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 instance, 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, 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=http://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=30sBy 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.
| Tip |
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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, 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=http://influx.example.com:8086Micrometer provides a hierarchical mapping to
JMX, primarily as a cheap and portable way to
view metrics locally. Micrometer provides a default HierarchicalNameMapper that governs
how a dimensional meter id is mapped to flat hierarchical names.
| Tip |
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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); }
New 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.
| Tip |
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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']
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.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
- 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:
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).exception, the simple class name of any exception that was thrown while handling the request.
To customize the tags, provide a @Bean that implements WebMvcTagsProvider.
Auto-configuration enables the instrumentation of all requests handled by WebFlux
controllers. You can also use a helper class, RouterFunctionMetrics, to instrument
applications that use WebFlux’s functional programming model.
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 for the annotation-based programming model 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).exception, the simple class name of any exception that was thrown while handling the request.
To customize the tags, provide a @Bean that implements WebFluxTagsProvider.
By default, metrics for the functional programming model 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).
To customize the tags, use the defaultTags method on your RouterFunctionMetrics
instance.
The instrumentation of any RestTemplate created using the auto-configured
RestTemplateBuilder is enabled. It is also possible to apply
MetricsRestTemplateCustomizer manually.
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 RestTemplate 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, provide a @Bean that implements
RestTemplateExchangeTagsProvider. There are convenience static functions in
RestTemplateExchangeTags.
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 a
metric named jdbc. Data source instrumentation results in gauges representing the
currently active, maximum allowed, and minimum allowed connections in the pool. Each of
these gauges has a name that is prefixed by jdbc.
Metrics are also tagged by the name of the DataSource computed based on the bean name.
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).
To 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"); }
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 54.1. Per-meter customizations
| Property | Description |
|---|---|
| Whether to deny meters from emitting any metrics. |
| Whether to publish a histogram suitable for computing aggregable (across dimension) percentile approximations. |
| 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.
| Tip |
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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 |