The following system metrics are exposed by Spring Boot:

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 data sources; you can add additional DataSourcePoolMetadataProvider beans if your favorite data source isn’t supported out of the box. See DataSourcePoolMetadataProvidersConfiguration for examples.

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

	Note
	Cache providers do not expose the hit/miss ratio in a consistent way. While some expose an aggregated value (i.e. the hit ratio since the last time the stats were cleared), others expose a temporal value (i.e. the hit ratio of the last second). Check your caching provider documentation for more details.

If two different cache managers happen to define the same cache, the name of the cache is prefixed by the name of the CacheManager bean.

It is possible to override part or all of those defaults by registering a bean with a customized version of CachePublicMetrics. By default, Spring Boot provides cache statistics for EhCache, Hazelcast, Infinispan, JCache and Guava. You can add additional CacheStatisticsProvider beans if your favorite caching library isn’t supported out of the box. See CacheStatisticsAutoConfiguration for examples.

If you are using Tomcat as your embedded servlet container, session metrics will automatically be exposed. The httpsessions.active and httpsessions.max keys provide the number of active and maximum sessions.

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.

The default implementation of GaugeService and CounterService provided by Spring Boot depends on the version of Java that you are using. With Java 8 (or better) the implementation switches to a high-performance version optimized for fast writes, backed by atomic in-memory buffers, rather than by the immutable but relatively expensive Metric<?> type (counters are approximately 5 times faster and gauges approximately twice as fast as the repository-based implementations). The Dropwizard metrics services (see below) are also very efficient even for Java 7 (they have backports of some of the Java 8 concurrency libraries), but they do not record timestamps for metric values. If performance of metric gathering is a concern then it is always advisable to use one of the high-performance options, and also to only read metrics infrequently, so that the writes are buffered locally and only read when needed.

	Note
	The old MetricRepository and its InMemoryMetricRepository implementation are not used by default if you are on Java 8 or if you are using Dropwizard metrics.

Spring Boot provides a couple of implementations of a marker interface called Exporter which can be used to copy metric readings from the in-memory buffers to a place where they can be analyzed and displayed. Indeed, if you provide a @Bean that implements the MetricWriter interface (or GaugeWriter for simple use cases) and mark it @ExportMetricWriter, then it will automatically be hooked up to an Exporter and fed metric updates every 5 seconds (configured via spring.metrics.export.delay-millis). In addition, any MetricReader that you define and mark as @ExportMetricReader will have its values exported by the default exporter.

The default exporter is a MetricCopyExporter which tries to optimize itself by not copying values that haven’t changed since it was last called (the optimization can be switched off using a flag spring.metrics.export.send-latest). Note also that the Dropwizard MetricRegistry has no support for timestamps, so the optimization is not available if you are using Dropwizard metrics (all metrics will be copied on every tick).

The default values for the export trigger (delay-millis, includes, excludes and send-latest) can be set as spring.metrics.export.*. Individual values for specific MetricWriters can be set as spring.metrics.export.triggers.<name>.* where <name> is a bean name (or pattern for matching bean names).

	Warning
	The automatic export of metrics is disabled if you switch off the default MetricRepository (e.g. by using Dropwizard metrics). You can get back the same functionality be declaring a bean of your own of type MetricReader and declaring it to be @ExportMetricReader.

52.8.1 Example: Export to Redis

If you provide a @Bean of type RedisMetricRepository and mark it @ExportMetricWriter the metrics are exported to a Redis cache for aggregation. The RedisMetricRepository has two important parameters to configure it for this purpose: prefix and key (passed into its constructor). It is best to use a prefix that is unique to the application instance (e.g. using a random value and maybe the logical name of the application to make it possible to correlate with other instances of the same application). The “key” is used to keep a global index of all metric names, so it should be unique “globally”, whatever that means for your system (e.g. two instances of the same system could share a Redis cache if they have distinct keys).

Example:

@Bean
@ExportMetricWriter
MetricWriter metricWriter(MetricExportProperties export) {
    return new RedisMetricRepository(connectionFactory,
        export.getRedis().getPrefix(), export.getRedis().getKey());
}

application.properties. 

spring.metrics.export.redis.prefix: metrics.mysystem.${spring.application.name:application}.${random.value:0000}
spring.metrics.export.redis.key: keys.metrics.mysystem

The prefix is constructed with the application name and id at the end, so it can easily be used to identify a group of processes with the same logical name later.

Note

It’s important to set both the key and the prefix. The key is used for all repository operations, and can be shared by multiple repositories. If multiple repositories share a key (like in the case where you need to aggregate across them), then you normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories).

	Tip
	The example above uses MetricExportProperties to inject and extract the key and prefix. This is provided to you as a convenience by Spring Boot, configured with sensible defaults. There is nothing to stop you using your own values as long as they follow the recommendations.

curl localhost:4242/api/query?start=1h-ago&m=max:counter.status.200.root
[
    {
        "metric": "counter.status.200.root",
        "tags": {
            "domain": "org.springframework.metrics",
            "process": "b968a76"
        },
        "aggregateTags": [],
        "dps": {
            "1430492872": 2,
            "1430492875": 6
        }
    }
]

@Value("${spring.application.name:application}.${random.value:0000}")
private String prefix = "metrics";

@Bean
@ExportMetricWriter
MetricWriter metricWriter() {
    return new StatsdMetricWriter(prefix, "localhost", 8125);
}

@Bean
@ExportMetricWriter
MetricWriter metricWriter(MBeanExporter exporter) {
    return new JmxMetricWriter(exporter);
}

There is an AggregateMetricReader that you can use to consolidate metrics from different physical sources. Sources for the same logical metric just need to publish them with a period-separated prefix, and the reader will aggregate (by truncating the metric names, and dropping the prefix). Counters are summed and everything else (i.e. gauges) take their most recent value.

This is very useful if multiple application instances are feeding to a central (e.g. Redis) repository and you want to display the results. Particularly recommended in conjunction with a MetricReaderPublicMetrics for hooking up to the results to the “/metrics” endpoint.

Example:

@Autowired
private MetricExportProperties export;

@Bean
public PublicMetrics metricsAggregate() {
    return new MetricReaderPublicMetrics(aggregatesMetricReader());
}

private MetricReader globalMetricsForAggregation() {
    return new RedisMetricRepository(this.connectionFactory,
        this.export.getRedis().getAggregatePrefix(), this.export.getRedis().getKey());
}

private MetricReader aggregatesMetricReader() {
    AggregateMetricReader repository = new AggregateMetricReader(
        globalMetricsForAggregation());
    return repository;
}

	Note
	The example above uses MetricExportProperties to inject and extract the key and prefix. This is provided to you as a convenience by Spring Boot, and the defaults will be sensible. They are set up in MetricExportAutoConfiguration.

	Note
	The MetricReaders above are not @Beans and are not marked as @ExportMetricReader because they are just collecting and analyzing data from other repositories, and don’t want to export their values.

A default MetricRegistry Spring bean will be created when you declare a dependency to the io.dropwizard.metrics:metrics-core library; you can also register you own @Bean instance if you need customizations. Users of the Dropwizard ‘Metrics’ library will find that Spring Boot metrics are automatically published to com.codahale.metrics.MetricRegistry. Metrics from the MetricRegistry are also automatically exposed via the /metrics endpoint

When Dropwizard metrics are in use, the default CounterService and GaugeService are replaced with a DropwizardMetricServices, which is a wrapper around the MetricRegistry (so you can @Autowired one of those services and use it as normal). You can also create “special” Dropwizard metrics by prefixing your metric names with the appropriate type (i.e. timer.*, histogram.* for gauges, and meter.* for counters).

If a MessageChannel bean called metricsChannel exists, then a MetricWriter will be created that writes metrics to that channel. The writer is automatically hooked up to an exporter (as for all writers), so all metric values will appear on the channel, and additional analysis or actions can be taken by subscribers (it’s up to you to provide the channel and any subscribers you need).