Configuración

1. Instala el rastreador de Java Datadog.

   1. Añade el rastreador de Datadog Java a tu archivo Docker:

      Dockerfile

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      ADD 'https://dtdg.co/latest-java-tracer' agent.jar
      ENV JAVA_TOOL_OPTIONS="-javaagent:agent.jar"

   2. Añade los artefactos del rastreador.

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      <dependency>
        <groupId>com.datadoghq</groupId>
        <artifactId>dd-trace-api</artifactId>
        <version>DD_TRACE_JAVA_VERSION_HERE</version>
      </dependency>

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      implementation 'com.datadoghq:dd-trace-api:DD_TRACE_JAVA_VERSION_HERE'

      Consulta las versiones de dd-trace-java para obtener la última versión del rastreador.

   Nota: Los Cloud Run Jobs se ejecutan hasta su finalización en lugar de servir peticiones, por lo que la instrumentación automática no creará un nivel superior “job (generic)” span (tramo). Para obtener visibilidad de extremo a extremo, cree su propia raíz span (tramo). Consulte las instrucciones de Java Custom Instrumentation.

   Para más información, consulta Rastreo de aplicaciones Java.

2. Instalar serverless-init.

   Serverless-init automatically creates a span for the duration of a task, even if the tracer is not installed. You can disable this by setting DD_APM_ENABLED=false. However, tracing is recommended because it is required for task-level visibility.
   Cloud Run Jobs requires serverless-init version 1.9.0 or later.
   Datadog publishes new releases of the serverless-init container image to Google's gcr.io, AWS's ECR, and on Docker Hub:

   hub.docker.com	gcr.io	public.ecr.aws
   datadog/serverless-init	gcr.io/datadoghq/serverless-init	public.ecr.aws/datadog/serverless-init

   Images are tagged based on semantic versioning, with each new version receiving three relevant tags:

   • 1, 1-alpine: use these to track the latest minor releases, without breaking changes
   • 1.x.x, 1.x.x-alpine: use these to pin to a precise version of the library
   • latest, latest-alpine: use these to follow the latest version release, which may include breaking changes

   Add the following instructions and arguments to your Dockerfile.

   COPY --from=datadog/serverless-init:<YOUR_TAG> /datadog-init /app/datadog-init
   ENTRYPOINT ["/app/datadog-init"]
   CMD ["./mvnw", "spring-boot:run"]
   

   Alternative configuration

   Datadog expects serverless-init to be the top-level application, with the rest of your app’s command line passed in for serverless-init to execute.

   If you already have an entrypoint defined inside your Dockerfile, you can instead modify the CMD argument.

   CMD ["/app/datadog-init", "./mvnw", "spring-boot:run"]
   

   If you require your entrypoint to be instrumented as well, you can instead swap your entrypoint and CMD arguments.

   ENTRYPOINT ["/app/datadog-init"]
   CMD ["/your_entrypoint.sh", "./mvnw", "spring-boot:run"]
   

   As long as your command to run is passed as an argument to datadog-init, you will receive full instrumentation.

   
   

3. Configurar logs.

   Para activar el registro, configura la variable de entorno DD_LOGS_ENABLED=true. Esto permite a serverless-init leer logs de stdout y stderr.

   Datadog también recomienda configurar la variable de entorno DD_LOGS_INJECTION=true y DD_SOURCE=java para activar el parseo avanzado de logs de Datadog.

   Si deseas que los logs de varias líneas se conserven en un único mensaje de logs, Datadog te recomienda escribir tus logs en formato JSON compacto. Por ejemplo, puedes utilizar una biblioteca de registro de terceros como Log4j 2:

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   private static final Logger logger = LogManager.getLogger(App.class);
   logger.info("Hello World!");

   resources/log4j2.xml

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   <Configuration>
     <Appenders>
       <Console name="Console"><JsonLayout compact="true" eventEol="true" properties="true"/></Console>
     </Appenders>
     <Loggers><Root level="info"><AppenderRef ref="Console"/></Root></Loggers>
   </Configuration>

   Para obtener más información, consulta Correlación de logs y trazas de Java.

4. Configurar tu aplicación.

   After the container is built and pushed to your registry, set the required environment variables for the Datadog Agent:

   • DD_API_KEY: Your Datadog API key, used to send data to your Datadog account. For privacy and safety, configure this API key as a Google Cloud Secret.
   • DD_SITE: Your Datadog site. For example, datadoghq.com.

   For more environment variables, see the Environment variables section on this page.

5. Add a service label in Google Cloud. In your Cloud Run service’s info panel, add a label with the following key and value:

   Key	Value
   service	The name of your service. Matches the value provided as the DD_SERVICE environment variable.

   See Configure labels for services in the Cloud Run documentation for instructions.

6. Set up a retention filter for Cloud Run Jobs traces. Datadog relies on traces to display executions and tasks in the UI. To ensure traces are retained, create a retention filter with the query @origin:cloudrunjobs and set the span retention rate to 100%.

   

7. Enviar métricas personalizadas.

   Para enviar métricas personalizadas, instala el cliente DogStatsD y consulta ejemplos de código. En serverless, solo se admite el tipo de métrica distribution.

Environment variables

Do not set the following environment variables in your serverless environment. They should only be set in non-serverless environments.

• DD_AGENT_HOST
• DD_TRACE_AGENT_URL

Solucionar problemas

This integration depends on your runtime having a full SSL implementation. If you are using a slim image, you may need to add the following command to your Dockerfile to include certificates:

RUN apt-get update && apt-get install -y ca-certificates

To have your Cloud Run services appear in the software catalog, you must set the DD_SERVICE, DD_VERSION, and DD_ENV environment variables.

Referencias adicionales

Más enlaces, artículos y documentación útiles:

Rastreo de aplicaciones JavaDOCUMENTACIÓN Correlación de logs y trazas de JavaDOCUMENTACIÓN