Google Cloud Run

개요

Google Cloud Run은 컨테이너 기반 애플리케이션을 배포하고 확장하기 위한 완전 관리형 서버리스 플랫폼입니다. Datadog은 Google Cloud 통합을 통해 Cloud Run에 대한 모니터링 및 로그 수집을 제공합니다. Datadog은 또한 추적, 커스텀 메트릭, 직접 로그 수집을 지원하기 위해 특별히 제작된 Agent로 Cloud Run 애플리케이션을 계측하기 위한 솔루션을 제공합니다.

전제 조건

Datadog API 키가 있고 Datadog 트레이싱 라이브러리에서 지원되는 프로그래밍 언어를 사용하고 있는지 확인하세요.

애플리케이션의 계측

Dockerfile 또는 buildpack의 두 가지 방법 중 하나로 애플리케이션을 계측할 수 있습니다.

Dockerfile

Datadog은 serverless-init 컨테이너 이미지의 새 릴리스를 Google의 gcr.io, AWS의 ECR 및 Docker Hub에 게시합니다.

dockerhub.iogcr.iopublic.ecr.aws
datadog/serverless-initgcr.io/datadoghq/serverless-initpublic.ecr.aws/datadog/serverless-init

이미지에는 시맨틱 버전 관리를 기반으로 태그가 지정되며, 새 버전마다 3개의 관련 태그가 지정됩니다:

  • 1, 1-alpine: 이를 사용하여 변경 사항 중단 없이 최신 마이너 릴리스를 추적하세요
  • 1.x.x, 1.x.x-alpine: 정확한 버전의 라이브러리에 고정할 때 사용하세요.
  • latest, latest-alpine: 주요 변경 사항이 포함된 최신 버전 릴리스를 따를 때 사용하세요.

serverless-init 작동 방식

serverless-init 애플리케이션은 프로세스를 래핑하고 이를 하위 프로세스로 실행합니다. 메트릭을 위한 DogStatsD 수신기와 추적을 위한 Trace Agent 수신기를 시작합니다. 애플리케이션의 stdout/stderr 스트림을 래핑하여 로그를 수집합니다. 부트스트래핑 후 serverless-init는 명령을 하위 프로세스로 실행합니다.

전체를 계측하려면 Docker 컨테이너 내에서 실행되는 첫 번째 명령으로 datadog-init를 호출하고 있는지 확인하세요. 이를 진입점으로 설정하거나 CMD의 첫 번째 인수로 설정하여 수행할 수 있습니다.

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-js-init /operator-build/node_modules /dd_tracer/node/
ENV DD_SERVICE=datadog-demo-run-nodejs
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["/nodejs/bin/node", "/path/to/your/app.js"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Copy the Datadog Node.JS tracer into your Docker image.

    COPY --from=datadog/dd-lib-js-init /operator-build/node_modules /dd_tracer/node/

    If you install the Datadog tracer library directly in your application, as outlined in the manual tracer instrumentation instructions, omit this step.

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-nodejs
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your binary application wrapped in the entrypoint. Adapt this line to your needs.

    CMD ["/nodejs/bin/node", "/path/to/your/app.js"]

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-js-init /operator-build/node_modules /dd_tracer/node/
ENV DD_SERVICE=datadog-demo-run-nodejs
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/app/datadog-init", "/nodejs/bin/node", "/path/to/your/app.js"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-js-init /operator-build/node_modules /dd_tracer/node/
ENV DD_SERVICE=datadog-demo-run-nodejs
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["/your_entrypoint.sh", "/nodejs/bin/node", "/path/to/your/app.js"]

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

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
RUN pip install --target /dd_tracer/python/ ddtrace
ENV DD_SERVICE=datadog-demo-run-python
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["/dd_tracer/python/bin/ddtrace-run", "python", "app.py"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Install the Datadog Python tracer.

    RUN pip install --target /dd_tracer/python/ ddtrace

    If you install the Datadog tracer library directly in your application, as outlined in the manual tracer instrumentation instructions, omit this step.

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-python
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your binary application wrapped in the entrypoint, launched by the Datadog trace library. Adapt this line to your needs.

    CMD ["/dd_tracer/python/bin/ddtrace-run", "python", "app.py"]

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
RUN pip install --target /dd_tracer/python/ ddtrace
ENV DD_SERVICE=datadog-demo-run-python
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/app/datadog-init", "/dd_tracer/python/bin/ddtrace-run", "python", "app.py"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
RUN pip install --target /dd_tracer/python/ ddtrace
ENV DD_SERVICE=datadog-demo-run-python
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["your_entrypoint.sh", "/dd_tracer/python/bin/ddtrace-run", "python", "app.py"]

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

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD 'https://dtdg.co/latest-java-tracer' /dd_tracer/java/dd-java-agent.jar
ENV DD_SERVICE=datadog-demo-run-java
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["./mvnw", "spring-boot:run"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Add the Datadog Java tracer to your Docker image.

    ADD 'https://dtdg.co/latest-java-tracer' /dd_tracer/java/dd-java-agent.jar

    If you install the Datadog tracer library directly in your application, as outlined in the manual tracer instrumentation instructions, omit this step.

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-java
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your binary application wrapped in the entrypoint. Adapt this line to your needs.

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

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD 'https://dtdg.co/latest-java-tracer' /dd_tracer/java/dd-java-agent.jar
ENV DD_SERVICE=datadog-demo-run-java
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/app/datadog-init", "./mvnw", "spring-boot:run"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD 'https://dtdg.co/latest-java-tracer' /dd_tracer/java/dd-java-agent.jar
ENV DD_SERVICE=datadog-demo-run-java
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
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.

Manually install the Go tracer before you deploy your application. Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENTRYPOINT ["/app/datadog-init"]
ENV DD_SERVICE=datadog-demo-run-go
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/path/to/your-go-binary"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-go
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Execute your binary application wrapped in the entrypoint. Adapt this line to your needs.

    CMD ["/path/to/your-go-binary"]

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENV DD_SERVICE=datadog-demo-run-go
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/app/datadog-init", "/path/to/your-go-binary"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENV DD_SERVICE=datadog-demo-run-go
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["your_entrypoint.sh", "/path/to/your-go-binary"]

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

Note: You can also use Orchestrion, a tool for automatically instrumenting Go code. Orchestrion is in private beta. For more information, open a GitHub issue in the Orchestrion repo, or contact Support.

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-dotnet-init /datadog-init/monitoring-home/ /dd_tracer/dotnet/
ENV DD_SERVICE=datadog-demo-run-dotnet
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["dotnet", "helloworld.dll"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Copy the Datadog .NET tracer into your Docker image.

    COPY --from=datadog/dd-lib-dotnet-init /datadog-init/monitoring-home/ /dd_tracer/dotnet/

    If you install the Datadog tracer library directly in your application, as outlined in the manual tracer instrumentation instructions, omit this step.

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-dotnet
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your binary application wrapped in the entrypoint. Adapt this line to your needs.

    CMD ["dotnet", "helloworld.dll"]

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-dotnet-init /datadog-init/monitoring-home/ /dd_tracer/dotnet/
ENV DD_SERVICE=datadog-demo-run-dotnet
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
CMD ["/app/datadog-init", "dotnet", "helloworld.dll"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
COPY --from=datadog/dd-lib-dotnet-init /datadog-init/monitoring-home/ /dd_tracer/dotnet/
ENV DD_SERVICE=datadog-demo-run-dotnet
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]
CMD ["your_entrypoint.sh", "dotnet", "helloworld.dll"]

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

Manually install the Ruby tracer before you deploy your application. See the example application.

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENV DD_SERVICE=datadog-demo-run-ruby
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENV DD_TRACE_PROPAGATION_STYLE=datadog
ENTRYPOINT ["/app/datadog-init"]
CMD ["rails", "server", "-b", "0.0.0.0"]

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. (Optional) add Datadog tags

    ENV DD_SERVICE=datadog-demo-run-ruby
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  3. This environment variable is needed for trace propagation to work properly in Cloud Run. Ensure that you set this variable for all Datadog-instrumented downstream services.

    ENV DD_TRACE_PROPAGATION_STYLE=datadog

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your binary application wrapped in the entrypoint. Adapt this line to your needs.

    CMD ["rails", "server", "-b", "0.0.0.0"]

Alternative configuration

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

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENV DD_SERVICE=datadog-demo-run-ruby
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENV DD_TRACE_PROPAGATION_STYLE=datadog
CMD ["/app/datadog-init", "rails", "server", "-b", "0.0.0.0"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ENV DD_SERVICE=datadog-demo-run-ruby
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENV DD_TRACE_PROPAGATION_STYLE=datadog
ENTRYPOINT ["/app/datadog-init"]
CMD ["your_entrypoint.sh", "rails", "server", "-b", "0.0.0.0"]

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

Add the following instructions and arguments to your Dockerfile.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD https://github.com/DataDog/dd-trace-php/releases/latest/download/datadog-setup.php /datadog-setup.php
RUN php /datadog-setup.php --php-bin=all
ENV DD_SERVICE=datadog-demo-run-php
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]

# use the following for an Apache and mod_php based image
RUN sed -i "s/Listen 80/Listen 8080/" /etc/apache2/ports.conf
EXPOSE 8080
CMD ["apache2-foreground"]

# use the following for an Nginx and php-fpm based image
RUN ln -sf /dev/stdout /var/log/nginx/access.log && ln -sf /dev/stderr /var/log/nginx/error.log
EXPOSE 8080
CMD php-fpm; nginx -g daemon off;

Note: The datadog-init entrypoint wraps your process and collects logs from it. To get logs working properly, ensure that your Apache, Nginx, or PHP processes are writing output to stdout.

Explanation

  1. Copy the Datadog serverless-init into your Docker image.

    COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init

  2. Copy and install the Datadog PHP tracer.

    ADD https://github.com/DataDog/dd-trace-php/releases/latest/download/datadog-setup.php /datadog-setup.php
RUN php /datadog-setup.php --php-bin=all

    If you install the Datadog tracer library directly in your application, as outlined in the manual tracer instrumentation instructions, omit this step.

  3. (Optional) Add Datadog tags.

    ENV DD_SERVICE=datadog-demo-run-php
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1

  4. Change the entrypoint to wrap your application in the Datadog serverless-init process. Note: If you already have an entrypoint defined inside your Dockerfile, see the alternative configuration.

    ENTRYPOINT ["/app/datadog-init"]

  5. Execute your application.

    Use the following for an apache and mod_php based image:

    RUN sed -i "s/Listen 80/Listen 8080/" /etc/apache2/ports.conf
EXPOSE 8080
CMD ["apache2-foreground"]

    Use the following for an nginx and php-fpm based image:

    RUN ln -sf /dev/stdout /var/log/nginx/access.log && ln -sf /dev/stderr /var/log/nginx/error.log
EXPOSE 8080
CMD php-fpm; nginx -g daemon off;

Alternative configuration: CMD argument

If you already have an entrypoint defined inside your Dockerfile, and you are using an Apache and mod_php based image, you can instead modify the CMD argument.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD https://github.com/DataDog/dd-trace-php/releases/latest/download/datadog-setup.php /datadog-setup.php
RUN php /datadog-setup.php --php-bin=all
ENV DD_SERVICE=datadog-demo-run-php
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
RUN sed -i "s/Listen 80/Listen 8080/" /etc/apache2/ports.conf
EXPOSE 8080
CMD ["/app/datadog-init", "apache2-foreground"]

If you require your entrypoint to be instrumented as well, you can swap your entrypoint and CMD arguments instead. For more information, see How serverless-init works.

COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
ADD https://github.com/DataDog/dd-trace-php/releases/latest/download/datadog-setup.php /datadog-setup.php
RUN php /datadog-setup.php --php-bin=all
ENV DD_SERVICE=datadog-demo-run-php
ENV DD_ENV=datadog-demo
ENV DD_VERSION=1
ENTRYPOINT ["/app/datadog-init"]

# use the following for an Apache and mod_php based image
RUN sed -i "s/Listen 80/Listen 8080/" /etc/apache2/ports.conf
EXPOSE 8080
CMD ["your_entrypoint.sh", "apache2-foreground"]

# use the following for an Nginx and php-fpm based image
RUN ln -sf /dev/stdout /var/log/nginx/access.log && ln -sf /dev/stderr /var/log/nginx/error.log
EXPOSE 8080
CMD your_entrypoint.sh php-fpm; your_entrypoint.sh nginx -g daemon off;

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

Buildpack

Pack Buildpacks는 Dockerfile을 사용하지 않고 컨테이너를 패키징하는 편리한 방법을 제공합니다.

먼저 트레이서를 수동으로 설치합니다.

이어서 다음 명령을 실행하여 애플리케이션을 구축합니다.

pack build --builder=gcr.io/buildpacks/builder \
--buildpack from=builder \
--buildpack datadog/serverless-buildpack:latest \
gcr.io/YOUR_PROJECT/YOUR_APP_NAME

참고: Buildpack 계측은 Alpine 이미지와 호환되지 않습니다.

애플리케이션 설정

컨테이너가 만들어진 후 레지스트리에 푸시되면 마지막 단계는 Datadog 에이전트를 위한 필수 환경 변수를 설정하는 것입니다:

  • DD_API_KEY: Datadog API 키는 Datadog 계정으로 데이터를 보내는 데 사용됩니다. 개인 정보 보호 및 보안 이슈 방지를 위해 Google Cloud Secret으로 설정해야 합니다.
  • DD_SITE: Datadog 엔드포인트와 웹사이트입니다. 페이지 오른쪽에서 사이트를 선택합니다. 사이트는 입니다.
  • DD_TRACE_ENABLED: 트레이싱 활성화를 위해 true로 설정합니다.
  • DD_TRACE_PROPAGATION_STYLE: 컨텍스트 전파 및 로그 추적 상관 관계를 사용하기 위해 datadog로 설정하세요.

환경 변수와 기능에 대한 자세한 정보는 추가 설정을 참조하세요.

다음 명령은 서비스를 배포하고 외부 연결이 서비스에 도달하도록 허용합니다. DD_API_KEY를 환경 변수로 설정하고 서비스가 포트 8080을 수신하도록 설정합니다.

shell
gcloud run deploy APP_NAME --image=gcr.io/YOUR_PROJECT/APP_NAME \
  --port=8080 \
  --update-env-vars=DD_API_KEY=$DD_API_KEY \
  --update-env-vars=DD_TRACE_ENABLED=true \
  --update-env-vars=DD_SITE='datadoghq.com' \
  --update-env-vars=DD_TRACE_PROPAGATION_STYLE='datadog' \

결과

구축이 완료되면 메트릭과 트레이스는 Datadog로 전송됩니다. Datadog에서 인프라스트럭처->서버리스로 이동하여 서버리스 메트릭과 트레이스를 확인합니다.

추가 설정

  • 고급 트레이싱: Datadog 에이전트는 이미 인기 프레임워크에 대한 기본 트레이싱을 제공합니다. 자세한 정보는 고급 트레이싱 가이드를 참조하세요.

  • 로그: Google Cloud 통합을 사용하는 경우 이미 로그가 수집됩니다. 대신 DD_LOGS_ENABLED 환경 변수를 true로 설정하여 직접 서버리스 계측으로 애플리케이션 로그를 캡처할 수 있습니다.

  • 커스텀 메트릭: DogStatsd 클라이언트를 사용해 커스텀 메트릭을 제출할 수 있습니다. Cloud Run 및 기타 서버리스 애플리케이션의 경우 분포 메트릭을 사용합니다. 분포는 avg, sum, max, min, count 집계를 기본적으로 제공합니다. 메트릭 요약 페이지에서 백분위수 집계(p50, p75, p90, p95, p99)를 활성화할 수 있습니다. 게이지 메트릭 유형에 대한 분포를 모니터링하려면 시간 및 공간 집계 모두에 대해 avg를 사용합니다. 개수 메트릭 유형에 대한 분포를 모니터링하려면 시간 및 공간 집계 모두에 sum을 사용합니다.

환경 변수

변수설명
DD_API_KEYDatadog API 키 - 필수
DD_SITEDatadog 사이트 - 필수
DD_LOGS_ENABLEDtrue인 경우 로그 (stdout 및 stderr)를 Datadog에 전송합니다. 기본값은 false입니다.
DD_LOGS_INJECTIONtrue인 경우 Java, Node, .NET 및 PHP에서 지원되는 로거에 대한 트레이스 데이터로 모든 로그를 보강합니다. Python, Go 및 Ruby에 대한 추가 문서를 참조하세요.
DD_TRACE_SAMPLE_RATE트레이스 수집 샘플링 속도 0.0 및 1.0을 제어합니다.
DD_SERVICE통합 서비스 태깅을 참조하세요.
DD_VERSION통합 서비스 태깅을 참조하세요.
DD_ENV통합 서비스 태깅을 참조하세요.
DD_SOURCE통합 서비스 태깅을 참조하세요.
DD_TAGS통합 서비스 태깅을 참조하세요.

트러블슈팅

이 통합은 전체 SSL 구현이 있는 런타임에 따라 달라집니다. 슬림 이미지를 사용하는 경우 인증서를 포함하려면 Dockerfile에 다음 명령을 추가해야 할 수도 있습니다.

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

참고 자료

Additional helpful documentation, links, and articles:

Cloud Run 서비스에서 트레이스, 로그, 커스텀 메트릭 수집블로그 more more