Datadog offers native Docker container monitoring, either by running the Agent on the host or running in a sidecar container. Which is the best way to run it? It ultimately depends on the tooling you have in place to manage the Agent's configuration. If you want to go Docker all the way, you can run the Agent as a sidecar and control its configuration with custom Dockerfiles.

Let's see what it looks like.

To build a meaningful setup, we start from the example that Docker put together to illustrate Compose. A simple python web application that connects to Redis to store the number of hits.

Here is the docker-compose.yml that powers the whole setup.

web:
  build: web
  command: python app.py
  ports:
   - "5000:5000"
  volumes:
   - web:/code # modified here to take into account the new app path
  links:
   - redis
redis:
  image: redis
# agent section
datadog:
  build: datadog
  links:
   - redis # ensures that redis is a host that the container can find
  environment:
   - API_KEY=__your_datadog_api_key_here__
  volumes:
   - /var/run/docker.sock:/var/run/docker.sock
   - /proc/mounts:/host/proc/mounts:ro
   - /sys/fs/cgroup:/host/sys/fs/cgroup:ro

Because the Agent needs to monitor redis it needs:

1. the proper redisdb.yaml in the container's /etc/dd-agent/conf.d
2. to find the redis node.

The Agent's Dockerfile takes care of #1.

FROM datadog/docker-dd-agent
ADD conf.d/redisdb.yaml /etc/dd-agent/conf.d/redisdb.yaml

And the Compose yaml files creates the link to redis with:

  links:
    - redis

How to test this?

1. Clone this repository
2. Update your API_KEY in docker-compose.yml
3. Run all containers with ./docker-compose run
4. Verify in Datadog that your container picks up the docker and redis metrics