Managing DatadogPodAutoscaler with Terraform

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Overview

The DatadogPodAutoscaler (DPA) is a Kubernetes custom resource definition (CRD) that enables autoscaling of Kubernetes workloads using Datadog Kubernetes Autoscaling (DKA). This guide demonstrates how to manage DatadogPodAutoscaler resources using Terraform and HashiCorp’s Kubernetes provider to deploy an autoscaling configuration.

Prerequisites

Before you begin, ensure you have the following:

  • Kubernetes cluster: A working Kubernetes cluster with access using kubectl
  • Terraform: Terraform installed (version 0.13 or later recommended)
  • Datadog API credentials: Valid Datadog API key and application key

Project structure

This guide uses a multi-stage deployment approach to ensure proper dependency creation for Terraform.

.
├── providers.tf              # Provider configurations
├── variables.tf              # Input variables
├── main.tf                   # Stage 1: Datadog secret and operator (CRDs)
├── terraform.tfvars          # Example variable values
├── datadogagent/             # Stage 2: DatadogAgent CRD resource
│   └── main.tf               # DatadogAgent manifest
├── nginx-dpa/                # Stage 3: Nginx application with DPA
│   └── main.tf               # Nginx namespace, deployment, and DPA

Deployment stages

A multi-stage deployment approach is essential when working with Kubernetes custom resource definitions (CRDs) and Terraform. This ordered approach is necessary to ensure that you create and install the dependencies required for each stage in the process.

Kubernetes CRDs must be installed in the cluster before you can create custom resources that use them. The DatadogPodAutoscaler CRD is created when you install the Datadog Operator in Stage 1. Terraform needs to know about these CRDs before it can manage resources that depend on them.

The Terraform Kubernetes provider discovers available resource types at initialization time. If you try to create a DatadogPodAutoscaler resource before the CRD is installed, Terraform will fail because it doesn’t recognize the custom resource type.

  1. Stage 1 (Datadog Operator and CRDs): Creates Datadog secret, Operator, and CRD
    • Datadog Operator using Helm (creates CRDs)
  2. Stage 2 (Datadog Agent): Deploys the Datadog Agent configured for Datadog Kubernetes Autoscaling
    • Datadog API and application secrets
    • DatadogAgent custom resource with Cluster Agent enabled
  3. Stage 3 (Autoscaled workload): Deploys application with DatadogPodAutoscaler
    • Nginx namespace and deployment
    • DatadogPodAutoscaler resource for autoscaling the nginx deployment

Set up configuration files

First, set up the following configuration files for each stage in the process.

Stage 1: Datadog Operator and CRDs

providers.tf

provider "kubernetes" {
  config_path = "~/.kube/config"
}

provider "helm" {
  kubernetes = {
    config_path = "~/.kube/config"
  }
}

main.tf

resource "helm_release" "datadog_operator" {
  name       = "datadog-operator"
  namespace  = "datadog"
  repository = "https://helm.datadoghq.com"
  chart      = "datadog-operator"
  version    = "2.11.1" # You can update to the latest stable version

  create_namespace = true
}

Stage 2: Datadog Agent

datadogagent/variables.tf

variable "datadog_api_key" {
  description = "Datadog API key"
  type        = string
  sensitive   = true
}

variable "datadog_app_key" {
  description = "Datadog application key"
  type        = string
  sensitive   = true
}

datadogagent/main.tf

provider "kubernetes" {
  config_path = "~/.kube/config"
}

resource "kubernetes_secret" "datadog_secret" {
  metadata {
    name      = "datadog-secret"
    namespace = "datadog"
  }

  data = {
    api-key = var.datadog_api_key
    app-key = var.datadog_app_key
  }

  type = "Opaque"
}

resource "kubernetes_manifest" "datadog" {
  manifest = {
    apiVersion = "datadoghq.com/v2alpha1"
    kind       = "DatadogAgent"
    metadata = {
      name      = "datadog"
      namespace = "datadog"
    }
    spec = {
      features = {
        autoscaling = {
          workload = {
            enabled = true
          }
        }
        eventCollection = {
          unbundleEvents = true
        }
      }
      global = {
        site = "datadoghq.com"
        credentials = {
          apiSecret = {
            secretName = "datadog-secret"
            keyName = "api-key"
          }
          appSecret = {
            secretName = "datadog-secret"
            keyName = "app-key"
          }
        }
        clusterName = "minikube-dka-demo"
        kubelet = {
          tlsVerify = false
        }
      }
      override = {
        clusterAgent = {
          env = [
            {
              name  = "DD_AUTOSCALING_FAILOVER_ENABLED"
              value = "true"
            }
          ]
        }
        nodeAgent = {
          env = [
            {
              name  = "DD_AUTOSCALING_FAILOVER_ENABLED"
              value = "true"
            }
          ]
        }
      }
    }
  }
}

Stage 3: Application with DatadogPodAutoscaler

nginx-dpa/main.tf

terraform {
  required_providers {
    kubernetes = {
      source  = "hashicorp/kubernetes"
      version = "~> 2.0"
    }
  }
}

provider "kubernetes" {
  config_path = "~/.kube/config"
}

# Create namespace for the application
resource "kubernetes_namespace" "nginx_demo" {
  metadata {
    name = "nginx-dka-demo"
  }
}

# Nginx deployment
resource "kubernetes_deployment" "nginx" {
  metadata {
    name      = "nginx"
    namespace = kubernetes_namespace.nginx_demo.metadata[0].name
    labels = {
      app = "nginx"
    }
  }

  spec {
    replicas = 3

    selector {
      match_labels = {
        app = "nginx"
      }
    }

    template {
      metadata {
        labels = {
          app = "nginx"
        }
      }

      spec {
        container {
          image = "nginx:latest"
          name  = "nginx"

          port {
            container_port = 80
          }

          resources {
            limits = {
              cpu    = "500m"
              memory = "512Mi"
            }
            requests = {
              cpu    = "250m"
              memory = "256Mi"
            }
          }
        }
      }
    }
  }
}

# Service for the nginx deployment
resource "kubernetes_service" "nginx" {
  metadata {
    name      = "nginx-service"
    namespace = kubernetes_namespace.nginx_demo.metadata[0].name
  }

  spec {
    selector = {
      app = "nginx"
    }

    port {
      port        = 80
      target_port = 80
    }

    type = "ClusterIP"
  }
}

# DatadogPodAutoscaler for nginx
resource "kubernetes_manifest" "datadogpodautoscaler_nginx_dka_demo_nginx" {
  manifest = {
    "apiVersion" = "datadoghq.com/v1alpha2"
    "kind" = "DatadogPodAutoscaler"
    "metadata" = {
      "name" = "nginx"
      "namespace" = "nginx-dka-demo"
    }
    "spec" = {
      "applyPolicy" = {
        "mode" = "Apply"
        "scaleDown" = {
          "rules" = [
            {
              "periodSeconds" = 1200
              "type" = "Percent"
              "value" = 20
            },
          ]
          "stabilizationWindowSeconds" = 600
          "strategy" = "Max"
        }
        "scaleUp" = {
          "rules" = [
            {
              "periodSeconds" = 120
              "type" = "Percent"
              "value" = 50
            },
          ]
          "stabilizationWindowSeconds" = 600
          "strategy" = "Max"
        }
        "update" = {
          "strategy" = "Auto"
        }
      }
      "constraints" = {
        "containers" = [
          {
            "enabled" = true
            "name" = "nginx"
          },
        ]
        "maxReplicas" = 100
        "minReplicas" = 3
      }
      "objectives" = [
        {
          "podResource" = {
            "name" = "cpu"
            "value" = {
              "type" = "Utilization"
              "utilization" = 70
            }
          }
          "type" = "PodResource"
        },
      ]
      "owner" = "Local"
      "targetRef" = {
        "apiVersion" = "apps/v1"
        "kind" = "Deployment"
        "name" = "nginx"
      }
    }
  }
}

Deployment instructions

After you have set up the configuration files for each stage, follow these steps to deploy the components in the correct order.

Stage 1: Datadog Operator and CRDs

Deploy the Datadog Operator and CRDs:

terraform init
terraform apply

Verify that the Datadog Operator and CRDs are deployed:

kubectl get crd
kubectl get pods -n datadog

You should see that the Datadog CRDs are created and the datadog-operator pod is running.

Stage 2: Datadog Agent

Create a terraform.tfvars file with your Datadog credentials:

cat > datadogagent/terraform.tfvars << EOF
datadog_api_key = "your-api-key-here"
datadog_app_key = "your-app-key-here"
EOF
Alternatively, set the TF_VAR_datadog_api_key and TF_VAR_datadog_app_key environment variables in your shell.

cd datadogagent
terraform init
terraform apply

Verify that the Datadog Agent is deployed:

kubectl get datadogagent -n datadog

You should see the Datadog Agent custom resource created. It should be in the Running state before proceeding. Also verify that the Datadog Agent and datadog-cluster-agent pods are running:

kubectl get pods -n datadog

Stage 3: Application with DatadogPodAutoscaler

Deploy the nginx application with DatadogPodAutoscaler:

cd ../nginx-dpa
terraform init
terraform apply

After deployment, verify that all components are working correctly.

Check Datadog Agent status:

kubectl get datadogagent -n datadog
kubectl describe datadogagent datadog -n datadog

Check DatadogPodAutoscaler status:

kubectl get datadogpodautoscaler -n nginx-dka-demo
kubectl describe datadogpodautoscaler nginx-autoscaler -n nginx-dka-demo

Congratulations, you have a workload managed by the Datadog Kubernetes Autoscaler!

Cleanup

To remove all resources, follow the reverse order of the deployment stages:

  1. Clean up the deployed application (Stage 3):

    cd nginx-dpa
terraform destroy

  2. Clean up the Datadog Agent (Stage 2):

    cd ../datadogagent
terraform destroy

  3. Clean up the Datadog Operator and CRDs (Stage 1):

    cd ..
terraform destroy

Troubleshooting

Debugging commands

Check DatadogPodAutoscaler events:

kubectl get events -n nginx-dka-demo --sort-by='.lastTimestamp'

Check Cluster Agent logs:

kubectl logs -n datadog -l agent.datadoghq.com/component=cluster-agent

Further reading

Más enlaces, artículos y documentación útiles:

Kubernetes autoscaling guide: determine which solution is right for your use caseBLOG more more Datadog Kubernetes AutoscalingDOCUMENTATION more more Datadog Cluster AgentDOCUMENTATION more more Terraform Kubernetes Provider documentationEXTERNAL SITE more more Kubernetes Horizontal Pod Autoscaling documentationEXTERNAL SITE more more