Elastic CloudとTerraformを併用する

terraform {
  required_version = ">= 1.0.0"

  required_providers {
    ec = {
      source  = "elastic/ec"
      version = "0.4.0"
    }
  }
}

provider "ec" {
}

resource "ec_deployment" "custom-deployment-id" {
  name                   = "My deployment identifier"

  region                 = "gcp-europe-west3"
  version                = "8.1.3"
  deployment_template_id = "gcp-memory-optimized-v2"

  elasticsearch {}

  kibana {}
}

After storing this you can run

terraform init
terraform validate
terraform apply auto-approve

Copy

output "elasticsearch_endpoint" {
  value = ec_deployment.custom-deployment-id.elasticsearch[0].https_endpoint
}

output "elasticsearch_username" {
  value = ec_deployment.custom-deployment-id.elasticsearch_username
}

output "elasticsearch_password" {
  value = ec_deployment.custom-deployment-id.elasticsearch_password
  sensitive = true
}

output "kibana_endpoint" {
  value = ec_deployment.custom-deployment-id.kibana[0].https_endpoint
}

Copy

output=$(terraform output -json)
endpoint=$(echo $output | jq -r ".elasticsearch_endpoint.value")
username=$(echo $output | jq -r ".elasticsearch_username.value")
password=$(echo $output | jq -r ".elasticsearch_password.value")
curl $endpoint -u $username:$password

Copy

resource "ec_deployment" "cluster_1" {
  name                   = "cluster_1"

  region                 = "gcp-europe-west3"
  version                = "8.1.3"
  deployment_template_id = "gcp-memory-optimized-v2"

  elasticsearch {}

  kibana {}
}

resource "ec_deployment" "cluster_2" {
  name                   = "cluster_2"

  region                 = "gcp-europe-west3"
  version                = "8.1.3"
  deployment_template_id = "gcp-memory-optimized-v2"

  elasticsearch {
    remote_cluster {
      deployment_id = ec_deployment.cluster_1.id
      alias         = ec_deployment.cluster_1.name
      ref_id        = ec_deployment.cluster_1.elasticsearch.0.ref_id
    }
  }

  kibana {}
}

Copy

# Cluster 1
PUT my-leader-index

# Cluster 2
PUT my-follower-index/_ccr/follow?wait_for_active_shards=1
{"remote_cluster":"cluster_1","leader_index":"my-leader-index"}

# Cluster 1
PUT /my-leader-index/_doc/my-doc?refresh=true
{"key":"value"}

# Cluster 2, repeat until hit count > 0, should take less than a second usually
GET /my-follower-index/_search

Copy

# Cluster 1
PUT /cluster1-index/_doc/1?refresh=true
{"cluster":"cluster1","name":"my name"}

# Cluster 2
PUT /cluster2-index/_doc/1?refresh=true
{"cluster":"cluster2","name":"my name"}

# Cluster 2
GET /cluster2-index,cluster_1:cluster1-index/_search

Copy

terraform {
  required_version = ">= 1.0.0"

  required_providers {
    ec = {
      source  = "elastic/ec"
      version = "0.4.0"
    }
    elasticstack = {
      source = "elastic/elasticstack",
      version = "0.3.3"
    }
  }
}

provider "ec" {
}

resource "ec_deployment" "custom-deployment-id" {
  name                   = "custom-deployment-id"

  region                 = "gcp-europe-west3"
  version                = "8.1.3"
  deployment_template_id = "gcp-memory-optimized-v2"

  elasticsearch {}

  kibana {}
}

provider "elasticstack" {
  elasticsearch {
    username  = ec_deployment.custom-deployment-id.elasticsearch_username
    password  = ec_deployment.custom-deployment-id.elasticsearch_password
    endpoints = [ec_deployment.custom-deployment-id.elasticsearch[0].https_endpoint]
  }
}

data "elasticstack_elasticsearch_ingest_processor_set" "set_field_terraform" {
  field = "pipeline-source"
  value = "terraform"
}

data "elasticstack_elasticsearch_ingest_processor_grok" "grok_the_log" {
  field    = "message"
  patterns = ["%%{TIMESTAMP_ISO8601:@timestamp} %%{LOGLEVEL:level} %%{GREEDYDATA:message}"]
}

resource "elasticstack_elasticsearch_ingest_pipeline" "ingest" {
  name = "my-ingest-pipeline"

  processors = [
    data.elasticstack_elasticsearch_ingest_processor_set.set_field_terraform.json,
    data.elasticstack_elasticsearch_ingest_processor_grok.grok_the_log.json
  ]
}

This creates a pipeline named my-ingest-pipeline after bringing up the cluster. You could now go to Management/Ingest Pipeline in Kibana and see that the pipeline has been created, or just run the following simulate pipeline call:

POST _ingest/pipeline/my-ingest-pipeline/_simulate
{
  "docs": [
    {
      "_source": {
        "message": "2022-03-03T12:34:56.789Z INFO ze message"
      }
    }
  ]
}

This will return the following document as part of the response:

"_source" : {
  "@timestamp" : "2022-03-03T12:34:56.789Z",
  "level" : "INFO",
  "pipeline-source" : "terraform",
  "message" : "ze message"
}

There is one final step, which is not yet as easy as it should be, but the following little trick does it at least for most of my PoCs.

Copy

terraform {
  required_version = ">= 1.0.0"

  required_providers {
    ec = {
      source  = "elastic/ec"
      version = "0.4.0"
    }
  }
}

provider "ec" {
}

data "local_file" "dashboard" {
    filename = "${path.module}/dashboard.ndjson"
}

resource "ec_deployment" "custom-deployment-id" {
  name                   = "custom-deployment-id"

  region                 = "gcp-europe-west3"
  version                = "8.1.2"
  deployment_template_id = "gcp-memory-optimized-v2"

  elasticsearch {}

  kibana {}
}

resource "null_resource" "store_local_dashboard" {
  provisioner "local-exec" {
    command = "curl -X POST -u ${ec_deployment.custom-deployment-id.elasticsearch_username}:${ec_deployment.custom-deployment-id.elasticsearch_password} ${ec_deployment.custom-deployment-id.kibana[0].https_endpoint}/api/saved_objects/_import?overwrite=true -H \"kbn-xsrf: true\" --form file=@dashboard.ndjson"
  }
  depends_on = [ec_deployment.custom-deployment-id]
  triggers = {
    dashboard_sha1 = "${sha1(file("dashboard.ndjson"))}"
  }
}

Copy