This document contains troubleshooting information for the following components:
To execute the troubleshooting commands for the Cluster Agent, you first need to be inside the Pod of the Cluster Agent or the node-based Agent. For this, use:
kubectl exec -it <DATADOG_CLUSTER_AGENT_POD_NAME> bash
To see what cluster level metadata is served by the Datadog Cluster Agent, run:
datadog-cluster-agent metamap
You should see the following result:
root@datadog-cluster-agent-8568545574-x9tc9:/# datadog-cluster-agent metamap
===============
Metadata Mapper
===============
Node detected: gke-test-default-pool-068cb9c0-sf1w
- Namespace: kube-system
- Pod: kube-dns-788979dc8f-hzbj5
Services: [kube-dns]
- Pod: kube-state-metrics-5587867c9f-xllnm
Services: [kube-state-metrics]
- Pod: kubernetes-dashboard-598d75cb96-5khmj
Services: [kubernetes-dashboard]
Node detected: gke-test-default-pool-068cb9c0-wntj
- Namespace: default
- Pod: datadog-cluster-agent-8568545574-x9tc9
Services: [datadog-custom-metrics-server dca]
- Namespace: kube-system
- Pod: heapster-v1.5.2-6d59ff54cf-g7q4h
Services: [heapster]
- Pod: kube-dns-788979dc8f-q9qkt
Services: [kube-dns]
- Pod: l7-default-backend-5d5b9874d5-b2lts
Services: [default-http-backend]
- Pod: metrics-server-v0.2.1-7486f5bd67-v827f
Services: [metrics-server]
To verify that the Datadog Cluster Agent is being queried, look for:
root@datadog-cluster-agent-8568545574-x9tc9:/# tail -f /var/log/datadog/cluster-agent.log
2018-06-11 09:37:20 UTC | DEBUG | (metadata.go:40 in GetPodMetadataNames) | CacheKey: agent/KubernetesMetadataMapping/ip-192-168-226-77.ec2.internal, with 1 services
2018-06-11 09:37:20 UTC | DEBUG | (metadata.go:40 in GetPodMetadataNames) | CacheKey: agent/KubernetesMetadataMapping/ip-192-168-226-77.ec2.internal, with 1 services
If you are not collecting events properly, ensure that DD_LEADER_ELECTION and DD_COLLECT_KUBERNETES_EVENTS are set to true, as well as the proper verbs listed in the RBAC (notably, watch events).
If you have enabled those, check the leader election status and the kube_apiserver check with the following command:
datadog-cluster-agent status
This should produce the following result:
root@datadog-cluster-agent-8568545574-x9tc9:/# datadog-cluster-agent status
[...]
Leader Election
===============
Leader Election Status: Running
Leader Name is: datadog-cluster-agent-8568545574-x9tc9
Last Acquisition of the lease: Mon, 11 Jun 2018 06:38:53 UTC
Renewed leadership: Mon, 11 Jun 2018 09:41:34 UTC
Number of leader transitions: 2 transitions
[...]
Running Checks
==============
kubernetes_apiserver
--------------------
Total Runs: 736
Metrics: 0, Total Metrics: 0
Events: 0, Total Events: 100
Service Checks: 3, Total Service Checks: 2193
[...]
You can check the status of the Datadog Cluster Agent by running the Agent status command:
datadog-agent status
If the Datadog Cluster Agent is enabled and correctly configured, you should see:
[...]
=====================
Datadog Cluster Agent
=====================
- Datadog Cluster Agent endpoint detected: https://XXX.XXX.XXX.XXX:5005
Successfully Connected to the Datadog Cluster Agent.
- Running: {Major:1 Minor:0 Pre:xxx Meta:xxx Commit:xxxxx}
Make sure the Cluster Agent service was created before the Agents’ Pods, so that the DNS is available in the environment variables:
root@datadog-agent-9d5bl:/# env | grep DATADOG_CLUSTER_AGENT | sort
DATADOG_CLUSTER_AGENT_PORT=tcp://10.100.202.234:5005
DATADOG_CLUSTER_AGENT_PORT_5005_TCP=tcp://10.100.202.234:5005
DATADOG_CLUSTER_AGENT_PORT_5005_TCP_ADDR=10.100.202.234
DATADOG_CLUSTER_AGENT_PORT_5005_TCP_PORT=5005
DATADOG_CLUSTER_AGENT_PORT_5005_TCP_PROTO=tcp
DATADOG_CLUSTER_AGENT_SERVICE_HOST=10.100.202.234
DATADOG_CLUSTER_AGENT_SERVICE_PORT=5005
DATADOG_CLUSTER_AGENT_SERVICE_PORT_AGENTPORT=5005
root@datadog-agent-9d5bl:/# echo ${DD_CLUSTER_AGENT_AUTH_TOKEN}
DD_CLUSTER_AGENT_AUTH_TOKEN=1234****9
Verify that the node-based Agent is using the Datadog Cluster Agent as a tag provider:
root@datadog-agent-9d5bl:/# cat /var/log/datadog/agent.log | grep "metadata-collector"
2018-06-11 06:59:02 UTC | INFO | (tagger.go:151 in tryCollectors) | kube-metadata-collector tag collector successfully started
Or look for error logs, such as:
2018-06-10 08:03:02 UTC | ERROR | Could not initialize the communication with the Datadog Cluster Agent, falling back to local service mapping: [...]
If you are having issues with the Custom Metrics Server:
Make sure you have the aggregation layer and the certificates set up.
Make sure the metrics you want to autoscale on are available. As you create the HPA, the Datadog Cluster Agent parses the manifest and queries Datadog to try to fetch the metric. If there is a typographic issue with your metric name, or if the metric does not exist within your Datadog application, the following error is raised:
2018-07-03 13:47:56 UTC | ERROR | (datadogexternal.go:45 in queryDatadogExternal) | Returned series slice empty
Run the datadog-cluster-agent status command to see the status of the External Metrics Provider process:
Custom Metrics Provider
=======================
External Metrics
================
ConfigMap name: datadog-hpa
Number of external metrics detected: 2
Errors with the External Metrics Provider process are displayed with this command. If you want more verbose output, run the flare command: datadog-cluster-agent flare.
The flare command generates a zip file containing the custom-metrics-provider.log where you can see output as follows:
Custom Metrics Provider
=======================
External Metrics
================
ConfigMap name: datadog-hpa
Number of external metrics detected: 2
hpa:
- name: nginxext
- namespace: default
labels:
- cluster: eks
metricName: redis.key
ts: 1532042322
valid: false
value: 0
hpa:
- name: nginxext
- namespace: default
labels:
- dcos_version: 1.9.4
metricName: docker.mem.limit
ts: 1.532042322
valid: true
value: 268435456
If the metric’s flag Valid is set to false, the metric is not considered in the HPA pipeline.
If you see the following message when describing the HPA manifest:
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True SucceededGetScale the HPA controller was able to get the target's current scale
ScalingActive False FailedGetExternalMetric the HPA was unable to compute the replica count: unable to get external metric default/nginx.net.request_per_s/&LabelSelector{MatchLabels:map[string]string{kube_container_name: nginx,},MatchExpressions:[],}: unable to fetch metrics from external metrics API: the server could not find the requested resource (get nginx.net.request_per_s.external.metrics.k8s.io)
Then it’s likely that you don’t have the proper RBAC set for the HPA. Make sure that kubectl api-versions shows:
autoscaling/v2beta1
[...]
external.metrics.k8s.io/v1beta1
The latter shows up if the Datadog Cluster Agent properly registers as an External Metrics Provider—and if you have the same service name referenced in the APIService for the External Metrics Provider, as well as the one for the Datadog Cluster Agent on port 8443. Also make sure you have created the RBAC from the Register the External Metrics Provider step.
If you see the following error when describing the HPA manifest:
Warning FailedComputeMetricsReplicas 3s (x2 over 33s) horizontal-pod-autoscaler failed to get nginx.net.request_per_s external metric: unable to get external metric default/nginx.net.request_per_s/&LabelSelector{MatchLabels:map[string]string{kube_container_name: nginx,},MatchExpressions:[],}: unable to fetch metrics from external metrics API: the server is currently unable to handle the request (get nginx.net.request_per_s.external.metrics.k8s.io)
Make sure the Datadog Cluster Agent is running, and the service exposing the port 8443, whose name is registered in the APIService, is up.
As Kubernetes autoscales your resources, the current target is weighted by the number of replicas of the scaled deployment. The value returned by the Datadog Cluster Agent is fetched from Datadog and should be proportionally equal to the current target times the number of replicas.
Example:
hpa:
- name: nginxext
- namespace: default
labels:
- app: puppet
- env: demo
metricName: nginx.net.request_per_s
ts: 1532042322
valid: true
value: 2472
The Cluster Agent fetched 2472, but the HPA indicates:
NAMESPACE NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
default nginxext Deployment/nginx 824/9 (avg) 1 3 3 41m
And indeed 824 * 3 replicas = 2472.
Disclaimer: The Datadog Cluster Agent processes the metrics set in different HPA manifests and queries Datadog to get values every 30 seconds, by default. Kubernetes queries the Datadog Cluster Agent every 30 seconds, by default. As this process is done asynchronously, you should not expect to see the above rule verified at all times, especially if the metric varies, but both frequencies are configurable in order to mitigate any issues.
When leader election is enabled, only the leader serves cluster check configurations to the node-based Agents. If only one replica of the Cluster Agent Pod is running, it is the leader. Otherwise, you can identify the name of the leader in the datadog-leader-election ConfigMap:
# kubectl get cm datadog-leader-election -o yaml
apiVersion: v1
kind: ConfigMap
metadata:
annotations:
control-plane.alpha.kubernetes.io/leader: '{"holderIdentity":"cluster-agent-rhttz", ... }'
In this case, the leader Pod is cluster-agent-rhttz. If the Pod is deleted or unresponsive, another Pod takes over automatically.
To ensure a configuration (static or Autodiscovered) is picked up by the Cluster Agent, use the configcheck command in the leader Cluster Agent:
# kubectl exec <CLUSTER_AGENT_POD_NAME> agent configcheck
...
=== http_check cluster check ===
Source: kubernetes-services
Instance ID: http_check:My service:6e5f4b16b4b433cc
name: My service
tags:
- kube_namespace:default
- kube_service:my-nginx
timeout: 1
url: http://10.15.246.109
~
Init Config:
{}
Auto-discovery IDs:
* kube_service://751adfe4-1280-11e9-a26b-42010a9c00c8
===
The clusterchecks command allows you to inspect the state of the dispatching logic, including:
# kubectl exec <CLUSTER_AGENT_POD_NAME> agent clusterchecks
=== 3 node-agents reporting ===
Name Running checks
default-pool-bce5cd34-7g24.c.sandbox.internal 0
default-pool-bce5cd34-slx3.c.sandbox.internal 2
default-pool-bce5cd34-ttw6.c.sandbox.internal 1
...
===== Checks on default-pool-bce5cd34-ttw6.c.sandbox.internal =====
=== http_check check ===
Source: kubernetes-services
Instance ID: http_check:My service:5b948dee172af830
empty_default_hostname: true
name: My service
tags:
- kube_namespace:default
- kube_service:my-nginx
- cluster_name:example
timeout: 1
url: http://10.15.246.109
~
Init Config:
{}
===
Note: the Instance ID is different from the configcheck command, as the instance is modified to add tags and options.
In this case, this configuration is dispatched to the default-pool-bce5cd34-ttw6 node. Troubleshooting continues with respect to the Agent Pod on that corresponding node.
The Agent configcheck command should show the instance, with the cluster-checks source:
# kubectl exec <NODE_AGENT_POD_NAME> agent configcheck
...
=== http_check check ===
Source: cluster-checks
Instance ID: http_check:My service:5b948dee172af830
empty_default_hostname: true
name: My service
tags:
- kube_namespace:default
- kube_service:my-nginx
- cluster_name:example
timeout: 1
url: http://10.15.246.109
~
Init Config:
{}
===
The Instance ID matches the one you had earlier.
The Agent status command should show the check instance running and reporting successfully.
# kubectl exec <NODE_AGENT_POD_NAME> agent status
...
http_check (3.1.1)
------------------
Instance ID: http_check:My service:5b948dee172af830 [OK]
Total Runs: 234
Metric Samples: Last Run: 3, Total: 702
Events: Last Run: 0, Total: 0
Service Checks: Last Run: 1, Total: 234
Average Execution Time : 90ms
Troubleshooting endpoint checks is similar to troubleshooting cluster checks. Differences occur on Node Agents, where scheduled endpoint checks appear alongside cluster checks.
Note: Endpoint checks are scheduled by Agents that run on the same node as the Pod(s) that back the endpoint(s) of the service. If an endpoint is not backed by a Pod, the Cluster Agent converts the check into a cluster check. This cluster check can be run by any Node Agent.
The Agent configcheck command shows the instance, with the endpoints-checks source:
# kubectl exec <NODE_AGENT_POD_NAME> agent configcheck
...
=== nginx check ===
Configuration provider: endpoints-checks
Configuration source: kube_endpoints:kube_endpoint_uid://default/nginx/
Instance ID: nginx:956741d8796d940c
nginx_status_url: http://10.0.0.75/nginx_status/
tags:
- pod_phase:running
- kube_deployment:nginx
- kube_service:nginx
- kube_namespace:default
- kube_endpoint_ip:10.0.0.75
- cluster_name:cluster
~
Init Config:
{}
Auto-discovery IDs:
* kube_endpoint_uid://default/nginx/10.0.0.75
* kubernetes_pod://4e733448-f57e-11e9-8123-42010af001ed
State: dispatched to gke-cluster-default-pool-4658d5d4-qfnt
===
The Agent status command should show the check instance running and reporting successfully.
# kubectl exec <NODE_AGENT_POD_NAME> agent status
...
nginx (4.0.0)
-------------
Instance ID: nginx:956741d8796d940c [OK]
Configuration Source: kube_endpoints:kube_endpoint_uid://default/nginx/
Total Runs: 443
Metric Samples: Last Run: 7, Total: 3,101
Events: Last Run: 0, Total: 0
Service Checks: Last Run: 1, Total: 443
Average Execution Time : 5ms
The Cluster Agent clusterchecks command shows the instance(s), with the kubernetes-endpoints source:
# kubectl exec <CLUSTER_AGENT_POD_NAME> agent clusterchecks
...
===== 3 Pod-backed Endpoints-Checks scheduled =====
=== nginx check ===
Configuration provider: kubernetes-endpoints
Configuration source: kube_endpoints:kube_endpoint_uid://default/nginx/
Instance ID: nginx:My Nginx Service Endpoints:f139adc46c81828e
name: My Nginx Endpoints
nginx_status_url: http://10.0.0.75/nginx_status/
tags:
- kube_service:nginx
- kube_namespace:default
- kube_endpoint_ip:10.0.0.75
- cluster_name:cluster
~
Init Config:
{}
Auto-discovery IDs:
* kube_endpoint_uid://default/nginx/10.0.0.75
* kubernetes_pod://4e733448-f57e-11e9-8123-42010af001ed
State: dispatched to gke-cluster-default-pool-4658d5d4-qfnt
===
...
Additional helpful documentation, links, and articles:
