Deploy a Grafana® service to visualize PostgreSQL® metrics#

Whether monitoring your data infrastructure or analyzing resource utilization based on metrics, Aiven for Grafana provides powerful visualizations and easy integrations for your Aiven services. A time-series database like M3DB can be used as backend to store PostgreSQL® database metrics to be queried by the Grafana dashboard. This example shows how to use the Aiven Terraform Provider to create an Aiven for PosgreSQL service, an Aiven for M3DB service, an Aiven for Grafana service, and the related service integrations programmatically.

flowchart LR PostgreSQL[Aiven for PostgreSQL] M3DB[Aiven for M3DB] Grafana[Aiven for Grafana] PostgreSQL ==>|si-metrics| M3DB Grafana ==>|si-dashboard| M3DB

In the above diagram, the PostgreSQL service metrics are pushed to M3DB which is then queried by a prebuilt Grafana dashboard. All three services are connected via Aiven Service Integrations, which lets your Aiven services talk to one another without you having to write complex integration codes. si-... in the above diagram stands for “Service Integration”.

Let’s cook!#

Be sure to check out the getting started guide to learn about the common files required to execute the following recipe. For example, you’ll need to declare the variables for project_name, api_token, and service_name_prefix.

Expand to check out the relevant common files needed for this recipe.

Navigate to a new folder and add the following files.

  1. Add the following to a new provider.tf file:

terraform {
  required_providers {
    aiven = {
      source  = "aiven/aiven"
      version = "~> 3.10.0"
    }
  }
}

provider "aiven" {
  api_token = var.aiven_api_token
}

You can also set the environment variable TF_VAR_aiven_api_token for the api_token property. With this, you don’t need to pass the -var-file flag when executing Terraform commands.

  1. To avoid including sensitive information in source control, the variables are defined here in the variables.tf file. You can then use a *.tfvars file with the actual values so that Terraform receives the values during runtime, and exclude it.

The variables.tf file defines the API token, the project name to use, and the prefix for the service name:

variable "aiven_api_token" {
  description = "Aiven console API token"
  type        = string
}

variable "project_name" {
  description = "Aiven console project name"
  type        = string
}

variable "service_name_prefix" {
  description = "A string to prepend to the service name"
  type        = string
}

  1. The var-values.tfvars file holds the actual values and is passed to Terraform using the -var-file= flag.

var-values.tfvars file:

aiven_api_token     = "<YOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE>"
project_name        = "<YOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE>"
service_name_prefix = "<YOUR-CHOICE-OF-A-SERVICE-NAME-PREFIX>"

services.tf file:

# PostgreSQL Service

resource "aiven_pg" "demo-pg" {
  project                 = var.project_name
  cloud_name              = "google-northamerica-northeast1"
  plan                    = "startup-8"
  service_name            = join("-", [var.service_name_prefix, "postgres"])
  termination_protection  = false
  maintenance_window_dow  = "sunday"
  maintenance_window_time = "10:00:00"
}

# M3DB Service

resource "aiven_m3db" "demo-m3db" {
  project                 = var.project_name
  cloud_name              = "google-northamerica-northeast1"
  plan                    = "startup-8"
  service_name            = join("-", [var.service_name_prefix, "m3db"])
  maintenance_window_dow  = "sunday"
  maintenance_window_time = "10:00:00"

  m3db_user_config {
    m3db_version = 1.5

    namespaces {
      name = "my_ns1"
      type = "unaggregated"
    }
  }
}

# Grafana Service

resource "aiven_grafana" "demo-grafana" {
  project                 = var.project_name
  cloud_name              = "google-northamerica-northeast1"
  plan                    = "startup-8"
  service_name            = join("-", [var.service_name_prefix, "grafana"])
  maintenance_window_dow  = "sunday"
  maintenance_window_time = "10:00:00"

  grafana_user_config {
    alerting_enabled = true

    public_access {
      grafana = true
    }
  }
}

# PostgreSQL-M3DB Metrics Service Integration

resource "aiven_service_integration" "postgresql_to_m3db" {
  project                  = var.project_name
  integration_type         = "metrics"
  source_service_name      = aiven_pg.demo-pg.service_name
  destination_service_name = aiven_m3db.demo-m3db.service_name
}

# M3DB-Grafana Dashboard Service Integration

resource "aiven_service_integration" "m3db-to-grafana" {
  project                  = var.project_name
  integration_type         = "dashboard"
  source_service_name      = aiven_grafana.demo-grafana.service_name
  destination_service_name = aiven_m3db.demo-m3db.service_name
}
Expand to check out how to execute the Terraform files.

The init command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.

terraform init

The plan command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.

terraform plan -var-file=var-values.tfvars

If you’re satisfied with the output of terraform plan, go ahead and run the terraform apply command which actually does the task or creating (or modifying) your infrastructure resources.

terraform apply -var-file=var-values.tfvars

At first, aiven_pg, aiven_m3db, and aiven_grafana resources are created. Once these three services are running, the resources that bridge them aiven_service_integration are created. Note the different integration_type used for each of these service integrations.

More resources#

You might find these related resources useful too: