---
title: "A beautiful GitOps day V - Monitoring and Logging Stack"
date: 2023-08-23
description: "Follow this opinionated guide as starter-kit for your own Kubernetes platform..."
tags: ["kubernetes", "monitoring", "logging", "prometheus", "loki", "grafana"]
---

{{< lead >}}
Use GitOps workflow for building a production grade on-premise Kubernetes cluster on cheap VPS provider, with complete CI/CD 🎉
{{< /lead >}}

This is the **Part V** of more global topic tutorial. [Back to guide summary]({{< ref "/posts/10-a-beautiful-gitops-day" >}}) for intro.

## Monitoring

Monitoring is a critical part of any production grade platform. It allows you to be proactive and react before your users are impacted. It also helps get a quick visualization of cluster architecture and current usage.

### Monitoring node pool

As well as storage pool, creating a dedicated node pool for monitoring stack is a good practice in order to scale it separately from the apps.

You now have a good understanding of how to create a node pool, so apply next configuration from our 1st Terraform project:

{{< highlight host="demo-kube-hcloud" file="kube.tf" >}}

```tf
module "hcloud_kube" {
  //...

  agent_nodepools = [
    //...
    {
      name              = "monitor"
      server_type       = "cx21"
      location          = "nbg1"
      count             = 1
      private_interface = "ens10"
      labels = [
        "node.kubernetes.io/server-usage=monitor"
      ]
      taints = [
        "node-role.kubernetes.io/monitor:NoSchedule"
      ]
    }
  ]
}
```

{{< /highlight >}}

### Prometheus Stack

When using k8s, the standard de facto is to install [Prometheus stack](https://artifacthub.io/packages/helm/prometheus-community/kube-prometheus-stack). It includes all necessary CRDs and components for a proper monitoring stack.

You have 2 choices to install it, are we using Flux or Terraform ? Flux include a full documentation of [how to install it with](https://fluxcd.io/flux/guides/monitoring/).

But remember previous chapter with the house analogies. I personally consider monitoring as part of my infrastructure. And I prefer to keep all my infrastructure configuration in Terraform, and only use Flux for apps. Moreover, the Prometheus stack is a pretty big Helm chart, and upgrading it can be a bit tricky. So I prefer to have a full control of it with Terraform.

Go back to 2nd Terraform project and let's apply this pretty big boy:

{{< highlight host="demo-kube-k3s" file="monitoring.tf" >}}

```tf
resource "kubernetes_namespace_v1" "monitoring" {
  metadata {
    name = "monitoring"
  }
}

resource "helm_release" "kube_prometheus_stack" {
  chart      = "kube-prometheus-stack"
  version    = "49.2.0"
  repository = "https://prometheus-community.github.io/helm-charts"

  name      = "kube-prometheus-stack"
  namespace = kubernetes_namespace_v1.monitoring.metadata[0].name

  set {
    name  = "prometheus.prometheusSpec.retention"
    value = "15d"
  }

  set {
    name  = "prometheus.prometheusSpec.retentionSize"
    value = "5GB"
  }

  set {
    name  = "prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues"
    value = "false"
  }

  set {
    name  = "prometheus.prometheusSpec.podMonitorSelectorNilUsesHelmValues"
    value = "false"
  }

  set {
    name  = "prometheus.prometheusSpec.enableRemoteWriteReceiver"
    value = "true"
  }

  set {
    name  = "prometheus.prometheusSpec.storageSpec.volumeClaimTemplate.spec.accessModes[0]"
    value = "ReadWriteOnce"
  }

  set {
    name  = "prometheus.prometheusSpec.storageSpec.volumeClaimTemplate.spec.resources.requests.storage"
    value = "8Gi"
  }

  set {
    name  = "prometheus.prometheusSpec.tolerations[0].key"
    value = "node-role.kubernetes.io/storage"
  }

  set {
    name  = "prometheus.prometheusSpec.tolerations[0].operator"
    value = "Exists"
  }

  set {
    name  = "prometheus.prometheusSpec.nodeSelector.node\\.kubernetes\\.io/server-usage"
    value = "monitor"
  }

  set {
    name  = "alertmanager.enabled"
    value = "false"
  }

  set {
    name  = "grafana.enabled"
    value = "false"
  }

  set {
    name  = "grafana.forceDeployDatasources"
    value = "true"
  }

  set {
    name  = "grafana.forceDeployDashboards"
    value = "true"
  }
}
```

{{< /highlight >}}

The application is deployed under `monitoring` namespace. It takes few minutes to be fully up and running. You can check the status with `kgpo -n monitoring`.

Important notes:

* We set a retention of **15 days** and **5 GB** of storage for Prometheus. Set this according to your needs.
* We allow `serviceMonitorSelector` and `podMonitorSelector` for scrapping monitor CRDs from all namespaces.
* We set `enableRemoteWriteReceiver` to allow remote write to databases for advanced specific usage, as by default Prometheus works with pull model on its own.
* As we don't set any storage class, the default one will be used, which is `local-path` when using K3s. If you want to use longhorn instead and benefit of automatic monitoring backup, you can set it with `...volumeClaimTemplate.spec.storageClassName`. But don't forget to deploy Longhorn manager by adding monitor toleration.
* As it's a huge chart, I want to minimize dependencies by disabling Grafana, as I prefer manage it separately. However, in this case we may set `grafana.forceDeployDatasources` and `grafana.forceDeployDashboards` to `true` in order to benefit of all included Kubernetes dashboards and automatic Prometheus datasource injection, and deploy them to config maps that can be used for next Grafana install by provisioning.

{{< alert >}}
As Terraform plan become slower and slower, you can directly apply one single resource by using `target` option. For example for applying only Prometheus stack, use `terraform apply -target=helm_release.kube_prometheus_stack`. It will save you a lot of time for testing.
{{< /alert >}}

And finally the ingress for external access:

{{< highlight host="demo-kube-k3s" file="monitoring.tf" >}}

```tf
resource "kubernetes_manifest" "prometheus_ingress" {
  manifest = {
    apiVersion = "traefik.io/v1alpha1"
    kind       = "IngressRoute"
    metadata = {
      name      = "prometheus"
      namespace = kubernetes_namespace_v1.monitoring.metadata[0].name
    }
    spec = {
      entryPoints = ["websecure"]
      routes = [
        {
          match = "Host(`prometheus.${var.domain}`)"
          kind  = "Rule"
          middlewares = [
            {
              name      = "middleware-ip"
              namespace = "traefik"
            },
            {
              name      = "middleware-auth"
              namespace = "traefik"
            }
          ]
          services = [
            {
              name = "prometheus-operated"
              port = "http-web"
            }
          ]
        }
      ]
    }
  }
}
```

{{< /highlight >}}

No go to `prometheus.kube.rocks`, after login you should access the Prometheus UI. Check under `/targets` that all targets are up and running. In previous chapters, because we have enabled monitoring for all our apps supporting metrics, you should see following available targets:

* 1 instance of Traefik
* 1 instance of cert-manager
* 1 instance of each PostgreSQL primary and read
* 2 instances of Redis
* 5 instances of Longhorn manager

This is exactly how it works, the `ServiceMonitor` custom resource is responsible to discover and centralize all metrics for prometheus, allowing automatic discovery without touch the Prometheus config. Use `kg smon -A` to list them all.

### Monitoring Flux

There is one missing however, let's add monitoring for flux. Go back to flux project and push following manifests:

{{< highlight host="demo-kube-flux" file="clusters/demo/flux-add-ons/flux-monitoring.yaml" >}}

```yaml
---
apiVersion: source.toolkit.fluxcd.io/v1
kind: GitRepository
metadata:
  name: flux-monitoring
  namespace: flux-system
spec:
  interval: 30m0s
  ref:
    branch: main
  url: https://github.com/fluxcd/flux2-monitoring-example
---
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
  name: monitoring-config
  namespace: flux-system
spec:
  interval: 1h0m0s
  path: ./monitoring/configs
  prune: true
  sourceRef:
    kind: GitRepository
    name: flux-monitoring
```

{{< /highlight >}}

The `spec.path` under `Kustomization` tells Flux to scrape [remote monitoring manifests](https://github.com/fluxcd/flux2-monitoring-example/tree/main/monitoring/configs), avoiding us to write all of them manually. It includes the `PodMonitor` as well as Grafana dashboards.

After some minutes, flux should be appearing in Prometheus targets.

[![Prometheus targets](prometheus-targets.png)](prometheus-targets.png)

### Grafana

We have the basement of our monitoring stack, it's time to get a UI to visualize all these metrics. Grafana is the most popular tool for that, and it's also available as Helm chart. Prepare some variables:

{{< highlight host="demo-kube-k3s" file="main.tf" >}}

```tf
variable "smtp_host" {
  sensitive = true
}

variable "smtp_port" {
  type = string
}

variable "smtp_user" {
  type      = string
  sensitive = true
}

variable "smtp_password" {
  type      = string
  sensitive = true
}

variable "grafana_db_password" {
  type      = string
  sensitive = true
}
```

{{< /highlight >}}

Create `grafana` database through pgAdmin with same user and according `grafana_db_password`.

{{< highlight host="demo-kube-k3s" file="terraform.tfvars" >}}

```tf
smtp_host            = "smtp.mailgun.org"
smtp_port            = "587"
smtp_user            = "xxx"
smtp_password        = "xxx"
```

{{< /highlight >}}

Apply next configuration to Terraform project:

{{< highlight host="demo-kube-k3s" file="grafana.tf" >}}

```tf
resource "helm_release" "grafana" {
  chart      = "grafana"
  version    = "6.58.9"
  repository = "https://grafana.github.io/helm-charts"

  name      = "grafana"
  namespace = kubernetes_namespace_v1.monitoring.metadata[0].name

  set {
    name  = "serviceMonitor.enabled"
    value = "true"
  }

  set {
    name  = "sidecar.datasources.enabled"
    value = "true"
  }

  set {
    name  = "sidecar.dashboards.enabled"
    value = "true"
  }

  set {
    name  = "env.GF_SERVER_DOMAIN"
    value = var.domain
  }

  set {
    name  = "env.GF_SERVER_ROOT_URL"
    value = "https://grafana.${var.domain}"
  }

  set {
    name  = "env.GF_SMTP_ENABLED"
    value = "true"
  }

  set {
    name  = "env.GF_SMTP_HOST"
    value = "${var.smtp_host}:${var.smtp_port}"
  }

  set {
    name  = "env.GF_SMTP_USER"
    value = var.smtp_user
  }

  set {
    name  = "env.GF_SMTP_PASSWORD"
    value = var.smtp_password
  }

  set {
    name  = "env.GF_SMTP_FROM_ADDRESS"
    value = "grafana@${var.domain}"
  }

  set {
    name  = "env.GF_DATABASE_TYPE"
    value = "postgres"
  }

  set {
    name  = "env.GF_DATABASE_HOST"
    value = "postgresql-primary.postgres"
  }

  set {
    name  = "env.GF_DATABASE_NAME"
    value = "grafana"
  }

  set {
    name  = "env.GF_DATABASE_USER"
    value = "grafana"
  }

  set {
    name  = "env.GF_DATABASE_PASSWORD"
    value = var.grafana_db_password
  }
}

resource "kubernetes_manifest" "grafana_ingress" {
  manifest = {
    apiVersion = "traefik.io/v1alpha1"
    kind       = "IngressRoute"
    metadata = {
      name      = "grafana"
      namespace = kubernetes_namespace_v1.monitoring.metadata[0].name
    }
    spec = {
      entryPoints = ["websecure"]
      routes = [
        {
          match = "Host(`grafana.${var.domain}`)"
          kind  = "Rule"
          services = [
            {
              name = "grafana"
              port = "service"
            }
          ]
        }
      ]
    }
  }
}
```

{{< /highlight >}}

We enable both data source and dashboard sidecars by setting `sidecar.datasources.enabled` and `sidecar.dashboards.enabled`. These sidecars will automatically inject all dashboards and data sources from `ConfigMap`, like those provided by Prometheus stack and Flux. `serviceMonitor.enabled` will create a `ServiceMonitor` for Prometheus to scrape Grafana metrics.

Grafana should be deploying and migrate database successfully. Let's log in immediately after in `https://grafana.kube.rocks/login` with admin account. You can get the password with `kg secret -n monitoring grafana -o jsonpath='{.data.admin-password}' | base64 -d`.

### Native dashboards

If you go to `https://grafana.kube.rocks/dashboards`, you should see a many dashboards available that should already perfectly work, giving you a complete vision of:

* Some core components of K8s, like coredns, kube api server, all kubelets
* Detail of pods, namespace, workloads
* Nodes thanks to Node exporter
* Prometheus and Grafana itself stats
* Flux stats

#### Prometheus

[![Prometheus](dashboards-prometheus.png)](dashboards-prometheus.png)

#### Nodes

[![Nodes](dashboards-nodes.png)](dashboards-nodes.png)

#### Cluster

[![Cluster compute](dashboards-cluster-compute.png)](dashboards-cluster-compute.png)
[![Cluster networks](dashboards-cluster-network.png)](dashboards-cluster-network.png)
[![Pods](dashboards-pods.png)](dashboards-pods.png)

#### Kube components

[![Kube API Server](dashboards-api-server.png)](dashboards-api-server.png)
[![Kubelets](dashboards-kubelets.png)](dashboards-kubelets.png)
[![CoreDNS](dashboards-coredns.png)](dashboards-coredns.png)

#### Flux

[![Flux](dashboards-flux.png)](dashboards-flux.png)

### Additional dashboards

You can easily import some additional dashboards by importing them from Grafana marketplace or include them in `ConfigMap` for automatic provisioning.

#### Traefik

[Link](https://grafana.com/dashboards/17346)

[![Traefik](dashboards-traefik.png)](dashboards-traefik.png)

#### cert-manager

[Link](https://github.com/monitoring-mixins/website/blob/master/assets/cert-manager/dashboards/cert-manager.json)

[![cert-manager](dashboards-cert-manager.png)](dashboards-cert-manager.png)

#### Longhorn

[Link](https://grafana.com/dashboards/16888)

[![Longhorn](dashboards-longhorn.png)](dashboards-longhorn.png)

#### PostgreSQL

[Link](https://grafana.com/dashboards/9628)

[![PostgreSQL](dashboards-postgresql.png)](dashboards-postgresql.png)

#### Redis

[Link](https://grafana.com/dashboards/763)

[![Redis](dashboards-redis.png)](dashboards-redis.png)

#### Other core components

Some other core components like etcd, scheduler, proxy, and controller manager need to have metrics enabled to be scraped. See K3s docs or [this issue](https://github.com/k3s-io/k3s/issues/3619).

From Terraform Hcloud project, use `control_planes_custom_config` for expose all remaining metrics endpoint:

{{< highlight host="demo-kube-hcloud" file="kube.tf" >}}

```tf
module "hcloud_kube" {
  //...

  control_planes_custom_config = {
    etcd-expose-metrics         = true,
    kube-scheduler-arg          = "bind-address=0.0.0.0",
    kube-controller-manager-arg = "bind-address=0.0.0.0",
    kube-proxy-arg              = "metrics-bind-address=0.0.0.0",
  }

  //...
}
```

{{< /highlight >}}

{{< alert >}}
As above config applies only at cluster initialization, you may change directly `/etc/rancher/k3s/config.yaml` instead and restart K3s server.
{{< /alert >}}

## Logging

Last but not least, we need to add a logging stack. The most popular one is [Elastic Stack](https://www.elastic.co/elastic-stack), but it's very resource intensive. A more lightweight option is to use [Loki](https://grafana.com/oss/loki/), also part of Grafana Labs.

In order to work on scalable mode, we need to have a S3 storage backend. We will reuse same S3 compatible storage as longhorn backup here, but it's recommended to use a separate bucket and credentials.

### Loki

Let's install it now:

{{< highlight host="demo-kube-k3s" file="logging.tf" >}}

```tf
resource "kubernetes_namespace_v1" "logging" {
  metadata {
    name = "logging"
  }
}

resource "helm_release" "loki" {
  chart      = "loki"
  version    = "5.15.0"
  repository = "https://grafana.github.io/helm-charts"

  name      = "loki"
  namespace = kubernetes_namespace_v1.logging.metadata[0].name

  set {
    name  = "loki.auth_enabled"
    value = "false"
  }

  set {
    name  = "loki.compactor.retention_enabled"
    value = "true"
  }

  set {
    name  = "loki.limits_config.retention_period"
    value = "24h"
  }

  set {
    name  = "loki.storage.bucketNames.chunks"
    value = var.s3_bucket
  }

  set {
    name  = "loki.storage.bucketNames.ruler"
    value = var.s3_bucket
  }

  set {
    name  = "loki.storage.bucketNames.admin"
    value = var.s3_bucket
  }

  set {
    name  = "loki.storage.s3.endpoint"
    value = var.s3_endpoint
  }

  set {
    name  = "loki.storage.s3.region"
    value = var.s3_region
  }

  set {
    name  = "loki.storage.s3.accessKeyId"
    value = var.s3_access_key
  }

  set {
    name  = "loki.storage.s3.secretAccessKey"
    value = var.s3_secret_key
  }

  set {
    name  = "read.replicas"
    value = "1"
  }

  set {
    name  = "backend.replicas"
    value = "1"
  }

  set {
    name  = "write.replicas"
    value = "2"
  }

  set {
    name  = "write.tolerations[0].key"
    value = "node-role.kubernetes.io/storage"
  }

  set {
    name  = "write.tolerations[0].effect"
    value = "NoSchedule"
  }

  set {
    name  = "write.nodeSelector.node-role\\.kubernetes\\.io/storage"
    type  = "string"
    value = "true"
  }

  set {
    name  = "monitoring.dashboards.namespace"
    value = kubernetes_namespace_v1.monitoring.metadata[0].name
  }

  set {
    name  = "monitoring.selfMonitoring.enabled"
    value = "false"
  }

  set {
    name  = "monitoring.selfMonitoring.grafanaAgent.installOperator"
    value = "false"
  }

  set {
    name  = "monitoring.lokiCanary.enabled"
    value = "false"
  }

  set {
    name  = "test.enabled"
    value = "false"
  }
}
```

{{< /highlight >}}

Use `loki.limits_config.retention_period` to set a maximum period retention. You need to set at least **2** for `write.replicas` or you'll get this 500 API error "*too many unhealthy instances in the ring*". As we force them to be deployed on storage nodes, be sure to have 2 storage nodes.

### Promtail

Okay so Loki is running but not fed, for that we'll deploy [Promtail](https://grafana.com/docs/loki/latest/clients/promtail/), which is a log collector that will be deployed on each node and collect logs from all pods and send them to Loki.

{{< highlight host="demo-kube-k3s" file="logging.tf" >}}

```tf
resource "helm_release" "promtail" {
  chart      = "promtail"
  version    = "6.15.0"
  repository = "https://grafana.github.io/helm-charts"

  name      = "promtail"
  namespace = kubernetes_namespace_v1.logging.metadata[0].name

  set {
    name  = "tolerations[0].effect"
    value = "NoSchedule"
  }

  set {
    name  = "tolerations[0].operator"
    value = "Exists"
  }

  set {
    name  = "serviceMonitor.enabled"
    value = "true"
  }
}
```

{{< /highlight >}}

Ha, finally a simple Helm chart ! Seems too good to be true. We just have to add generic `tolerations` in order to deploy Promtail `DaemonSet` on every node for proper log scrapping.

### Loki data source

Because we are GitOps, we want to have all Loki dashboards and data sources automatically configured. It's already done for dashboards, but we need to add a data source.

Let's apply next Terraform resource:

{{< highlight host="demo-kube-k3s" file="logging.tf" >}}

```tf
resource "kubernetes_config_map_v1" "loki_grafana_datasource" {
  metadata {
    name      = "loki-grafana-datasource"
    namespace = kubernetes_namespace_v1.monitoring.metadata[0].name
    labels = {
      grafana_datasource = "1"
    }
  }

  data = {
    "datasource.yaml" = <<EOF
apiVersion: 1
datasources:
- name: Loki
  type: loki
  uid: loki
  url: http://loki-gateway.logging/
  access: proxy
EOF
  }
}
```

{{< /highlight >}}

Now go to `https://grafana.kube.rocks/connections/datasources/edit/loki` and ensure that Loki respond correctly by click on *Test*.

Go can now admire logs in Loki UI at `https://grafana.kube.rocks/explore` !

[![Loki explore](loki-explore.png)](loki-explore.png)

### Loki dashboards

We have nothing more to do, all dashboards are already provided by Loki Helm chart.

[![Loki explore](dashboards-loki.png)](dashboards-loki.png)

## 5th check ✅

We now have a full monitoring suite with performant logging collector ! What a pretty massive subject done. At this stage, you have a good starting point to run many apps on your cluster with high scalability and observability. We are done for the pure **operational** part. It's finally time to tackle the **building** part for a complete development stack. Go [next part]({{< ref "/posts/16-a-beautiful-gitops-day-6" >}}) to begin with continuous integration.
