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Micrometer Metrics

Micrometer provides an abstraction layer for metrics collection. It defines an API for basic meter types, like counters, gauges, timers, and distribution summaries, along with a MeterRegistry API that generalizes metrics collection and propagation for different backend monitoring systems.

Micrometer is the recommended approach to metrics for Quarkus.
By default, the metrics are exposed on the main HTTP server. If you would like to surface metrics from a separate management port, see the Managed interface section.

Micrometer and monitoring system extensions

Quarkus Micrometer extensions are structured in the same way as the Micrometer project. The quarkus-micrometer extension provides core Micrometer support and runtime integration. Other Quarkus and Quarkiverse extensions use the Quarkus Micrometer extension to provide support for other monitoring systems.

Quarkus extensions:

  • micrometer

  • micrometer-registry-prometheus

Quarkiverse extensions (may be incomplete):

  • micrometer-registry-azure-monitor

  • micrometer-registry-datadog

  • micrometer-registry-graphite

  • micrometer-registry-influx

  • micrometer-registry-jmx

  • micrometer-registry-newrelic-telemetry

  • micrometer-registry-otlp

  • micrometer-registry-signalfx

  • micrometer-registry-stackdriver

  • micrometer-registry-statsd

To add support for Prometheus metrics to your application, for example, use the micrometer-registry-prometheus extension. It will bring in the Quarkus Micrometer extension and Micrometer core libraries as dependencies.

Add the extension to your project using following command (from your project directory):

CLI
quarkus extension add micrometer-registry-prometheus
Maven
./mvnw quarkus:add-extension -Dextensions='micrometer-registry-prometheus'
Gradle
./gradlew addExtension --extensions='micrometer-registry-prometheus'

This will add the following to your build file:

pom.xml
<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-micrometer-registry-prometheus</artifactId>
</dependency>
build.gradle
implementation("io.quarkus:quarkus-micrometer-registry-prometheus")

And you’re all set!

A similar process applies for other meter registry extensions. To use the Micrometer StackDriver MeterRegistry, for example, you would use the quarkus-micrometer-registry-stackdriver extension from the Quarkiverse:

CLI
quarkus extension add io.quarkiverse.micrometer.registry:quarkus-micrometer-registry-stackdriver
Maven
./mvnw quarkus:add-extension -Dextensions='io.quarkiverse.micrometer.registry:quarkus-micrometer-registry-stackdriver'
Gradle
./gradlew addExtension --extensions='io.quarkiverse.micrometer.registry:quarkus-micrometer-registry-stackdriver'
pom.xml
<dependency>
    <groupId>io.quarkiverse.micrometer.registry</groupId>
    <artifactId>quarkus-micrometer-registry-stackdriver</artifactId>
</dependency>
build.gradle
implementation("io.quarkiverse.micrometer.registry:quarkus-micrometer-registry-stackdriver")

Other registry implementations

If the Micrometer registry you would like to use does not yet have an associated extension, use the quarkus-micrometer extension and bring in the Micrometer meter registry dependency directly:

pom.xml
<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-micrometer</artifactId>
</dependency>
<dependency>
    <groupId>com.acme</groupId>
    <artifactId>custom-micrometer-registry</artifactId>
    <version>...</version>
</dependency>
build.gradle
implementation("io.quarkus:quarkus-micrometer")
implementation("com.acme:custom-micrometer-registry")

You will then need to specify your own provider to configure and initialize the MeterRegistry, as discussed in the next section.

Create a customized MeterRegistry

Use a custom @Produces method to create and configure a your own MeterRegistry if you need to.

The following example customizes the line format used for StatsD:

@Produces
@Singleton (1)
public StatsdMeterRegistry createStatsdMeterRegistry(StatsdConfig statsdConfig, Clock clock) { (2)
    // define what to do with lines
    Consumer<String> lineLogger = line -> logger.info(line);

    // inject a configuration object, and then customize the line builder
    return StatsdMeterRegistry.builder(statsdConfig)
          .clock(clock)
          .lineSink(lineLogger)
          .build();
}
1 The method returns a @Singleton.
2 The method returns the specific type of MeterRegistry

This example corresponds to the following instructions in the Micrometer documentation: Micrometer StatsD: Customizing the Metrics Sink

Use MicroProfile Config to inject any configuration attributes you need to configure the registry. Most Micrometer registry extensions, like quarkus-micrometer-registry-statsd, provide registry-specific configuration objects that are integrated with the Quarkus configuration model. The Quarkiverse GitHub Repository can be a useful implementation reference.

Create your own metrics

Metrics data is used in the aggregate to observe how data changes over time. This data is used for trend analysis, anomaly detection, and alerting. Data is stored by backend monitoring systems in time series databases, with new values appended to the end of the series.

Metrics are constructed lazily. You may not see any data for the metric you’re looking for until you’ve performed an action that will create it, like visiting an endpoint.

Naming conventions

Meter names should use dots to separate segments, a.name.like.this. Micrometer applies naming conventions to convert registered meter names to match the expectations of backend monitoring systems.

Given the following declaration of a timer: registry.timer("http.server.requests"), applied naming conventions will emit the following metrics for different monitoring systems:

  • Prometheus: http_server_requests_duration_seconds

  • Atlas: httpServerRequests

  • Graphite: http.server.requests

  • InfluxDB: http_server_requests

Define dimensions for aggregation

Metrics, single numerical measurements, often have additional data captured with them. This ancillary data is used to group or aggregate metrics for analysis. The Micrometer API refers to this dimensional data as tags, but you may it referred to as "labels" or "attributes" in other documentation sources.

Micrometer is built primariliy for backend monitoring systems that support dimensional data (metric names that are enchriched with key/value pairs). For heirarchical systems that only support a flat metric name, Micrometer will flatten the set of key/value pairs (sorted by key) and add them to the name.

Tags can be specified when a meter is registered with a MeterRegistry or using a Meter Filter.

See the Micrometer documentation for additional advice on tag naming.

Each unique combination of metric name and dimension produces a unique time series. Using an unbounded set of dimensional data can lead to a "cardinality explosion", an exponential increase in the creation of new time series.

Obtain a reference to a MeterRegistry

To register meters, you need a reference to a MeterRegistry, which is configured and maintained by the Micrometer extension.

Use one of the following methods to obtain a reference to a MeterRegistry:

  1. Use CDI Constructor injection:

    package org.acme.micrometer;
    
    import io.micrometer.core.instrument.MeterRegistry;
    
    import jakarta.ws.rs.GET;
    import jakarta.ws.rs.Path;
    import jakarta.ws.rs.Produces;
    
    @Path("/example")
    @Produces("text/plain")
    public class ExampleResource {
    
        private final MeterRegistry registry;
    
        ExampleResource(MeterRegistry registry) {
            this.registry = registry;
        }
    }
  2. Use a MeterRegistry member variable and use @Inject:

        @Inject
        MeterRegistry registry;
  3. Use the global MeterRegistry:

        MeterRegistry registry = Metrics.globalRegistry;

Gauges

Gauges measure a value that can increase or decrease over time, like the speedometer on a car. Gauges can be useful when monitoring the statistics for a cache or collection.

Gauge values are sampled rather than set; there is no record of how the value associated with a gauge may have changed between measurements.

Micrometer provides a few mechanisms for creating gauges:

  1. Wrap construction of a collection to monitor its size:

    List<String> list = registry.gaugeCollectionSize("fantastic.list", (1)
            Tags.of("key", "value") // optional (2)
            new ArrayList<>());  (3)
    1 Create a new gauge, list.size, using the dot-separated convention.
    2 Associate tags with the gauge. Gauge tag values are constant, and must be assigned at construction time.
    3 Construct the array list whose size should be observed.
  2. Use a builder to create a Gauge that will call a function:

    Gauge.builder("jvm.threads.peak", threadBean, ThreadMXBean::getPeakThreadCount) (1)
        .baseUnit(BaseUnits.THREADS) // optional (2)
        .description("The peak live thread count...") // optional (3)
        .tags("key", "value") // optional (4)
        .register(registry); (5)
    1 Create a new gauge called jvm.threads.peak that will call getPeakThreadCount on threadBean, an instance of ThreadMXBean
    2 Define the base unit, see BaseUnits.java for predefined values.
    3 Provide a description of the Gauge
    4 Associate tags with the gauge
    5 Register the Gauge with the MeterRegistry

See Gauges in the Micrometer documentation for more information and examples. Of note are two special cases: TimeGauge for measuring time, and a MultiGauge for reporting several criteria together.

Micrometer does not create strong references to the objects it observes by default. Depending on the registry, Micrometer either omits gauges that observe objects that have been garbage-collected entirely or uses NaN (not a number) as the observed value.

When should you use a gauge? Only use a gauge when you can’t use something else. Gauges can be less straight-forward to use than other meters. If what you are measuring can be counted (because the value always increments), use a counter instead.

Counters

Counters measure values that only increase. Use one of the methods below to create a counter.

  1. Use a convenience method on the MeterRegistry:

    registry.counter("example.prime.number", "type", "prime"); (1) (2)
    1 example.prime.number is the counter name.
    2 type is a dimensional tag with value prime.
  2. Use Counter.builder to provide a description and units:

    Counter.builder("count.me") (1)
        .baseUnit("beans")            // optional (2)
        .description("a description") // optional (3)
        .tags("region", "test")       // optional (4)
        .register(registry);
    1 Create a new counter called count.me
    2 Define a custom base unit. See BaseUnits.java for predefined values.
    3 Provide a description for the counter
    4 Associate tags with the counter
  3. Annotate a method

    @Counted(value = "counted.method", extraTags = { "extra", "annotated" }) (1) (2)
    void countThisMethod(){
        ...
    }
    1 A CDI interceptor will create and register a counter called counted.method
    2 The interceptor-created counter will have the "extra" dimension tag with value "annotated"

See Counters in the Micrometer documentation for more information and examples, including the less common FunctionCounter that can be used to measure the result returned by an always increasing function.

When should you use a counter? Use a counter if you are doing something that can not be either timed or summarized. If you want to understand more about how a value is changing, a timer (when the base unit of measurement is time) or a distribution summary might be more appropriate.

Summaries and Timers

Timers and distribution summaries in Micrometer are very similar. Both meters record data, and can capture additional histogram or percentile data. While distribution summaries can be use for arbitrary types of data, timers are optimized for measuring time and durations.

Timers and distribution summaries store at least three values internally:

  • the aggregation of all recorded values as a sum

  • the number of values that have been recorded (a counter)

  • the highest value seen within a decaying time window (a gauge).

Create a distribution summary

Use a distribution summary to record a value, not time. Use one of the following methods to create a distribution summary.

  1. Use a convenience method on the MeterRegistry:

    registry.summary("bytes.written", "protocol", "http"); (1) (2)
    1 bytes.written is the summary name
    2 protocol is a dimensional tag with value http.
  2. Use DistributionSummary.builder to provide a description and units:

    DistributionSummary.builder("response.size") (1)
        .baseUnit("bytes")            // optional (2)
        .description("a description") // optional (3)
        .tags("protocol", "http")     // optional (4)
        .register(registry);
    1 Create a new distribution summary called response.size
    2 Use bytes as a base unit. See BaseUnits.java for predefined values.
    3 Provide a description for the distribution summary
    4 Associate tags with the distribution summary

Create a timer

Timers measure short-duration latencies and how often they occur. Negative values are not supported, and longer durations could cause an overflow of the total time (Long.MAX_VALUE nanoseconds (292.3 years)).

Use one of the following methods to construct a timer.

  1. Use a convenience method on the MeterRegistry:

    registry.timer("fabric.selection", "primary", "blue"); (1) (2)
    1 fabric.selection is the summary name
    2 primary is a dimensional tag with value blue.
  2. Use Timer.builder to provide a description and units:

    Timer.builder("my.timer")        (1) (2)
        .description("description ") // optional (3)
        .tags("region", "test")      // optional (4)
        .register(registry);
    1 Create a new timer called my.timer
    2 Timers measure time, and will convert it into the units required by the monitoring backend
    3 Provide a description for the distribution summary
    4 Associate tags with the timer
  3. Annotate a method

    @Timed(value = "call", extraTags = {"region", "test"}) (1) (2)
    1 A CDI interceptor will create and register a timer called call
    2 The interceptor-created timer will have the "region" dimension tag with value "test"

Measure durations with Timers

Micrometer provides the following convenience mechanisms for recording durations.

  1. Wrap the invocation of a Runnable:

    timer.record(() -> noReturnValue());
  2. Wrap the invocation of a Callable:

    timer.recordCallable(() -> returnValue());
  3. Create a wrapped Runnable for repeated invocation:

    Runnable r = timer.wrap(() -> noReturnValue());
  4. Create a wrapped Callable for repeated invocation:

    Callable c = timer.wrap(() -> returnValue());
  5. Use a Sample for more complex code paths:

    Sample sample = Timer.start(registry); (1)
    
    doStuff; (2)
    
    sample.stop(registry.timer("my.timer", "response", response.status())); (3)
    1 We create a sample, which records the start of the timer.
    2 The sample can be passed along as context
    3 We can choose the timer when the sample is stopped. This example uses a response status as a tag identifying the timer, which won’t be known until processing is complete.

Histograms and percentiles

Both timers and distribution summaries can be configured to emit additional statistics, like histogram data, precomputed percentiles, or service level objective (SLO) boundaries. See Timers and Distribution Summaries in the Micrometer documentation for more information and examples, including memory footprint estimation for both types.

The count, sum, and histogram data associated with timers and distribution summaries can be re-aggregated across dimensions (or across a series of instances).

Precomputed percentile values can not. Percentiles are unique to each dataset (the 90th percentile of this collection of measurements).

Automatically generated metrics

The Micrometer extension automatically times HTTP server requests. Following Prometheus naming conventions for timers, look for http_server_requests_seconds_count, http_server_requests_seconds_sum, and http_server_requests_seconds_max. Dimensional labels have been added for the requested uri, the HTTP method (GET, POST, etc.), the status code (200, 302, 404, etc.), and a more general outcome field.

Ignoring endpoints

You can disable measurement of HTTP endpoints using the quarkus.micrometer.binder.http-server.ignore-patterns property. This property accepts a comma-separated list of simple regex match patterns identifying URI paths that should be ignored. For example, setting quarkus.micrometer.binder.http-server.ignore-patterns=/example/prime/[0-9]+ will ignore a request to http://localhost:8080/example/prime/7919. A request to http://localhost:8080/example/gauge/7919 would still be measured.

URI templates

The micrometer extension will make a best effort at representing URIs containing path parameters in templated form. Using examples from above, a request to http://localhost:8080/example/prime/7919 should appear as an attribute of http_server_requests_seconds_* metrics with a value of uri=/example/prime/{number}.

Use the quarkus.micrometer.binder.http-server.match-patterns property if the correct URL can not be determined. This property accepts a comma-separated list defining an association between a simple regex match pattern and a replacement string. For example, setting quarkus.micrometer.binder.http-server.match-patterns=/example/prime/[0-9]+=/example/{jellybeans} would use the value /example/{jellybeans} for the uri attribute any time the requested uri matches /example/prime/[0-9]+.

Exported metrics format

By default, the metrics are exported using the Prometheus format application/openmetrics-text, you can revert to the former format by specifying the Accept request header to text/plain (curl -H "Accept: text/plain" localhost:8080/q/metrics/).

Customizing Micrometer

Quarkus provides a variety of way to customize Micrometer.

Use MeterFilter to customize emitted tags and metrics

Micrometer uses MeterFilter instances to customize the metrics emitted by MeterRegistry instances. The Micrometer extension will detect MeterFilter CDI beans and use them when initializing MeterRegistry instances.

@Singleton
public class CustomConfiguration {

    @ConfigProperty(name = "deployment.env")
    String deploymentEnv;

    /** Define common tags that apply only to a Prometheus Registry */
    @Produces
    @Singleton
    @MeterFilterConstraint(applyTo = PrometheusMeterRegistry.class)
    public MeterFilter configurePrometheusRegistries() {
        return MeterFilter.commonTags(Arrays.asList(
                Tag.of("registry", "prometheus")));
    }

    /** Define common tags that apply globally */
    @Produces
    @Singleton
    public MeterFilter configureAllRegistries() {
        return MeterFilter.commonTags(Arrays.asList(
                Tag.of("env", deploymentEnv)));
    }

    /** Enable histogram buckets for a specific timer */
    @Produces
    @Singleton
    public MeterFilter enableHistogram() {
        return new MeterFilter() {
            @Override
            public DistributionStatisticConfig configure(Meter.Id id, DistributionStatisticConfig config) {
                if(id.getName().startsWith("myservice")) {
                    return DistributionStatisticConfig.builder()
                        .percentiles(0.5, 0.95)     // median and 95th percentile, not aggregable
                        .percentilesHistogram(true) // histogram buckets (e.g. prometheus histogram_quantile)
                        .build()
                        .merge(config);
                }
                return config;
            }
        };
    }
}

In this example, a singleton CDI bean will produce two different MeterFilter beans. One will be applied only to Prometheus MeterRegistry instances (using the @MeterFilterConstraint qualifier), and another will be applied to all MeterRegistry instances. An application configuration property is also injected and used as a tag value. Additional examples of MeterFilters can be found in the official documentation.

Use HttpServerMetricsTagsContributor for server HTTP requests

By providing CDI beans that implement io.quarkus.micrometer.runtime.HttpServerMetricsTagsContributor, user code can contribute arbitrary tags based on the details of HTTP request

Use MeterRegistryCustomizer for arbitrary customizations to meter registries

By providing CDI beans that implement io.quarkus.micrometer.runtime.MeterRegistryCustomizer user code has the change to change the configuration of any MeterRegistry that has been activated. Unless an implementation is annotated with @io.quarkus.micrometer.runtime.MeterRegistryCustomizerConstraint, the customization applies to all MeterRegistry instances.

Does Micrometer support annotations?

Micrometer does define two annotations, @Counted and @Timed, that can be added to methods. The @Timed annotation will wrap the execution of a method and will emit the following tags in addition to any tags defined on the annotation itself: class, method, and exception (either "none" or the simple class name of a detected exception).

Parameters to @Counted and @Timed can be annotated with @MeterTag to dynamically assign meaningful tag values.

MeterTag.resolver can be used to extract a tag from a method parameter, by creating a bean implementing io.micrometer.common.annotation.ValueResolver and referring to this class: `@MeterTag(resolver=CustomResolver.class)

MeterTag.expression is also supported, but you have to implement the evaluation of the expression by creating a bean implementing io.micrometer.common.annotation.ValueExpressionResolver that can evaluate expressions.

enum Currency { USD, EUR }

@Singleton
class EnumOrdinalResolver implements ValueResolver {
    @Override
    public String resolve(Object parameter) {
        if(parameter instanceof Enum) {
            return String.valueOf(((Enum<?>) parameter).ordinal());
        }
        return null;
    }
}

@Singleton
public class MyExpressionResolver implements ValueExpressionResolver {
    @Override
    public String resolve(String expression, Object parameter) {
        return someParser.parse(expression).evaluate(parameter);
    }
}

// tags = type=with_enum, currency=${currency.toString()}
@Timed(value="time_something", extraTags = {"type", "with_enum"})
public Something calculateSomething(@MeterTag Currency currency) { ... }

// tags = type=with_enum, the_currency=${currency.toString()}
@Timed(value="time_something", extraTags = {"type", "with_enum"})
public Something calculateSomething(@MeterTag(key="the_currency") Currency currency) { ... }

// tags = type=with_enum, currency=${currency.ordinal()}
@Timed(value="time_something", extraTags = {"type", "with_enum"})
public Something calculateSomething(@MeterTag(resolver=EnumOrdinalResolver.class) Currency currency) { ... }

// tags = type=with_enum, currency=${currency.ordinal()}
@Timed(value="time_something", extraTags = {"type", "with_enum"})
public Something calculateSomething(@MeterTag(expression="currency.ordinal()") Currency currency) { ... }
Provided tag values MUST BE of LOW-CARDINALITY. High-cardinality values can lead to performance and storage issues in your metrics backend (a "cardinality explosion"). Tag values should not use end-user data, since those could be high-cardinality.

Many methods, like REST endpoint methods or Vert.x Routes, are counted and timed by the micrometer extension out of the box.

Support for the MicroProfile Metrics API

If you use the MicroProfile Metrics API in your application, the Micrometer extension will create an adaptive layer to map those metrics into the Micrometer registry. Note that naming conventions between the two systems is different, so the metrics that are emitted when using MP Metrics with Micrometer will change.

Use a MeterFilter to remap names or tags according to your conventions.

@Produces
@Singleton
public MeterFilter renameApplicationMeters() {
    final String targetMetric = MPResourceClass.class.getName() + ".mpAnnotatedMethodName";

    return MeterFilter() {
        @Override
        public Meter.Id map(Meter.Id id) {
            if (id.getName().equals(targetMetric)) {
                // Drop the scope tag (MP Registry type: application, vendor, base)
                List<Tag> tags = id.getTags().stream().filter(x -> !"scope".equals(x.getKey()))
                        .collect(Collectors.toList());
                // rename the metric
                return id.withName("my.metric.name").replaceTags(tags);
            }
            return id;
        }
    };
}

Ensure the following dependency is present if you require the MicroProfile Metrics API:

pom.xml
<dependency>
    <groupId>org.eclipse.microprofile.metrics</groupId>
    <artifactId>microprofile-metrics-api</artifactId>
</dependency>
build.gradle
implementation("org.eclipse.microprofile.metrics:microprofile-metrics-api")
The MP Metrics API compatibility layer may be moved to a different extension in the future.

Management interface

By default, the metrics are exposed on the main HTTP server.

You can expose them on a separate network interface and port by setting quarkus.management.enabled=true in your application configuration. Note that this property is a build-time property. The value cannot be overridden at runtime.

If you enable the management interface without customizing the management network interface and port, the metrics are exposed under: http://0.0.0.0:9000/q/metrics.

You can configure the path of each exposed format using:

quarkus.micrometer.export.json.enabled=true # Enable json metrics
quarkus.micrometer.export.json.path=metrics/json
quarkus.micrometer.export.prometheus.path=metrics/prometheus

With such a configuration, the json metrics will be available from http://0.0.0.0:9000/q/metrics/json. The prometheus metrics will be available from http://0.0.0.0:9000/q/metrics/prometheus.

Refer to the management interface reference for more information.

Configuration Reference

Configuration property fixed at build time - All other configuration properties are overridable at runtime

Configuration property

Type

Default

Micrometer metrics support.

Micrometer metrics support is enabled by default.

Environment variable: QUARKUS_MICROMETER_ENABLED

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boolean

true

Micrometer MeterRegistry discovery.

Micrometer MeterRegistry implementations discovered on the classpath will be enabled automatically by default.

Environment variable: QUARKUS_MICROMETER_REGISTRY_ENABLED_DEFAULT

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boolean

true

Micrometer MeterBinder discovery.

Micrometer MeterBinder implementations discovered on the classpath will be enabled automatically by default.

Environment variable: QUARKUS_MICROMETER_BINDER_ENABLED_DEFAULT

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boolean

true

Outbound HTTP request metrics support.

Support for HTTP client metrics will be enabled if Micrometer support is enabled, the REST client feature is enabled, and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_CLIENT_ENABLED

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boolean

Inbound HTTP metrics support.

Support for HTTP server metrics will be enabled if Micrometer support is enabled, an extension serving HTTP traffic is enabled, and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_SERVER_ENABLED

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boolean

Micrometer JVM metrics support.

Support for JVM metrics will be enabled if Micrometer support is enabled, and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_JVM

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boolean

Kafka metrics support.

Support for Kafka metrics will be enabled if Micrometer support is enabled, the Kafka Consumer or Producer interface is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_KAFKA_ENABLED

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boolean

Redis client metrics support.

Support for Redis metrics will be enabled if Micrometer support is enabled, the Quarkus Redis client extension is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_REDIS_ENABLED

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boolean

Stork metrics support.

Support for Stork metrics will be enabled if Micrometer support is enabled, the Quarkus Stork extension is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_STORK_ENABLED

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boolean

gRPC Server metrics support.

Support for gRPC server metrics will be enabled if Micrometer support is enabled, the gRPC server interfaces are on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_GRPC_SERVER_ENABLED

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boolean

gRPC Client metrics support.

Support for gRPC client metrics will be enabled if Micrometer support is enabled, the gRPC client interfaces are on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_GRPC_CLIENT_ENABLED

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boolean

Kafka metrics support.

Support for Reactive Messaging metrics will be enabled if Micrometer support is enabled, MessageObservationCollector interface is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_MESSAGING_ENABLED

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boolean

Eclipse MicroProfile Metrics support.

Support for MicroProfile Metrics will be enabled if Micrometer support is enabled and the MicroProfile Metrics dependency is present:

<dependency>
  <groupId>org.eclipse.microprofile.metrics</groupId>
  <artifactId>microprofile-metrics-api</artifactId>
</dependency>

The Micrometer extension currently provides a compatibility layer that supports the MP Metrics API, but metric names and recorded values will be different. Note that the MP Metrics compatibility layer will move to a different extension in the future.

Environment variable: QUARKUS_MICROMETER_BINDER_MP_METRICS_ENABLED

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boolean

Micrometer System metrics support.

Support for System metrics will be enabled if Micrometer support is enabled, and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_SYSTEM

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boolean

Vert.x metrics support.

Support for Vert.x metrics will be enabled if Micrometer support is enabled, Vert.x MetricsOptions is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_VERTX_ENABLED

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boolean

Netty metrics support.

Support for Netty metrics will be enabled if Micrometer support is enabled, the Netty allocator classes are on the classpath and either this value is true, or this value is unset and quarkus.micrometer.binder-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_BINDER_NETTY_ENABLED

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boolean

Support for export to JSON format. Off by default.

Environment variable: QUARKUS_MICROMETER_EXPORT_JSON_ENABLED

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boolean

false

The path for the JSON metrics endpoint. The default value is metrics. By default, this value will be resolved as a path relative to ${quarkus.http.non-application-root-path}. If the management interface is enabled, the value will be resolved as a path relative to ${quarkus.management.root-path}.

Environment variable: QUARKUS_MICROMETER_EXPORT_JSON_PATH

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string

metrics

Statistics like max, percentiles, and histogram counts decay over time to give greater weight to recent samples. Samples are accumulated to such statistics in ring buffers which rotate after the expiry, with this buffer length.

Environment variable: QUARKUS_MICROMETER_EXPORT_JSON_BUFFER_LENGTH

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int

3

Statistics like max, percentiles, and histogram counts decay over time to give greater weight to recent samples. Samples are accumulated to such statistics in ring buffers which rotate after this expiry, with a particular buffer length.

Environment variable: QUARKUS_MICROMETER_EXPORT_JSON_EXPIRY

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Duration

P3D

Support for export to Prometheus.

Support for Prometheus will be enabled if Micrometer support is enabled, the PrometheusMeterRegistry is on the classpath and either this value is true, or this value is unset and quarkus.micrometer.registry-enabled-default is true.

Environment variable: QUARKUS_MICROMETER_EXPORT_PROMETHEUS_ENABLED

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boolean

The path for the prometheus metrics endpoint (produces text/plain). The default value is metrics and is resolved relative to the non-application endpoint (q), e.g. ${quarkus.http.root-path}/${quarkus.http.non-application-root-path}/metrics. If an absolute path is specified (/metrics), the prometheus endpoint will be served from the configured path.

If the management interface is enabled, the value will be resolved as a path relative to ${quarkus.management.root-path} (q by default), e.g. http://${quarkus.management.host}:${quarkus.management.port}/${quarkus.management.root-path}/metrics. If an absolute path is specified (/metrics), the prometheus endpoint will be served from the configured path, e.g. http://${quarkus.management.host}:${quarkus.management.port}/metrics.

Environment variable: QUARKUS_MICROMETER_EXPORT_PROMETHEUS_PATH

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string

metrics

By default, this extension will create a Prometheus MeterRegistry instance.

Use this attribute to veto the creation of the default Prometheus MeterRegistry.

Environment variable: QUARKUS_MICROMETER_EXPORT_PROMETHEUS_DEFAULT_REGISTRY

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boolean

true

Comma-separated list of regular expressions used to specify uri labels in http metrics.

Outbount HTTP client instrumentation will attempt to transform parameterized resource paths, /item/123, into a generic form, /item/{id}, to reduce the cardinality of uri label values.

Patterns specified here will take precedence over those computed values.

For example, if /item/\\\\d+=/item/custom or /item/[0-9]+=/item/custom is specified in this list, a request to a matching path (/item/123) will use the specified replacement value (/item/custom) as the value for the uri label. Note that backslashes must be double escaped as \\\\.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_CLIENT_MATCH_PATTERNS

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list of string

Comma-separated list of regular expressions defining uri paths that should be ignored (not measured).

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_CLIENT_IGNORE_PATTERNS

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list of string

Maximum number of unique URI tag values allowed. After the max number of tag values is reached, metrics with additional tag values are denied by filter.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_CLIENT_MAX_URI_TAGS

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int

100

Comma-separated list of regular expressions used to specify uri labels in http metrics.

Vertx instrumentation will attempt to transform parameterized resource paths, /item/123, into a generic form, /item/{id}, to reduce the cardinality of uri label values.

Patterns specified here will take precedence over those computed values.

For example, if /item/\\\\d+=/item/custom or /item/[0-9]+=/item/custom is specified in this list, a request to a matching path (/item/123) will use the specified replacement value (/item/custom) as the value for the uri label. Note that backslashes must be double escaped as \\\\.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_SERVER_MATCH_PATTERNS

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list of string

Comma-separated list of regular expressions defining uri paths that should be ignored (not measured).

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_SERVER_IGNORE_PATTERNS

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list of string

Suppress non-application uris from metrics collection. This will suppress all metrics for non-application endpoints using ${quarkus.http.root-path}/${quarkus.http.non-application-root-path}.

Suppressing non-application uris is enabled by default.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_SERVER_SUPPRESS_NON_APPLICATION_URIS

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boolean

true

Maximum number of unique URI tag values allowed. After the max number of tag values is reached, metrics with additional tag values are denied by filter.

Environment variable: QUARKUS_MICROMETER_BINDER_HTTP_SERVER_MAX_URI_TAGS

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int

100

Prometheus registry configuration properties.

A property source for configuration of the Prometheus MeterRegistry, see https://micrometer.io/docs/registry/prometheus.

Environment variable: QUARKUS_MICROMETER_EXPORT_PROMETHEUS__CONFIGURATION_PROPERTY_NAME_

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String

About the Duration format

To write duration values, use the standard java.time.Duration format. See the Duration#parse() Java API documentation for more information.

You can also use a simplified format, starting with a number:

  • If the value is only a number, it represents time in seconds.

  • If the value is a number followed by ms, it represents time in milliseconds.

In other cases, the simplified format is translated to the java.time.Duration format for parsing:

  • If the value is a number followed by h, m, or s, it is prefixed with PT.

  • If the value is a number followed by d, it is prefixed with P.

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