설정

1. Datadog Python SDK를 설치합니다.

   requirements.txt 또는 pyproject.toml에 ddtrace를 추가합니다. 이렇게 하면 컨테이너 이미지를 빌드하고 배포할 때 SDK가 이미지에 포함됩니다. 최신 버전은 PyPI에서 확인할 수 있습니다.

   requirements.txt

   Copy

   ddtrace==<VERSION>

   자세한 정보는 Python 애플리케이션 추적을 참조하세요.

2. serverless-init을 사이드카로 설치합니다.

   Setup

   Install the Datadog CLI

   npm install -g @datadog/datadog-ci @datadog/datadog-ci-plugin-cloud-run
   

   Install the gcloud CLI and authenticate with gcloud auth login.

   Configuration

   Configure the Datadog site and Datadog API key, and define the service name to use in Datadog.

   export DATADOG_SITE="<DATADOG_SITE>"
   export DD_API_KEY="<DD_API_KEY>"
   export DD_SERVICE="<SERVICE_NAME>"
   

   Instrument

   If you are new to Datadog serverless monitoring, launch the Datadog CLI in interactive mode to guide your first installation for a quick start.

   datadog-ci cloud-run instrument -i
   

   To set up the Datadog sidecar for your applications, run the instrument command after your normal deployment. You can specify multiple services to instrument by passing multiple --service flags.

   datadog-ci cloud-run instrument --project <GCP-PROJECT-ID> --service <CLOUD-RUN-SERVICE-NAME> --region <GCP-REGION>
   

   You can pin to a specific image with the --sidecar-image flag. See the latest releases on Docker Hub.

   Additional parameters can be found in the CLI documentation.

   The Datadog Terraform module for Google Cloud Run wraps the google_cloud_run_v2_service resource and automatically configures your Cloud Run app for Datadog Serverless Monitoring by adding required environment variables and the serverless-init sidecar.

   If you don’t already have Terraform set up, install Terraform, create a new directory, and make a file called main.tf.

   Then, add the following to your Terraform configuration, updating it as necessary based on your needs:

   variable "datadog_api_key" {
     description = "Your Datadog API key"
     type        = string
     sensitive   = true
   }
   
   module "my-cloud-run-app" {
     source  = "DataDog/cloud-run-datadog/google"
     version = "~> 1.0"
   
     project  = "my-gcp-project"
     name     = "my-cloud-run-app"
     location = "us-central1"
   
     datadog_api_key = var.datadog_api_key
     datadog_service = "test-service" // your application service
     datadog_version = "0.0.0" // your code version
     datadog_env     = "prod" // your application environment
     
     datadog_enable_logging = true
   
     deletion_protection = false
     build_config = {
       function_target          = "helloHttp" // your function entry point
       image_uri                = "us-docker.pkg.dev/cloudrun/container/hello"
       base_image               = "us-central1-docker.pkg.dev/serverless-runtimes/google-22-full/runtimes/your-runtime" // base image for your runtime
       enable_automatic_updates = true
     }
     template = {
       containers = [
         {
           name  = "main"
           image = "us-docker.pkg.dev/cloudrun/container/hello"
           base_image_uri = "us-central1-docker.pkg.dev/serverless-runtimes/google-22-full/runtimes/your-runtime" // base image for your runtime
           resources = {
             limits = {
               cpu    = "1"
               memory = "512Mi"
             }
           }
           ports = {
             container_port = 8080
           }
           env = [
             { name = "DD_TRACE_ENABLED", value = "true" },
           ]
         },
       ]
     }
   }
   

   See the Environment Variables for more information on the configuration options available through the env.

   Ensure the container port for the main container is the same as the one exposed in your Dockerfile/service.

   If you haven’t already, initialize your Terraform project:

   terraform init
   

   To deploy your app, run:

   terraform apply
   

   After deploying your Cloud Run app, you can manually modify your app’s settings to enable Datadog monitoring.

   1. Create a Volume with In-Memory volume type.

   2. Add a new container with image URL: gcr.io/datadoghq/serverless-init:<YOUR_TAG>. See the latest releases on Docker Hub to pin a specific version.

   3. Add the volume mount to every container in your application. Choose a path such as /shared-volume, and remember it for the next step.

   4. Add the following environment variables to your serverless-init sidecar container:

      • DD_SERVICE: A name for your service. For example, gcr-sidecar-test.
      • DD_ENV: A name for your environment. For example, dev.
      • DD_SERVERLESS_LOG_PATH: Your log path. For example, /shared-volume/logs/*.log. The path must begin with the mount path you defined in the previous step.
      • DD_API_KEY: Your Datadog API key.
      • FUNCTION_TARGET: The entry point of your function. For example, Main.

      For a list of all environment variables, including additional tags, see Environment variables.

3. 로그를 설정합니다.

   이전 단계에서 공유 볼륨을 생성했습니다. DD_SERVERLESS_LOG_PATH 환경 변수를 설정했을 수도 있으며, 기본값은 /shared-volume/logs/app.log입니다.

   이 단계에서는 DD_SERVERLESS_LOG_PATH에 설정된 파일에 로그를 기록하도록 로깅 라이브러리를 구성합니다. 로그/트레이스 상관관계 및 기타 기능에 사용할 사용자 지정 형식을 설정할 수도 있습니다. Datadog은 다음 환경 변수를 설정할 것을 권장합니다.

   • PYTHONUNBUFFERED=1: 메인 컨테이너에서 설정합니다. Python 출력이 버퍼링되지 않고 컨테이너 로그에 즉시 표시되도록 합니다.
   • DD_LOGS_INJECTION=true: 메인 컨테이너에서 설정합니다. 지원되는 로거에 대하여 로그/트레이스 상관관계를 활성화합니다.
   • DD_SOURCE=python: 사이드카 컨테이너에서 설정합니다. 고급 Datadog 로그 구문 분석을 활성화합니다.

   그런 다음 로깅 라이브러리를 업데이트하세요. 예를 들어, Python의 기본 logging 라이브러리를 사용할 수 있습니다.

   Copy

   LOG_FILE = "/shared-volume/logs/app.log"
   os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True)
   
   FORMAT = ('%(asctime)s %(levelname)s [%(name)s] [%(filename)s:%(lineno)d] '
           '[dd.service=%(dd.service)s dd.env=%(dd.env)s dd.version=%(dd.version)s dd.trace_id=%(dd.trace_id)s dd.span_id=%(dd.span_id)s] '
           '- %(message)s')
   
   logging.basicConfig(
       level=logging.INFO,
       format=FORMAT,
       handlers=[
           logging.FileHandler(LOG_FILE),
           logging.StreamHandler(sys.stdout)
       ]
   )
   logger = logging.getLogger(__name__)
   logger.level = logging.INFO
   
   logger.info('Hello world!')

   자세한 정보는 Python 로그 및 트레이스 상호 연결하기를 참조하세요.

4. 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.

5. 커스텀 메트릭을 전송합니다.

   커스텀 메트릭을 전송하려면, DogStatsD 클라이언트를 설치하고 코드 예시를 참고하세요. Serverless Monitoring에서는 분포 메트릭 유형만 지원됩니다.

6. 프로파일링을 활성화합니다(미리 보기).

   Continuous Profiler를 활성화하려면, 애플리케이션 컨테이너에 DD_PROFILING_ENABLED=true 환경 변수를 설정하고 함수 파일 상단에 import ddtrace.auto를 추가합니다.

   Copy

   import ddtrace.auto
   
   # ... rest of your function code

   Datadog Continuous Profiler는 2세대 Cloud Run 함수에서 미리 보기로 제공됩니다.

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

Pub/Sub을 사용한 분산 추적

애플리케이션 컨테이너에서 DD_TRACE_INFERRED_PROXY_SERVICES_ENABLED=true를 설정하세요. 이렇게 하면 푸시 요청에 대해 추론된 gcp.pubsub.receive 스팬이 생성됩니다.

Google Cloud Pub/Sub 푸시 구독 추적을 사용하려면 ddtrace 버전 4.8.0 이상이 필요합니다.

To get end-to-end distributed traces between Pub/Sub producers and Cloud Run, configure your push subscriptions with the --push-no-wrapper and --push-no-wrapper-write-metadata flags. This moves message attributes from the JSON body to HTTP headers, allowing Datadog to extract producer trace context and create span links.

For more information, see Producer-aware tracing for Google Cloud Pub/Sub and Cloud Run and Payload unwrapping in the Google Cloud documentation.

Configure push subscriptions for full trace visibility

Create a new push subscription:

gcloud pubsub subscriptions create order-processor-sub \
  --topic=orders \
  --push-endpoint=https://order-processor-xyz.run.app/pubsub \
  --push-no-wrapper \
  --push-no-wrapper-write-metadata

Update an existing push subscription:

gcloud pubsub subscriptions update order-processor-sub \
  --push-no-wrapper \
  --push-no-wrapper-write-metadata

Configure Eventarc Pub/Sub triggers

Eventarc Pub/Sub triggers use push subscriptions as the underlying delivery mechanism. When you create an Eventarc trigger, GCP automatically creates a managed push subscription. However, Eventarc does not expose --push-no-wrapper-write-metadata as a trigger creation parameter, so you must manually update the auto-created subscription.

1. Create the Eventarc trigger:

   gcloud eventarc triggers create order-processor-trigger \
     --destination-run-service=order-processor \
     --destination-run-region=us-central1 \
     --event-filters="type=google.cloud.pubsub.topic.v1.messagePublished" \
     --transport-topic=projects/my-project/topics/orders \
     --location=us-central1
   

2. Find the auto-created subscription:

   gcloud pubsub subscriptions list \
     --filter="topic:projects/my-project/topics/orders" \
     --format="table(name,pushConfig.pushEndpoint)"
   

   Example output:

   NAME                                                          PUSH_ENDPOINT
   eventarc-us-central1-order-processor-trigger-abc-sub-def      https://order-processor-xyz.run.app
   

3. Update the subscription for trace propagation:

   gcloud pubsub subscriptions update \
     eventarc-us-central1-order-processor-trigger-abc-sub-def \
     --push-no-wrapper \
     --push-no-wrapper-write-metadata
   

문제 해결

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.

추가 자료

추가적인 유용한 설명서, 링크 및 기사:

Python 애플리케이션 추적하기설명서 Python 로그 및 트레이스 상호 연결하기설명서