////
This guide is maintained in the main Quarkus repository
and pull requests should be submitted there:
https://github.com/quarkusio/quarkus/tree/master/docs/src/main/asciidoc
////
= Quarkus - Micrometer Metrics

include::./attributes.adoc[]

This guide demonstrates how your Quarkus application can utilize the Micrometer
metrics library for runtime and application metrics.

Apart from application-specific metrics, which are described in this guide, you may also utilize built-in metrics
exposed by various Quarkus extensions. These are described in the guide for each particular extension that supports
built-in metrics.

IMPORTANT: Micrometer is the recommended approach to metrics for Quarkus.

== Prerequisites

To complete this guide, you need:

* less than 15 minutes
* an IDE
* JDK 1.8+ installed with `JAVA_HOME` configured appropriately
* Apache Maven {maven-version}

== Architecture

In this example, we build a very simple microservice which offers one REST endpoint and that determines
if a number is prime.

== Solution

We recommend that you follow the instructions in the next sections and create the application step by step.
However, you can go right to the completed example.

Clone the Git repository: `git clone {quickstarts-clone-url}`, or download an {quickstarts-archive-url}[archive].

The solution is located in the `micrometer-quickstart` {quickstarts-tree-url}/micrometer-quickstart[directory].

== Creating the Maven Project

Micrometer defines a core library and a set of additional libraries that support different monitoring systems.
Quarkus Micrometer extensions are structured similarly: `quarkus-micrometer` provides core micrometer support and
runtime integration and other supporting Quarkus and Quarkiverse extensions bring in additional dependencies
and requirements to support specific monitoring systems.

For this example, we'll use the Prometheus registry.

First, we need a new project. Create a new project with the following command:

[source,bash,subs=attributes+]
----
mvn io.quarkus:quarkus-maven-plugin:{quarkus-version}:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=micrometer-quickstart \
    -DclassName="org.acme.micrometer.PrimeNumberResource" \
    -Dpath="/" \
    -Dextensions="resteasy,micrometer-registry-prometheus"
cd micrometer-quickstart
----

This command generates a Maven project, that imports the `micrometer-registry-prometheus` extension as a dependency.
This extension will load the core `micrometer` extension as well as additional library dependencies required to support
prometheus.

If you already have your Quarkus project configured, you can add the `micrometer-registry-prometheus` extension
to your project by running the following command in your project base directory:

[source,bash]
----
./mvnw quarkus:add-extension -Dextensions="micrometer-registry-prometheus"
----

This will add the following to your `pom.xml`:

[source,xml]
----
<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-micrometer-registry-prometheus</artifactId>
</dependency>
----

== Writing the application

The application consists of a single class that implements an algorithm for checking whether a number is prime.
This algorithm is exposed over a REST interface. With the Micrometer extension enabled, metrics for all http
server requests are collected automatically.

We do want to add a few other metrics to demonstrate how those types work:

* A counter will be incremented for every prime number discovered
* A gauge will store the highest prime number discovered
* A timer will record the time spent testing if ia number is prime.

[source,java]
----
package org.acme.micrometer;

import io.micrometer.core.instrument.MeterRegistry;

import javax.ws.rs.GET;
import javax.ws.rs.Path;
import javax.ws.rs.PathParam;
import javax.ws.rs.Produces;
import javax.ws.rs.core.MediaType;
import java.util.concurrent.atomic.LongAccumulator;
import java.util.function.Supplier;

@Path("/")
public class PrimeNumberResource {

    private final LongAccumulator highestPrime = new LongAccumulator(Long::max, 0);
    private final MeterRegistry registry;

    PrimeNumberResource(MeterRegistry registry) {
        this.registry = registry;

        // Create a gauge that uses the highestPrimeNumberSoFar method
        // to obtain the highest observed prime number
        registry.gauge("prime.number.max", this,
                PrimeNumberResource::highestObservedPrimeNumber);
    }

    @GET
    @Path("/{number}")
    @Produces(MediaType.TEXT_PLAIN)
    public String checkIfPrime(@PathParam("number") long number) {
        if (number < 1) {
            return "Only natural numbers can be prime numbers.";
        }
        if (number == 1) {
            return "1 is not prime.";
        }
        if (number == 2) {
            return "2 is prime.";
        }
        if (number % 2 == 0) {
            return number + " is not prime, it is divisible by 2.";
        }

        Supplier<String> supplier = () -> {
            for (int i = 3; i < Math.floor(Math.sqrt(number)) + 1; i = i + 2) {
                if (number % i == 0) {
                    return number + " is not prime, is divisible by " + i + ".";
                }
            }
            highestPrime.accumulate(number);
            return number + " is prime.";
        };

        return registry.timer("prime.number.test").wrap(supplier).get();
    }

    /**
     * This method is called by the registered {@code highest.prime.number} gauge.
     * @return the highest observed prime value
     */
    long highestObservedPrimeNumber() {
        return highestPrime.get();
    }
}
----

== Running and using the application

To run the microservice in dev mode, use `./mvnw clean compile quarkus:dev`

=== Generate some values for the metrics

First, ask the endpoint whether some numbers are prime numbers.

[source,bash]
----
curl localhost:8080/350
----

The application will respond that 350 is not a prime number because it can be divided by 2.

Now for some large prime number so that the test takes a bit more time:

[source,bash]
----
curl localhost:8080/629521085409773
----

The application will respond that 629521085409773 is a prime number.
If you want, try some more calls with numbers of your choice.

=== Review the generated metrics

To view the metrics, execute `curl localhost:8080/q/metrics/`

Prometheus-formatted metrics will be returned in plain text in no particular order.

The application above has only one custom gauge that measures the time
spent determining whether or not a number is prime. The Micrometer extension
enables additional system, jvm, and http metrics. A subset of the collected metrics
are shown below.

[source,text]
----
# HELP http_server_requests_seconds
# TYPE http_server_requests_seconds summary
http_server_requests_seconds_count{method="GET",outcome="SUCCESS",status="200",uri="/{number}",} 4.0
http_server_requests_seconds_sum{method="GET",outcome="SUCCESS",status="200",uri="/{number}",} 0.041501773
# HELP http_server_requests_seconds_max
# TYPE http_server_requests_seconds_max gauge
http_server_requests_seconds_max{method="GET",outcome="SUCCESS",status="200",uri="/{number}",} 0.038066359
# HELP jvm_threads_peak_threads The peak live thread count since the Java virtual machine started or peak was reset
# TYPE jvm_threads_peak_threads gauge
jvm_threads_peak_threads 56.0
# HELP http_server_connections_seconds_max
# TYPE http_server_connections_seconds_max gauge
http_server_connections_seconds_max 0.102580737
# HELP http_server_connections_seconds
# TYPE http_server_connections_seconds summary
http_server_connections_seconds_active_count 5.0
http_server_connections_seconds_duration_sum 0.175032815
# HELP system_load_average_1m The sum of the number of runnable entities queued to available processors and the number of runnable entities running on the available processors averaged over a period of time
# TYPE system_load_average_1m gauge
system_load_average_1m 2.4638671875
# HELP http_server_bytes_written_max
# TYPE http_server_bytes_written_max gauge
http_server_bytes_written_max 39.0
# HELP http_server_bytes_written
# TYPE http_server_bytes_written summary
http_server_bytes_written_count 4.0
http_server_bytes_written_sum 99.0
# HELP jvm_classes_loaded_classes The number of classes that are currently loaded in the Java virtual machine
# TYPE jvm_classes_loaded_classes gauge
jvm_classes_loaded_classes 9341.0
# HELP prime_number_max
# TYPE prime_number_max gauge
prime_number_max 887.0
# HELP jvm_info JVM version info
# TYPE jvm_info gauge
jvm_info{runtime="OpenJDK Runtime Environment",vendor="Oracle Corporation",version="13.0.2+8",} 1.0
# HELP jvm_classes_unloaded_classes_total The total number of classes unloaded since the Java virtual machine has started execution
# TYPE jvm_classes_unloaded_classes_total counter
jvm_classes_unloaded_classes_total 28.0
# HELP prime_number_test_seconds
# TYPE prime_number_test_seconds summary
prime_number_test_seconds_count 3.0
prime_number_test_seconds_sum 1.94771E-4
# HELP prime_number_test_seconds_max
# TYPE prime_number_test_seconds_max gauge
prime_number_test_seconds_max 1.76162E-4
# HELP http_server_bytes_read
# TYPE http_server_bytes_read summary
http_server_bytes_read_count 4.0
http_server_bytes_read_sum 0.0
# HELP http_server_bytes_read_max
# TYPE http_server_bytes_read_max gauge
http_server_bytes_read_max 0.0
# HELP jvm_threads_states_threads The current number of threads having NEW state
# TYPE jvm_threads_states_threads gauge
jvm_threads_states_threads{state="runnable",} 37.0
jvm_threads_states_threads{state="blocked",} 0.0
jvm_threads_states_threads{state="waiting",} 15.0
jvm_threads_states_threads{state="timed-waiting",} 4.0
jvm_threads_states_threads{state="new",} 0.0
jvm_threads_states_threads{state="terminated",} 0.0
# HELP jvm_buffer_memory_used_bytes An estimate of the memory that the Java virtual machine is using for this buffer pool
# TYPE jvm_buffer_memory_used_bytes gauge
jvm_buffer_memory_used_bytes{id="mapped",} 0.0
jvm_buffer_memory_used_bytes{id="direct",} 149521.0
# HELP jvm_memory_committed_bytes The amount of memory in bytes that is committed for the Java virtual machine to use
# TYPE jvm_memory_committed_bytes gauge
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'profiled nmethods'",} 1.1403264E7
jvm_memory_committed_bytes{area="heap",id="G1 Survivor Space",} 4194304.0
jvm_memory_committed_bytes{area="heap",id="G1 Old Gen",} 9.2274688E7
jvm_memory_committed_bytes{area="nonheap",id="Metaspace",} 4.9803264E7
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'non-nmethods'",} 2555904.0
jvm_memory_committed_bytes{area="heap",id="G1 Eden Space",} 6.9206016E7
jvm_memory_committed_bytes{area="nonheap",id="Compressed Class Space",} 6815744.0
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'non-profiled nmethods'",} 2555904.0
----

Note that metrics appear lazily, you often won't see any data for your endpoint until
something tries to access it, etc.

== Using MeterFilter to configure 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.

[source,java]
----
@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.

== Using other Registry implementations

If you aren't using Prometheus, you have a few options. Some Micrometer registry implementations
have been wrapped in
https://github.com/quarkiverse/quarkiverse-micrometer-registry[Quarkiverse extensions].
To use the Micrometer StackDriver MeterRegistry, for example, you would use the
`quarkus-micrometer-registry-stackdriver` extension:

[source,xml]
----
<dependency>
    <groupId>io.quarkiverse</groupId>
    <artifactId>quarkus-micrometer-registry-stackdriver</artifactId>
</dependency>
----

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 packaged MeterRegistry dependency directly:

[source,xml]
----
<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-micrometer</artifactId>
</dependency>
<dependency>
    <groupId>com.acme</groupId>
    <artifactId>custom-micrometer-registry</artifactId>
</dependency>
----

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

== Creating a customized MeterRegistry

Use a custom `@Produces` method to create and configure a customized `MeterRegistry` if you need to.

The following example customizes the line format used for StatsD:

[source,java]
----
@Produces
@Singleton
public StatsdMeterRegistry createStatsdMeterRegistry(StatsdConfig statsdConfig, Clock clock) {
    // 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();
}
----

This example corresponds to the following instructions in the Micrometer documentation:
https://micrometer.io/docs/registry/statsD#_customizing_the_metrics_sink

Note that the method returns the specific type of `MeterRegistry` as a `@Singleton`. Use MicroProfile Config
to inject any configuration attributes you need to configure the registry. Most Micrometer registry extensions,
like `quarkus-micrometer-registry-statsd`, define a producer for registry-specific configuration objects
that are integrated with the Quarkus configuration model.

== 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 will change, but you can use MeterFilters to remap names or tags to what your dashboards require.

[source,java]
----
@Produces
@Singleton
public MeterFilter renameApplicationMeters() {
    final String targetMetric = PrimeResource.class.getName() + ".highestPrimeNumberSoFar";

    return MeterFilter() {
        @Override
        public Meter.Id map(Meter.Id id) {
            // rename the specified metric (remove package), and drop the scope tag
            // you could also use this to prepend a scope tag (application, base, vendor, if present) to the metric name
            if (id.getName().equals(targetMetric)) {
                List<Tag> tags = id.getTags().stream().filter(x -> !"scope".equals(x.getKey()))
                        .collect(Collectors.toList());
                return id.withName("highestPrimeNumberSoFar").replaceTags(tags);
            }
            return id;
        }
    };
}
----

NOTE: The MP Metrics API compatibility layer will be moved to a different extension in the future.

== Configuration Reference

include::{generated-dir}/config/quarkus-micrometer.adoc[opts=optional, leveloffset=+1]
