AWS Big Data Blog

Achieve cross-Region resilience with Amazon OpenSearch Ingestion

Cross-Region deployments provide increased resilience to maintain business continuity during outages, natural disasters, or other operational interruptions. Many large enterprises, design and deploy special plans for readiness during such situations. They rely on solutions built with AWS services and features to improve their confidence and response times. Amazon OpenSearch Service is a managed service for OpenSearch, a search and analytics engine at scale. OpenSearch Service provides high availability within an AWS Region through its Multi-AZ deployment model and provides Regional resiliency with cross-cluster replication. Amazon OpenSearch Serverless is a deployment option that provides on-demand auto scaling, to which we continue to bring in many features.

With the existing cross-cluster replication feature in OpenSearch Service, you designate a domain as a leader and another as a follower, using an active-passive replication model. Although this model offers a way to continue operations during Regional impairment, it requires you to manually configure the follower. Additionally, after recovery, you need to reconfigure the leader-follower relationship between the domains.

In this post, we outline two solutions that provide cross-Region resiliency without needing to reestablish relationships during a failback, using an active-active replication model with Amazon OpenSearch Ingestion (OSI) and Amazon Simple Storage Service (Amazon S3). These solutions apply to both OpenSearch Service managed clusters and OpenSearch Serverless collections. We use OpenSearch Serverless as an example for the configurations in this post.

Solution overview

We outline two solutions in this post. In both options, data sources local to a region write to an OpenSearch ingestion (OSI) pipeline configured within the same region. The solutions are extensible to multiple Regions, but we show two Regions as an example as Regional resiliency across two Regions is a popular deployment pattern for many large-scale enterprises.

You can use these solutions to address cross-Region resiliency needs for OpenSearch Serverless deployments and active-active replication needs for both serverless and provisioned options of OpenSearch Service, especially when the data sources produce disparate data in different Regions.

Prerequisites

Complete the following prerequisite steps:

  1. Deploy OpenSearch Service domains or OpenSearch Serverless collections in all the Regions where resiliency is needed.
  2. Create S3 buckets in each Region.
  3. Configure AWS Identity and Access Management (IAM) permissions needed for OSI. For instructions, refer to Amazon S3 as a source. Choose Amazon Simple Queue Service (Amazon SQS) as the method for processing data.

After you complete these steps, you can create two OSI pipelines one in each Region with the configurations detailed in the following sections.

Use OpenSearch Ingestion (OSI) for cross-Region writes

In this solution, OSI takes the data that is local to the Region it’s in and writes it to the other Region. To facilitate cross-Region writes and increase data durability, we use an S3 bucket in each Region. The OSI pipeline in the other Region reads this data and writes to the collection in its local Region. The OSI pipeline in the other Region follows a similar data flow.

While reading data, you have choices: Amazon SQS or Amazon S3 scans. For this post, we use Amazon SQS because it helps provide near real-time data delivery. This solution also facilitates writing directly to these local buckets in the case of pull-based OSI data sources. Refer to Source under Key concepts to understand the different types of sources that OSI uses.

The following diagram shows the flow of data.

The data flow consists of the following steps:

  1. Data sources local to a Region write their data to the OSI pipeline in their Region. (This solution also supports sources directly writing to Amazon S3.)
  2. OSI writes this data into collections followed by S3 buckets in the other Region.
  3. OSI reads the other Region data from the local S3 bucket and writes it to the local collection.
  4. Collections in both Regions now contain the same data.

The following snippets shows the configuration for the two pipelines.

#pipeline config for cross region writes
version: "2"
write-pipeline:
  source:
    http:
      path: "/logs"
  processor:
    - parse_json:
  sink:
    # First sink to same region collection
    - opensearch:
        hosts: [ "https://abcdefghijklmn.us-east-1.aoss.amazonaws.com" ]
        aws:
          sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
          region: "us-east-1"
          serverless: true
        index: "cross-region-index"
    - s3:
        # Second sink to cross region S3 bucket
        aws:
          sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
          region: "us-east-2"
        bucket: "osi-cross-region-bucket"
        object_key:
          path_prefix: "osi-crw/%{yyyy}/%{MM}/%{dd}/%{HH}"
        threshold:
          event_collect_timeout: 60s
        codec:
          ndjson:
The code for the write pipeline is as follows: 

#pipeline config to read data from local S3 bucket
version: "2"
read-write-pipeline:
  source:
    s3:
      # S3 source with SQS 
      acknowledgments: true
      notification_type: "sqs"
      compression: "none"
      codec:
        newline:
      sqs:
        queue_url: "https://sqs.us-east-1.amazonaws.com/1234567890/my-osi-cross-region-write-q"
        maximum_messages: 10
        visibility_timeout: "60s"
        visibility_duplication_protection: true
      aws:
        region: "us-east-1"
        sts_role_arn: "arn:aws:iam::123567890:role/pipe-line-role"
  processor:
    - parse_json:
  route:
  # Routing uses the s3 keys to ensure OSI writes data only once to local region 
    - local-region-write: "contains(/s3/key, \"osi-local-region-write\")"
    - cross-region-write: "contains(/s3/key, \"osi-cross-region-write\")"
  sink:
    - pipeline:
        name: "local-region-write-cross-region-write-pipeline"
    - pipeline:
        name: "local-region-write-pipeline"
        routes:
        - local-region-write
local-region-write-cross-region-write-pipeline:
  # Read S3 bucket with cross-region-write
  source:
    pipeline: 
      name: "read-write-pipeline"
  sink:
   # Sink to local-region managed OpenSearch service 
    - opensearch:
        hosts: [ "https://abcdefghijklmn.us-east-1.aoss.amazonaws.com" ]
        aws:
          sts_role_arn: "arn:aws:iam::12345678890:role/pipeline-role"
          region: "us-east-1"
          serverless: true
        index: "cross-region-index"
local-region-write-pipeline:
  # Read local-region write  
  source:
    pipeline: 
      name: "read-write-pipeline"
  processor:
    - delete_entries:
        with_keys: ["s3"]
  sink:
     # Sink to cross-region S3 bucket 
    - s3:
        aws:
          sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
          region: "us-east-2"
        bucket: "osi-cross-region-write-bucket"
        object_key:
          path_prefix: "osi-cross-region-write/%{yyyy}/%{MM}/%{dd}/%{HH}"
        threshold:
          event_collect_timeout: "60s"
        codec:
          ndjson:

To separate management and operations, we use two prefixes, osi-local-region-write and osi-cross-region-write, for buckets in both Regions. OSI uses these prefixes to copy only local Region data to the other Region. OSI also creates the keys s3.bucket and s3.key to decorate documents written to a collection. We remove this decoration while writing across Regions; it will be added back by the pipeline in the other Region.

This solution provides near real-time data delivery across Regions, and the same data is available across both Regions. However, although OpenSearch Service contains the same data, the buckets in each Region contain only partial data. The following solution addresses this.

Use Amazon S3 for cross-Region writes

In this solution, we use the Amazon S3 Region replication feature. This solution supports all the data sources available with OSI. OSI again uses two pipelines, but the key difference is that OSI writes the data to Amazon S3 first. After you complete the steps that are common to both solutions, refer to Examples for configuring live replication for instructions to configure Amazon S3 cross-Region replication. The following diagram shows the flow of data.

The data flow consists of the following steps:

  1. Data sources local to a Region write their data to OSI. (This solution also supports sources directly writing to Amazon S3.)
  2. This data is first written to the S3 bucket.
  3. OSI reads this data and writes to the collection local to the Region.
  4. Amazon S3 replicates cross-Region data and OSI reads and writes this data to the collection.

The following snippets show the configuration for both pipelines.

version: "2"
s3-write-pipeline:
  source:
    http:
      path: "/logs"
  processor:
    - parse_json:
  sink:
    # Write to S3 bucket that has cross region replication enabled
    - s3:
        aws:
          sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
          region: "us-east-2"
        bucket: "s3-cross-region-bucket"
        object_key:
          path_prefix: "pushedlogs/%{yyyy}/%{MM}/%{dd}/%{HH}"
        threshold:
          event_collect_timeout: 60s
          event_count: 2
        codec:
          ndjson:

The code for the write pipeline is as follows:

version: "2"
s3-read-pipeline:
  source:
    s3:
      acknowledgments: true
      notification_type: "sqs"
      compression: "none"
      codec:
        newline:
      # Configure SQS to notify OSI pipeline
      sqs:
        queue_url: "https://sqs.us-east-2.amazonaws.com/1234567890/my-s3-crr-q"
        maximum_messages: 10
        visibility_timeout: "15s"
        visibility_duplication_protection: true
      aws:
        region: "us-east-2"
        sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
  processor:
    - parse_json:
  # Configure OSI sink to move the files from S3 to OpenSearch Serverless
  sink:
    - opensearch:
        hosts: [ "https://abcdefghijklmn.us-east-1.aoss.amazonaws.com" ]
        aws:
          # Role must have access to S3 OpenSearch Pipeline and OpenSearch Serverless
          sts_role_arn: "arn:aws:iam::1234567890:role/pipeline-role"
          region: "us-east-1"
          serverless: true
        index: "cross-region-index"

The configuration for this solution is relatively simpler and relies on Amazon S3 cross-Region replication. This solution makes sure that the data in the S3 bucket and OpenSearch Serverless collection are the same in both Regions.

For more information about the SLA for this replication and metrics that are available to monitor the replication process, refer to S3 Replication Update: Replication SLA, Metrics, and Events.

Impairment scenarios and additional considerations

Let’s consider a Regional impairment scenario. For this use case, we assume that your application is powered by an OpenSearch Serverless collection as a backend. When a region is impaired, these applications can simply failover to the OpenSearch Serverless collection in the other Region and continue operations without interruption, because the entirety of the data present before the impairment is available in both collections.

When the Region impairment is resolved, you can failback to the OpenSearch Serverless collection in that Region either immediately or after you allow some time for the missing data to be backfilled in that Region. The operations can then continue without interruption.

You can automate these failover and failback operations to provide a seamless user experience. This automation is not in scope of this post, but will be covered in a future post.

The existing cross-cluster replication solution, requires you to manually reestablish a leader-follower relationship, and restart replication from the beginning once recovered from an impairment. But the solutions discussed here automatically resume replication from the point where it last left off. If for some reason only Amazon OpenSearch service that is collections or domain were to fail, the data is still available in a local buckets and it will be back filled as soon the collection or domain becomes available.

You can effectively use these solutions in an active-passive replication model as well. In those scenarios, it’s sufficient to have minimum set of resources in the replication Region like a single S3 bucket. You can modify this solution to solve different scenarios using additional services like Amazon Managed Streaming for Apache Kafka (Amazon MSK), which has a built-in replication feature.

When building cross-Region solutions, consider cross-Region data transfer costs for AWS. As a best practice, consider adding a dead-letter queue to all your production pipelines.

Conclusion

In this post, we outlined two solutions that achieve Regional resiliency for OpenSearch Serverless and OpenSearch Service managed clusters. If you need explicit control over writing data cross Region, use solution one. In our experiments with few KBs of data majority of writes completed within a second between two chosen regions. Choose solution two if you need simplicity the solution offers. In our experiments replication completed completely in a few seconds. 99.99% of objects will be replicated within 15 minutes.  These solutions also serve as an architecture for an active-active replication model in OpenSearch Service using OpenSearch Ingestion.

You can also use OSI as a mechanism to search for data available within other AWS services, like Amazon S3, Amazon DynamoDB, and Amazon DocumentDB (with MongoDB compatibility). For more details, see Working with Amazon OpenSearch Ingestion pipeline integrations.

About the Authors

Muthu Pitchaimani is a Search Specialist with Amazon OpenSearch Service. He builds large-scale search applications and solutions. Muthu is interested in the topics of networking and security, and is based out of Austin, Texas.

Aruna Govindaraju is an Amazon OpenSearch Specialist Solutions Architect and has worked with many commercial and open source search engines. She is passionate about search, relevancy, and user experience. Her expertise with correlating end-user signals with search engine behavior has helped many customers improve their search experience.