Spring Boot Actuator provides dependency management and auto-configuration for Micrometer, an application metrics facade that supports numerous monitoring systems, including:
![[Tip]](images/tip.png) | Tip |
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To learn more about Micrometer’s capabilities, please refer to its reference documentation, in particular the concepts section. |
Spring Boot auto-configures a composite MeterRegistry and adds a registry to the composite for each of the supported implementations that it finds on the classpath.
Having a dependency on micrometer-registry-{system} in your runtime classpath is enough for Spring Boot to configure the registry.
Most registries share common features. For instance, you can disable a particular registry even if the Micrometer registry implementation is on the classpath. For example, to disable Datadog:
management.metrics.export.datadog.enabled=falseSpring Boot will also add any auto-configured registries to the global static composite registry on the Metrics class unless you explicitly tell it not to:
management.metrics.use-global-registry=falseYou can register any number of MeterRegistryCustomizer beans to further configure the registry, such as applying common tags, before any meters are registered with the registry:
@Bean MeterRegistryCustomizer<MeterRegistry> metricsCommonTags() { return registry -> registry.config().commonTags("region", "us-east-1"); }
You can apply customizations to particular registry implementations by being more specific about the generic type:
@Bean MeterRegistryCustomizer<GraphiteMeterRegistry> graphiteMetricsNamingConvention() { return registry -> registry.config().namingConvention(MY_CUSTOM_CONVENTION); }
With that setup in place you can inject MeterRegistry in your components and register metrics:
@Component public class SampleBean { private final Counter counter; public SampleBean(MeterRegistry registry) { this.counter = registry.counter("received.messages"); } public void handleMessage(String message) { this.counter.increment(); // handle message implementation } }
Spring Boot also configures built-in instrumentation (i.e. MeterBinder implementations) that you can control via configuration or dedicated annotation markers.
By default, the AppOptics registry pushes metrics to api.appoptics.com/v1/measurements periodically.
To export metrics to SaaS AppOptics, your API token must be provided:
management.metrics.export.appoptics.api-token=YOUR_TOKENBy default, metrics are exported to Atlas running on your local machine. The location of the Atlas server to use can be provided using:
management.metrics.export.atlas.uri=https://atlas.example.com:7101/api/v1/publishDatadog registry pushes metrics to datadoghq periodically. To export metrics to Datadog, your API key must be provided:
management.metrics.export.datadog.api-key=YOUR_KEYYou can also change the interval at which metrics are sent to Datadog:
management.metrics.export.datadog.step=30sDynatrace registry pushes metrics to the configured URI periodically. To export metrics to Dynatrace, your API token, device ID, and URI must be provided:
management.metrics.export.dynatrace.api-token=YOUR_TOKEN management.metrics.export.dynatrace.device-id=YOUR_DEVICE_ID management.metrics.export.dynatrace.uri=YOUR_URI
You can also change the interval at which metrics are sent to Dynatrace:
management.metrics.export.dynatrace.step=30sBy default, metrics are exported to Elastic running on your local machine. The location of the Elastic server to use can be provided using the following property:
management.metrics.export.elastic.host=https://elastic.example.com:8086By default, metrics are exported to Ganglia running on your local machine. The Ganglia server host and port to use can be provided using:
management.metrics.export.ganglia.host=ganglia.example.com management.metrics.export.ganglia.port=9649
By default, metrics are exported to Graphite running on your local machine. The Graphite server host and port to use can be provided using:
management.metrics.export.graphite.host=graphite.example.com management.metrics.export.graphite.port=9004
Micrometer provides a default HierarchicalNameMapper that governs how a dimensional meter id is mapped to flat hierarchical names.
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To take control over this behaviour, define your |
@Bean public GraphiteMeterRegistry graphiteMeterRegistry(GraphiteConfig config, Clock clock) { return new GraphiteMeterRegistry(config, clock, MY_HIERARCHICAL_MAPPER); }
By default, the Humio registry pushes metrics to cloud.humio.com periodically. To export metrics to SaaS Humio, your API token must be provided:
management.metrics.export.humio.api-token=YOUR_TOKENYou should also configure one or more tags to identify the data source to which metrics will be pushed:
management.metrics.export.humio.tags.alpha=a management.metrics.export.humio.tags.bravo=b
By default, metrics are exported to Influx running on your local machine. The location of the Influx server to use can be provided using:
management.metrics.export.influx.uri=https://influx.example.com:8086Micrometer provides a hierarchical mapping to JMX, primarily as a cheap and portable way to view metrics locally.
By default, metrics are exported to the metrics JMX domain.
The domain to use can be provided using:
management.metrics.export.jmx.domain=com.example.app.metricsMicrometer provides a default HierarchicalNameMapper that governs how a dimensional meter id is mapped to flat hierarchical names.
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To take control over this behaviour, define your |
@Bean public JmxMeterRegistry jmxMeterRegistry(JmxConfig config, Clock clock) { return new JmxMeterRegistry(config, clock, MY_HIERARCHICAL_MAPPER); }
By default, metrics are exported to KairosDB running on your local machine. The location of the KairosDB server to use can be provided using:
management.metrics.export.kairos.uri=https://kairosdb.example.com:8080/api/v1/datapointsNew Relic registry pushes metrics to New Relic periodically. To export metrics to New Relic, your API key and account id must be provided:
management.metrics.export.newrelic.api-key=YOUR_KEY management.metrics.export.newrelic.account-id=YOUR_ACCOUNT_ID
You can also change the interval at which metrics are sent to New Relic:
management.metrics.export.newrelic.step=30sPrometheus expects to scrape or poll individual app instances for metrics.
Spring Boot provides an actuator endpoint available at /actuator/prometheus to present a Prometheus scrape with the appropriate format.
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The endpoint is not available by default and must be exposed, see exposing endpoints for more details. |
Here is an example scrape_config to add to prometheus.yml:
scrape_configs: - job_name: 'spring' metrics_path: '/actuator/prometheus' static_configs: - targets: ['HOST:PORT']
For ephemeral or batch jobs which may not exist long enough to be scraped, Prometheus Pushgateway support can be used to expose their metrics to Prometheus. To enable Prometheus Pushgateway support, add the following dependency to your project:
<dependency> <groupId>io.prometheus</groupId> <artifactId>simpleclient_pushgateway</artifactId> </dependency>
When the Prometheus Pushgateway dependency is present on the classpath, Spring Boot auto-configures a PrometheusPushGatewayManager bean.
This manages the pushing of metrics to a Prometheus Pushgateway.
The PrometheusPushGatewayManager can be tuned using properties under management.metrics.export.prometheus.pushgateway.
For advanced configuration, you can also provide your own PrometheusPushGatewayManager bean.
SignalFx registry pushes metrics to SignalFx periodically. To export metrics to SignalFx, your access token must be provided:
management.metrics.export.signalfx.access-token=YOUR_ACCESS_TOKENYou can also change the interval at which metrics are sent to SignalFx:
management.metrics.export.signalfx.step=30sMicrometer ships with a simple, in-memory backend that is automatically used as a fallback if no other registry is configured. This allows you to see what metrics are collected in the metrics endpoint.
The in-memory backend disables itself as soon as you’re using any of the other available backend. You can also disable it explicitly:
management.metrics.export.simple.enabled=falseThe StatsD registry pushes metrics over UDP to a StatsD agent eagerly. By default, metrics are exported to a StatsD agent running on your local machine. The StatsD agent host and port to use can be provided using:
management.metrics.export.statsd.host=statsd.example.com management.metrics.export.statsd.port=9125
You can also change the StatsD line protocol to use (default to Datadog):
management.metrics.export.statsd.flavor=etsyWavefront registry pushes metrics to Wavefront periodically. If you are exporting metrics to Wavefront directly, your API token must be provided:
management.metrics.export.wavefront.api-token=YOUR_API_TOKENAlternatively, you may use a Wavefront sidecar or an internal proxy set up in your environment that forwards metrics data to the Wavefront API host:
management.metrics.export.wavefront.uri=proxy://localhost:2878 | Tip |
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If publishing metrics to a Wavefront proxy (as described in the documentation), the host must be in the |
You can also change the interval at which metrics are sent to Wavefront:
management.metrics.export.wavefront.step=30sSpring 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 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
- Spring Integration metrics
Auto-configuration enables the instrumentation of requests handled by Spring MVC.
When management.metrics.web.server.auto-time-requests is true, this instrumentation occurs for all requests.
Alternatively, when set to false, you can enable instrumentation by adding @Timed to a request-handling method:
@RestController @Timedpublic class MyController { @GetMapping("/api/people") @Timed(extraTags = { "region", "us-east-1" })
@Timed(value = "all.people", longTask = true)
public List<Person> listPeople() { ... } }
| A controller class to enable timings on every request handler in the controller. |
| A method to enable for an individual endpoint. This is not necessary if you have it on the class, but can be used to further customize the timer for this particular endpoint. |
| A method with |
By default, metrics are generated with the name, http.server.requests.
The name can be customized by setting the management.metrics.web.server.requests-metric-name property.
By default, Spring MVC-related metrics are tagged with the following information:
| Tag | Description |
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| Simple class name of any exception that was thrown while handling the request. |
| Request’s method (for example, |
| Request’s outcome based on the status code of the response.
1xx is |
| Response’s HTTP status code (for example, |
| Request’s URI template prior to variable substitution, if possible (for example, |
To customize the tags, provide a @Bean that implements WebMvcTagsProvider.
Auto-configuration enables the instrumentation of all requests handled by WebFlux controllers and functional handlers.
By default, metrics are generated with the name http.server.requests.
You can customize the name by setting the management.metrics.web.server.requests-metric-name property.
By default, WebFlux-related metrics are tagged with the following information:
| Tag | Description |
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| Simple class name of any exception that was thrown while handling the request. |
| Request’s method (for example, |
| Request’s outcome based on the status code of the response.
1xx is |
| Response’s HTTP status code (for example, |
| Request’s URI template prior to variable substitution, if possible (for example, |
To customize the tags, provide a @Bean that implements WebFluxTagsProvider.
Auto-configuration enables the instrumentation of requests handled by the Jersey JAX-RS implementation.
When management.metrics.web.server.auto-time-requests is true, this instrumentation occurs for all requests.
Alternatively, when set to false, you can enable instrumentation by adding @Timed to a request-handling method:
@Component @Path("/api/people") @Timed
| On a resource class to enable timings on every request handler in the resource. |
| On a method to enable for an individual endpoint. This is not necessary if you have it on the class, but can be used to further customize the timer for this particular endpoint. |
| On a method with |
By default, metrics are generated with the name, http.server.requests.
The name can be customized by setting the management.metrics.web.server.requests-metric-name property.
By default, Jersey server metrics are tagged with the following information:
| Tag | Description |
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| Simple class name of any exception that was thrown while handling the request. |
| Request’s method (for example, |
| Request’s outcome based on the status code of the response.
1xx is |
| Response’s HTTP status code (for example, |
| Request’s URI template prior to variable substitution, if possible (for example, |
To customize the tags, provide a @Bean that implements JerseyTagsProvider.
Spring Boot Actuator manages the instrumentation of both RestTemplate and WebClient.
For that, you have to get injected with an auto-configured builder and use it to create instances:
RestTemplateBuilderforRestTemplateWebClient.BuilderforWebClient
It is also possible to apply manually the customizers responsible for this instrumentation, namely MetricsRestTemplateCustomizer and MetricsWebClientCustomizer.
By default, metrics are generated with the name, http.client.requests.
The name can be customized by setting the management.metrics.web.client.requests-metric-name property.
By default, metrics generated by an instrumented client are tagged with the following information:
method, the request’s method (for example,GETorPOST).uri, the request’s URI template prior to variable substitution, if possible (for example,/api/person/{id}).status, the response’s HTTP status code (for example,200or500).clientName, the host portion of the URI.
To customize the tags, and depending on your choice of client, you can provide a @Bean that implements RestTemplateExchangeTagsProvider or WebClientExchangeTagsProvider.
There are convenience static functions in RestTemplateExchangeTags and WebClientExchangeTags.
Auto-configuration enables the instrumentation of all available Caches on startup with metrics prefixed with cache.
Cache instrumentation is standardized for a basic set of metrics.
Additional, cache-specific metrics are also available.
The following cache libraries are supported:
- Caffeine
- EhCache 2
- Hazelcast
- Any compliant JCache (JSR-107) implementation
Metrics are tagged by the name of the cache and by the name of the CacheManager that is derived from the bean name.
![[Note]](images/note.png) | Note |
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Only caches that are available on startup are bound to the registry.
For caches created on-the-fly or programmatically after the startup phase, an explicit registration is required.
A |
Auto-configuration enables the instrumentation of all available DataSource objects with metrics prefixed with jdbc.connections.
Data source instrumentation results in gauges representing the currently active, maximum allowed, and minimum allowed connections in the pool.
Metrics are also tagged by the name of the DataSource computed based on the bean name.
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By default, Spring Boot provides metadata for all supported data sources; you can add additional |
Also, Hikari-specific metrics are exposed with a hikaricp prefix.
Each metric is tagged by the name of the Pool (can be controlled with spring.datasource.name).
Auto-configuration enables the instrumentation of all available Hibernate EntityManagerFactory instances that have statistics enabled with a metric named hibernate.
Metrics are also tagged by the name of the EntityManagerFactory that is derived from the bean name.
To enable statistics, the standard JPA property hibernate.generate_statistics must be set to true.
You can enable that on the auto-configured EntityManagerFactory as shown in the following example:
spring.jpa.properties.hibernate.generate_statistics=trueTo register custom metrics, inject MeterRegistry into your component, as shown in the following example:
class Dictionary { private final List<String> words = new CopyOnWriteArrayList<>(); Dictionary(MeterRegistry registry) { registry.gaugeCollectionSize("dictionary.size", Tags.empty(), this.words); } // … }
If you find that you repeatedly instrument a suite of metrics across components or applications, you may encapsulate this suite in a MeterBinder implementation.
By default, metrics from all MeterBinder beans will be automatically bound to the Spring-managed MeterRegistry.
If you need to apply customizations to specific Meter instances you can use the io.micrometer.core.instrument.config.MeterFilter interface.
By default, all MeterFilter beans will be automatically applied to the micrometer MeterRegistry.Config.
For example, if you want to rename the mytag.region tag to mytag.area for all meter IDs beginning with com.example, you can do the following:
@Bean public MeterFilter renameRegionTagMeterFilter() { return MeterFilter.renameTag("com.example", "mytag.region", "mytag.area"); }
Common tags are generally used for dimensional drill-down on the operating environment like host, instance, region, stack, etc. Commons tags are applied to all meters and can be configured as shown in the following example:
management.metrics.tags.region=us-east-1 management.metrics.tags.stack=prod
The example above adds region and stack tags to all meters with a value of us-east-1 and prod respectively.
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The order of common tags is important if you are using Graphite.
As the order of common tags cannot be guaranteed using this approach, Graphite users are advised to define a custom |
In addition to MeterFilter beans, it’s also possible to apply a limited set of customization on a per-meter basis using properties.
Per-meter customizations apply to any all meter IDs that start with the given name.
For example, the following will disable any meters that have an ID starting with example.remote
management.metrics.enable.example.remote=falseThe following properties allow per-meter customization:
Table 57.1. Per-meter customizations
| Property | Description |
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| Whether to deny meters from emitting any metrics. |
| Whether to publish a histogram suitable for computing aggregable (across dimension) percentile approximations. |
| Publish less histogram buckets by clamping the range of expected values. |
| Publish percentile values computed in your application |
| Publish a cumulative histogram with buckets defined by your SLAs. |
For more details on concepts behind percentiles-histogram, percentiles and sla refer to the "Histograms and percentiles" section of the micrometer documentation.
Spring Boot provides a metrics endpoint that can be used diagnostically to examine the metrics collected by an application.
The endpoint is not available by default and must be exposed, see exposing endpoints for more details.
Navigating to /actuator/metrics displays a list of available meter names.
You can drill down to view information about a particular meter by providing its name as a selector, e.g. /actuator/metrics/jvm.memory.max.
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The name you use here should match the name used in the code, not the name after it has been naming-convention normalized for a monitoring system it is shipped to.
In other words, if |
You can also add any number of tag=KEY:VALUE query parameters to the end of the URL to dimensionally drill down on a meter, e.g. /actuator/metrics/jvm.memory.max?tag=area:nonheap.
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The reported measurements are the sum of the statistics of all meters matching the meter name and any tags that have been applied.
So in the example above, the returned "Value" statistic is the sum of the maximum memory footprints of "Code Cache", "Compressed Class Space", and "Metaspace" areas of the heap.
If you just wanted to see the maximum size for the "Metaspace", you could add an additional |