It supports Java, Clojure, Scala, JRuby, and anything else that runs on the JVM.

• Using
  • Assets
  • Javadocs
• Instrumenting
  • Counter
  • Gauge
  • Summary
  • Histogram
  • Labels
  • Registering Metrics
• Included Collectors
  • Logging
  • Caches
  • Hibernate
  • Jetty
• Exporting
  • HTTP
• Exporting to a Pushgateway
• Bridges
  • Graphite
• Custom Collectors
• Contact

If you use Maven, you can simply reference the assets below. The latest version can be found on in the maven repository for io.prometheus.

<!-- The client -->
<dependency>
  <groupId>io.prometheus</groupId>
  <artifactId>simpleclient</artifactId>
  <version>0.8.0</version>
</dependency>
<!-- Hotspot JVM metrics-->
<dependency>
  <groupId>io.prometheus</groupId>
  <artifactId>simpleclient_hotspot</artifactId>
  <version>0.8.0</version>
</dependency>
<!-- Exposition HTTPServer-->
<dependency>
  <groupId>io.prometheus</groupId>
  <artifactId>simpleclient_httpserver</artifactId>
  <version>0.8.0</version>
</dependency>
<!-- Pushgateway exposition-->
<dependency>
  <groupId>io.prometheus</groupId>
  <artifactId>simpleclient_pushgateway</artifactId>
  <version>0.8.0</version>
</dependency>

There are canonical examples defined in the class definition Javadoc of the client packages.

Documentation can be found at the Java Client Github Project Page.

Four types of metrics are offered: Counter, Gauge, Summary and Histogram. See the documentation on metric types and instrumentation best practices on how to use them.

Counters go up, and reset when the process restarts.

import io.prometheus.client.Counter;
class YourClass {
  static final Counter requests = Counter.build()
     .name("requests_total").help("Total requests.").register();

  void processRequest() {
    requests.inc();
    // Your code here.
  }
}

Gauges can go up and down.

class YourClass {
  static final Gauge inprogressRequests = Gauge.build()
     .name("inprogress_requests").help("Inprogress requests.").register();

  void processRequest() {
    inprogressRequests.inc();
    // Your code here.
    inprogressRequests.dec();
  }
}

There are utilities for common use cases:

gauge.setToCurrentTime(); // Set to current unixtime.

As an advanced use case, a Gauge can also take its value from a callback by using the setChild() method. Keep in mind that the default inc(), dec() and set() methods on Gauge take care of thread safety, so when using this approach ensure the value you are reporting accounts for concurrency.

Summaries track the size and number of events.

class YourClass {
  static final Summary receivedBytes = Summary.build()
     .name("requests_size_bytes").help("Request size in bytes.").register();
  static final Summary requestLatency = Summary.build()
     .name("requests_latency_seconds").help("Request latency in seconds.").register();

  void processRequest(Request req) {
    Summary.Timer requestTimer = requestLatency.startTimer();
    try {
      // Your code here.
    } finally {
      receivedBytes.observe(req.size());
      requestTimer.observeDuration();
    }
  }
}

There are utilities for timing code and support for quantiles. Essentially quantiles aren't aggregatable and add some client overhead for the calculation.

class YourClass {
  static final Summary requestLatency = Summary.build()
    .quantile(0.5, 0.05)   // Add 50th percentile (= median) with 5% tolerated error
    .quantile(0.9, 0.01)   // Add 90th percentile with 1% tolerated error
    .name("requests_latency_seconds").help("Request latency in seconds.").register();

  void processRequest(Request req) {
    requestLatency.time(new Runnable() {
      public abstract void run() {
        // Your code here.
      }
    });


    // Or the Java 8 lambda equivalent
    requestLatency.time(() -> {
      // Your code here.
    });
  }
}

Histograms track the size and number of events in buckets. This allows for aggregatable calculation of quantiles.

class YourClass {
  static final Histogram requestLatency = Histogram.build()
     .name("requests_latency_seconds").help("Request latency in seconds.").register();

  void processRequest(Request req) {
    Histogram.Timer requestTimer = requestLatency.startTimer();
    try {
      // Your code here.
    } finally {
      requestTimer.observeDuration();
    }
  }
}

The default buckets are intended to cover a typical web/rpc request from milliseconds to seconds. They can be overridden with the buckets() method on the Histogram.Builder.

There are utilities for timing code:

class YourClass {
  static final Histogram requestLatency = Histogram.build()
     .name("requests_latency_seconds").help("Request latency in seconds.").register();

  void processRequest(Request req) {
    requestLatency.time(new Runnable() {
      public abstract void run() {
        // Your code here.
      }
    });


    // Or the Java 8 lambda equivalent
    requestLatency.time(() -> {
      // Your code here.
    });
  }
}

All metrics can have labels, allowing grouping of related time series.

See the best practices on naming and labels.

Taking a counter as an example:

class YourClass {
  static final Counter requests = Counter.build()
     .name("my_library_requests_total").help("Total requests.")
     .labelNames("method").register();

  void processGetRequest() {
    requests.labels("get").inc();
    // Your code here.
  }
}

The best way to register a metric is via a static final class variable as is common with loggers.

static final Counter requests = Counter.build()
   .name("my_library_requests_total").help("Total requests.").labelNames("path").register();

Using the default registry with variables that are static is ideal since registering a metric with the same name is not allowed and the default registry is also itself static. You can think of registering a metric, more like registering a definition (as in the TYPE and HELP sections). The metric 'definition' internally holds the samples that are reported and pulled out by Prometheus. Here is an example of registering a metric that has no labels.

class YourClass {
  static final Gauge activeTransactions = Gauge.build()
     .name("my_library_transactions_active")
     .help("Active transactions.")
     .register();

  void processThatCalculates(String key) {
    activeTransactions.inc();
    try {
        // Perform work.
    } finally{
        activeTransactions.dec();
    }
  }
}

To create timeseries with labels, include labelNames() with the builder. The labels() method looks up or creates the corresponding labelled timeseries. You might also consider storing the labelled timeseries as an instance variable if it is appropriate. It is thread safe and can be used multiple times, which can help performance.

class YourClass {
  static final Counter calculationsCounter = Counter.build()
     .name("my_library_calculations_total").help("Total calls.")
     .labelNames("key").register();

  void processThatCalculates(String key) {
    calculationsCounter.labels(key).inc();
    // Run calculations.
  }
}

The Java client includes collectors for garbage collection, memory pools, JMX, classloading, and thread counts. These can be added individually or just use the DefaultExports to conveniently register them.

DefaultExports.initialize();

There are logging collectors for log4j, log4j2 and logback.

To register the Logback collector can be added to the root level like so:

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <include resource="org/springframework/boot/logging/logback/base.xml"/>

    <appender name="METRICS" class="io.prometheus.client.logback.InstrumentedAppender" />

    <root level="INFO">
        <appender-ref ref="METRICS" />
    </root>

</configuration>

To register the log4j collector at root level:

<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE log4j:configuration SYSTEM "log4j.dtd">
<log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">
    <appender name="METRICS" class="io.prometheus.client.log4j.InstrumentedAppender"/>
    <root>
        <priority value ="info" />
        <appender-ref ref="METRICS" />
    </root>
</log4j:configuration>

To register the log4j2 collector at root level:

<?xml version="1.0" encoding="UTF-8"?>
<Configuration packages="io.prometheus.client.log4j2">
    <Appenders>
        <Prometheus name="METRICS"/>
    </Appenders>
    <Loggers>
        <Root level="info">
            <AppenderRef ref="METRICS"/>
        </Root>
    </Loggers>
</Configuration>

To register the Guava cache collector, be certain to add recordStats() when building the cache and adding it to the registered collector.

CacheMetricsCollector cacheMetrics = new CacheMetricsCollector().register();

Cache<String, String> cache = CacheBuilder.newBuilder().recordStats().build();
cacheMetrics.addCache("myCacheLabel", cache);

The Caffeine equivalent is nearly identical. Again, be certain to call recordStats() when building the cache so that metrics are collected.

CacheMetricsCollector cacheMetrics = new CacheMetricsCollector().register();

Cache<String, String> cache = Caffeine.newBuilder().recordStats().build();
cacheMetrics.addCache("myCacheLabel", cache);

There is a collector for Hibernate which allows to collect metrics from one or more SessionFactory instances.

If you want to collect metrics from a single SessionFactory, you can register the collector like this:

new HibernateStatisticsCollector(sessionFactory, "myapp").register();

In some situations you may want to collect metrics from multiple factories. In this case just call add() on the collector for each of them.

new HibernateStatisticsCollector()
    .add(sessionFactory1, "myapp1")
    .add(sessionFactory2, "myapp2")
    .register();

If you are using Hibernate in a JPA environment and only have access to the EntityManager or EntityManagerFactory, you can use this code to access the underlying SessionFactory:

SessionFactory sessionFactory = entityManagerFactory.unwrap(SessionFactory.class);

There is a collector for recording various Jetty server metrics. You can do it by registering the collector like this:

// Configure StatisticsHandler.
StatisticsHandler stats = new StatisticsHandler();
stats.setHandler(server.getHandler());
server.setHandler(stats);
// Register collector.
new JettyStatisticsCollector(stats).register();

Also, you can collect QueuedThreadPool metrics. If there is a single QueuedThreadPool to keep track of, use the following:

new QueuedThreadPoolStatisticsCollector(queuedThreadPool, "myapp").register();

If you want to collect multiple QueuedThreadPool metrics, also you can achieve it like this:

new QueuedThreadPoolStatisticsCollector()
    .add(queuedThreadPool1, "myapp1")
    .add(queuedThreadPool2, "myapp2")
    .register();

There is a servlet filter available for measuring the duration taken by servlet requests. The metric-name init parameter is required, and is the name of the metric prometheus will expose for the timing metrics. Help text via the help init parameter is not required, although it is highly recommended. The number of buckets is overridable, and can be configured by passing a comma-separated string of doubles as the buckets init parameter. The granularity of path measuring is also configurable, via the path-components init parameter. By default, the servlet filter will record each path differently, but by setting an integer here, you can tell the filter to only record up to the Nth slashes. That is, all requests with greater than N "/" characters in the servlet URI path will be measured in the same bucket and you will lose that granularity.

The code below is an example of the XML configuration for the filter. You will need to place this (replace your own values) code in your webapp/WEB-INF/web.xml file.

<filter>
  <filter-name>prometheusFilter</filter-name>
  <filter-class>io.prometheus.client.filter.MetricsFilter</filter-class>
  <init-param>
    <param-name>metric-name</param-name>
    <param-value>webapp_metrics_filter</param-value>
  </init-param>
  <init-param>
    <param-name>help</param-name>
    <param-value>This is the help for your metrics filter</param-value>
  </init-param>
  <init-param>
    <param-name>buckets</param-name>
    <param-value>0.005,0.01,0.025,0.05,0.075,0.1,0.25,0.5,0.75,1,2.5,5,7.5,10</param-value>
  </init-param>
  <!-- Optionally override path components; anything less than 1 (1 is the default)
       means full granularity -->
  <init-param>
    <param-name>path-components</param-name>
    <param-value>1</param-value>
  </init-param>
</filter>

<!-- You will most likely want this to be the first filter in the chain
(therefore the first <filter-mapping> in the web.xml file), so that you can get
the most accurate measurement of latency. -->
<filter-mapping>
  <filter-name>prometheusFilter</filter-name>
  <url-pattern>/*</url-pattern>
</filter-mapping>

Additionally, you can instantiate your servlet filter directly in Java code. To do this, you just need to call the non-empty constructor. The first parameter, the metric name, is required. The second, help, is optional but highly recommended. The last two (path-components, and buckets) are optional and will default sensibly if omitted.

There is a Spring AOP collector that allows you to annotate methods that you would like to instrument with a Summary, but without going through the process of manually instantiating and registering your metrics classes. To use the metrics annotations, simply add simpleclient_spring_web as a dependency, annotate a configuration class with @EnablePrometheusTiming, then annotate your Spring components as such:

@Controller
public class MyController {
  @RequestMapping("/")
  @PrometheusTimeMethod(name = "my_controller_path_duration_seconds", help = "Some helpful info here")
  public Object handleMain() {
    // Do something
  }
}

There are several options for exporting metrics.

Metrics are usually exposed over HTTP, to be read by the Prometheus server.

There are HTTPServer, Servlet, SpringBoot, and Vert.x integrations included in the client library. The simplest of these is the HTTPServer:

HTTPServer server = new HTTPServer(1234);

To add Prometheus exposition to an existing HTTP server using servlets, see the MetricsServlet. It also serves as a simple example of how to write a custom endpoint.

To expose the metrics used in your code, you would add the Prometheus servlet to your Jetty server:

Server server = new Server(1234);
ServletContextHandler context = new ServletContextHandler();
context.setContextPath("/");
server.setHandler(context);

context.addServlet(new ServletHolder(new MetricsServlet()), "/metrics");

All HTTP expostion integrations support restricting which time series to return using ?name[]= URL parameters. Due to implementation limitations, this may have false negatives.

The Pushgateway allows ephemeral and batch jobs to expose their metrics to Prometheus.

void executeBatchJob() throws Exception {
  CollectorRegistry registry = new CollectorRegistry();
  Gauge duration = Gauge.build()
     .name("my_batch_job_duration_seconds").help("Duration of my batch job in seconds.").register(registry);
  Gauge.Timer durationTimer = duration.startTimer();
  try {
    // Your code here.

    // This is only added to the registry after success,
    // so that a previous success in the Pushgateway isn't overwritten on failure.
    Gauge lastSuccess = Gauge.build()
       .name("my_batch_job_last_success").help("Last time my batch job succeeded, in unixtime.").register(registry);
    lastSuccess.setToCurrentTime();
  } finally {
    durationTimer.setDuration();
    PushGateway pg = new PushGateway("127.0.0.1:9091");
    pg.pushAdd(registry, "my_batch_job");
  }
}

A separate registry is used, as the default registry may contain other metrics such as those from the Process Collector. See the Pushgateway documentation for more information.

PushGateway pushgateway = new PushGateway("127.0.0.1:9091");
pushgateway.setConnectionFactory(new BasicAuthHttpConnectionFactory("my_user", "my_password"));

PushGateway pushgateway = new PushGateway("127.0.0.1:9091");
pushgateway.setConnectionFactory(new MyHttpConnectionFactory());

where

class MyHttpConnectionFactory implements HttpConnectionFactory {
    @Override
    public HttpURLConnection create(String url) throws IOException {
        HttpURLConnection connection = (HttpURLConnection) new URL(url).openConnection();
        // add any connection preparation logic you need
        return connection;
    }
}

It is also possible to expose metrics to systems other than Prometheus. This allows you to take advantage of Prometheus instrumentation even if you are not quite ready to fully transition to Prometheus yet.

Metrics are pushed over TCP in the Graphite plaintext format.

Graphite g = new Graphite("localhost", 2003);
// Push the default registry once.
g.push(CollectorRegistry.defaultRegistry);

// Push the default registry every 60 seconds.
Thread thread = g.start(CollectorRegistry.defaultRegistry, 60);
// Stop pushing.
thread.interrupt();
thread.join();

Sometimes it is not possible to directly instrument code, as it is not in your control. This requires you to proxy metrics from other systems.

To do so you need to create a custom collector (which will need to be registered as a normal metric), for example:

class YourCustomCollector extends Collector {
  List<MetricFamilySamples> collect() {
    List<MetricFamilySamples> mfs = new ArrayList<MetricFamilySamples>();
    // With no labels.
    mfs.add(new GaugeMetricFamily("my_gauge", "help", 42));
    // With labels
    GaugeMetricFamily labeledGauge = new GaugeMetricFamily("my_other_gauge", "help", Arrays.asList("labelname"));
    labeledGauge.addMetric(Arrays.asList("foo"), 4);
    labeledGauge.addMetric(Arrays.asList("bar"), 5);
    mfs.add(labeledGauge);
    return mfs;
  }
}

// Registration
static final YourCustomCollector requests = new YourCustomCollector().register()

SummaryMetricFamily works similarly.

A collector may implement a describe method which returns metrics in the same format as collect (though you don't have to include the samples). This is used to predetermine the names of time series a CollectorRegistry exposes and thus to detect collisions and duplicate registrations.

Usually custom collectors do not have to implement describe. If describe is not implemented and the CollectorRegistry was created with auto_describe=True (which is the case for the default registry) then collect will be called at registration time instead of describe. If this could cause problems, either implement a proper describe, or if that's not practical have describe return an empty list.

DropwizardExports collector is available to proxy metrics from Dropwizard.

// Dropwizard MetricRegistry
MetricRegistry metricRegistry = new MetricRegistry();
new DropwizardExports(metricRegistry).register();

By default Dropwizard metrics are translated to Prometheus sample sanitizing their names, i.e. replacing unsupported chars with _, for example:

Dropwizard metric name:
org.company.controller.save.status.400
Prometheus metric:
org_company_controller_save_status_400

It is also possible add custom labels and name to newly created Samples by using a CustomMappingSampleBuilder with custom MapperConfigs:

// Dropwizard MetricRegistry
MetricRegistry metricRegistry = new MetricRegistry();
MapperConfig config = new MapperConfig();
// The match field in MapperConfig is a simplified glob expression that only allows * wildcard.
config.setMatch("org.company.controller.*.status.*");
// The new Sample's template name.
config.setName("org.company.controller");
Map<String, String> labels = new HashMap<String,String>();
// ... more configs
// Labels to be extracted from the metric. Key=label name. Value=label template
labels.put("name", "${0}");
labels.put("status", "${1}");
config.setLabels(labels);

SampleBuilder sampleBuilder = new CustomMappingSampleBuilder(Arrays.asList(config));
new DropwizardExports(metricRegistry, sampleBuilder).register();

When a new metric comes to the collector, MapperConfigs are scanned to find the first one that matches the incoming metric name. The name set in the configuration will be used and labels will be extracted. Using the CustomMappingSampleBuilder in the previous example leads to the following result:

Dropwizard metric name
org.company.controller.save.status.400
Prometheus metric
org_company_controller{name="save",status="400"}

Template with placeholders can be used both as names and label values. Placeholders are in the ${n} format where n is the zero based index of the Dropwizard metric name wildcard group we want to extract.

The Prometheus Users Mailinglist is the best place to ask questions.

Details for those wishing to develop the library can be found on the wiki