What Is OpenSearch migration?

OpenSearch migration refers to the process of moving data, configurations, and workflows from an existing Elasticsearch system (or another search platform) to OpenSearch. OpenSearch, a community-driven open source search and analytics suite, is a fork of Elasticsearch and Kibana.

Migrating to OpenSearch means transferring indices, mappings, dashboards, and associated settings to ensure that search and analytics capabilities remain functional after the switch. This migration can include multiple steps such as cluster assessment, creating data snapshots, restoring or replaying data, and validating index and query compatibility.

Successful migration requires understanding both source and target platform differences, especially around features, version compatibility, APIs, and plugins. Planning and executing a migration ensures minimal data loss, service disruption, and alignment with security and compliance requirements inherent to search engines.

How to migrate from Elasticsearch to OpenSearch

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Why migrate to OpenSearch

Migrating to OpenSearch offers several practical advantages, especially for teams seeking greater control, flexibility, and transparency in their search and analytics infrastructure. OpenSearch is maintained by a broad open source community and avoids vendor lock-in, making it a reliable option for long-term deployment.

Benefits of migrating to OpenSearch include:

  • Open licensing: OpenSearch is fully open source under the Apache 2.0 license, enabling unrestricted use, modification, and distribution.
  • Community-driven development: New features, updates, and patches are driven by community contributions, offering transparency and faster innovation cycles.
  • No vendor lock-in: Users retain control over their infrastructure and deployment choices without dependency on a commercial license or proprietary software.
  • Elasticsearch compatibility: OpenSearch maintains compatibility with Elasticsearch 7.10 APIs, simplifying the migration of applications and integrations.
  • Customizable plugins and extensions: OpenSearch supports a plugin-based architecture, allowing teams to extend functionality with custom features or integrate community plugins.
  • Active security and access controls: Built-in security features include role-based access control (RBAC), TLS encryption, and audit logging without requiring a commercial license.
  • Cost efficiency: Avoids licensing fees and supports cost-effective scaling for large datasets and distributed environments.
    Integrated dashboards: Comes with OpenSearch Dashboards, a Kibana fork, for visualizing and interacting with search data in a familiar interface.

Related content: Read our guide to OpenSearch tutorial

OpenSearch migration approaches

Reindexing

Reindexing is a migration technique that copies documents from a source index or cluster into a target index. It’s commonly used when migrating data within the same OpenSearch environment, consolidating indexes, or transferring subsets of data. This approach leverages the Reindex API, which reads documents from the _source field of the source index and writes them into a pre-created destination index.

Here are the steps involved:

  1. Enable the _source field: Ensure that the _source field is enabled in the source index. Reindexing depends on this field to read document content.
  2. Create the destination index: Manually create the destination index before reindexing. You must define its settings, mappings, and shard configuration, as OpenSearch does not copy them automatically.
  3. Configure for cross-cluster reindexing (optional): If reindexing across clusters, configure the remote field with the source cluster’s host URL and authentication credentials to allow data transfer from the remote cluster.
  4. Optimize for large datasets: For large reindex operations, temporarily set the number of replicas in the destination index to 0 to improve ingestion speed. Re-enable them after completion.
  5. Control reindexing throughput: Use the requests_per_second parameter to throttle the reindex rate and minimize cluster impact. The default is unlimited (-1).
  6. Enable parallel processing: Use the slices parameter to split the reindex job into parallel tasks. Set it to a number or use auto for automatic slicing based on cluster capacity.
  7. Apply filters and field selection: Use queries to filter documents and _source field selection to limit which fields are reindexed.
  8. Transform documents with scripts or pipelines: Use scripts or ingest pipelines to modify documents during reindexing. You can change field values, set operation types (noop, delete, create), or reroute documents to different indexes.
  9. Handle version conflicts: To avoid errors from version mismatches, use the version_type setting and set conflicts to proceed if you want the process to continue on version conflict.

Snapshot and restore

Snapshot and restore is a migration method that involves exporting data from a source OpenSearch cluster and importing it into a new one. This technique is useful when migrating across infrastructure, between OpenSearch versions, or when architectural changes are required. It supports large datasets and enables rollback since the original cluster remains untouched.

General steps for snapshot and restore migration:

  • Prepare the source cluster: Ensure the source cluster is healthy and operational. Resolve any issues with index or shard allocation to avoid incomplete snapshots.
  • Configure snapshot repository: Set up a shared repository that both clusters can access. This can be a shared filesystem, Amazon S3, Azure Blob Storage, or another compatible backend.
  • Take snapshots: Create full or incremental snapshots of indexes and cluster state. Schedule incremental snapshots if minimizing downtime is critical.
  • Provision the target cluster: Deploy a new OpenSearch cluster with the desired version and architecture. This can include infrastructure changes like node configurations or scaling requirements.
  • Register repository on target cluster: Configure the same snapshot repository on the new cluster so it can access and restore the saved data.
  • Restore snapshots: Restore selected indexes or the entire dataset. Avoid restoring system indexes unless intentionally replicating the original cluster configuration.
  • Modify restored settings: Adjust index-level settings such as replica counts or shard configurations to match the target cluster. Rename indexes if needed to avoid naming conflicts.
  • Validate and test: Verify that data has been restored correctly. Test application functionality and query responses. Confirm all performance and access requirements are met.
  • Switch traffic to the new cluster: After validation, update applications and services to point to the new cluster. Keep the source cluster available as a fallback until fully transitioned.

Snapshot and restore is a flexible approach but may require downtime unless change data capture is implemented. It supports forward migration by one major version, and for larger version jumps, an intermediate upgrade step may be necessary.

Migration assistant

Migration Assistant is a guided migration tool to simplify and automate the process of upgrading or moving workloads to OpenSearch. It offers a low-risk, resilient path for migrating both Elasticsearch and OpenSearch clusters, particularly in complex environments with live traffic or multi-version upgrade requirements.

This method uses a combination of tools to handle key migration phases: data transfer, live traffic replication, and system validation. It supports features like Capture and Replay to mirror real-world usage, a CLI-based migration console for orchestration, and backfill capabilities to restore historical data from snapshots. These tools allow users to compare behavior and performance between clusters before making the switch.

This method is best suited for organizations that need to:

  • Perform multi-version upgrades
  • Minimize service interruptions
  • Run side-by-side comparisons of performance and functionality
  • Revert changes if issues are detected during migration

Though it requires additional infrastructure and setup, the benefits in risk reduction and automation make it well-suited for production workloads with strict availability and consistency requirements. (NOTE: As of the publication of this article the OpenSearch Migration Assistant doesn’t support all features, such as ISM, but broader feature support is expected soon).

Related content: Migration methods – pros and cons

Best practices for OpenSearch migration

Consider the following practices when migrating to OpenSearch.

1. Plan downtime or use zero-downtime strategies

Careful migration planning includes anticipating potential downtime during key phases, such as snapshot creation, restoration, or index backfills. For less critical systems or where short service disruptions are acceptable, schedule downtime during low-traffic maintenance windows and clearly communicate timelines with stakeholders to reduce business impact. Document a detailed runbook and rollback strategy for unexpected issues.

For high-availability or mission-critical environments, implement zero-downtime migration strategies. These can include blue-green deployments, dual-read/write or event streaming between clusters, and phased cutovers. Zero-downtime approaches minimize service interruptions but require more sophisticated tooling, data pipelines, and exhaustive testing. Consider your SLAs, complexity tradeoffs, and the tolerance for temporary overhead.

2. Optimize index settings before data load

Review and adjust index settings in OpenSearch before large-scale data loads or reindexing. Tune shard counts, replicas, refresh intervals, and mapping types for optimal performance. Temporarily defer expensive index operations such as automatic refresh or replication during initial bulk loading to speed up ingestion and reduce operational strain on the new cluster.

After data loading is complete, revert index settings to production values to regain resiliency and correctness. Validate that all index mappings, analyzers, and settings match application expectations. Pre- and post-load index optimizations reduce outage risk, improve ingest performance, and prevent post-migration surprises in query results or operational costs.

3. Secure snapshots and access credentials

Secure backup snapshots and cluster credentials to prevent data loss and unauthorized access. Use encrypted storage for repositories, restrict snapshot access to privileged users, and audit permissions regularly. Store credentials and keys in secure vaults or secret management systems, not in scripts or plain text files, to minimize exposure risk during and after migration.

Before, during, and after migration, rotate credentials and access tokens where feasible. Remove old or temporary access credentials from source and destination systems post-migration. Document your access controls and backup retention policies to demonstrate compliance with regulatory requirements and internal governance standards.

4. Automate repeatable steps with scripts

Automate migration steps, such as snapshot creation, index restoration, metadata export/import, and validation tasks, using scripts or infrastructure-as-code tooling. Automation ensures consistency, speeds up multi-environment migrations, and reduces the risk of human error. Where possible, version control migration scripts and share internally for peer review and reuse.

Test all automation scripts in a staging environment before production runs. Parameterize scripts so they adapt to cluster size, repository paths, or index names as needed. Post-migration, retain and update your automation assets for use in scaling, further migrations, or disaster recovery scenarios. Automation improves reliability and operational efficiency.

5. Regularly monitor logs and metrics

Establish comprehensive monitoring for both source and OpenSearch environments during and after migration. Collect cluster health metrics (heap usage, search and indexing throughput, disk utilization), monitor logs for errors or deprecation warnings, and set alerts for anomalies such as node dropouts or unassigned shards. Proactive monitoring allows rapid response to problems that can arise during migration phases.

Continue monitoring after migration cutover to detect and resolve emerging issues. Track application query latency, error rates, and end-user experience. Use metrics to inform post-migration tuning, scaling, and to demonstrate successful migration performance. Regular, detailed monitoring ensures continued data integrity, reliability, and user satisfaction with your OpenSearch deployment.

Seamlessly migrate from Elasticsearch to OpenSearch with Instaclustr

The journey to OpenSearch can unlock new levels of freedom and flexibility for data infrastructure, but the migration path often raises questions and challenges. Instaclustr for OpenSearch provides a clear and seamless transition, empowering organizations to harness the full potential of this powerful, open source search and analytics suite. The Instaclustr Managed Platform is designed to handle the heavy lifting, allowing teams to focus on innovation rather than operational complexity. Instaclustr helps streamline the migration process, ensuring a smooth and secure transfer of data with minimal downtime.

The move to OpenSearch is more than just a change of software; it’s a strategic decision to embrace a truly open, community-driven ecosystem. Unlike the restrictions that have emerged in the Elasticsearch environment, OpenSearch offers the assurance of the Apache 2.0 license, meaning no vendor lock-in and complete control. This open source nature translates into significant cost-effectiveness. By migrating to Instaclustr’s Managed OpenSearch, users benefit from predictable pricing and optimized infrastructure, eliminating the licensing fees and unpredictable costs often associated with proprietary software.

Instaclustr support extends far beyond the initial migration offering a comprehensive managed service that includes proactive monitoring, expert 24/7 support, and continuous performance optimization. Instaclustr ensures OpenSearch clusters are always secure, scalable, and running at peak efficiency. Users gain a trusted guide dedicated to helping them navigate the open source landscape with confidence to achieve business goals.

Discover more about Instaclustr support for OpenSearch: