• Guarantee up-time in the event I need to switch due to cost increases
• Re-deploy the stack to a client that has adopted another cloud provider

Anything that I find I can monitor or pull metrics from ends up in Grafana. I currently use three data sources with dashboards and alerts pulling and presenting information from all of them;

• Prometheus: For monitoring real-time metrics such as CPU usage and weather
• InfluxDB: For storing and monitoring historical metrics, such as stock market data
• Loki: For monitoring system logs
• Elasticsearch: For storing transactions, documents and RSS feeds.

I chose a Managed Kubernetes offering as a basis for deployment as opposed to virtual machines or self-hosted Kubernetes for two reasons;

• Uptime is guaranteed by the vendor
• I don’t have to maintain a Kubernetes cluster at a systems-level

Deploying a DigitalOcean Kubernetes Cluster

Droplet layout

I’m deploying my stack with two dedicated right-sized nodes living in two separate node pools. One is labelled as fixed and will contain only one instance, whereas the other is scalable with a maximum of three. With this design, I accomplish three essentials for the architecture;

• Cost Efficiency
  • I prevent over-provisioning by right-sizing, and rely on DigitalOcean’s control plane to scale the node pool when necessary, such as a larger dataset being held in memory by a data source
• Availability & Scalability
  • At-least one nodes should be available at all time, allowing for a single node failure to keep applications running. This is separate from a HA Control Plane, which offers a different benefit.

Cluster Location

lon1 is about 17km away from where I live, so I’ve deployed my cluster there. The location is not really important for my use case as I plan to do all data ingestion from within, but it’s cool to think it’s just a few streets down from me.

I played with the idea of taking availability even further by deploying to both ams1 and lon1 in a warm-standby style, but that’s a story for another post.

DOKS Cluster Resource in Terraform

resource "digitalocean_kubernetes_cluster" "primary" {
  name = "zai-lon1"
  region = "lon1"
  version = "1.30.4-do.0"
  vpc_uuid = digitalocean_vpc.primary.id
  auto_upgrade = false
  surge_upgrade = true
  ha = false
  registry_integration = true

  node_pool {
    name = "k8s-lon1-dedicated"
    size = "s-1vcpu-1gb-amd"
    node_count = 1
  }

  node_pool {
    name = "k8s-lon1-burst"
    size = "s-1vcpu-1gb-amd"
    node_count = 1
  }

  tags = local.resource_tags

  maintenance_policy {
    day = "saturday"
    start_time = "04:00"
  }
}

Deploying A Load Balancer

Traefik is my load balancer of choice. It’s written in Go, performant and integrates very well into the Kubernetes ecosystem. I’m not using it as a load balancer but as an ‘application gateway’, so that I can have a single IP address handle routing to many Kubernetes services based on HTTP Headers such as domain names or paths.

I’m also using it as a front for all of the web services in the cluster, so I can manage TLS certificates from the same location for all applications, not a unique configuration per-application. I’m not so concerned about inter-service TLS communication at this point, but rather over the public internet.

Traefik has its’ own form of an Operator that works with the Ingress resource definition within Kubernetes. When a resource is created, Traefik will automatically create a route based on the specification. This means that I can easily declare that https://grafana.zai.dev on the LoadBalancer will route to the grafana service on port 80.

The Traefik helm chart by default creates a LoadBalancer resource, which configures DigitalOcean to reserve a static public IP address that can be reached from the public internet. I don’t need to provide any additional configuration.

resource "helm_release" "traefik" {
  name       = "traefik"
  repository = "https://traefik.github.io/charts"
  chart      = "traefik"
  version    = "30.1.0"
}

Deploying an Identity Provider

Authentication is required since Grafana is accessible from the public. With the same mindset for applying Traefik, I’d like to centrally control authentication & authorization rather than defining it on a per-application level.

I adopted Keycloak as it acts as an Identity Provider and / or Broker, supporting both OpenID Connect and SAML. OIDC is a common standard across many apps, including Grafana.

I use GitHub as an Identity Provider for Keycloak, and Keycloak as an Identity Provider for Grafana. I take this approach as it;

• Allows me to integrate more OIDC or SAML compatible applications into my own provider
• Reduces management of external accounts to a single point (rather than configuring GitHub per-application)
• Allows me to add additional roles on-top of GitHub accounts required for Grafana to recognize who’s an Administrator.
• Allows me to integrate LDAP in the future

I won’t go through deploying the Keycloak configuration in this post (a future one is coming with more detail on configuring Keycloak), but based on the OAuth2 specification, I have available to me the client_id, client_secret, auth_url, token_url and api_url that I pipe into the grafana.ini in the next stage. I can receive these details from GitHub directly by creating an OAuth2 application.

Deploying Grafana with Helm

To be agile in my deployments, I’m isolating the Grafana container from any configuration by deploying any configuration to Grafana through ConfigMaps. With that, I can truly version control the running version of Grafana without worrying about losing any stored work such as dashboards.

Provisionable elements, such as Dashboards, Alerts and Datasources can be loaded into Grafana by using its’ provisioning directory, defined below as /etc/grafana/provisioning. By using Kubernetes ConfigMaps, I can mount my configuration into Grafana outside of the instance itself.

By enabling the sidecar containers, I save myself from needing to maintain this volumeMount, as these act as operators monitoring for ConfigMaps with specific labels (described below), mounting them into the Grafana pod and instructing Grafana to reload the configuration without restarting the instance.

Within the grafana.ini file, auth.generic_oauth instructs grafana how to connect with an identity provider. Here, I pipe in the values received from Keycloak (or GitHub) above. To force that credentials are given from Keycloak, I enforce the disable_login_form setting.

The $__file{} operator reads a variable from a file on disk, allowing me to further protect the OAuth2 credentials by storing them in a Secret. I use HashiCorp Vault to protect secrets through ServiceAccount, but that’s outside the scope of this post.

role_attribute_path allows me to map user roles defined within Keycloak to Grafana roles, allowing me to centralize “how to define an administrator” across multiple applications, while scopes instructs Keycloak on what data Grafana requires in order to successfully authenticate and authorize.

Finally, ingress is the bridge between the Grafana instance and the load balancer. Within the Helm chart, an Ingress resource will be created that will point to the Service created by the chart, accessible on the domain grafana.zai.dev.

tls provides instructions on how to load the TLS Certificate associated with grafana.zai.dev. In my case, I store the certificate inside a Secret named grafana-tls.

resource "helm_release" "grafana" {
  name       = local.grafana_deployment_name
  repository = local.grafana_repository
  chart      = "grafana"
  version    = var.grafana_chart_version

  values = [
    yamlencode({ "grafana.ini" = {
      analytics = {
        check_for_updates = true
      },
      grafana_net = {
        url = "https://grafana.net"
      },
      log = {
        mode = "console"
      },
      paths = {
        data         = "/var/lib/grafana/",
        logs         = "/var/log/grafana",
        plugins      = "/var/lib/grafana/plugins",
        provisioning = "/etc/grafana/provisioning"
      },
      server = {
        domain   = "grafana.zai.dev",
        root_url = "https://grafana.zai.dev"
      },
      "auth.generic_oauth" = {
        enabled             = true,
        name                = "Keycloak",
        allow_sign_up       = true,
        client_id           = "$__file{/etc/secrets/oidc_credentials/id}",
        client_secret       = "$__file{/etc/secrets/oidc_credentials/secret}",
        disable_login_form  = true
        auth_url            = "$__file{/etc/secrets/oidc_credentials/auth_url}",
        token_url           = "$__file{/etc/secrets/oidc_credentials/token_url}",
        api_url             = "$__file{/etc/secrets/oidc_credentials/api_url}",
        scopes              = "openid profile email offline_access roles",
        role_attribute_path = "contains(realm_access.roles[*], 'admin') && 'Admin' || contains(realm_access.roles[*], 'editor') && 'Editor' || 'Viewer'"
      } 
      },
      "sidecar" = {
        "datasources" = { "enabled" = true },
        "alerts" = { "enabled" = true },
        "dashboards" = { "enabled" = true }
      },
      ingress = {
        enabled = true,
        hosts   = ["grafana.zai.dev"]
        tls = [
          {
            secretName = "grafana-tls",
            hosts = ["grafana.zai.dev"]
          }
        ]
      },
      assertNoLeakedSecrets = false,
    })
  ]
}

Deploying Datasources for Grafana

[]PersistentVolume are key to reliability. Without these, each data source has nowhere to store their data across crashes or reboot. All the Helm charts for each data source, by default, create a PersistentVolumeClaim and rely on the creation of a PersistentVolume with matching labels by an external factor, human or automated.

Deploying Prometheus

Prometheus is ideal for alerting on real-time numeric metrics, and doesn’t require much configuration in a small facility configuration. It includes the entire prometheus stack: AlertManager, Push Gateway and a node metrics exporter.

It contains an operator that provides Kubernetes service discovery by hooking into onto the Service creation loop and looks for prometheus.io/* annotations, and instructs prometheus to start scraping from them. At a minimum, these annotations look like;

• prometheus.io/scrape=true
  • Tells prometheus to actively scrape this Service
• prometheus.io/path=/metrics
  • Prometheus scrapes on HTTP. It will request this path.
• prometheus.io/port=9090
  • Prometheus will connect to a HTTP server on this port within the services’ Endpoint

This means that I don’t have to modify Prometheus configuration directly when expanding the services that my Kubernetes cluster is hosting. By simple appending annotations to any new services that expose metrics in OpenTelemetry format, I will immediately get data visible within Grafana from it.

resource "helm_release" "prometheus" {
  name       = "prometheus"
  repository = "https://prometheus-community.github.io/helm-charts"
  chart      = "prometheus"
  version    = "25.26.0"
}

Deploying Elasticsearch

Elasticsearch is great for analyzing documents and transactions where the data-type varies. It’s defined as a search engine. I use this data source for analyzing articles and stock market transactions.

My first problem was how resource-hungry Elasticsearch is in its’ nature. I had to dial down it’s memory usage to match the amount of content I was putting it through. 512Mb appears to be the right number for it to function as 256Mb causes it to fail to initialize. Increasing this value alongside the replicas value will give me higher availability.

Because of the 512Mb limit, I had to upsize my Kubernetes node as it would report that there was insufficient memory to deploy Elasticsearch.

To get data into Elasticsearch, I use the Elasticsearch Telegraf exporter and connect the input either RabbitMQ, a web socket or a HTTP polling feed. When I’m generating data through Python or Node.JS, I don’t push the data directly from the code, rather pushing the data through RabbitMQ for Telegraf to handle. I do this so that I can throttle the amount of data going through to elasticsearch that may take the service down.

resource "helm_release" "elasticsearch" {
  name       = "elasticsearch"
  repository = "https://helm.elastic.co"
  chart      = "elasticsearch"
  version    = "8.5.1"

  set {
    name  = "replicas"
    value = 1
  }

  set {
    name = "resources.requests.memory"
    value = "1Gi"
  }

  set {
    name = "resources.limits.memory"
    value = "1Gi"
  }

  set {
    name = "heapSize"
    value = "512Mi"
  }

  set {
    name  = "minimumMasterNodes"
    value = 1
  }

  set {
    name  = "volumeClaimTemplate.resources.requests.storage"
    value = "4Gi"
  }

  set {
    name = "cluster.initialMasterNodes"
    value = "elasticsearch-master"
  }
}

• cluster.initialMasterNodes is needed in this helm chart as it instructs Elasticsearch to “find itself”. elasticsearch-master is the name of the Kubernetes Service that gets created, and in turn, will instruct the kube-dns service to return the IP Address of the elasticsearch instance when requesting elasticsearch-master.
• I restrict the size of the DigitalOcean volume through volumeClaimTemplate.resources.requests.storage, as by default it’s around 20Gi.
• minimumMasterNodes and replicas are restricted to 1 as I don’t need more than one instance of Elasticsearch. If I increase the amount of replicas and begin to shard, Grafana shouldn’t need additional configuration to cater for that.

Deploying InfluxDB

InfluxDB is my time-series database of choice when working with historical data that will need batch processing at some point (e.g: Grafana Alerting), such as Apple HealthKit and stock market data. Flux, the syntax used by InfluxDB, is extremely powerful in comparison to PromQL. But with more complexity comes a performance hit.

I also use Telegraf to ingest data into InfluxDB, with inputs pointing solely at RabbitMQ. I use NodeJS to listen to websocket streams and push data points to RabbitMQ for ingestion. Because of the amount of streaming data I plan to put into InfluxDB, I set persistence.size to a high amount of 12GB.

As the chart version hadn’t been updated in a while, using an image tag that was causing me some errors, I manually set the image.tag to the latest available version.

resource "helm_release" "influx" {
  name = "influxdb"
  repository = "https://helm.influxdata.com/"
  chart = "influxdb2"
  version = "2.1.2"

  set {
    name = "image.tag"
    value = "2.7.10"
  }

  set {
    name = "persistence.size"
    value = "12Gi"
  }
}

Deploying Loki

Loki is the most complex to configure, but I find it more intuitive (for Grafana) as a way to store system and application logs. I deploy it in a single binary configuration, and use DigitalOcean Spaces as the backend storage for logs themselves. Relying on a block storage may prove problematic as millions of messages would require constant re-provisioning of storage.

resource "helm_release" "loki" {
  name = "loki"
  repository = "https://grafana.github.io/helm-charts"
  chart = "loki"
  version = "6.18.0"

  values = [
    yamlencode({
        loki = {
          commonConfig = {
            replication_factor = 1
          }
          storage = {
            type = "s3"
            bucketNames = {
              chunks = "<SPACES_BUCKET>",
              ruler = "<SPACES_BUCKET>",
              admin = "<SPACES_BUCKET>",
            },
            s3 = {
              s3 = "s3://<SPACES_URL>",
              endpoint = "lon1.digitaloceanspaces.com",
              region = "lon1",
              secretAccessKey = "<SPACES_KEY>",
              accessKeyId = "<SPACES_ID>",
            }
          }
          schemaConfig = {
            configs = [
              {
                from = "2024-04-01",
                store = "tsdb",
                object_store = "s3",
                schema = "v13",
                index = {
                  "prefix" = "loki_index_",
                  "period" = "24h"
                }
              }
            ]
          },
        },
        deploymentMode = "SingleBinary",
        backend = { replicas = 0 },
        read = { replicas = 0 },
        write = { replicas = 0 },
        singleBinary = { replicas = 1 },
        chunksCache = { allocatedMemory = 2048 }
      })
  ]
}

Pushing logs to Loki

Loki exposes an API Endpoint for pushing logs to like the Prometheus Push Gateway, which accepts logs in a OpenTelemetry-compatible format. One tool, Promtail, will follow all container logs created by all pods in a Kubernetes cluster and stream them to the Loki push API.

resource "helm_release" "promtail" {
  name = "promtail"
  repository = "https://grafana.github.io/helm-charts"
  chart = "promtail"
  version = "6.16.6"

  values = [
    yamlencode({
        config = {
          clients = [{url = "http://loki-gateway/loki/api/v1/push", tenant_id = "zai"}]
        }
      })
  ]
}

• loki-gateway is the default name of the Kubernetes Service created by the Loki helm chart. The kube-dns service will return the Endpoint IP Address of the Loki instance.

Deploying Provisioned Components for Grafana

Deploying Grafana with sidecar containers provisions operators that listen for []ConfigMap with specific labels for Dashboards, Alerts and Datasources. Simply, it takes the value of the ConfigMap and puts it into Grafana’s provisioning directory.

Grafana’s provisioning directory is defined by paths.provisioning within grafana.ini, which can be injected upon deploying the Grafana helm chart within the "grafana.ini" key. In my case, this path is /etc/grafana/provisioning.

Grafana natively will read its’ provisioning directory and load them into the instance, regardless if its’ containerized or running on the system directly.

Provisioning Dashboards

For dashboards, a label of grafana_dashboard needs to exist, but the value is irrelevant. I use templatefile() to load the file as string into main.json. This will allow me in the future to handle the renaming of data sources used within a Dashboard, or to manipulate a dashboard directly from Terraform.

I design dashboards directly within Grafana, export them as JSON and store them alongside the Terraform module for use by the ConfigMap. In the following resource, my exported dashboard will end up under /etc/grafana/provisioning/main.json.

Within the export menu, Grafana does provide the option to export using HCL (Terraform). I don’t opt for this option as this requires Grafana to be up and running in order to execute the resource. With the approach of declaring Dashboards via ConfigMap, I can re-deploy the dashboard in one go and remove the direct dependency on the Grafana instance running.

resource "kubernetes_config_map" "grafana_dashboards" {
  metadata {
    name = "grafana-dashboards"
    labels = {
      grafana_dashboard = "1"
    }
  }

  data = {
    "main.json" = templatefile("/path/to/dashboard.json", {})
  }
}

Provisioning Alerts

I follow the same approach as above for declaring alerts, with the exception that grafana_alert is the expected label from the sidecar.

resource "kubernetes_config_map" "grafana_alerts" {
  metadata {
    name = "grafana-alerts"
    labels = {
      grafana_alert = "1"
    }
  }

  data = {
    "alerts.json" = templatefile("/path/to/alert.json", {})
  }
}

Provisioning Data-sources

I build the configuration myself when it comes to data sources. The specification varies between each data source. Thanks to using Terraform to deploy each data source, I can re-use the variables used to define the Service names of each data source so that Grafana can find them correctly.

Provisioning Prometheus as a data source

resource "kubernetes_config_map" "prometheus_grafana_discovery" {
  metadata {
    name = "prometheus-grafana-datasource"
    labels = {
      grafana_datasource = "prometheus"
    }
  }

  data = {
    "prometheus.yml" = yamlencode({
        apiVersion = 1,
        datasources = [
          {
            name = var.prometheus_deployment_name,
            type = "prometheus",
            url = "http://${var.prometheus_deployment_name}.${helm_release.prometheus.namespace}.svc.cluster.local",
            access = "proxy"
          }
        ]
    })
  }
}

With the above resource declared from Kubernetes, I then just manipulate datasources = [] to match the following specifications for each datasource;

Specification for Loki

"apiVersion": 1
"datasources":
- "jsonData":
    "httpHeaderName1": "X-Scope-OrgID"
  "name": "prometheus-server"
  "secureJsonData":
    "httpHeaderValue1": "1"
  "type": "loki"
  "url": "http://loki.default.svc.cluster.local"

• X-Scope-OrgID is a trick to inject an Organization ID into the HTTP Header so that Grafana gets authenticated by Loki.
• loki is the default name of the Kubernetes service created by the Helm chart

Specification for Elasticsearch

Elasticsearch needs one declaration per index (if splitting the data by index). I create an index for each source of data being ingested into Elastic, and postfix it with the date of ingestion.

For authentication, I use the password for the elastic as defined by the Helm chart. By default, this is randomly generated and stored within a Secret. I also use the tlsSkipVerify flag as additional configuration is needed for elasticsearch to use a TLS certificate that’s respected by Grafana. Since the traffic is internal, I’m not that concerned by this at this point.

elasticsearch-master is the default name of the service created by the Helm chart.

"apiVersion": 1
"datasources":
- "basicAuth": true
  "basicAuthUser": "elastic"
  "jsonData":
    "index": "twelvedata-*"
    "timeField": "@timestamp"
    "tlsSkipVerify": true
  "name": "Elasticsearch (Twelve Data)"
  "secureJsonData":
    "basicAuthPassword": "<ELASTIC_PASSWORD>"
  "type": "elasticsearch"
  "url": "https://elasticsearch-master:9200"
- "basicAuth": true
  "basicAuthUser": "elastic"
  "jsonData":
    "index": "coinbase-*"
    "timeField": "@timestamp"
    "tlsSkipVerify": true
  "name": "Elasticsearch (Coinbase)"
  "secureJsonData":
    "basicAuthPassword": "<ELASTIC_PASSWORD>"
  "type": "elasticsearch"
  "url": "https://elasticsearch-master:9200"

Specification for InfluxDB

"apiVersion": 1
"datasources":
- "jsonData":
    "default_bucket": "default"
    "organization": "influxdata"
    "version": "Flux"
  "name": "InfluxDB"
  "secureJsonData":
    "token": "<API KEY>"
  "type": "influxdb"
  "url": "http://influxdb-influxdb2:80"

• The Flux version forces InfluxDB v2, which in turn requires a default_bucket and organization. These values are defined by the Helm chart, but its’ default values are used here.
• token is also defined by the Helm chart and stored within a Secret. I opt for using the randomly generated default.
• influxdb-influxdb2 is the default name of the Service created by the Helm chart.

With all this in place, I have a Terraform module that deploys a Grafana stack onto DigitalOcean’s Kubernetes platform, while maintaining portability.

I’d built a Prometheus exporter for Exoscale, allowing me to visualize cloud spending and resource usage from a central location alongside AWS and DigitalOcean.

The Exoscale exporter is built using Go and leverages the latest version of Exoscale’s Go API, egoscale v3 and includes basic integration tests and automatic package building for all major platforms and architectures.

Some of the metrics exported are;

• Organization Information: Usage, Address, API Keys
• Compute Resource Summary: Instances, Kubernetes, Node Pools
• Storage Resource Summary: SOS Buckets & Usage, Block Volumes
• Networking Resource Summary: Domain & Records, Load Balancers

By integrating organizational data from Exoscale into the Prometheus ecosystem, I can now configure alerts for spending or resource usage on either Exoscale specifically or for all platforms using AlertManager.

I can also identify where I may have left resources behind using Grafana, in the event I’m manually creating them or my IaC executions didn’t do a proper clean-up.

I decided to deploy the exporter to my Kubernetes cluster, scraping based on the default interval of 2 minutes. This is roughly a good balance between;

• When a new billing amount gets updated (hourly)
• How often infrastructure elements themselves gets updated (could be on a minutely-basis)
• How much data gets consumed by the time-series

I chose Kubernetes cluster rather than a server-less solution or a dedicated VM so that I can optimize the costs of running the exporter by sharing resources, in addition to abstracting the cloud provider away from the application.