Kubernetes Log collection

Docs > Container Monitoring > Kubernetes > Kubernetes Log collection

The Agent has two ways to collect logs: from the Docker socket, and from the Kubernetes log files (automatically handled by Kubernetes). Datadog recommends using the Kubernetes log file logic when:

Docker is not the runtime, or
More than 10 containers are used on each node

The Docker API is optimized to get logs from one container at a time; when there are many containers in the same node, collecting logs through the Docker socket might be consuming much more resources than going through the Kubernetes log files logic.

Log collection

In order to start collecting your application logs you must be running the Datadog Agent in your Kubernetes cluster. To enable log collection with your Agent, follow the instructions below:

Update your datadog-agent.yaml manifest with:

apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    credentials:
      apiKey: <DATADOG_API_KEY>

  features:
    logCollection:
      enabled: true
      containerCollectAll: true

See the sample manifest with logs and metrics collection enabled for a complete example. You can set the features.logCollection.containerCollectAll to true to collect logs from all discovered containers by default. When set to false (default), you need to specify Autodiscovery log configurations to enable log collection.

Then apply the new configuration:

kubectl apply -n $DD_NAMESPACE -f datadog-agent.yaml

Unprivileged

(Optional) To run an unprivileged installation, add the following to the DatadogAgent Custom Resource:

apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    credentials:
      apiKey: <DATADOG_API_KEY>

  features:
    logCollection:
      enabled: true
      containerCollectAll: true

  override:
    nodeAgent:
      securityContext:
        runAsUser: <USER_ID>
        supplementalGroups:
          - <DOCKER_GROUP_ID>

where <USER_ID> is the UID to run the agent and <DOCKER_GROUP_ID> is the group ID owning the docker or containerd socket.

To enable log collection with Helm, update your datadog-values.yaml file with the following log collection configuration, then upgrade your Datadog Helm chart:

datadog:
  ## @param logs - object - required
  ## Enable logs agent and provide custom configs
  #
  logs:
    ## @param enabled - boolean - optional - default: false
    ## Enables this to activate Datadog Agent log collection.
    #
    enabled: true

    ## @param containerCollectAll - boolean - optional - default: false
    ## Enable this to allow log collection for all containers.
    #
    containerCollectAll: true

You can set the datadog.logs.containerCollectAll to true to collect logs from all discovered containers by default. When set to false (default), you need to specify Autodiscovery log configurations to enable log collection.

Unprivileged

(Optional) To run an unprivileged installation, add the following in the values.yaml file:

datadog:
  securityContext:
      runAsUser: <USER_ID>
      supplementalGroups:
        - <DOCKER_GROUP_ID>

where <USER_ID> is the UID to run the agent and <DOCKER_GROUP_ID> is the group ID owning the docker or containerd socket.

To configure log collection using a DaemonSet, see DaemonSet Log Collection.

Warning: When running an unprivileged installation, the agent needs to be able to read the log files in /var/log/pods. With containerd, the log files in /var/log/pods are readable by members of the root group. With the above instructions, the agent is still running with the root group so, it works. With docker, the logs files in /var/log/pods are symbolic links to /var/lib/docker/containers which is traversable only by the root user. As a consequence, with docker, it’s not possible for a non-root agent to read pod logs in /var/log/pods. The docker socket must be mounted in the agent container so that it can get pods logs through the docker daemon.

Note: If you do want to collect logs from /var/log/pods even if the Docker socket is mounted, set the environment variable DD_LOGS_CONFIG_K8S_CONTAINER_USE_FILE (or logs_config.k8s_container_use_file in datadog.yaml) to true in order to force the Agent to go for the file collection mode.

Autodiscovery

The goal of Autodiscovery is to apply a Datadog integration log configuration when running an Agent check against a given container. See how to configure Agent integrations when running the Agent on a host for more context on this logic.

To configure an integration with Autodiscovery, use the following parameters:

Parameter	Required	Description
`<LOG_CONFIG>`	No	For Agent v6.5+, configuration for the `logs:` section for the given Datadog-`<INTEGRATION_NAME>`

Discover the full list of Agent integrations that are Autodiscovery ready with examples for those parameters

Each tab in sections below shows a different way to apply integration templates to a given container. The available methods are:

Configuration

Note: When configuring the service value through pod annotations, Datadog recommends using unified service tagging as a best practice. Unified service tagging ties all Datadog telemetry together, including logs, through the use of three standard tags: env, service, and version. To learn how to configure your environment with unified tagging, refer to the dedicated unified service tagging documentation.

Integration templates can be stored in your Kubernetes pod annotations. With Autodiscovery, the Agent detects if it’s running on Kubernetes and automatically searches all pod annotations for integration templates.

To apply a specific configuration to a given container, Autodiscovery identifies containers by name, NOT image. It tries to match <CONTAINER_IDENTIFIER> to .spec.containers[0].name, not .spec.containers[0].image. To configure your Datadog integration Autodiscovery on a given <CONTAINER_IDENTIFIER> within your pod, add the following annotations to your pod:

apiVersion: v1
kind: Pod
# (...)
metadata:
  name: '<POD_NAME>'
  annotations:
    ad.datadoghq.com/<CONTAINER_IDENTIFIER>.logs: '[<LOG_CONFIG>]'
    # (...)
spec:
  containers:
    - name: '<CONTAINER_IDENTIFIER>'
# (...)

To apply two different integration templates to two different containers: <CONTAINER_IDENTIFIER_1> and <CONTAINER_IDENTIFIER_2> within your pod, add the following annotations to your pod:

apiVersion: v1
kind: Pod
# (...)
metadata:
  name: '<POD_NAME>'
  annotations:
    ad.datadoghq.com/<CONTAINER_IDENTIFIER_1>.logs: '[<LOG_CONFIG_1>]'
    # (...)
    ad.datadoghq.com/<CONTAINER_IDENTIFIER_2>.logs: '[<LOG_CONFIG_2>]'
spec:
  containers:
    - name: '<CONTAINER_IDENTIFIER_1>'
    # (...)
    - name: '<CONTAINER_IDENTIFIER_2>'
# (...)

Note: If you define your Kubernetes pods directly with kind: Pod, add each pod’s annotations directly under its metadata section. If you define pods indirectly with replication controllers, replica sets, or deployments, add pod annotations under .spec.template.metadata.

Storing templates as local files and mounting them inside the containerized Agent doesn’t require an external service or a specific orchestration platform. The downside is that you need to restart your Agent containers each time you change, add, or remove templates. The Agent looks for Autodiscovery templates in the mounted /conf.d directory.

Since Agent v6.2.0 (and v5.24.0), the default templates use the default port for the monitored software, instead of auto-detecting it. If you need to use a different port, provide a custom Autodiscovery template either in Docker container labels or Kubernetes pod annotations.

These integration templates are meant for basic cases. If you need a custom Datadog integration configuration to enable extra options, use different container identifiers—or use template variables indexing and write your own auto-configuration file:

Create a conf.d/<INTEGRATION_NAME>.d/conf.yaml file on your host and add your custom auto-configuration.
Mount your host conf.d/ folder to the containerized Agent’s conf.d folder.

Note: This is only supported when collecting logs through the Docker Socket, and not when using the Kubernetes log file method. To use the Docker Socket for log collection in a Kubernetes environment, ensure Docker is the runtime and DD_LOGS_CONFIG_K8S_CONTAINER_USE_FILE has been set to false.

Example auto-configuration file:

ad_identifiers:
  <INTEGRATION_AUTODISCOVERY_IDENTIFIER>

logs:
  <LOGS_CONFIG>

See the Autodiscovery Container Identifiers documentation for information on the <INTEGRATION_AUTODISCOVERY_IDENTIFIER>.

Note: You don’t need to set up the <INTEGRATIONS_NAME> since the Agent infers it from the file name directly.

On Kubernetes, you can use ConfigMaps. Reference the template below and the Kubernetes Custom Integrations documentation.

kind: ConfigMap
apiVersion: v1
metadata:
  name: "<NAME>-config-map"
  namespace: default
data:
  <INTEGRATION_NAME>-config: |-
    ad_identifiers:
      <INTEGRATION_AUTODISCOVERY_IDENTIFIER>
    logs:
      <LOGS_CONFIG>

See the Autodiscovery Container Identifiers documentation for information on the <INTEGRATION_AUTODISCOVERY_IDENTIFIER>.

Autodiscovery can use Consul, Etcd, and Zookeeper as integration template sources. To use a key-value store, configure it in the Agent datadog.yaml configuration file and mount this file inside the containerized Agent. Alternatively, pass your key-value store as environment variables to the containerized Agent.

Configure in datadog.yaml:

In the datadog.yaml file, set the <KEY_VALUE_STORE_IP> address and <KEY_VALUE_STORE_PORT> of your key-value store:

config_providers:
  - name: etcd
    polling: true
    template_dir: /datadog/check_configs
    template_url: '<KV_STORE_IP>:<KV_STORE_PORT>'
    username:
    password:

  - name: consul
    polling: true
    template_dir: datadog/check_configs
    template_url: '<KV_STORE_IP>:<KV_STORE_PORT>'
    ca_file:
    ca_path:
    cert_file:
    key_file:
    username:
    password:
    token:

  - name: zookeeper
    polling: true
    template_dir: /datadog/check_configs
    template_url: '<KV_STORE_IP>:<KV_STORE_PORT>'
    username:
    password:

Then restart the Agent to apply the configuration change.

Configure in environment variables:

With the key-value store enabled as a template source, the Agent looks for templates under the key /datadog/check_configs. Autodiscovery expects a key-value hierarchy like this:

/datadog/
  check_configs/
    <CONTAINER_IDENTIFIER>/
      - logs: ["<LOGS_CONFIG>"]
    ...

Note: To apply a specific configuration to a given container, Autodiscovery identifies containers by image when using the key-value stores by trying to match <CONTAINER_IDENTIFIER> to .spec.containers[0].image.

You can customize logs collection per integration within confd. This method mounts the desired configuration onto the Agent container.

confd:
  <INTEGRATION_NAME>.yaml: |-
    ad_identifiers:
      - <INTEGRATION_AUTODISCOVERY_IDENTIFIER>
    init_config:
    instances:
      (...)
    logs:
      <LOGS_CONFIG>

Examples - Datadog Redis integration

The following pod annotation defines the integration template for redis containers with a custom password parameter and tags all its logs with the correct source and service attributes, including custom tags :

apiVersion: v1
kind: Pod
metadata:
  name: redis
  annotations:
    ad.datadoghq.com/redis.logs: '[{"source": "redis","service": "redis","tags": ["env:prod"]}]'
  labels:
    name: redis
spec:
  containers:
    - name: redis
      image: redis:latest
      ports:
        - containerPort: 6379

The following ConfigMap defines the integration template for redis containers with the source and service attributes for collecting logs and tags all its logs with the correct source and service attributes, including custom tags :

kind: ConfigMap
apiVersion: v1
metadata:
  name: redisdb-config-map
  namespace: default
data:
  redisdb-config: |-
    ad_identifiers:
      - redis
      - redis-test
    logs:
      - source: redis
        service: redis
        tags:
          - env:prod

In the manifest, define the volumeMounts and volumes:

# (...)
        volumeMounts:
        # (...)
          - name: redisdb-config-map
            mountPath: /conf.d/redisdb.d
        # (...)
      volumes:
      # (...)
        - name: redisdb-config-map
          configMap:
            name: redisdb-config-map
            items:
              - key: redisdb-config
                path: conf.yaml
# (...)

The following etcd commands create a Redis integration template with a custom password parameter and tags all its logs with the correct source and service attributes:

etcdctl mkdir /datadog/check_configs/redis
etcdctl set /datadog/check_configs/redis/logs '[{"source": "redis", "service": "redis", "tags": ["env:prod"]}]'

Notice that each of the three values is a list. Autodiscovery assembles list items into the integration configurations based on shared list indexes. In this case, it composes the first (and only) check configuration from check_names[0], init_configs[0] and instances[0].

Unlike auto-conf files, key-value stores may use the short OR long image name as container identifiers, for example, redis OR redis:latest.

The following configuration defines the integration template for Redis containers with the source and service attributes for collecting logs:

confd:
  redis.yaml: |-
    ad_identifiers:
      - redis
    logs:
      - source: redis
        service: redis
        tags: env:prod

Note: The above configuration collects only logs from this integration. If you are already collecting other data from the Redis integration, you can append the logs section to your existing configuration.

Examples - Log collection from file configured in an annotation

Datadog recommends that you use the stdout and stderr output streams for containerized applications, so that you can more automatically set up log collection. However, the Agent can also directly collect logs from a file based on an annotation. To collect these logs, use ad.datadoghq.com/<CONTAINER_IDENTIFIER>.logs with a type: file and path configuration. Logs collected from files with such an annotation are automatically tagged with the same set of tags as logs coming from the container itself.

These file paths are relative to the Agent. Therefore, the directory containing the log file needs to be mounted into both the application and Agent container so the Agent can have proper visibility.

For example, you can do this with a shared hostPath volume. The Pod below is emitting logs into the file /var/log/example/app.log. This is done in the /var/log/example directory, where a volume and volumeMount have set this as a hostPath.

apiVersion: v1
kind: Pod
metadata:
  name: logger
  annotations:
    ad.datadoghq.com/busybox.logs: |
      [{
          "type": "file",
          "path": "/var/log/example/app.log",
          "source": "example-source",
          "service": "example-service"
      }]      
spec:
  containers:
   - name: busybox
     image: busybox
     command: [ "/bin/sh", "-c", "--" ]
     args: [ "while true; do sleep 1; echo `date` example file log >> /var/log/example/app.log; done;" ]
     volumeMounts:
     - name: applogs
       mountPath: /var/log/example
  volumes:
     - name: applogs
       hostPath:
         path: /var/log/example

The equivalent volume and volumeMount path need to be set in the Agent container so it can read that same log file.

  containers:
  - name: agent
    # (...)
    volumeMounts:
    - mountPath: /var/log/example
      name: applogs
    # (...)
  volumes:
  - name: applogs
    hostPath:
      path: /var/log/example
    # (...)

Note: When using this kind of annotation with a container, stdout and stderr logs are not collected automatically from the container. If collection from both the container and a file are needed it should be explicitly enabled in the annotation. For example:

ad.datadoghq.com/<CONTAINER_IDENTIFIER>.logs: |
  [
    {"type":"file","path":"/var/log/example/app.log","source":"file","service":"example-service"},
    {"source":"container","service":"example-service"}
  ]

When using this kind of combination, source and service have no default value for logs collected from a file and should be explicitly set in the annotation.

Advanced log collection

Use Autodiscovery log labels to apply advanced log collection processing logic, for example:

Filter containers

It is possible to manage from which containers you want to collect logs. This can be useful to prevent the collection of the Datadog Agent logs. See the Container Discovery Management to learn more.

Short lived containers

By default the Agent looks every 5 seconds for new containers.

For Agent v6.12+, short lived container logs (stopped or crashed) are automatically collected when using the K8s file log collection method (through /var/log/pods). This also includes the collection init container logs.

Troubleshooting

If you are missing tags on Kubernetes logs, this may be because the Agent’s internal tagger does not yet have the related container or pod tags when logs are sent. To make the Log Agent wait a few seconds for the tagger to be ready, you can use the environment variable DD_LOGS_CONFIG_TAGGER_WARMUP_DURATION to set how many seconds to wait. The default value is 0.

# The number of seconds the Log Agent waits for the internal tagger to add the related container or pod tags to the logs before the logs are sent.
# For example, in order to set the Log Agent to wait five seconds, use an integer in the value:
tagger_warmup_duration: 5