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In this example, we’ll use Dash0 to collect and visualize metrics and traces produced by an otel4s application. Unlike the Jaeger example, you don’t need to set up a collector service locally—telemetry is sent directly to the Dash0 API.
Dash0 offers a 14-day free trial. After that, 1 million spans cost 20 cents. It provides robust analysis and visualization tools for exploring telemetry data.

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Project Setup

Configure your project with the required dependencies: 
// Add to build.sbt
libraryDependencies ++= Seq(
  "org.typelevel" %% "otel4s-oteljava" % "0.15.0",
  "io.opentelemetry" % "opentelemetry-exporter-otlp" % "1.59.0" % Runtime,
  "io.opentelemetry" % "opentelemetry-sdk-extension-autoconfigure" % "1.59.0" % Runtime
)

run / fork := true
javaOptions += "-Dotel.java.global-autoconfigure.enabled=true"
javaOptions += "-Dotel.service.name=dash0-example"
javaOptions += "-Dotel.exporter.otlp.endpoint=https://ingress.eu-west-1.aws.dash0.com"
The endpoint URL may differ depending on your cloud region. Check the Dash0 settings for your specific endpoint.

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OpenTelemetry SDK Configuration

The OpenTelemetry SDK can be configured via system properties or environment variables. See the full list of environment variable configurations for more options.

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Acquiring a Dash0 Auth Token

1
Create an Account
2
Sign up at dash0.com/sign-up.
3
Log In
4
Access your Dash0 account.
6
Go to the organization settings page.
7
Generate Auth Token
8
Under “Auth Tokens”, generate a new auth token.
9
Find Your Endpoint
10
Under “Endpoints”, find the value for -Dotel.exporter.otlp.endpoint specific to your cloud region.
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Use Different Tokens
12
Create separate auth tokens and datasets for test, production, and local development to organize your data.

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Dash0 Configuration

Configure the auth token and dataset name using environment variables: 
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer auth_token,Dash0-Dataset=otel-metrics"
Headers:
  • Authorization - Bearer token for authentication
  • Dash0-Dataset - The dataset name for metrics (defaults to default if not set)
Each service’s traces will automatically land in a dataset named after the otel.service.name configuration.

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Application Example

This example demonstrates both metrics and tracing: 
import java.util.concurrent.TimeUnit
import cats.effect.{Async, IO, IOApp}
import cats.effect.std.Console
import cats.effect.std.Random
import cats.syntax.all._
import org.typelevel.otel4s.{Attribute, AttributeKey}
import org.typelevel.otel4s.oteljava.OtelJava
import org.typelevel.otel4s.metrics.Histogram
import org.typelevel.otel4s.trace.Tracer
import scala.concurrent.duration._

trait Work[F[_]] {
  def doWork: F[Unit]
}

object Work {
  def apply[F[_]: Async: Tracer: Console](histogram: Histogram[F, Double]): Work[F] =
    new Work[F] {
      def doWork: F[Unit] =
        Tracer[F].span("Work.DoWork").use { span =>
          span.addEvent("Starting the work.") *>
            doWorkInternal(steps = 10) *>
            span.addEvent("Finished working.")
        }

      def doWorkInternal(steps: Int): F[Unit] = {
        val step = Tracer[F]
          .span("internal", Attribute(AttributeKey.long("steps"), steps.toLong))
          .surround {
            for {
              random <- Random.scalaUtilRandom
              delay <- random.nextIntBounded(1000)
              _ <- Async[F].sleep(delay.millis)
              _ <- Console[F].println("Doin' work")
            } yield ()
          }

        val metered = histogram.recordDuration(TimeUnit.MILLISECONDS).surround(step)

        if (steps > 0) metered *> doWorkInternal(steps - 1) else metered
      }
    }
}

object TracingExample extends IOApp.Simple {
  def run: IO[Unit] = {
    OtelJava
      .autoConfigured[IO]()
      .evalMap { otel4s =>
        otel4s.tracerProvider.get("com.service.runtime")
          .flatMap { implicit tracer: Tracer[IO] =>
            for {
              meter <- otel4s.meterProvider.get("com.service.runtime")
              histogram <- meter.histogram[Double]("work.execution.duration").create
              _ <- Work[IO](histogram).doWork
            } yield ()
          }
      }
      .use_
  }
}

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Running the Application

export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer auth_token,Dash0-Dataset=otel-metrics"
sbt run

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Viewing Data in Dash0

Navigate to https://app.dash0.com/ to query and visualize your collected traces and metrics.

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Traces

You’ll see the trace hierarchy with:
  • Parent span “Work.DoWork”
  • Nested “internal” spans
  • Events marking work start and completion
  • Attributes including the “steps” counter
  • Timing information for each span

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Metrics

The work.execution.duration histogram will show:
  • Execution time distribution
  • Statistical aggregations
  • Time-series visualization
  • Histogram buckets
Use Dash0’s query and visualization tools to:
  • Create custom dashboards
  • Set up alerts
  • Analyze performance patterns
  • Correlate metrics with traces

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