Java supports adding instrumentation code through the use of a command line argument, javaagent.

1. Download the latest version of Datadog’s Java tracing library.
2. Place the tracing library inside your project. It must be included with your deployment. If you are using the azure-webapp-maven plugin, you can add the Java tracing library as a resource entry with type lib.
3. Set the environment variable JAVA_OPTS with --javaagent:/home/site/lib/dd-java-agent.jar. When your application is deployed, the Java tracer is copied to /home/site/lib/dd-java-agent.jar.

Instrumentation starts when the application is launched.

1. Add the ddtrace package to your project using your package manager.
2. Initialize the tracer by doing one of the following:
   • Set NODE_OPTIONS with --require=dd-trace/init
   • Include the tracer in your application’s entrypoint file:

     const tracer = require('dd-trace').init({ logInjection: true, });
     

     This also configures trace log correlation.

Add the Datadog.Trace.Bundle Nuget package to your project. See the Nuget package page for more details.

For example:

dotnet add package Datadog.Trace.Bundle --version 3.21.0

Run the following script to install Datadog’s PHP tracing library: startup.sh:

#!/usr/bin/env bash

echo "Setting up Datadog tracing for PHP"
DD_PHP_TRACER_VERSION=1.8.3
DD_PHP_TRACER_URL=https://github.com/DataDog/dd-trace-php/releases/download/${DD_PHP_TRACER_VERSION}/datadog-setup.php

echo "Installing PHP tracer from ${DD_PHP_TRACER_URL}"
if curl -LO --fail "${DD_PHP_TRACER_URL}"; then
    eval "php datadog-setup.php --php-bin=all"
else
    echo "Downloading the tracer was unsuccessful"
    return
fi

# This line is can be uncommented if the project contains an nginx configuration in the project root
# cp /home/site/wwwroot/default /etc/nginx/sites-available/default && service nginx reload

service nginx reload

This bash script is intended to run as the startup command, which installs the tracing module into PHP and then restarts the NGINX service.

1. Add ddtrace to your project.

2. Modify your startup command. Your new command should run ddtrace-run with your old command as an argument. That is: if your startup command is foo, modify it to run ddtrace-run foo.

   For example:

   ddtrace-run gunicorn --bind=0.0.0.0 --timeout 600 quickstartproject.wsgi
   

Locally

Install the Datadog CLI and Azure CLI, and login to your Azure account using the Azure CLI by running az login.

Then, run the following command to set up the sidecar container:

export DD_API_KEY=<DATADOG_API_KEY>
export DD_SITE=<DATADOG_SITE>
datadog-ci aas instrument -s <subscription-id> -g <resource-group-name> -n <app-service-name>

Set your Datadog site to . Defaults to datadoghq.com.

Additional flags, like --service and --env, can be used to set the service and environment tags. For a full list of options, run datadog-ci aas instrument --help.

Azure Cloud Shell

To use the Datadog CLI in Azure Cloud Shell, open cloud shell and use npx to run the CLI directly. Set your API key and site in the DD_API_KEY and DD_SITE environment variables, and then run the CLI:

export DD_API_KEY=<DATADOG_API_KEY>
export DD_SITE=<DATADOG_SITE>
npx @datadog/datadog-ci@4 aas instrument -s <subscription-id> -g <resource-group-name> -n <app-service-name>

The Datadog Terraform module for Linux Web Apps wraps the azurerm_linux_web_app resource and automatically configures your Web 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
}

provider "azurerm" {
  features {}
  subscription_id = "00000000-0000-0000-0000-000000000000" // Replace with your subscription ID
}

resource "azurerm_service_plan" "my_asp" {
  name                = "my-app-service-plan" // Replace with your app service plan name
  resource_group_name = "my-resource-group"   // Replace with your resource group name
  os_type             = "Linux"
  location            = "eastus"
  sku_name            = "P1v2"
}

module "my_web_app" {
  source  = "DataDog/web-app-datadog/azurerm//modules/linux"
  version = "~> 1.0"

  name                = "my-web-app"        // Replace with your web app name
  resource_group_name = "my-resource-group" // Replace with your resource group name
  service_plan_id     = azurerm_service_plan.my_asp.id
  location            = "eastus"

  datadog_api_key = var.datadog_api_key
  datadog_service = "my-service" // Replace with your service name
  datadog_env     = "prod"       // Replace with your environment (e.g. prod, staging)
  datadog_version = "0.0.0"      // Replace with your application version

  site_config = {
    application_stack = {
      python_version = "3.13" // change for your specific runtime
    }
  }
  app_settings = {
    DD_TRACE_ENABLED = "true" // Example setting
  }
}

Finally, run terraform apply, and follow any prompts.

The Datadog Linux Web App module only deploys the Web App resource, so you need to deploy your code separately.

1. Configure environment variables. In Azure, add the following key-value pairs in Settings > Environment Variables > App Settings:

DD_API_KEY

Value: Your Datadog API key.
See Organization Settings > API Keys in Datadog.

DD_SITE

Value:
Your Datadog site. Defaults to datadoghq.com.
Use the “Datadog Site” drop-down menu on this page’s right navigation bar to select your site.

DD_SERVICE

Value: Your application’s service name.
Defaults to the name field value in package.json.
See Unified Service Tagging for more information on the service tag.

DD_ENV

Value: Your application’s environment name.
There is no default value for this field.
See Unified Service Tagging for more information on the env tag.

DD_VERSION

Value: Your application’s version.
There is no default value for this field.
See Unified Service Tagging for more information on the version tag.

DD_SERVERLESS_LOG_PATH

Value: The log path the sidecar uses to collect logs.
Where you write your logs. For example, /home/LogFiles/*.log or /home/LogFiles/myapp/*.log.

DD_AAS_INSTANCE_LOGGING_ENABLED

Value: false
When true, log collection is automatically configured for the additional file path: /home/LogFiles/*$COMPUTERNAME*.log

DD_AAS_INSTANCE_LOG_FILE_DESCRIPTOR

Value: An optional file descriptor used for more precise log tailing.
Recommended for scenarios with frequent log rotation. For example, setting _default_docker configures the log tailer to ignore rotated files and focus only on Azure’s active log file.

WEBSITES_ENABLE_APP_SERVICE_STORAGE

Value: true
Setting this environment variable to true allows the /home/ mount to persist and be shared with the sidecar.

2. Configure a sidecar container for Datadog.

   1. In Azure, navigate to Deployment > Deployment Center. Select the Containers tab.
   2. Click Add and select Custom container.
   3. In the Edit container form, provide the following:
      • Image source: Other container registries
      • Image type: Public
      • Registry server URL: index.docker.io
      • Image and tag: datadog/serverless-init:latest
      • Port: 8126
      • Environment Variables: Include all previously configured Datadog environment variables.
   4. Select Apply.

3. Restart your application.

   If you modified a startup command, restart your application. Azure automatically restarts the application when new Application Settings are saved.