeService provides a submit method.

Here is a simple example that counts the number of times that a method is invoked:

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.actuate.metrics.CounterService;
import org.springframework.stereotype.Service;

@Service
public class MyService {

    private final CounterService counterService;

    @Autowired
    public MyService(CounterService counterService) {
        this.counterService = counterService;
    }

    public void exampleMethod() {
        this.counterService.increment("services.system.myservice.invoked");
    }

}

	Tip
	You can use any string as a metric name but you should follow guidelines of your chosen store/graphing technology. Some good guidelines for Graphite are available on Matt Aimonetti’s Blog.

To add additional metrics that are computed every time the metrics endpoint is invoked, simply register additional PublicMetrics implementation bean(s). By default, all such beans are gathered by the endpoint. You can easily change that by defining your own MetricsEndpoint.

Metric service implementations are usually bound to a MetricRepository. A MetricRepository is responsible for storing and retrieving metric information. Spring Boot provides an InMemoryMetricRepository and a RedisMetricRepository out of the box (the in-memory repository is the default) but you can also write your own. The MetricRepository interface is actually composed of higher level MetricReader and MetricWriter interfaces. For full details refer to the Javadoc.

There’s nothing to stop you hooking a MetricRepository with back-end storage directly into your app, but we recommend using the default InMemoryMetricRepository (possibly with a custom Map instance if you are worried about heap usage) and populating a back-end repository through a scheduled export job. In that way you get some buffering in memory of the metric values and you can reduce the network chatter by exporting less frequently or in batches. Spring Boot provides an Exporter interface and a few basic implementations for you to get started with that.

User of the Coda Hale ‘Metrics’ library will automatically find that Spring Boot metrics are published to com.codahale.metrics.MetricRegistry. A default com.codahale.metrics.MetricRegistry Spring bean will be created when you declare a dependency to the com.codahale.metrics:metrics-core library; you can also register you own @Bean instance if you need customizations.

Users can create Coda Hale metrics by prefixing their metric names with the appropriate type (e.g. histogram.*, meter.*).

If the ‘Spring Messaging’ jar is on your classpath a MessageChannel called metricsChannel is automatically created (unless one already exists). All metric update events are additionally published as ‘messages’ on that channel. Additional analysis or actions can be taken by clients subscribing to that channel.