EFK Stack on Kubernetes (Part 2)
By: Appfleet Team
Tags: Docker, Kubernetes, Monitoring

This is the final part of our Kubernetes logging series. In case you missed part 1 you can find it here. In this tutorial we will learn about configuring Filebeat to run as a DaemonSet in our Kubernetes cluster in order to ship logs to the Elasticsearch backend. We are using Filebeat instead of FluentD or FluentBit because it is an extremely lightweight utility and has a first class support for Kubernetes. It is best for production level setups.

1. Deployment Architecture

Filebeat will run as a DaemonSet in our Kubernetes cluster. It will be:

Deployed in a separate namespace called Logging.

Pods will be scheduled on both Master nodes and Worker Nodes.

Master Node pods will forward api-server logs for audit and cluster administration purposes.

Client Node pods will forward workload related logs for application observability.

2.1 Creating Filebeat ServiceAccount and ClusterRole

Deploy the following manifest to create the required permissions for Filebeat pods.

apiVersion: v1

kind: Namespace

metadata:

name: logging

---

apiVersion: v1

kind: ServiceAccount

metadata:

name: filebeat

namespace: logging

labels:

k8s-app: filebeat

---

apiVersion: rbac.authorization.k8s.io/v1beta1

kind: ClusterRole

metadata:

name: filebeat

namespace: logging

labels:

k8s-app: filebeat

rules:

- apiGroups: [""] # "" indicates the core API group

resources:

- namespaces

- pods

verbs:

- get

- watch

- list

---

apiVersion: rbac.authorization.k8s.io/v1beta1

kind: ClusterRoleBinding

metadata:

name: filebeat

namespace: logging

subjects:

- kind: ServiceAccount

name: filebeat

namespace: kube-system

roleRef:

kind: ClusterRole

name: filebeat

apiGroup: rbac.authorization.k8s.io

We should make sure that ClusterRole permissions are as limited as possible from security point of view. If either of the pod associated with this service account gets compromised then the attacker would not be able to gain access entire cluster or applications running in it.

2.2 Creating Filebeat ConfigMap

Use the following manifest to create a ConfigMap which will be used by Filebeat pods.

apiVersion: v1

kind: Namespace

metadata:

name: logging

---

apiVersion: v1

kind: ConfigMap

metadata:

name: filebeat-config

namespace: logging

labels:

k8s-app: filebeat

kubernetes.io/cluster-service: "true"

data:

filebeat.yml: |-

filebeat.config:

# inputs:

# path: ${path.config}/inputs.d/*.yml

# reload.enabled: true

modules:

path: ${path.config}/modules.d/*.yml

reload.enabled: true



filebeat.autodiscover:

providers:

- type: kubernetes

hints.enabled: true

include_annotations: ["artifact.spinnaker.io/name","ad.datadoghq.com/tags"]

include_labels: ["app.kubernetes.io/name"]

labels.dedot: true

annotations.dedot: true

templates:

- condition:

equals:

kubernetes.namespace: myapp #Set the namespace in which your app is running, can add multiple conditions in case of more than 1 namespace.

config:

- type: docker

containers.ids:

- "${data.kubernetes.container.id}"

multiline:

pattern: '^[A-Za-z ]+[0-9]{2} (?:[01]\d|2[0123]):(?:[012345]\d):(?:[012345]\d)'. #Timestamp regex for the app logs. Change it as per format.

negate: true

match: after

- condition:

equals:

kubernetes.namespace: elasticsearch

config:

- type: docker

containers.ids:

- "${data.kubernetes.container.id}"

multiline:

pattern: '^\[[0-9]{4}-[0-9]{2}-[0-9]{2}|^[0-9]{4}-[0-9]{2}-[0-9]{2}T'

negate: true

match: after



processors:

- add_cloud_metadata: ~

- drop_fields:

when:

has_fields: ['kubernetes.labels.app']

fields:

- 'kubernetes.labels.app'



output.elasticsearch:

hosts: ['${ELASTICSEARCH_HOST:elasticsearch}:${ELASTICSEARCH_PORT:9200}']

Important concepts for the Filebeat ConfigMap:

hints.enabled : This activates Filebeat’s hints module for Kubernetes. By using this we can use pod annotations to pass config directly to Filebeat pod. We can specify different multiline patterns and various other types of config. More about this can be read here.

: This activates Filebeat’s hints module for Kubernetes. By using this we can use pod annotations to pass config directly to Filebeat pod. We can specify different multiline patterns and various other types of config. More about this can be read here. include_annotations : Setting this to true enables Filebeat to retain any pod annotation for a particular log entry. These annotations can be later used to filter logs in the Kibana console.

: Setting this to true enables Filebeat to retain any pod annotation for a particular log entry. These annotations can be later used to filter logs in the Kibana console. include_labels : Setting this to true enables Filebeat to retain any pod labels for a particular log entry. These labels can be later used to filter logs in the Kibana console.

: Setting this to true enables Filebeat to retain any pod labels for a particular log entry. These labels can be later used to filter logs in the Kibana console. We can also filter logs for a particular namespace and then can process the log entries accordingly. Here docker log processor is used. We can also use different multiline patterns for different namespaces.

The output is set to Elasticsearch because we are using Elasticsearch as the storage backend. Alternatively, this can also point to Redis, Logstash, Kafka or even a File. More this can be read here.

Cloud metadata processor includes some host specific fields in the log entry. This is helpful when we try to filter logs specific to a particular worker node.

2.3 Deploying Filebeat DaemonSet

Use the manifest below to deploy the Filebeat DaemonSet.

apiVersion: v1

kind: Namespace

metadata:

name: logging

---

apiVersion: extensions/v1beta1

kind: DaemonSet

metadata:

name: filebeat

namespace: logging

labels:

k8s-app: filebeat

spec:

template:

metadata:

labels:

k8s-app: filebeat

spec:

serviceAccountName: filebeat

terminationGracePeriodSeconds: 30

tolerations:

- effect: NoSchedule

key: node-role.kubernetes.io/master

containers:

- name: filebeat

image: elastic/filebeat:6.5.4

args: [

"-c", "/usr/share/filebeat/filebeat.yml",

"-e",

]

env:

- name: ELASTICSEARCH_HOST

value: elasticsearch.elasticsearch

- name: ELASTICSEARCH_PORT

value: "9200"

securityContext:

runAsUser: 0

# If using Red Hat OpenShift uncomment this:

#privileged: true

resources:

limits:

memory: 200Mi

requests:

cpu: 100m

memory: 100Mi

volumeMounts:

- name: config

mountPath: /usr/share/filebeat/filebeat.yml

readOnly: true

subPath: filebeat.yml

- name: inputs

mountPath: /usr/share/filebeat/inputs.d

readOnly: true

- name: data

mountPath: /usr/share/filebeat/data

- name: varlibdockercontainers

mountPath: /var/lib/docker/containers

readOnly: true

volumes:

- name: config

configMap:

defaultMode: 0600

name: filebeat-config

- name: varlibdockercontainers

hostPath:

path: /var/lib/docker/containers

- name: inputs

configMap:

defaultMode: 0600

name: filebeat-inputs

# data folder stores a registry of read status for all files, so we don't send everything again on a Filebeat pod restart

- name: data

hostPath:

path: /var/lib/filebeat-data

type: DirectoryOrCreate

---

Let’s see what is going on here:

Logs for each pod are written to /var/log/docker/containers . We are mounting this directory from the host to the Filebeat pod and then Filebeat processes the logs according to the provided configuration.

. We are mounting this directory from the host to the Filebeat pod and then Filebeat processes the logs according to the provided configuration. We have set the env var ELASTICSEARCH_HOST to elasticsearch.elasticsearch to refer to the Elasticsearch client service which was created in part 1 of this article. In case you already have an Elasticsearch cluster running the env var should be set to point to it.

to elasticsearch.elasticsearch to refer to the Elasticsearch client service which was created in part 1 of this article. In case you already have an Elasticsearch cluster running the env var should be set to point to it. Please note the following the setting in the manifest:

...

tolerations:

- effect: NoSchedule

key: node-role.kubernetes.io/master

...

This makes sure that our Filebeat DaemonSet schedules a pod on the master node as well.

Once the Filebeat DaemonSet is deployed we can check if our pods get scheduled properly.

root$ kubectl -n logging get pods -o wide

NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES

filebeat-4kchs 1/1 Running 0 6d 100.96.8.2 ip-10-10-30-206.us-east-2.compute.internal <none> <none>

filebeat-6nrpc 1/1 Running 0 6d 100.96.7.6 ip-10-10-29-252.us-east-2.compute.internal <none> <none>

filebeat-7qs2s 1/1 Running 0 6d 100.96.1.6 ip-10-10-30-161.us-east-2.compute.internal <none> <none>

filebeat-j5xz6 1/1 Running 0 6d 100.96.5.3 ip-10-10-28-186.us-east-2.compute.internal <none> <none>

filebeat-pskg5 1/1 Running 0 6d 100.96.64.4 ip-10-10-29-142.us-east-2.compute.internal <none> <none>

filebeat-vjdtg 1/1 Running 0 6d 100.96.65.3 ip-10-10-30-118.us-east-2.compute.internal <none> <none>

filebeat-wm24j 1/1 Running 0 6d 100.96.0.4 ip-10-10-28-162.us-east-2.compute.internal <none> <none>



root$ kubectl -get nodes -o wide

NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME

ip-10-10-28-162.us-east-2.compute.internal Ready master 6d v1.14.8 10.10.28.162 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-28-186.us-east-2.compute.internal Ready node 6d v1.14.8 10.10.28.186 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-29-142.us-east-2.compute.internal Ready master 6d v1.14.8 10.10.29.142 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-29-252.us-east-2.compute.internal Ready node 6d v1.14.8 10.10.29.252 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-30-118.us-east-2.compute.internal Ready master 6d v1.14.8 10.10.30.118 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-30-161.us-east-2.compute.internal Ready node 6d v1.14.8 10.10.30.161 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

ip-10-10-30-206.us-east-2.compute.internal Ready node 6d v1.14.8 10.10.30.206 <none> Debian GNU/Linux 9 (stretch) 4.9.0-9-amd64 docker://18.6.3

If we tail the logs for one of the pods we can clearly see that it connected to Elasticsearch and has started harvester for the files. The snippet below shows this:

2019-11-19T06:22:03.435Z INFO log/input.go:138 Configured paths: [/var/lib/docker/containers/c2b29f5e06eb8affb2cce7cf2501f6f824a2fd83418d09823faf4e74a5a51eb7/*.log]

2019-11-19T06:22:03.435Z INFO input/input.go:114 Starting input of type: docker; ID: 4134444498769889169

2019-11-19T06:22:04.786Z INFO input/input.go:149 input ticker stopped

2019-11-19T06:22:04.786Z INFO input/input.go:167 Stopping Input: 4134444498769889169

2019-11-19T06:22:19.295Z INFO [monitoring] log/log.go:144 Non-zero metrics in the last 30s {"monitoring": {"metrics": {"beat":{"cpu":{"system":{"ticks":641680,"time":{"ms":16}},"total":{"ticks":2471920,"time":{"ms":180},"value":2471920},"user":{"ticks":1830240,"time":{"ms":164}}},"handles":{"limit":{"hard":1048576,"soft":1048576},"open":20},"info":{"ephemeral_id":"007e8090-7c62-4b44-97fb-e74e8177dc54","uptime":{"ms":549390018}},"memstats":{"gc_next":47281968,"memory_alloc":29021760,"memory_total":156062982472}},"filebeat":{"events":{"added":111,"done":111},"harvester":{"closed":2,"open_files":15,"running":13}},"libbeat":{"config":{"module":{"running":0}},"output":{"events":{"acked":108,"batches":15,"total":108},"read":{"bytes":69},"write":{"bytes":123536}},"pipeline":{"clients":1847,"events":{"active":0,"filtered":3,"published":108,"total":111},"queue":{"acked":108}}},"registrar":{"states":{"current":87,"update":111},"writes":{"success":18,"total":18}},"system":{"load":{"1":0.98,"15":1.71,"5":1.59,"norm":{"1":0.0613,"15":0.1069,"5":0.0994}}}}}}



2019-11-19T06:22:49.295Z INFO [monitoring] log/log.go:144 Non-zero metrics in the last 30s {"monitoring": {"metrics": {"beat":{"cpu":{"system":{"ticks":641720,"time":{"ms":44}},"total":{"ticks":2472030,"time":{"ms":116},"value":2472030},"user":{"ticks":1830310,"time":{"ms":72}}},"handles":{"limit":{"hard":1048576,"soft":1048576},"open":20},"info":{"ephemeral_id":"007e8090-7c62-4b44-97fb-e74e8177dc54","uptime":{"ms":549420018}},"memstats":{"gc_next":47281968,"memory_alloc":38715472,"memory_total":156072676184}},"filebeat":{"events":{"active":12,"added":218,"done":206},"harvester":{"open_files":15,"running":13}},"libbeat":{"config":{"module":{"running":0}},"output":{"events":{"acked":206,"batches":24,"total":206},"read":{"bytes":102},"write":{"bytes":269666}},"pipeline":{"clients":1847,"events":{"active":12,"published":218,"total":218},"queue":{"acked":206}}},"registrar":{"states":{"current":87,"update":206},"writes":{"success":24,"total":24}},"system":{"load":{"1":1.22,"15":1.7,"5":1.58,"norm":{"1":0.0763,"15":0.1063,"5":0.0988}}}}}}



2019-11-19T06:23:19.295Z INFO [monitoring] log/log.go:144 Non-zero metrics in the last 30s {"monitoring": {"metrics": {"beat":{"cpu":{"system":{"ticks":641750,"time":{"ms":28}},"total":{"ticks":2472110,"time":{"ms":72},"value":2472110},"user":{"ticks":1830360,"time":{"ms":44}}},"handles":{"limit":{"hard":1048576,"soft":1048576},"open":20},"info":{"ephemeral_id":"007e8090-7c62-4b44-97fb-e74e8177dc54","uptime":{"ms":549450017}},"memstats":{"gc_next":47281968,"memory_alloc":43140256,"memory_total":156077100968}},"filebeat":{"events":{"active":-12,"added":43,"done":55},"harvester":{"open_files":15,"running":13}},"libbeat":{"config":{"module":{"running":0}},"output":{"events":{"acked":55,"batches":12,"total":55},"read":{"bytes":51},"write":{"bytes":70798}},"pipeline":{"clients":1847,"events":{"active":0,"published":43,"total":43},"queue":{"acked":55}}},"registrar":{"states":{"current":87,"update":55},"writes":{"success":12,"total":12}},"system":{"load":{"1":0.99,"15":1.67,"5":1.49,"norm":{"1":0.0619,"15":0.1044,"5":0.0931}}}}}}



2019-11-19T06:23:25.261Z INFO log/harvester.go:255 Harvester started for file: /var/lib/docker/containers/ccb7dc75ecc755734f6befc4965b9fdae74d59810914101eadf63daa69eb62e2/ccb7dc75ecc755734f6befc4965b9fdae74d59810914101eadf63daa69eb62e2-json.log



2019-11-19T06:23:49.295Z INFO [monitoring] log/log.go:144 Non-zero metrics in the last 30s {"monitoring": {"metrics": {"beat":{"cpu":{"system":{"ticks":641780,"time":{"ms":28}},"total":{"ticks":2472310,"time":{"ms":196},"value":2472310},"user":{"ticks":1830530,"time":{"ms":168}}},"handles":{"limit":{"hard":1048576,"soft":1048576},"open":21},"info":{"ephemeral_id":"007e8090-7c62-4b44-97fb-e74e8177dc54","uptime":{"ms":549480018}},"memstats":{"gc_next":47789200,"memory_alloc":31372376,"memory_total":156086697176,"rss":-1064960}},"filebeat":{"events":{"active":16,"added":170,"done":154},"harvester":{"open_files":16,"running":14,"started":1}},"libbeat":{"config":{"module":{"running":0}},"output":{"events":{"acked":153,"batches":24,"total":153},"read":{"bytes":115},"write":{"bytes":207569}},"pipeline":{"clients":1847,"events":{"active":16,"filtered":1,"published":169,"total":170},"queue":{"acked":153}}},"registrar":{"states":{"current":87,"update":154},"writes":{"success":25,"total":25}},"system":{"load":{"1":0.87,"15":1.63,"5":1.41,"norm":{"1":0.0544,"15":0.1019,"5":0.0881}}}}}}

Once we have all our pods running, then we can create an index pattern of the type filebeat-* in Kibana. Filebeat indexes are generally timestamped. As soon as we create the index pattern all the searchable available fields can be seen and should be imported.

Lastly, we can search through our application logs and create dashboards if needed.

It is highly recommended to JSON logger in our applications because it makes log processing extremely easy and messages can be parsed easily.

3. Conclusion

This concludes our logging set-up. All of the provided configuration files have been tested in production environments and are readily deployable. Feel free to reach out should you have any questions around it.