Introducing self-managed knowledge sources for Amazon OpenSearch Ingestion

Enterprise prospects more and more undertake Amazon OpenSearch Ingestion (OSI) to convey knowledge into Amazon OpenSearch Service for numerous use instances. These embrace petabyte-scale log analytics, real-time streaming, safety analytics, and looking out semi-structured key-value or doc knowledge. OSI makes it easy, with simple integrations, to ingest knowledge from many AWS companies, together with Amazon DynamoDB, Amazon Easy Storage Service (Amazon S3), Amazon Managed Streaming for Apache Kafka (Amazon MSK), and Amazon DocumentDB (with MongoDB compatibility).

As we speak we’re saying help for ingesting knowledge from self-managed OpenSearch/Elasticsearch and Apache Kafka clusters. These sources can both be on Amazon Elastic Compute Cloud (Amazon EC2) or on-premises environments.

On this put up, we define the steps to get began with these sources.

Resolution overview

OSI helps the AWS Cloud Improvement Package (AWS CDK), AWS CloudFormation, the AWS Command Line Interface (AWS CLI), Terraform, AWS APIs, and the AWS Administration Console to deploy pipelines. On this put up, we use the console to show how one can create a self-managed Kafka pipeline.

Stipulations

To verify OSI can join and skim knowledge efficiently, the next circumstances must be met:

• Community connectivity to knowledge sources – OSI is usually deployed in a public community, such because the web, or in a digital personal cloud (VPC). OSI deployed in a buyer VPC is ready to entry knowledge sources in the identical or totally different VPC and on the web with an connected web gateway. In case your knowledge sources are in one other VPC, widespread strategies for community connectivity embrace direct VPC peering, utilizing a transit gateway, or utilizing buyer managed VPC endpoints powered by AWS PrivateLink. In case your knowledge sources are in your company knowledge heart or different on-premises setting, widespread strategies for community connectivity embrace AWS Direct Join and utilizing a community hub like a transit gateway. The next diagram reveals a pattern configuration of OSI working in a VPC and utilizing Amazon OpenSearch Service as a sink. OSI runs in a service VPC and creates an Elastic Community interface (ENI) within the buyer VPC. For self-managed knowledge supply these ENIs are used for studying knowledge from on-premises setting. OSI creates an VPC endpoint within the service VPC to ship knowledge to the sink.
  
• Identify decision for knowledge sources – OSI makes use of an Amazon Route 53 resolver. This resolver mechanically solutions queries to names native to a VPC, public domains on the web, and data hosted in personal hosted zones. In the event you’re are utilizing a non-public hosted zone, ensure you have a DHCP possibility set enabled, connected to the VPC utilizing AmazonProvidedDNS as area title server. For extra data, see Work with DHCP possibility units. Moreover, you should use resolver inbound and outbound endpoints for those who want a posh decision schemes with circumstances which are past a easy personal hosted zone.
• Certificates verification for knowledge supply names – OSI helps solely SASL_SSL for transport for Apache Kafka supply. Inside SASL, Amazon OpenSearch Service helps most authentication mechanisms like PLAIN, SCRAM, IAM, GSAPI and others. When utilizing SASL_SSL, ensure you have entry to certificates wanted for OSI to authenticate. For self-managed OpenSearch knowledge sources, be certain verifiable certificates are put in on the clusters. Amazon OpenSearch Service doesn’t help insecure communication between OSI and OpenSearch. Certificates verification can’t be turned off. Particularly, the “insecure” configuration possibility just isn’t supported.
• Entry to AWS Secrets and techniques Supervisor – OSI makes use of AWS Secrets and techniques Supervisor to retrieve credentials and certificates wanted to speak with self-managed knowledge sources. For extra data, see Create and handle secrets and techniques with AWS Secrets and techniques Supervisor.
• IAM position for pipelines – You want an AWS Id and Entry Administration (IAM) pipeline position to jot down to knowledge sinks. For extra data, see Id and Entry Administration for Amazon OpenSearch Ingestion.

Create a pipeline with self-managed Kafka as a supply

After you full the stipulations, you’re able to create a pipeline to your knowledge supply. Full the next steps:

1. On the OpenSearch Service console, select Pipelines below Ingestion within the navigation pane.
   
2. Select Create pipeline.
   
3. Select Streaming below Use case within the navigation pane.
4. Choose Self managed Apache Kafka below Ingestion pipeline blueprints and select Choose blueprint.
   

It will populate a pattern configuration for this pipeline.

5. Present a reputation for this pipeline and select the suitable pipeline capability.
6. Beneath Pipeline configuration, present your pipeline configuration in YAML format. The next code snippet reveals pattern configuration in YAML for SASL_SSL authentication:

   model: '2'
   kafka-pipeline:
     supply:
       kafka:
         acknowledgments: true
         bootstrap_servers:
           - 'node-0.instance.com:9092'
         encryption:
           sort: "ssl"
           certificates: '${{aws_secrets:kafka-cert}}'
           
         authentication:
           sasl:
             plain:
               username: '${{aws_secrets:secrets and techniques:username}}'
               password: '${{aws_secrets:secrets and techniques:password}}'
         subjects:
           - title: on-prem-topic
             group_id: osi-group-1
     processor:
       - grok:
           match:
             message:
               - '%{COMMONAPACHELOG}'
       - date:
           vacation spot: '@timestamp'
           from_time_received: true
     sink:
       - opensearch:
           hosts: ["https://search-domain-12345567890.us-east-1.es.amazonaws.com"]
           aws:
             area: us-east-1
             sts_role_arn: 'arn:aws:iam::123456789012:position/pipeline-role'
           index: "on-prem-kakfa-index"
   extension:
     aws:
       secrets and techniques:
         kafka-cert:
           secret_id: kafka-cert
           area: us-east-1
           sts_role_arn: 'arn:aws:iam::123456789012:position/pipeline-role'
         secrets and techniques:
           secret_id: secrets and techniques
           area: us-east-1
           sts_role_arn: 'arn:aws:iam::123456789012:position/pipeline-role'

7. Select Validate pipeline and ensure there aren’t any errors.
8. Beneath Community configuration, select Public entry or VPC entry. (For this put up, we select VPC entry).
9. In the event you selected VPC entry, specify your VPC, subnets, and an applicable safety group so OSI can attain the outgoing ports for the info supply.
10. Beneath VPC attachment choices, choose Connect to VPC and select an applicable CIDR vary.
    

OSI assets are created in a service VPC managed by AWS that’s separate from the VPC you selected within the final step. This choice permits you to configure what CIDR ranges OSI ought to use inside this service VPC. The selection exists so you may make positive there isn’t any tackle collision between CIDR ranges in your VPC that’s connected to your on-premises community and this service VPC. Many pipelines in your account can share similar CIDR ranges for this service VPC.

11. Specify any elective tags and log publishing choices, then select Subsequent.
12. Evaluation the configuration and select Create pipeline.

You may monitor the pipeline creation and any log messages within the Amazon CloudWatch Logs log group you specified. Your pipeline ought to now be efficiently created. For extra details about how one can provision capability for the efficiency of this pipeline, see the part Advisable Compute Items (OCUs) for the MSK pipeline in Introducing Amazon MSK as a supply for Amazon OpenSearch Ingestion.

Create a pipeline with self-managed OpenSearch as a supply

The steps for making a pipeline for self-managed OpenSearch are just like the steps for creating one for Kafka. In the course of the blueprint choice, select Information Migration below Use case and choose Self managed OpenSearch/Elasticsearch. OpenSearch Ingestion can supply knowledge from all variations of OpenSearch and Elasticsearch from model 7.0  to  model 7.10.

The next blueprint reveals a pattern configuration YAML for this knowledge supply:

model: "2"
opensearch-migration-pipeline:
  supply:
    opensearch:
      acknowledgments: true
      hosts: [ "https://node-0.example.com:9200" ]
      username: "${{aws_secrets:secret:username}}"
      password: "${{aws_secrets:secret:password}}"
      indices:
        embrace:
        - index_name_regex: "opensearch_dashboards_sample_data*"
        exclude:
          - index_name_regex: '..*'
  sink:
    - opensearch:
        hosts: [ "https://search-domain-12345567890.us-east-1.es.amazonaws.com" ]
        aws:
          sts_role_arn: "arn:aws:iam::123456789012:position/pipeline-role"
          area: "us-east-1"
        index: "on-prem-os"
extension:
  aws:
    secrets and techniques:
      secret:
        secret_id: "self-managed-os-credentials"
        area: "us-east-1"
        sts_role_arn: "arn:aws:iam::123456789012:position/pipeline-role"
        refresh_interval: PT1H

Issues for self-managed OpenSearch knowledge supply

Certificates put in on the OpenSearch cluster have to be verifiable for OSI to hook up with this knowledge supply earlier than studying knowledge. Insecure connections are presently not supported.

After you’re related, be certain the cluster has ample learn bandwidth to permit for OSI to learn knowledge. Use the Min and Max OCU setting to restrict OSI learn bandwidth consumption. Your learn bandwidth will differ relying upon knowledge quantity, variety of indexes, and provisioned OCU capability. Begin small and enhance the variety of OCUs to steadiness between obtainable bandwidth and acceptable migration time.

This supply is often meant for one-time migration of information and never as steady ingestion to maintain knowledge in sync between knowledge sources and sinks.

OpenSearch Service domains help distant reindexing, however that consumes assets in your domains. Utilizing OSI will transfer this compute out of the area, and OSI can obtain considerably larger bandwidth than distant reindexing, thereby leading to quicker migration occasions.

OSI doesn’t help deferred replay or visitors recording immediately; check with Migration Assistant for Amazon OpenSearch Service in case your migration wants these capabilities.

Conclusion

On this put up, we launched self-managed sources for OpenSearch Ingestion that allow you to ingest knowledge from company knowledge facilities or different on-premises environments. OSI additionally helps numerous different knowledge sources and integrations. Check with Working with Amazon OpenSearch Ingestion pipeline integrations to study these different knowledge sources.

Concerning the Authors

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

Arjun Nambiar is a Product Supervisor with Amazon OpenSearch Service. He focuses on ingestion applied sciences that allow ingesting knowledge from all kinds of sources into Amazon OpenSearch Service at scale. Arjun is concerned about large-scale distributed techniques and cloud-centered applied sciences, and relies out of Seattle, Washington.