Amazon Elastic Beanstalk

For a single Docker container setup, install the Datadog Agent in Elastic Beanstalk using Advanced Environment Customization with Configuration Files (.ebextensions):

1. Create a folder named .ebextensions in the root of your application source bundle.
2. Download 99datadog.config or 99datadog-amazon-linux-2.config (for Amazon Linux 2) to .ebextensions.
3. Change the value of DD_API_KEY within option_settings in 99datadog.config to your Datadog API Key.
4. It is recommended to pin a specific Agent version by setting DD_AGENT_VERSION under option_settings, to ensure all hosts run the same version of the Agent.
5. Deploy your application with the Elastic Beanstalk Console, EB CLI, or AWS CLI.

Add more settings to datadog.yaml by updating the "/configure_datadog_yaml.sh" section of 99datadog.config. The line below enables the Datadog Process Agent:

echo -e "process_config:\n  enabled: \"true\"\n" >> /etc/datadog-agent/datadog.yaml

Trace collection

To enable tracing for single Docker containers:

1. Update the "/configure_datadog_yaml.sh" section in 99datadog.config file with apm_non_local_traffic, formatted like this:

   
   echo -e "apm_config:\n  enabled: \"true\"\n" >> /etc/datadog-agent/datadog.yaml
   
   echo -e "  apm_non_local_traffic: \"true\"\n" >> /etc/datadog-agent/datadog.yaml
   

2. Set up the tracing libraries to direct traces to the Gateway IP of the bridge network, which defaults to 172.17.0.1 from inside the application container. (If you’re not sure this is the Gateway IP, run docker inspect <container id> to confirm.)

For all languages, set the environment variable DD_AGENT_HOST to the Gateway IP. Alternatively, for the languages below, set the host name programmatically using:

Python

from ddtrace import tracer

tracer.configure(hostname="172.17.0.1")

Node.js

const tracer = require('dd-trace');

tracer.init({ hostname: "172.17.0.1" });

Ruby

require 'ddtrace'

Datadog.configure do |c|
  c.tracer hostname: "172.17.0.1")
end

Go

package main

import (
    "gopkg.in/DataDog/dd-trace-go.v1/ddtrace/tracer"
)

func main() {
  tracer.Start(tracer.WithAgentAddr("172.17.0.1"))
  defer tracer.Stop()
  
  // ...
}

For multiple Docker containers, use the containerized Datadog Agent to monitor Docker usage with a file named Dockerrun.aws.json.

A Dockerrun.aws.json file is an Elastic Beanstalk—specific JSON file that describes how to deploy a set of Docker containers as an Elastic Beanstalk application. You can use this file for a multicontainer Docker environment. Dockerrun.aws.json describes the containers to deploy to each container instance in the environment and the data volumes to create on the host instance for the containers to mount.

A Dockerrun.aws.json file can be used on its own or zipped up with additional source code in a single archive. Source code that is archived with Dockerrun.aws.json is deployed to container instances and accessible in the /var/app/current/ directory. Use the volumes section of the config to provide mount points for the containers running on the instance and the mountPoints section of the embedded container definitions to mount them from the containers.

The following code sample illustrates a Dockerrun.aws.json declaring the Datadog Agent. Update the containerDefinitions section with your Datadog API Key, tags (optional), and any additional container definitions. If needed, this file can be zipped with additional content as described above. For more info about the syntax of this file, see Multicontainer Docker configuration.

Notes:

• For high resource usage, you may need a higher memory limit.
• To ensure all hosts run the same Agent version, it is recommended to change agent:7 to a specific minor version of the Docker image.

{
    "AWSEBDockerrunVersion": 2,
    "volumes": [
        {
            "name": "docker_sock",
            "host": {
                "sourcePath": "/var/run/docker.sock"
            }
        },
        {
            "name": "proc",
            "host": {
                "sourcePath": "/proc/"
            }
        },
        {
            "name": "cgroup",
            "host": {
                "sourcePath": "/cgroup/"
            }
        }
    ],
    "containerDefinitions": [
        {
            "name": "dd-agent",
            "image": "gcr.io/datadoghq/agent:7",
            "environment": [
                {
                    "name": "DD_API_KEY",
                    "value": "<YOUR_DD_API_KEY>"
                },
                {
                    "name": "DD_SITE",
                    "value": "<YOUR_DD_SITE>"
                },
                {
                    "name": "DD_TAGS",
                    "value": "<SIMPLE_TAG>, <KEY:VALUE_TAG>"
                }
            ],
            "memory": 256,
            "mountPoints": [
                {
                    "sourceVolume": "docker_sock",
                    "containerPath": "/var/run/docker.sock",
                    "readOnly": false
                },
                {
                    "sourceVolume": "proc",
                    "containerPath": "/host/proc",
                    "readOnly": true
                },
                {
                    "sourceVolume": "cgroup",
                    "containerPath": "/host/sys/fs/cgroup",
                    "readOnly": true
                }
            ]
        }
    ]
}

Creating the environment

Once the container definition is ready, ship it to Elastic Beanstalk. For specific instructions, see Multicontainer Docker Environments in the AWS Elastic Beanstalk documentation.

DogStatsD

To collect custom metrics from your application container using DogStatsD in the Multicontainer Docker Environment, add the following to your Dockerrun.aws.json:

1. Add the environment variable DD_DOGSTATSD_NON_LOCAL_TRAFFIC under the dd-agent container:

   {
     "name": "DD_DOGSTATSD_NON_LOCAL_TRAFFIC",
     "value": "true"
   }
   

2. Add a link to the dd-agent container under your application container:

   "links": [ "dd-agent:dd-agent"]
   

See DogStatsD and Docker for additional information.

For a no container setup, install the Datadog Agent in Elastic Beanstalk using Advanced Environment Customization with Configuration Files (.ebextensions):

1. Create a folder named .ebextensions in the root of your application source bundle.
2. Download 99datadog.config or 99datadog-amazon-linux-2.config (for Amazon Linux 2) to .ebextensions.
3. Change the value of DD_API_KEY within option_settings in 99datadog.config to your Datadog API Key.
4. It is recommended to pin a specific Agent version by setting DD_AGENT_VERSION under option_settings, to ensure all hosts run the same version of the Agent.
5. Deploy your application with the Elastic Beanstalk Console, EB CLI, or AWS CLI.

Add more settings to datadog.yaml by updating the "/configure_datadog_yaml.sh" section of 99datadog.config. The line below enables the Datadog Process Agent:

echo -e "process_config:\n  enabled: \"true\"\n" >> /etc/datadog-agent/datadog.yaml

Trace collection

When the application isn’t containerized and the Datadog Agent is configured with 99datadog.config, tracing is enabled without any additional configuration, provided the application is instrumented with the tracing library setup.