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:
To deploy your app, run:
After deploying your Cloud Run app, you can manually modify your app’s settings to enable Datadog monitoring.
Create a Volume with In-Memory volume type.
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.
Add the volume mount to every container in your application. Choose a path such as /shared-volume, and remember it for the next step.
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.
