Introducing Terraform help for Amazon OpenSearch Ingestion

Right now, we’re launching Terraform help for Amazon OpenSearch Ingestion. Terraform is an infrastructure as code (IaC) instrument that helps you construct, deploy, and handle cloud sources effectively. OpenSearch Ingestion is a completely managed, serverless knowledge collector that delivers real-time log, metric, and hint knowledge to Amazon OpenSearch Service domains and Amazon OpenSearch Serverless collections. On this submit, we clarify how you should utilize Terraform to deploy OpenSearch Ingestion pipelines. For instance, we use an HTTP supply as enter and an Amazon OpenSearch Service area (Index) as output.

Answer overview

The steps on this submit deploy a publicly accessible OpenSearch Ingestion pipeline with Terraform, together with different supporting sources which are wanted for the pipeline to ingest knowledge into Amazon OpenSearch. We’ve got applied the Tutorial: Ingesting knowledge into a site utilizing Amazon OpenSearch Ingestion, utilizing Terraform.

We create the next sources with Terraform:

The pipeline that you simply create exposes an HTTP supply as enter and an Amazon OpenSearch sink to avoid wasting batches of occasions.

Stipulations

To observe the steps on this submit, you want the next:

• An energetic AWS account.
• Terraform put in in your native machine. For extra data, see Set up Terraform.
• The required IAM permissions required to create the AWS sources utilizing Terraform.
• awscurl for sending HTTPS requests by the command line with AWS Sigv4 authentication. For directions on putting in this instrument, see the GitHub repo.

Create a listing

In Terraform, infrastructure is managed as code, known as a undertaking. A Terraform undertaking incorporates varied Terraform configuration information, corresponding to important.tf, supplier.tf, variables.tf, and output.df . Let’s create a listing on the server or machine that we will use to connect with AWS providers utilizing the AWS Command Line Interface (AWS CLI):

mkdir osis-pipeline-terraform-example

Change to the listing.

cd osis-pipeline-terraform-example

Create the Terraform configuration

Create a file to outline the AWS sources.

Enter the next configuration in important.tf and save your file:

terraform 
  required_providers 
    aws = 
      supply  = "hashicorp/aws"
      model = "~> 5.36"
    
  

  required_version = ">= 1.2.0"


supplier "aws" 
  area = "eu-central-1"


knowledge "aws_region" "present" 
knowledge "aws_caller_identity" "present" 
locals 
    account_id = knowledge.aws_caller_identity.present.account_id


output "ingest_endpoint_url" 
  worth = tolist(aws_osis_pipeline.instance.ingest_endpoint_urls)[0]


useful resource "aws_iam_role" "instance" 
  title = "exampleosisrole"
  assume_role_policy = jsonencode(
    Model = "2012-10-17"
    Assertion = [
      
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Sid    = ""
        Principal = 
          Service = "osis-pipelines.amazonaws.com"
        
      ,
    ]
  )


useful resource "aws_opensearch_domain" "take a look at" {
  domain_name           = "osi-example-domain"
  engine_version = "OpenSearch_2.7"
  cluster_config 
    instance_type = "r5.massive.search"
  
  encrypt_at_rest 
    enabled = true
  
  domain_endpoint_options 
    enforce_https       = true
    tls_security_policy = "Coverage-Min-TLS-1-2-2019-07"
  
  node_to_node_encryption 
    enabled = true
  
  ebs_options 
    ebs_enabled = true
    volume_size = 10
  
 access_policies = <<EOF

  "Model": "2012-10-17",
  "Assertion": [
    
      "Effect": "Allow",
      "Principal": 
        "AWS": "$aws_iam_role.example.arn"
      ,
      "Action": "es:*"
    
  ]


EOF

}

useful resource "aws_iam_policy" "instance" 
  title = "osis_role_policy"
  description = "Coverage for OSIS pipeline position"
  coverage = jsonencode(
    Model = "2012-10-17",
    Assertion = [
        
          Action = ["es:DescribeDomain"]
          Impact = "Permit"
          Useful resource = "arn:aws:es:$knowledge.aws_region.present.title:$native.account_id:area/*"
        ,
        
          Motion = ["es:ESHttp*"]
          Impact = "Permit"
          Useful resource = "arn:aws:es:$knowledge.aws_region.present.title:$native.account_id:area/osi-test-domain/*"
        
    ]
)


useful resource "aws_iam_role_policy_attachment" "instance" 
  position       = aws_iam_role.instance.title
  policy_arn = aws_iam_policy.instance.arn


useful resource "aws_cloudwatch_log_group" "instance" 
  title = "/aws/vendedlogs/OpenSearchIngestion/example-pipeline"
  retention_in_days = 365
  tags = 
    Title = "AWS Weblog OSIS Pipeline Instance"
  


useful resource "aws_osis_pipeline" "instance" 
  pipeline_name               = "example-pipeline"
  pipeline_configuration_body = <<-EOT
            model: "2"
            example-pipeline:
              supply:
                http:
                  path: "/test_ingestion_path"
              processor:
                - date:
                    from_time_received: true
                    vacation spot: "@timestamp"
              sink:
                - opensearch:
                    hosts: ["https://$aws_opensearch_domain.test.endpoint"]
                    index: "application_logs"
                    aws:
                      sts_role_arn: "$aws_iam_role.instance.arn"   
                      area: "$knowledge.aws_region.present.title"
        EOT
  max_units                   = 1
  min_units                   = 1
  log_publishing_options 
    is_logging_enabled = true
    cloudwatch_log_destination 
      log_group = aws_cloudwatch_log_group.instance.title
    
  
  tags = 
    Title = "AWS Weblog OSIS Pipeline Instance"
  
  

Create the sources

Initialize the listing:

Overview the plan to see what sources might be created:

Apply the configuration and reply sure to run the plan:

The method may take round 7–10 minutes to finish.

Check the pipeline

After you create the sources, you must see the ingest_endpoint_url output displayed. Copy this worth and export it in your surroundings variable:

export OSIS_PIPELINE_ENDPOINT_URL=<Exchange with worth copied>

Ship a pattern log with awscurl. Exchange the profile along with your acceptable AWS profile for credentials:

awscurl --service osis --region eu-central-1 -X POST -H "Content material-Kind: software/json" -d '["time":"2014-08-11T11:40:13+00:00","remote_addr":"122.226.223.69","status":"404","request":"GET http://www.k2proxy.com//hello.html HTTP/1.1","http_user_agent":"Mozilla/4.0 (compatible; WOW64; SLCC2;)"]' https://$OSIS_PIPELINE_ENDPOINT_URL/test_ingestion_path

You need to obtain a 200 OK as a response.

To confirm that the info was ingested within the OpenSearch Ingestion pipeline and saved within the OpenSearch, navigate to the OpenSearch and get its area endpoint. Exchange the <OPENSEARCH ENDPOINT URL> within the snippet given under and run it.

awscurl --service es --region eu-central-1 -X GET https://<OPENSEARCH ENDPOINT URL>/application_logs/_search | json_pp 

You need to see the output as under:

Clear up

To destroy the sources you created, run the next command and reply sure when prompted:

The method may take round 30–35 minutes to finish.

Conclusion

On this submit, we confirmed how you should utilize Terraform to deploy OpenSearch Ingestion pipelines. AWS affords varied sources so that you can rapidly begin constructing pipelines utilizing OpenSearch Ingestion and use Terraform to deploy them. You should utilize varied built-in pipeline integrations to rapidly ingest knowledge from Amazon DynamoDB, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Safety Lake, Fluent Bit, and lots of extra. The next OpenSearch Ingestion blueprints mean you can construct knowledge pipelines with minimal configuration adjustments and handle them with ease utilizing Terraform. To be taught extra, try the Terraform documentation for Amazon OpenSearch Ingestion.

In regards to the Authors

Rahul Sharma is a Technical Account Supervisor at Amazon Net Companies. He’s passionate concerning the knowledge applied sciences that assist leverage knowledge as a strategic asset and is predicated out of NY city, New York.

Farhan Angullia is a Cloud Software Architect at AWS Skilled Companies, based mostly in Singapore. He primarily focuses on fashionable purposes with microservice software program patterns, and advocates for implementing strong CI/CD practices to optimize the software program supply lifecycle for purchasers. He enjoys contributing to the open supply Terraform ecosystem in his spare time.

Arjun Nambiar is a Product Supervisor with Amazon OpenSearch Service. He focusses on ingestion applied sciences that allow ingesting knowledge from all kinds of sources into Amazon OpenSearch Service at scale. Arjun is interested by massive scale distributed techniques and cloud-native applied sciences and is predicated out of Seattle, Washington.

Muthu Pitchaimani is a Search Specialist with Amazon OpenSearch Service. He builds large-scale search purposes and options. Muthu is within the matters of networking and safety, and is predicated out of Austin, Texas.