In my previous article I showed how to use the Kops tool to create a production ready Kubernetes cluster on the Amazon Web Services (AWS) cloud hosting platform. This time I will guide you in installing into this cluster the free and open source Elasticsearch search engine and its graphical counterpart Kibana, as a visual log database. This will provide you with a powerful system for storing logs from containers running in Kubernetes and navigating and searching them in an appealing graphical interface.

We will also install Fluentd as this component is responsible for transmitting the standard Kubernetes logs to Elasticsearch. These three components are together colloquially known as the EFK stack, a self explanatory acronym (an older variation on this stack is known as ELK, which is the same except that it uses Logstash instead of Fluentd).

Before going the route of maintaining your own Elasticsearch cluster, however, you might want to instead consider using the managed service from Elastic themselves, Elastic Cloud. The reason being that administrating Elasticsearch can be a lot of work, as many people experienced with the system will tell you it can be tricky to keep running smoothly and that it’s a task better outsourced to an external service (i.e. Elastic Cloud).

Official Kubernetes Add-On

Before going any further, it should be mentioned that there is a standard add-on for installing EFK in Kubernetes clusters, as part of the official Kubernetes repository. The current version of the add-on (corresponding to Elasticearch 5.6.2) is based on a contribution by yours truly to bring it up to date (5.5.1 at the time). The guide presented in this article is based on said add-on.

I will go through each part of the stack in succession, and provide the corresponding Kubernetes manifest files, and explain shortly how it functions. In order to install the logging stack in your Kubernetes cluster, apply the manifests via kubectl:

kubectl apply -f *.yaml

Elasticsearch

These manifests install Elasticsearch itself as a StatefulSet of two pods that will allocate a persistent volume of 20 GB per pod (make sure to pick a size that make sense for your workload). A Service is created in front of the StatefulSet pods to load balance them.

Elasticsearch is also configured to run under the service account elasticsearch-logging, which gets bound to the role of the same name in order for it to have the right permissions.

es-statefulset.yaml:

# RBAC authn and authz apiVersion: v1 kind: ServiceAccount metadata: name: elasticsearch-logging namespace: kube-system labels: k8s-app: elasticsearch-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: name: elasticsearch-logging labels: k8s-app: elasticsearch-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile rules: - apiGroups: - "" resources: - "services" - "namespaces" - "endpoints" verbs: - "get" --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: namespace: kube-system name: elasticsearch-logging labels: k8s-app: elasticsearch-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile subjects: - kind: ServiceAccount name: elasticsearch-logging namespace: kube-system apiGroup: "" roleRef: kind: ClusterRole name: elasticsearch-logging apiGroup: "" --- # Elasticsearch deployment itself apiVersion: apps/v1beta1 kind: StatefulSet metadata: name: elasticsearch-logging namespace: kube-system labels: k8s-app: elasticsearch-logging version: v5.6.2 kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile spec: serviceName: elasticsearch-logging replicas: 2 selector: matchLabels: k8s-app: elasticsearch-logging version: v5.6.2 template: metadata: labels: k8s-app: elasticsearch-logging version: v5.6.2 kubernetes.io/cluster-service: "true" spec: serviceAccountName: elasticsearch-logging containers: - image: gcr.io/google-containers/elasticsearch:v5.6.2 name: elasticsearch-logging resources: # need more cpu upon initialization, therefore burstable class limits: cpu: 1000m requests: cpu: 100m ports: - containerPort: 9200 name: db protocol: TCP - containerPort: 9300 name: transport protocol: TCP volumeMounts: - name: elasticsearch-logging mountPath: /data env: - name: "NAMESPACE" valueFrom: fieldRef: fieldPath: metadata.namespace initContainers: - image: alpine:3.6 command: ["/sbin/sysctl", "-w", "vm.max_map_count=262144"] name: elasticsearch-logging-init securityContext: privileged: true volumeClaimTemplates: - metadata: name: elasticsearch-logging spec: accessModes: ["ReadWriteOnce"] resources: requests: storage: 20Gi

es-service.yaml:

apiVersion: v1 kind: Service metadata: name: elasticsearch-logging namespace: kube-system labels: k8s-app: elasticsearch-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile kubernetes.io/name: "Elasticsearch" spec: ports: - port: 9200 protocol: TCP targetPort: db selector: k8s-app: elasticsearch-logging

Elasticsearch Curator

Alongside Elasticsearch itself we deploy a service called Elasticsearch Curator, which does automatic maintenance of your Elasticsearch cluster. In our case we make it delete indices older than three days. If you want to tweak this configuration, Base64 decode the values for actionfile.yaml and/or config.yaml_ in es-curator-secret.yaml (Kubernetes requires secret values to be Base64 encoded), make your changes and re-insert the Base64 encoded contents of respective files.

es-curator.yaml:

apiVersion: apps/v1beta1 kind: Deployment metadata: name: es-curator namespace: kube-system labels: k8s-app: es-curator spec: replicas: 1 selector: matchLabels: k8s-app: es-curator template: metadata: labels: k8s-app: es-curator spec: containers: - name: es-curator image: aknudsen/es-curator-service:5.3.0-1 imagePullPolicy: IfNotPresent args: ["--config", "/etc/config/config.yml", "/etc/config/action_file.yml"] volumeMounts: - name: config-volume mountPath: /etc/config volumes: - name: config-volume secret: secretName: curator-config

es-curator-secret.yaml:

apiVersion: v1 kind: Secret metadata: name: curator-config namespace: kube-system type: Opaque data: action_file.yml: 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 config.yml: Y2xpZW50OgogIGhvc3RzOgogICAgLSBlbGFzdGljc2VhcmNoLWxvZ2dpbmcKICBwb3J0OiA5MjAwCiAgdXJsX3ByZWZpeDoKICB1c2Vfc3NsOiBGYWxzZQogIGNlcnRpZmljYXRlOgogIGNsaWVudF9jZXJ0OgogIGNsaWVudF9rZXk6CiAgc3NsX25vX3ZhbGlkYXRlOiBGYWxzZQogIGh0dHBfYXV0aDogZWxhc3RpYzpjaGFuZ2VtZQogIHRpbWVvdXQ6IDMwCiAgbWFzdGVyX29ubHk6IEZhbHNlCmxvZ2dpbmc6CiAgbG9nbGV2ZWw6IElORk8KICBsb2dmaWxlOgogIGxvZ2Zvcm1hdDogZGVmYXVsdAogIGJsYWNrbGlzdDogWydlbGFzdGljc2VhcmNoJywgJ3VybGxpYjMnXQo=

Fluentd

Fluentd is installed as a DaemonSet, which means that a corresponding pod will run on every Kubernetes worker node in order to collect its logs (and send them to Elasticsearch). Furthermore, the pods run as the service account fluentd-es which is bound to the cluster role with the same name in order to have the necessary permissions.

fluentd-es-configmap.yaml:

kind: ConfigMap apiVersion: v1 data: containers.input.conf: |- # This configuration file for Fluentd / td-agent is used # to watch changes to Docker log files. The kubelet creates symlinks that # capture the pod name, namespace, container name & Docker container ID # to the docker logs for pods in the /var/log/containers directory on the host. # If running this fluentd configuration in a Docker container, the /var/log # directory should be mounted in the container. # # These logs are then submitted to Elasticsearch which assumes the # installation of the fluent-plugin-elasticsearch & the # fluent-plugin-kubernetes_metadata_filter plugins. # See https://github.com/uken/fluent-plugin-elasticsearch & # https://github.com/fabric8io/fluent-plugin-kubernetes_metadata_filter for # more information about the plugins. # # Example # ======= # A line in the Docker log file might look like this JSON: # # {"log":"2014/09/25 21:15:03 Got request with path wombat

", # "stream":"stderr", # "time":"2014-09-25T21:15:03.499185026Z"} # # The time_format specification below makes sure we properly # parse the time format produced by Docker. This will be # submitted to Elasticsearch and should appear like: # $ curl 'http://elasticsearch-logging:9200/_search?pretty' # ... # { # "_index" : "logstash-2014.09.25", # "_type" : "fluentd", # "_id" : "VBrbor2QTuGpsQyTCdfzqA", # "_score" : 1.0, # "_source":{"log":"2014/09/25 22:45:50 Got request with path wombat

", # "stream":"stderr","tag":"docker.container.all", # "@timestamp":"2014-09-25T22:45:50+00:00"} # }, # ... # # The Kubernetes fluentd plugin is used to write the Kubernetes metadata to the log # record & add labels to the log record if properly configured. This enables users # to filter & search logs on any metadata. # For example a Docker container's logs might be in the directory: # # /var/lib/docker/containers/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b # # and in the file: # # 997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b-json.log # # where 997599971ee6... is the Docker ID of the running container. # The Kubernetes kubelet makes a symbolic link to this file on the host machine # in the /var/log/containers directory which includes the pod name and the Kubernetes # container name: # # synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log # -> # /var/lib/docker/containers/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b-json.log # # The /var/log directory on the host is mapped to the /var/log directory in the container # running this instance of Fluentd and we end up collecting the file: # # /var/log/containers/synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log # # This results in the tag: # # var.log.containers.synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log # # The Kubernetes fluentd plugin is used to extract the namespace, pod name & container name # which are added to the log message as a kubernetes field object & the Docker container ID # is also added under the docker field object. # The final tag is: # # kubernetes.var.log.containers.synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log # # And the final log record look like: # # { # "log":"2014/09/25 21:15:03 Got request with path wombat

", # "stream":"stderr", # "time":"2014-09-25T21:15:03.499185026Z", # "kubernetes": { # "namespace": "default", # "pod_name": "synthetic-logger-0.25lps-pod", # "container_name": "synth-lgr" # }, # "docker": { # "container_id": "997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b" # } # } # # This makes it easier for users to search for logs by pod name or by # the name of the Kubernetes container regardless of how many times the # Kubernetes pod has been restarted (resulting in a several Docker container IDs). # Example: # {"log":"[info:2016-02-16T16:04:05.930-08:00] Some log text here

","stream":"stdout","time":"2016-02-17T00:04:05.931087621Z"} <source> type tail path /var/log/containers/*.log pos_file /var/log/es-containers.log.pos time_format %Y-%m-%dT%H:%M:%S.%NZ tag kubernetes.* format json read_from_head true </source> system.input.conf: |- # Example: # 2015-12-21 23:17:22,066 [salt.state ][INFO ] Completed state [net.ipv4.ip_forward] at time 23:17:22.066081 <source> type tail format /^(?<time>[^ ]* [^ ,]*)[^\[]*\[[^\]]*\]\[(?<severity>[^ \]]*) *\] (?<message>.*)$/ time_format %Y-%m-%d %H:%M:%S path /var/log/salt/minion pos_file /var/log/es-salt.pos tag salt </source> # Example: # Dec 21 23:17:22 gke-foo-1-1-4b5cbd14-node-4eoj startupscript: Finished running startup script /var/run/google.startup.script <source> type tail format syslog path /var/log/startupscript.log pos_file /var/log/es-startupscript.log.pos tag startupscript </source> # Examples: # time="2016-02-04T06:51:03.053580605Z" level=info msg="GET /containers/json" # time="2016-02-04T07:53:57.505612354Z" level=error msg="HTTP Error" err="No such image: -f" statusCode=404 <source> type tail format /^time="(?<time>[^)]*)" level=(?<severity>[^ ]*) msg="(?<message>[^"]*)"( err="(?<error>[^"]*)")?( statusCode=($<status_code>\d+))?/ path /var/log/docker.log pos_file /var/log/es-docker.log.pos tag docker </source> # Example: # 2016/02/04 06:52:38 filePurge: successfully removed file /var/etcd/data/member/wal/00000000000006d0-00000000010a23d1.wal <source> type tail # Not parsing this, because it doesn't have anything particularly useful to # parse out of it (like severities). format none path /var/log/etcd.log pos_file /var/log/es-etcd.log.pos tag etcd </source> # Multi-line parsing is required for all the kube logs because very large log # statements, such as those that include entire object bodies, get split into # multiple lines by glog. # Example: # I0204 07:32:30.020537 3368 server.go:1048] POST /stats/container/: (13.972191ms) 200 [[Go-http-client/1.1] 10.244.1.3:40537] <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/kubelet.log pos_file /var/log/es-kubelet.log.pos tag kubelet </source> # Example: # I1118 21:26:53.975789 6 proxier.go:1096] Port "nodePort for kube-system/default-http-backend:http" (:31429/tcp) was open before and is still needed <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/kube-proxy.log pos_file /var/log/es-kube-proxy.log.pos tag kube-proxy </source> # Example: # I0204 07:00:19.604280 5 handlers.go:131] GET /api/v1/nodes: (1.624207ms) 200 [[kube-controller-manager/v1.1.3 (linux/amd64) kubernetes/6a81b50] 127.0.0.1:38266] <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/kube-apiserver.log pos_file /var/log/es-kube-apiserver.log.pos tag kube-apiserver </source> # Example: # I0204 06:55:31.872680 5 servicecontroller.go:277] LB already exists and doesn't need update for service kube-system/kube-ui <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/kube-controller-manager.log pos_file /var/log/es-kube-controller-manager.log.pos tag kube-controller-manager </source> # Example: # W0204 06:49:18.239674 7 reflector.go:245] pkg/scheduler/factory/factory.go:193: watch of *api.Service ended with: 401: The event in requested index is outdated and cleared (the requested history has been cleared [2578313/2577886]) [2579312] <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/kube-scheduler.log pos_file /var/log/es-kube-scheduler.log.pos tag kube-scheduler </source> # Example: # I1104 10:36:20.242766 5 rescheduler.go:73] Running Rescheduler <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/rescheduler.log pos_file /var/log/es-rescheduler.log.pos tag rescheduler </source> # Example: # I0603 15:31:05.793605 6 cluster_manager.go:230] Reading config from path /etc/gce.conf <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/glbc.log pos_file /var/log/es-glbc.log.pos tag glbc </source> # Example: # I0603 15:31:05.793605 6 cluster_manager.go:230] Reading config from path /etc/gce.conf <source> type tail format multiline multiline_flush_interval 5s format_firstline /^\w\d{4}/ format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/ time_format %m%d %H:%M:%S.%N path /var/log/cluster-autoscaler.log pos_file /var/log/es-cluster-autoscaler.log.pos tag cluster-autoscaler </source> # Logs from systemd-journal for interesting services. <source> type systemd filters [{ "_SYSTEMD_UNIT": "docker.service" }] pos_file /var/log/gcp-journald-docker.pos read_from_head true tag docker </source> <source> type systemd filters [{ "_SYSTEMD_UNIT": "kubelet.service" }] pos_file /var/log/gcp-journald-kubelet.pos read_from_head true tag kubelet </source> <source> type systemd filters [{ "_SYSTEMD_UNIT": "node-problem-detector.service" }] pos_file /var/log/gcp-journald-node-problem-detector.pos read_from_head true tag node-problem-detector </source> forward.input.conf: |- # Takes the messages sent over TCP <source> type forward </source> monitoring.conf: |- # Prometheus Exporter Plugin # input plugin that exports metrics <source> @type prometheus </source> <source> @type monitor_agent </source> # input plugin that collects metrics from MonitorAgent <source> @type prometheus_monitor <labels> host ${hostname} </labels> </source> # input plugin that collects metrics for output plugin <source> @type prometheus_output_monitor <labels> host ${hostname} </labels> </source> # input plugin that collects metrics for in_tail plugin <source> @type prometheus_tail_monitor <labels> host ${hostname} </labels> </source> output.conf: |- # Enriches records with Kubernetes metadata <filter kubernetes.**> type kubernetes_metadata </filter> <match **> type elasticsearch log_level info include_tag_key true host elasticsearch-logging port 9200 logstash_format true # Set the chunk limits. buffer_chunk_limit 2M buffer_queue_limit 8 flush_interval 5s # Never wait longer than 5 minutes between retries. max_retry_wait 30 # Disable the limit on the number of retries (retry forever). disable_retry_limit # Use multiple threads for processing. num_threads 2 </match> metadata: name: fluentd-es-config-v0.1.0 namespace: kube-system labels: addonmanager.kubernetes.io/mode: Reconcile

fluentd-es-ds.yaml:

apiVersion: v1 kind: ServiceAccount metadata: name: fluentd-es namespace: kube-system labels: k8s-app: fluentd-es kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: name: fluentd-es labels: k8s-app: fluentd-es kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile rules: - apiGroups: - "" resources: - "namespaces" - "pods" verbs: - "get" - "watch" - "list" --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: name: fluentd-es labels: k8s-app: fluentd-es kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile subjects: - kind: ServiceAccount name: fluentd-es namespace: kube-system apiGroup: "" roleRef: kind: ClusterRole name: fluentd-es apiGroup: "" --- apiVersion: apps/v1beta1 kind: DaemonSet metadata: name: fluentd-es-v2.0.2 namespace: kube-system labels: k8s-app: fluentd-es version: v2.0.2 kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile spec: template: metadata: labels: k8s-app: fluentd-es kubernetes.io/cluster-service: "true" version: v2.0.2 # This annotation ensures that fluentd does not get evicted if the node # supports critical pod annotation based priority scheme. # Note that this does not guarantee admission on the nodes (#40573). annotations: scheduler.alpha.kubernetes.io/critical-pod: '' spec: serviceAccountName: fluentd-es containers: - name: fluentd-es image: gcr.io/google-containers/fluentd-elasticsearch:v2.0.2 env: - name: FLUENTD_ARGS value: --no-supervisor -q resources: limits: memory: 500Mi requests: cpu: 100m memory: 200Mi volumeMounts: - name: varlog mountPath: /var/log - name: varlibdockercontainers mountPath: /var/lib/docker/containers readOnly: true - name: libsystemddir mountPath: /host/lib readOnly: true - name: config-volume mountPath: /etc/fluent/config.d nodeSelector: beta.kubernetes.io/fluentd-ds-ready: "true" terminationGracePeriodSeconds: 30 volumes: - name: varlog hostPath: path: /var/log - name: varlibdockercontainers hostPath: path: /var/lib/docker/containers # It is needed to copy systemd library to decompress journals - name: libsystemddir hostPath: path: /usr/lib64 - name: config-volume configMap: name: fluentd-es-config-v0.1.0

Kibana

There’s not much to say about the Kibana manifests; we install a Deployment, which ensures that one pod is always running, and a Service in front of it (which is capable of load balancing in case there should be several pods in parallel).

kibana-deployment.yaml:

apiVersion: apps/v1beta1 kind: Deployment metadata: name: kibana-logging namespace: kube-system labels: k8s-app: kibana-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile spec: replicas: 1 selector: matchLabels: k8s-app: kibana-logging template: metadata: labels: k8s-app: kibana-logging spec: containers: - name: kibana-logging image: docker.elastic.co/kibana/kibana:5.6.2 resources: # need more cpu upon initialization, therefore burstable class limits: cpu: 1000m requests: cpu: 100m env: - name: ELASTICSEARCH_URL value: [http://elasticsearch-logging:9200](http://elasticsearch-logging:9200) - name: SERVER_BASEPATH value: /api/v1/proxy/namespaces/kube-system/services/kibana-logging - name: XPACK_MONITORING_ENABLED value: "false" - name: XPACK_SECURITY_ENABLED value: "false" ports: - containerPort: 5601 name: ui protocol: TCP

kibana-service.yaml

apiVersion: v1 kind: Service metadata: name: kibana-logging namespace: kube-system labels: k8s-app: kibana-logging kubernetes.io/cluster-service: "true" addonmanager.kubernetes.io/mode: Reconcile kubernetes.io/name: "Kibana" spec: ports: - port: 5601 protocol: TCP targetPort: ui selector: k8s-app: kibana-logging

Scripted Setup

As described in the previous article in this series, we have made a Python script to create a Kubernetes cluster on AWS with the help of the kops tool. This script also installs the EFK stack within the cluster (in addition to Prometheus Operator for monitoring), so you might give it a spin if you are creating your Kubernetes clusters from scratch on AWS or you could simply use its EFK manifests to integrate with your own cluster(s).

In future articles we will detail installation of the Prometheus Operator monitoring stack plus other Kubernetes practices that we make use of at Coder Society. Stay posted!