--- title: Instrumenting a Node.js Container App with Sidecar description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: >- Docs > Serverless > Azure Container Apps > Sidecar Instrumentation > Instrumenting a Node.js Container App with Sidecar --- # Instrumenting a Node.js Container App with Sidecar ## Setup{% #setup %} 1. **Install the Datadog Node.js tracer**. 1. In your main application, install the `dd-trace` package. ```shell npm install dd-trace ``` 1. Initialize the Node.js tracer with the `NODE_OPTIONS` environment variable: ```dockerfile ENV NODE_OPTIONS="--require dd-trace/init" ``` For more information, see [Tracing Node.js applications](https://docs.datadoghq.com/tracing/trace_collection/automatic_instrumentation/dd_libraries/nodejs/). 1. **Install serverless-init as a sidecar**. 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 {% tab title="Datadog CLI" %} Locally: Install the Datadog CLI ```shell npm install -g @datadog/datadog-ci @datadog/datadog-ci-plugin-container-app ``` Install the [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli) and authenticate with `az login`. To set up the Datadog sidecar for your applications, configure the [Datadog site](https://docs.datadoghq.com/getting_started/site/) and Datadog API key, and run the `instrument` command *after* your normal deployment: ```shell export DD_SITE="" export DD_API_KEY="" datadog-ci container-app instrument -s -g -n ``` You can also instrument multiple applications using the full resource IDs: ```shell datadog-ci container-app instrument \ --resource-id "/subscriptions//resourceGroups//providers/Microsoft.App/containerApps/" \ --resource-id "/subscriptions//resourceGroups//providers/Microsoft.App/containerApps/" ``` Azure Cloud Shell: To use the Datadog CLI in [Azure Cloud Shell](https://portal.azure.com/#cloudshell/), open a cloud shell, set your API key and site in the `DD_API_KEY` and `DD_SITE` environment variables, and use `npx` to run the CLI directly. ```shell export DD_API_KEY= export DD_SITE= npx @datadog/datadog-ci container-app instrument -s -g -n ``` Additional parameters can be found in the [CLI documentation](https://github.com/DataDog/datadog-ci/tree/master/packages/plugin-container-app#arguments). {% /tab %} {% tab title="Terraform" %} The [Datadog Terraform module for Container Apps](https://registry.terraform.io/modules/DataDog/container-app-datadog/azurerm/latest) wraps the [`azurerm_container_app`](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/container_app) resource and automatically configures your Azure Container 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](https://developer.hashicorp.com/terraform/install), 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: ```tf variable "datadog_api_key" { description = "Your Datadog API key" type = string sensitive = true } provider "azurerm" { features {} subscription_id = "00000000-0000-0000-0000-000000000000" // Replace with your subscription ID } resource "azurerm_container_app_environment" "my_env" { name = "my-container-app-env" // Replace with your container app environment name resource_group_name = "my-resource-group" // Replace with your resource group name location = "eastus" } module "my_container_app" { source = "DataDog/container-app-datadog/azurerm" version = "~> 1.0" name = "my-container-app" // Replace with your container app name resource_group_name = "my-resource-group" // Replace with your resource group name container_app_environment_id = azurerm_container_app_environment.my_env.id datadog_api_key = var.datadog_api_key datadog_site = "datadoghq.com" // Replace with your Datadog site datadog_service = "my-service" // Replace with your service name datadog_env = "dev" // Replace with your environment (e.g. prod, staging, dev) datadog_version = "0.1.0" // Replace with your application version revision_mode = "Single" workload_profile_name = "Consumption" ingress = { external_enabled = true target_port = 8080 traffic_weight = [{ percentage = 100 latest_revision = true }] } template = { container = [{ cpu = 0.5 memory = "1Gi" image = "docker.io/your-docker-image:latest" // Replace with your Docker image name = "main" }] } } ``` Finally, run `terraform apply`, and follow any prompts. The [Datadog Container App module](https://registry.terraform.io/modules/DataDog/container-app-datadog/azurerm/latest) only deploys the Container App resource, so you need to build and push your container separately. 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: ```shell terraform init ``` To deploy your app, run: ```shell terraform apply ``` {% /tab %} {% tab title="Bicep" %} Configuration: Update your existing Container App bicep to include the necessary Datadog App Settings and sidecar, as follows: ```bicep @secure() param datadogApiKey string param datadogSite string param service string = 'my-service' param env string = 'dev' param version string = '0.0.0' resource containerApp 'Microsoft.App/containerApps@2024-03-01' = { // ... properties: { template: { volumes: [ { name: 'shared-volume' storageType: 'EmptyDir' } // Additional volumes ] containers: [ { name: 'main' image: 'index.docker.io/your/image:tag' // Replace with your Application Image resources: { cpu: 1 memory: '2Gi' } env: [ { name: 'DD_ENV', value: env } { name: 'DD_SERVICE', value: name } { name: 'DD_VERSION', value: version } { name: 'DD_LOGS_INJECTION', value: 'true' } // Additional tracing/application env vars ] volumeMounts: [ { volumeName: 'shared-volume', mountPath: '/shared-volume' } // Additional volume mounts ] } { name: 'datadog-sidecar' image: 'index.docker.io/datadog/serverless-init:latest' resources: { cpu: '0.5' memory: '1Gi' } env: [ { name: 'DD_AZURE_SUBSCRIPTION_ID', value: subscription().subscriptionId } { name: 'DD_AZURE_RESOURCE_GROUP', value: resourceGroup().name } { name: 'DD_API_KEY', value: datadogApiKey } { name: 'DD_SITE', value: datadogSite } { name: 'DD_SERVICE', value: service } { name: 'DD_ENV', value: env } { name: 'DD_VERSION', value: version } // set this to wherever you write logs in the shared volume: { name: 'DD_SERVERLESS_LOG_PATH', value: '/shared-volume/logs/app.log' } ] volumeMounts: [{ volumeName: 'shared-volume', mountPath: '/shared-volume' }] } ] scale: { minReplicas: 1, maxReplicas: 1, rules: [] } } } } ``` Redeploy your updated template: ```shell az deployment group create --resource-group --template-file