It supports Java, Clojure, Scala, JRuby, and anything else that runs on the JVM.
- Using
- Instrumenting
- Included Collectors
- Exporting
- Exporting to a Pushgateway
- Bridges
- 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>0.0.21</version>
</dependency>
<!-- Hotspot JVM metrics-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_hotspot</artifactId>
<version>0.0.21</version>
</dependency>
<!-- Exposition servlet-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_servlet</artifactId>
<version>0.0.21</version>
</dependency>
<!-- Pushgateway exposition-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_pushgateway</artifactId>
<version>0.0.21</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() {
inprogressRequest.inc();
// Your code here.
inprogressRequest.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.timer(new Runnable() {
public abstract void run() {
// Your code here.
}
});
// Or the Java 8 lambda equivalent
requestLatency.timer(() -> {
// 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.timer(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();###Logging
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 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 reqeusts 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>net.cccnext.ssp.portal.spring.filter.PrometheusMetricsFilter</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 instaniating 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 Servlet, SpringBoot, and Vert.x integrations included in the client library.
To add Prometheus exposition to an existing HTTP server, 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");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.
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 and HistogramMetricFamily work 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_desribe=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.
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