In Datadog, the Containers Explorer page (formerly known as Live Containers) provides real-time visibility into all containers across your environment.

Inspired by htop, ctop, and kubectl, Containers Explorer gives you complete coverage of your container infrastructure. View data in a continuously updated table with resource metrics at two-second resolution, faceted search, and streaming container logs.

Configuration

Set up container collection

Collecting container telemetry for Containers Explorer is enabled by default for most Datadog Agent installations.

-v /etc/passwd:/etc/passwd:ro
-e DD_PROCESS_CONFIG_CONTAINER_COLLECTION_ENABLED=true

apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    credentials:
      apiKey: <DATADOG_API_KEY>
      appKey: <DATADOG_APP_KEY>
  features:
    liveContainerCollection:
      enabled: true

datadog:
  # (...)
  processAgent:
    containerCollection: true

datadog:
  #(...)
  clusterName: <YOUR_CLUSTER_NAME>
  #(...)
  processAgent:
    containerCollection: true

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

Include or exclude containers

By default, Datadog automatically discovers all containers.

You can include or exclude containers from collection by using these environment variables:

• DD_CONTAINER_EXCLUDE: Blocklist of containers to exclude
• DD_CONTAINER_INCLUDE: Allowlist of containers to include

Regular expressions are supported.

Example: Including and excluding containers

To exclude all Debian containers (image:debian) except containers with a name starting with frontend (name:frontend.*):

docker run -d --name datadog-agent \
           (...) \
           -e DD_CONTAINER_EXCLUDE="image:debian" \
           -e DD_CONTAINER_INCLUDE="name:frontend.*"

apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    credentials:
      apiKey: <DATADOG_API_KEY>
  override:
    nodeAgent:
      env:
        - name: DD_CONTAINER_EXCLUDE
          value: "image:debian"
        - name: DD_CONTAINER_INCLUDE
          value: "name:frontend.*"

datadog:
  containerExclude: "image:debian"
  containerInclude: "name:frontend.*"

"environment": [
  {
    "name": "DD_CONTAINER_EXCLUDE",
    "value": "image:debian"
  },
  {
    "name": "DD_CONTAINER_INCLUDE",
    "value": "name:frontend.*"
  },
  ...
]

For more granular configuration options, see Container Discovery Management

Scrub sensitive information from manifests

To help prevent leaking sensitive data, the Agent can be configured to scrub the collected Kubernetes YAML manifests. This scrubbing feature is applied to:

• Annotation values
• Label values
• Probe configurations (HTTP headers and commands)
• Environment variables
• Container exec commands

The scrubbing algorithm attempts to detect key-value pairs containing secrets based on a set of sensitive keywords, replacing corresponding values with ********. This logic is applied to structured key-value pairs (such as environment variables) as well as values that look like JSON or YAML content, which may contain key-value pairs within the content.

Scrubbing is enabled by default using the following sensitive keywords:

• password
• passwd
• mysql_pwd
• access_token
• auth_token
• api_key
• apikey
• pwd
• secret
• credentials
• stripetoken

You can supply additional sensitive keywords by providing a space-delimited list in the environment variable: DD_ORCHESTRATOR_EXPLORER_CUSTOM_SENSITIVE_WORDS. This adds to the default words and does not overwrite them. To use this environment variable, you must configure it for following Agents:

env:
    - name: DD_ORCHESTRATOR_EXPLORER_CUSTOM_SENSITIVE_WORDS
      value: "customword1 customword2 customword3"

Note: Any additional sensitive words must be provided as lowercase strings. The Agent converts text to lowercase before matching for sensitive words. If the sensitive word is password, MY_PASSWORD=1234 is scrubbed to MY_PASSWORD=******** because the Agent converts MY_PASSWORD to my_password, which mean the sensitive word PASSWORD does not match anything.

For example, because password is a sensitive word, the scrubber changes <MY_PASSWORD> in any of the following to a string of asterisks, ***********:

password <MY_PASSWORD>
password=<MY_PASSWORD>
password: <MY_PASSWORD>
password::::== <MY_PASSWORD>
config={"password":"<MY_PASSWORD>"}

However, the scrubber does not scrub paths that contain sensitive words. For example, it does not overwrite /etc/vaultd/secret/haproxy-crt.pem with /etc/vaultd/******/haproxy-crt.pem even though secret is a sensitive word.

Searching, filtering, and pivoting

String search

Containers are, by their nature, extremely high cardinality objects. Datadog’s flexible string search matches substrings in the container name, ID, or image fields.

To combine multiple string searches into a complex query, you can use any of the following Boolean operators:

AND

Intersection: both terms are in the selected events (if nothing is added, AND is taken by default)
Example: java AND elasticsearch

OR

Union: either term is contained in the selected events
Example: java OR python

NOT / !

Exclusion: the following term is NOT in the event. You may use the word NOT or ! character to perform the same operation
Example: java NOT elasticsearch or java !elasticsearch

Use parentheses to group operators together. For example, (NOT (elasticsearch OR kafka) java) OR python.

Filtering and pivoting

The screenshot below displays a system that has been filtered down to a Kubernetes cluster of 25 nodes. RSS and CPU utilization on containers is reported compared to the provisioned limits on the containers, when they exist. Here, it is apparent that the containers in this cluster are over-provisioned. You could use tighter limits and bin packing to achieve better utilization of resources.

Container environments are dynamic and can be hard to follow. The following screenshot displays a view that has been pivoted by kube_service and host—and, to reduce system noise, filtered to kube_namespace:default. You can see what services are running where, and how saturated key metrics are:

You could pivot by ECS ecs_task_name and ecs_task_version to understand changes to resource utilization between updates.

Tagging

Containers are tagged with all existing host-level tags, as well as with metadata associated with individual containers.

All containers are tagged by image_name, including integrations with popular orchestrators, such as ECS and Kubernetes, which provide further container-level tags. Additionally, each container is decorated with Docker, ECS, or Kubernetes icons so you can tell which are being orchestrated at a glance.

ECS containers are tagged by:

• task_name
• task_version
• ecs_cluster

Kubernetes containers are tagged by:

• pod_name
• kube_service
• kube_namespace
• kube_replica_set
• kube_daemon_set
• kube_job
• kube_deployment
• kube_cluster_name

If you have a configuration for Unified Service Tagging in place, Datadog automatically picks up env, service, and version tags. Having these tags available lets you tie together APM, logs, metrics, and container data.

Views

The Containers page includes Scatter Plot and Timeseries views, and a table to better organize your container data by fields such as container name, status, and start time.

Scatter plot

Use the scatter plot analytic to compare two metrics with one another in order to better understand the performance of your containers.

You can switch between the “Scatter Plot” and “Timeseries” tabs in the collapsible Summary Graphs section in the Containers page:

By default, the graph groups by the short_image tag key. The size of each dot represents the number of containers in that group, and clicking on a dot displays the individual containers and hosts that contribute to the group.

The options at the top of the graph allow you to control your scatter plot analytic:

• Selection of metrics to display.
• Selection of the aggregation method for both metrics.
• Selection of the scale of both X and Y axis (Linear/Log).

Real-time monitoring

While actively working with the containers page, metrics are collected at a 2-second resolution. This is important for volatile metrics such as CPU. In the background, for historical context, metrics are collected at 10s resolution.

Container logs

View streaming logs for any container like docker logs -f or kubectl logs -f in Datadog. Click any container in the table to inspect it. Click the Logs tab to see real-time data from live tail or indexed logs for any time in the past.

Live tail

With live tail, all container logs are streamed. Pausing the stream helps you read logs that are quickly being written; unpause to continue streaming.

Streaming logs can be searched with simple string matching. See Live Tail for more details.

Note: Streaming logs are not persisted, and entering a new search or refreshing the page clears the stream.

Indexed logs

You can see indexed logs that you have chosen to index and persist by selecting a corresponding timeframe. Indexing allows you to filter your logs using tags and facets. For example, to search for logs with an Error status, type status:error into the search box. Autocompletion can help you locate the particular tag that you want. Key attributes about your logs are already stored in tags, which enables you to search, filter, and aggregate as needed.

Additional information

• Real-time (2s) data collection is turned off after 30 minutes. To resume real-time collection, refresh the page.
• RBAC settings can restrict Kubernetes metadata collection. See the RBAC entities for the Datadog Agent.
• In Kubernetes the health value is the containers’ readiness probe, not its liveness probe.

Further reading

추가 유용한 문서, 링크 및 기사:

Monitor your Kubernetes operators to keep applications running smoothlyBLOG A deep dive into CPU requests and limits in KubernetesBLOG Expedite infrastructure investigations with Kubernetes AnomaliesBLOG Practical tips for rightsizing your Kubernetes workloadsBLOG Accelerate Kubernetes issue resolution with AI-powered guided remediationBLOG