Prometheus
Akka Insights can report metrics to Prometheus, using a backend plugin integrated with the Prometheus JVM client.
Cinnamon dependency
First make sure that your build is configured to use the Cinnamon Agent and has instrumentations enabled, such as Akka instrumentation or Akka HTTP instrumentation.
Here is the core Cinnamon Prometheus dependency, but note that you also need to select an exporter.
- sbt
-
libraryDependencies += Cinnamon.library.cinnamonPrometheus
- Maven
-
<dependency> <groupId>com.lightbend.cinnamon</groupId> <artifactId>cinnamon-prometheus</artifactId> <version>2.20.3</version> </dependency>
- Gradle
-
dependencies { implementation group: 'com.lightbend.cinnamon', name: 'cinnamon-prometheus', version: '2.20.3' }
Exporters
There are several options for exporting metrics to Prometheus. Metrics are usually exposed over HTTP, to be read by the Prometheus server. Custom exporters can also be created.
HTTP server
The HTTP server exporter starts a simple stand-alone server for exporting metrics in Prometheus format.
Add the Prometheus HTTP server dependency to your build:
- sbt
-
libraryDependencies += Cinnamon.library.cinnamonPrometheusHttpServer
- Maven
-
<dependency> <groupId>com.lightbend.cinnamon</groupId> <artifactId>cinnamon-prometheus-httpserver</artifactId> <version>2.20.3</version> </dependency>
- Gradle
-
dependencies { implementation group: 'com.lightbend.cinnamon', name: 'cinnamon-prometheus-httpserver', version: '2.20.3' }
You need to enable the HTTP server exporter using configuration. The host and port for the server can also be configured.
- Required
-
cinnamon.prometheus { exporters += http-server }
- Example
-
cinnamon.prometheus { exporters += http-server http-server { host = "localhost" port = 9091 } }
- Reference
-
cinnamon.prometheus { http-server { # Host to bind the Prometheus HTTP server # Defaults to the wildcard address when empty (bind to all interfaces) host = "" # Port to bind the Prometheus HTTP server port = 9001 # Whether to use daemon threads for the HTTP server daemon = false } }
NoteThese settings are defined in the
reference.conf
. You only need to specify any of these settings when you want to override the defaults.
Prometheus metrics will now be available at the configured server location. Using the defaults, this is at http://localhost:9001/metrics.
Custom metrics
Custom metrics of type Counter
and Gauge
are exposed as a Prometheus Gauge. A Rate
is exposed as a Prometheus Counter
and a Recorder
becomes either a Summary
or Histogram
depending on the further configuration.
Configuration
Host and application labels are automatically reported with Prometheus metrics, based on the shared Cinnamon metadata. To disable these extra labels, use the unique-dimensions
setting. See the example below.
The Prometheus Summary metric type is used by default for Cinnamon Recorders
, which are used for timing metrics and other distribution-based metrics. Summaries calculate configurable quantiles over a sliding time window, along with a total count of observations and the min, max, and sum of all observed values. The quantiles and the sliding time window for the default Summary metric can be configured, as well as the precision to use (for the underlying HDR Histogram). See the example and reference configuration below for details, and the Prometheus documentation on summaries for more information. For configuring Summaries differently for separate Recorders, or for using Prometheus Histogram metrics, see configuring metric hints and histograms.
- Required
-
There is nothing to configure to use the default settings for Prometheus metrics. See the Reference tab for defaults.
- Example
-
cinnamon.prometheus { # exclude the host and application labels unique-dimensions = off summary { # determine quantiles to use quantiles = [0.5, 0.9, 0.95] # configure the sliding time window max-age = 1m # after 1m, all observations will be discarded age-buckets = 6 # discards one bucket every 10s # configure the precision of the underlying HDR Histogram significant-value-digits = 3 } }
- Reference
-
cinnamon.prometheus { # Prometheus exporters to load exporters = ${?cinnamon.chmetrics.exporters} [] # Whether to include "unique dimensions" as labels. # These are labels that are unique to this client, # such as host name and application identifier. unique-dimensions = on # Using the default registry will enable the user to create "native" Prometheus metrics and # Cinnamon metrics in the Prometheus default registry. Enabling this feature means that any # Prometheus metric, regardless of how it is created, will be exposed via this exporter. use-default-registry = off # Default settings for Prometheus Summary metrics (default used for Cinnamon Recorder) # See https://prometheus.io/docs/practices/histograms/ for more information summary { # Quantiles used for summaries quantiles = [0.5, 0.95, 0.99] # Duration of the time window for summaries (how long observations are kept) max-age = 10m # Number of buckets used to implement the sliding time window for summaries age-buckets = 5 # Specify the precision to use. This is the number of significant decimal digits of the # underlying HdrHistogram. Must be a non-negative integer between 0 and 5. significant-value-digits = 2 # Set the initial dynamic range (and memory usage) of the underlying HdrHistogram. highest-to-lowest-value-ratio = 1000 } # Default settings for Prometheus Histogram metrics histogram {} # Settings specific to metric hints hints {} }
NoteThese settings are defined in the
reference.conf
. You only need to specify any of these settings when you want to override the defaults.
Metric hints
Metrics—either created by Cinnamon instrumentation or custom metrics—can be given hints that will passed to the metric backend. These hints allow particular metrics to be configured differently from the default configuration.
By default, the Cinnamon Recorder
metric type is backed by a Summary
in the Prometheus backend. If you need a particular recorder to always use a specially configured Summary, or if you require a Histogram with predetermined buckets to be used, then you can do this by labelling the recorder with a hint and specifying the Summary or Histogram configuration.
For example, using the custom metrics API you can pass in the hint when creating a custom recorder:
- Scala
-
val recorder = CinnamonMetrics(context).createRecorder("recorder", hints = Set("custom-recorder"))
- Java
-
final Set<String> hints = ImmutableSet.of("custom-recorder"); final Recorder recorder = CinnamonMetrics.get(context).createRecorder("recorder", hints);
You can then configure this hint in the cinnamon.prometheus.hints
section using the name of the hint. For example, here’s custom configuration for a Summary but scoped just for this hint:
cinnamon.prometheus {
hints {
custom-recorder {
summary {
quantiles = [0.5, 0.9]
}
}
}
}
Histograms
A Histogram counts observations in configurable buckets. As each bucket is just a cumulative counter, Histograms can be aggregated across dimensions. Because the histogram buckets are predetermined and fixed, there needs to be some idea of the range and distribution of values that will be observed. Since Cinnamon Recorders are used for a variety of different metrics, there’s no one-size-fits-all configuration for Recorders backed by Histograms, and so Histograms can only be configured in the Cinnamon Prometheus backend via metric hints.
If you’re using the custom metrics API you can specify a hint when creating a custom recorder:
- Scala
-
val recorder = CinnamonMetrics(context).createRecorder("recorder", hints = Set("custom-recorder"))
- Java
-
final Set<String> hints = ImmutableSet.of("custom-recorder"); final Recorder recorder = CinnamonMetrics.get(context).createRecorder("recorder", hints);
You can then configure this hint in the cinnamon.prometheus.hints
section using the name of the hint and configuration for a histogram
. A histogram is configured by specifying the upper bounds of the buckets. There are a few ways to do this.
Each bucket is a separate time series in Prometheus. Having many buckets could impact performance.
Histogram buckets
The simplest way to configure a Histogram is to specify the bucket boundaries directly. For example, here’s configuration for the custom recorder using the buckets
setting:
cinnamon.prometheus {
hints {
custom-recorder {
histogram {
buckets = [1, 10, 100]
}
}
}
}
Histogram linear buckets
If the upper bounds form a linear sequence then this can be specified by providing the starting value, the width of the buckets, and the number of buckets. For example, this configuration:
cinnamon.prometheus {
hints {
custom-recorder {
histogram {
linear-buckets {
start = 1
width = 2
count = 5
}
}
}
}
}
Would configure the histogram with these upper bounds for the buckets:
1.0,
3.0,
5.0,
7.0,
Histogram exponential buckets
If the upper bounds form an exponential sequence then this can be specified by providing the starting value, the factor, and the number of buckets. For example, this configuration:
cinnamon.prometheus {
hints {
custom-recorder {
histogram {
exponential-buckets {
start = 1
factor = 2
count = 4
}
}
}
}
}
Would configure the histogram with these upper bounds for the buckets:
1.0,
2.0,
4.0,
8.0
Histogram duration buckets
If the Histogram backs a Recorder that records time durations, then the buckets can be specified using durations and automatically converted to the base time unit (note that Cinnamon provided timers are in nanoseconds by default):
cinnamon.prometheus {
hints {
custom-recorder {
histogram {
durations {
buckets = [1ms, 3ms, 10ms, 30ms, 100ms, 300ms, 1s, 3s]
unit = nanoseconds
}
}
}
}
}
Custom exporter
It’s possible to create custom exporters, which will have access to the CollectorRegistry
from the Prometheus client.
The PrometheusExporter
interface should be implemented, which contains a stop
method, and the constructor for the exporter should accept a CollectorRegistry
and can optionally take Config
and LoggingProvider
parameters. The custom exporter is enabled in configuration, by providing the fully qualified class name. Optional extra configuration settings can be used which will be available via the Config
parameter.
For example, here is the configuration for setting up a custom exporter:
cinnamon.prometheus {
exporters += custom-exporter
custom-exporter {
exporter-class = "prometheus.sample.CustomExporter"
some-setting = 1234
}
}
And here is an outline for implementing a custom exporter, in Scala or Java:
- Scala
-
package prometheus.sample import com.lightbend.cinnamon.logging.Logger import com.lightbend.cinnamon.logging.LoggingProvider import com.lightbend.cinnamon.prometheus.PrometheusExporter import com.typesafe.config.Config import io.prometheus.client.CollectorRegistry // the Config and LoggingProvider are optional parameters which will be injected class CustomExporter(registry: CollectorRegistry, config: Config, logging: LoggingProvider) extends PrometheusExporter { // can create a Cinnamon logger val log: Logger = logging.get(this.getClass) // the config is already scoped to the "custom-exporter" section val someSetting: Int = config.getInt("some-setting") // set up and start exporter with registry in the constructor log.info("Starting custom exporter...") override def stop(): Unit = { // stop and clean up the exporter here // called when the Cinnamon backends are shutting down log.info("Stopping custom exporter...") } }
- Java
-
package prometheus.sample; import com.lightbend.cinnamon.logging.Logger; import com.lightbend.cinnamon.logging.LoggingProvider; import com.lightbend.cinnamon.prometheus.PrometheusExporter; import com.typesafe.config.Config; import io.prometheus.client.CollectorRegistry; public final class CustomExporter implements PrometheusExporter { private final Logger log; private final int someSetting; // the Config and LoggingProvider are optional parameters which will be injected public CustomExporter(CollectorRegistry registry, Config config, LoggingProvider logging) { // can create a Cinnamon logger this.log = logging.get(this.getClass()); // the config is already scoped to the "custom-exporter" section this.someSetting = config.getInt("some-setting"); // set up and start exporter with registry in the constructor log.info("Starting custom exporter..."); } public int getSomeSetting() { return someSetting; } @Override public void stop() { // stop and clean up the exporter here // called when the Cinnamon backends are shutting down log.info("Stopping custom exporter..."); } }
Reusing the default registry
It is also possible to have the Cinnamon Prometheus exporter use the Prometheus default registry. By doing so, all metrics created via the native Prometheus client and via Cinnamon will end up in the same registry. This will enable the Cinnamon Prometheus exporter to expose all metrics regardless of how they have been created.
By default this feature is set to off
and here below is the configuration for how to enable it:
- Default registry setting
-
cinnamon.prometheus { use-default-registry = on }
Prometheus Docker developer sandbox
Cinnamon provides a Docker-based developer sandbox environment for Prometheus.