How-To: Set up Fluentd, Elastic search and Kibana in Kubernetes

How-To: Set up Fluentd, Elastic search and Kibana in Kubernetes

How to install Fluentd, Elastic Search, and Kibana to search logs in Kubernetes

Prerequisites

Kubernetes (> 1.14)
kubectl
Helm 3

Install Elastic search and Kibana

Create a Kubernetes namespace for monitoring tools
```
kubectl create namespace dapr-monitoring
```

Add the helm repo for Elastic Search

helm repo add elastic https://helm.elastic.co
helm repo update

Install Elastic Search using Helm

By default, the chart creates 3 replicas which must be on different nodes. If your cluster has fewer than 3 nodes, specify a smaller number of replicas. For example, this sets the number of replicas to 1:
```
helm install elasticsearch elastic/elasticsearch -n dapr-monitoring --set replicas=1
```
Otherwise:
```
helm install elasticsearch elastic/elasticsearch -n dapr-monitoring
```
If you are using minikube or simply want to disable persistent volumes for development purposes, you can do so by using the following command:
```
helm install elasticsearch elastic/elasticsearch -n dapr-monitoring --set persistence.enabled=false,replicas=1
```

Install Kibana

helm install kibana elastic/kibana -n dapr-monitoring

Ensure that Elastic Search and Kibana are running in your Kubernetes cluster

$ kubectl get pods -n dapr-monitoring
NAME                            READY   STATUS    RESTARTS   AGE
elasticsearch-master-0          1/1     Running   0          6m58s
kibana-kibana-95bc54b89-zqdrk   1/1     Running   0          4m21s

Install Fluentd

Install config map and Fluentd as a daemonset

Download these config files:
- fluentd-config-map.yaml
- fluentd-dapr-with-rbac.yaml
Note: If you already have Fluentd running in your cluster, please enable the nested json parser so that it can parse JSON-formatted logs from Dapr.

Apply the configurations to your cluster:
```
kubectl apply -f ./fluentd-config-map.yaml
kubectl apply -f ./fluentd-dapr-with-rbac.yaml
```

Ensure that Fluentd is running as a daemonset. The number of FluentD instances should be the same as the number of cluster nodes. In the example below, there is only one node in the cluster:

$ kubectl get pods -n kube-system -w
NAME                          READY   STATUS    RESTARTS   AGE
coredns-6955765f44-cxjxk      1/1     Running   0          4m41s
coredns-6955765f44-jlskv      1/1     Running   0          4m41s
etcd-m01                      1/1     Running   0          4m48s
fluentd-sdrld                 1/1     Running   0          14s

Install Dapr with JSON formatted logs

Install Dapr with enabling JSON-formatted logs

helm repo add dapr https://dapr.github.io/helm-charts/
helm repo update
helm install dapr dapr/dapr --namespace dapr-system --set global.logAsJson=true

Enable JSON formatted log in Dapr sidecar

Add the dapr.io/log-as-json: "true" annotation to your deployment yaml. For example:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: pythonapp
  namespace: default
  labels:
    app: python
spec:
  replicas: 1
  selector:
    matchLabels:
      app: python
  template:
    metadata:
      labels:
        app: python
      annotations:
        dapr.io/enabled: "true"
        dapr.io/app-id: "pythonapp"
        dapr.io/log-as-json: "true"
...

Search logs

Note: Elastic Search takes a time to index the logs that Fluentd sends.

Port-forward from localhost to svc/kibana-kibana

$ kubectl port-forward svc/kibana-kibana 5601 -n dapr-monitoring
Forwarding from 127.0.0.1:5601 -> 5601
Forwarding from [::1]:5601 -> 5601
Handling connection for 5601
Handling connection for 5601

Browse to http://localhost:5601
Expand the drop-down menu and click Management → Stack Management
On the Stack Management page, select Data → Index Management and wait until dapr-* is indexed.
Once dapr-* is indexed, click on Kibana → Index Patterns and then the Create index pattern button.
Define a new index pattern by typing dapr* into the Index Pattern name field, then click the Next step button to continue.
Configure the primary time field to use with the new index pattern by selecting the @timestamp option from the Time field drop-down. Click the Create index pattern button to complete creation of the index pattern.
The newly created index pattern should be shown. Confirm that the fields of interest such as scope, type, app_id, level, etc. are being indexed by using the search box in the Fields tab.

Note: If you cannot find the indexed field, please wait. The time it takes to search across all indexed fields depends on the volume of data and size of the resource that the elastic search is running on.
To explore the indexed data, expand the drop-down menu and click Analytics → Discover.
In the search box, type in a query string such as scope:* and click the Refresh button to view the results.

Note: This can take a long time. The time it takes to return all results depends on the volume of data and size of the resource that the elastic search is running on.

References

Last modified October 14, 2021: upmerge v1.4 into v1.5 (#1851) (36bb4e5c)