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WIP - Bootstrap Infrastructure

This repo contains a reference infrastructure setup that you can change to your own liking.

The idea being, whether you are developing an application/set of services/operators you'd want a consistent environment

Motivation

Local and Proper/Production setup

Requirements

Common Requirements

Local Deployment Requirements

Production Deployment Requirements

Quick Start

Minikube setup (OSX)

Start minikube, configure it with your vm-driver of choice and resources. Keep in mind that a big elasticsearch cluster might require more than the default amount of --memory.

All the commands are assuming your current working directory is the root of this repository.

$ minikube start --vm-driver hyperkit --memory 8192 --cpus 4

Helm Initialization

$ kubectl create serviceaccount tiller --namespace kube-system
$ kubectl create clusterrolebinding tiller-role-binding --clusterrole cluster-admin --serviceaccount=kube-system:tiller
$ helm init --service-account tiller

Installing Prometheus and Graphana

Install prometheus operator:

kubectl create namespace prometheus
helm install stable/prometheus \
     --name prometheus \
     --namespace prometheus \
     --set alertmanager.persistentVolume.storageClass="standard" \
     --set server.persistentVolume.storageClass="standard"

And grafana (take note of the password):

kubectl create namespace grafana
helm install stable/grafana \
     --name grafana \
     --namespace grafana \
     --set persistence.storageClassName="standard" \
     --set adminPassword="bootstrapadmin" \
     --set datasources."datasources\.yaml".apiVersion=1 \
     --set datasources."datasources\.yaml".datasources[0].name=Prometheus \
     --set datasources."datasources\.yaml".datasources[0].type=prometheus \
     --set datasources."datasources\.yaml".datasources[0].url=http://prometheus-server.prometheus.svc.cluster.local \
     --set datasources."datasources\.yaml".datasources[0].access=proxy \
     --set datasources."datasources\.yaml".datasources[0].isDefault=true \
     --set service.type=LoadBalancer

After installing the chart, you can grab the pod name:

kubectl get pods -n grafana
.
.
.
NAME                      READY   STATUS    RESTARTS   AGE
grafana-9995876cc-j2kdz   1/1     Running   1          1m

Access it via port-forwarding 3000, which in the above case is grafana-9995876cc:

kubectl port-forward -n grafana POD_NAME 3000

You can import a custom dashboard that will display basic cluster health: https://grafana.com/dashboards/6417

To do so navigate to http://localhost:3000, on the hover menu import a dashboard: Importing Dashboard

Fill in the ID and the datasource: ID

ID

Elasticsearch

More info: env/elastic

First we will install the elasticsearch-operator:

helm install --name es-operator \
     --namespace kube-system \
     ./charts/elasticsearch-operator

After that you should see, that the CustomResourceDefinition has been created:

➜  bootstrap-infra git:(master) ✗ kubectl get CustomResourceDefinitions
NAME                                         CREATED AT
elasticsearchclusters.enterprises.upmc.com   2019-06-22T11:44:46Z
➜  bootstrap-infra git:(master) ✗ kubectl get elasticsearchclusters
No resources found.
➜  bootstrap-infra git:(master) ✗ kubectl get foo
error: the server doesn't have a resource type "foo"

After that, we will be launching the EFK stack:

helm install --name efk \
    --namespace logging \
    ./charts/efk \
    -f ./env/elastic/values.minikube.yaml

And you should see:

➜  bootstrap-infra git:(master) ✗ k get pods -n logging
NAME                                     READY   STATUS              RESTARTS   AGE
efk-kibana-d7b549b5c-n7b82               0/1     ContainerCreating   0          33s
es-client-efk-cluster-664f8fcb94-r2qb8   0/1     ContainerCreating   0          33s
es-data-efk-cluster-default-0            0/1     ContainerCreating   0          32s
es-master-efk-cluster-default-0          0/1     ContainerCreating   0          33s
fluent-bit-wlv62                         1/1     Running             0          33s

This also deploys an elasticsearch-curator which purges the indices every 7 days. This is configurable in values.yaml.

You can access the deployed kibana by grabbing the name and port-forwarding like in the case of grafana like so:

kubectl port-forward efk-kibana-d7b549b5c-n7b82 5601 -n logging

After that, you will be asked to create an index pattern and select a time filter. You can use kube* to match across all indices (which come in as kubernetes_cluster-YYYY.MM.DD) and @timestamp for the filter:

Indices

Filter

Cleaning Up

To stop the minikube cluster:

minikube stop

And to clean it up and delete the artifacts:

minikube delete

Production Setup (AWS EKS Example)

Setup the cluster using the provided terraform configs from the major cloud providers (or add your own). All the outputs from the terraform configs include the guide on how to setup kubectl for the deployed cluster.

Terraform

From

Helm Initialization

$ kubectl create serviceaccount tiller --namespace kube-system
$ kubectl create clusterrolebinding tiller-role-binding --clusterrole cluster-admin --serviceaccount=kube-system:tiller
$ helm init --service-account tiller

Installing Prometheus and Graphana

Install prometheus operator:

kubectl create namespace prometheus
helm install stable/prometheus \
     --name prometheus \
     --namespace prometheus \
     --set alertmanager.persistentVolume.storageClass="gp2" \
     --set server.persistentVolume.storageClass="gp2"

And grafana (take note of the password):

kubectl create namespace grafana
helm install stable/grafana \
     --name grafana \
     --namespace grafana \
     --set persistence.storageClassName="gp2" \
     --set adminPassword="bootstrapadmin" \
     --set datasources."datasources\.yaml".apiVersion=1 \
     --set datasources."datasources\.yaml".datasources[0].name=Prometheus \
     --set datasources."datasources\.yaml".datasources[0].type=prometheus \
     --set datasources."datasources\.yaml".datasources[0].url=http://prometheus-server.prometheus.svc.cluster.local \
     --set datasources."datasources\.yaml".datasources[0].access=proxy \
     --set datasources."datasources\.yaml".datasources[0].isDefault=true \
     --set service.type=LoadBalancer

Elasticsearch

More info: env/elastic

Before installing deployments with passed in values.yaml inspect them individually, for examples when setting up zones for ES cluster:

zones: ["us-east-1a", "us-east-1b", "us-east-1c"]

First we will install the elasticsearch-operator:

helm install --name es-operator \
     --namespace kube-system \
     ./charts/elasticsearch-operator

After that, we will be launching the EFK stack:

helm install --name efk \
    --namespace logging \
    ./charts/efk \
    -f ./env/elastic/values.yaml

This also deploys an elasticsearch-curator which purges the indices every 7 days. This is configurable in values.yaml.

Kibana is accessible via port-forward.

Folder Structure

The folder structure is grouped as follows:

  • /charts - contains packaged charts (i.e. those not provided by the official stable repository), chart scripts, and related information
    • /charts/observability - aggregated logging, metrics, elasticsearch etc.
    • /charts/controller - Prometheus, Let's Encrypter issuers
  • /tf - terraform config charts for deploying
    • AWS EKS
    • GCP GKE
    • DO Kubernetes Engine
    • tectonic
  • /env - infrastructure environment, contains the
    • /env/monitoring - grafana, kibana
    • /env/elasticsearch - elasticsearch cluster

TODOs

  • Create some basic setup scripts and/or CLI tools to automate all of the bootstrapping phase (especially locally)
  • Describe pushing docker / images workflow with local deployment