Migrate from Apache Solr to OpenSearch with confidence. Learn why organizations are moving now and how to execute a secure, scalable, and future-ready search migration.

Organizations running Apache Solr are increasingly finding themselves at a crossroads. While Solr has served the search community well for nearly two decades, the landscape of enterprise search is shifting rapidly. This guide provides a comprehensive roadmap for migrating from Apache Solr to OpenSearch, covering everything from strategic rationale to hands-on migration steps.

Why Move Now?

The search engine landscape has fundamentally changed. Apache Solr, once the undisputed leader in open-source search, is showing signs of age. As the Solr maintainers themselves have acknowledged, the project is run by a small group of passionate developers who, despite their best efforts, struggle to keep pace with competitors like OpenSearch.

The evidence is clear: while Solr continues to receive maintenance updates in its 9.x lifecycle, its release velocity has slowed considerably compared to OpenSearch's aggressive development cadence. OpenSearch 3.x arrived in 2025 with major performance improvements, enhanced ML and observability features, and continued investment in vector search capabilities. Meanwhile, Solr's community momentum has been thinning, and the project lacks the funding and roadmap aggression of its rivals.

The OpenSearch Advantage

OpenSearch offers compelling benefits that make migration worthwhile:

Modern AI and Search Capabilities: OpenSearch provides out-of-the-box support for vector search, hybrid search (combining lexical and semantic approaches), and native ML integrations. These capabilities are essential for building AI-augmented search experiences that users now expect.

Fully Managed Operations Available: With Amazon OpenSearch Service, teams eliminate the operational burden of managing clusters, handling upgrades, and maintaining infrastructure. The service automatically handles hardware provisioning, software patching, data backups, and blue/green deployments.

Cloud-Native Scalability: OpenSearch was built with cloud-native deployments in mind, offering automatic scalability, native AWS service integrations (S3, Kinesis, IAM), and high availability across multiple availability zones.

Rich Analytics and Visualization: OpenSearch Dashboards provides powerful, integrated visualization capabilities. Solr, by contrast, relies on third-party tools like Banana or Tableau that require additional configuration and maintenance.

Cost Efficiency: While OpenSearch may appear more expensive than self-managed Solr at first glance, the total cost of ownership tells a different story. Factor in the personnel, time, and effort required for routine Solr maintenance, and OpenSearch often comes out ahead.

For a detailed feature-by-feature comparison, see our Apache Solr vs OpenSearch: Comparison and Key Differences.

Challenges and Considerations

Before diving into migration, it's essential to understand what you're getting into. Migrating from Solr to OpenSearch isn't a simple lift-and-shift operation—there's no direct one-to-one mapping between the two systems.

Key Challenges to Anticipate

API and Feature Differences: While both platforms use Apache Lucene at their core, they expose different APIs. Solr uses a file-based approach with XML configuration, whereas OpenSearch is entirely API-driven with JSON-based configuration. Your application code will need changes. This also translates to client libraries of course.

Query Language Translation: Solr uses query parsers (Standard, DisMax, eDisMax) while OpenSearch uses Query DSL, a JSON-based query language. Your search queries will need to be rewritten. Some nuances exist—for example, term-specific boosting in multi-word queries works differently between platforms.

Schema vs. Mapping: Solr's global schema approach differs from OpenSearch's index-based mappings. You'll need to translate field types, analyzers, tokenizers, and filters, though the underlying Lucene foundation means most concepts map reasonably well.

Configuration Migration: Solr's solrconfig.xml settings don't have direct equivalents in OpenSearch. Commit intervals, circuit breakers, slow query logging, and other settings need to be reconfigured using OpenSearch's index settings and cluster settings APIs.

Custom Plugins: Any custom Solr plugins or customizations will need to be recreated or migrated to work with OpenSearch's plugin architecture.

Security Setup: OpenSearch security involves configuring IAM roles, fine-grained access control, authentication backends, and encryption—a different model from Solr's security approach.

Team Adoption and Maintenance Ramp Up: Perhaps the biggest challenge is change management. Your team is comfortable with Solr's tools and workflows. Factor in training time and expect a learning curve.

Feature Parity Gaps

Some Solr features don't have direct OpenSearch equivalents:

  • Certain specialized query parsers may require workarounds
  • Multi-field aliases work differently
  • Dynamic schema capabilities (updating field types in place) require reindexing in OpenSearch

Planning Your Migration

Before anything else, document your existing Solr configuration thoroughly:

  • Number of shards and replicas
  • Primary shard sizes
  • CPU specifications
  • Total memory and JVM allocation
  • Disk capacity and type
  • Query patterns and performance baselines
  • Custom analyzers and field types in use

Choose Your Migration Strategy

Two primary approaches exist:

Minimal Downtime Migration: Run Solr and OpenSearch in parallel (active-active), gradually shifting traffic and validating results before full cutover. This approach is more complex but ensures zero downtime.

Downtime-Tolerant Migration: If your application can tolerate maintenance windows, simplify the migration by switching over completely during planned downtime.

Size Your OpenSearch Infrastructure

Since both platforms use Lucene, performance is reasonably predictable and comparable. Match or exceed your current Solr resource allocation per shard. Use AWS's OpenSearch Service pricing documentation to select equivalent instance types, and treat the migration as an opportunity to address any existing performance bottlenecks.

Plan Your Proof of Concept

After completing schema mapping and key configurations, conduct a PoC to validate:

  • Search Relevance and Accuracy: Ensure migrated queries produce equivalent or better results
  • Latency: Measure response times under typical and peak conditions
  • User Experience: Validate that search behavior meets user expectations

Migration Steps

With planning complete, let's walk through the technical migration process.

Step 1: Schema Migration

The schema is the foundation of your search implementation. In Solr, the schema.xml or managed-schema file defines fields, dynamic fields, copy fields, and field types with their associated analyzers. OpenSearch uses index mappings and settings to achieve the same goals.

Understanding the Terminology Shift

Solr Term OpenSearch Term Notes
Collection Index Both represent a set of searchable documents
Schema Mapping Both define document structure
FieldType Analyzer (and more) Solr combines data types and analysis; OpenSearch separates them
DynamicField Dynamic Mapping Both automatically handle new fields matching patterns
copyField copy_to Copies field values to a destination field

Type Mappings

Since both platforms use Lucene, field types map reasonably well:

Solr Type OpenSearch Type
string keyword
text_general text (with analyzer)
pint integer
plong long
pfloat float
pdouble double
pdate date
boolean boolean
binary binary
xy_point xy_point
location (lat/lon) geo_point

Migrating Analyzers

Both platforms use Lucene tokenizers and filters, making analyzer migration relatively straightforward. Standard tokenizers, whitespace tokenizers, lowercase filters, ASCII folding—all have direct equivalents.

Example Solr analyzer:

<fieldType name="text_general" class="solr.TextField">
  <analyzer type="index">
    <tokenizer class="solr.StandardTokenizerFactory"/>
    <filter class="solr.LowerCaseFilterFactory"/>
    <filter class="solr.ASCIIFoldingFilterFactory"/>
  </analyzer>
</fieldType>

Equivalent OpenSearch analyzer:

{
  "settings": {
    "analysis": {
      "analyzer": {
        "text_general": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "asciifolding"]
        }
      }
    }
  }
}

We have previosuly open-sourced a Python script that automates the conversion of Solr schema files to OpenSearch index mappings. The tool handles basic field types, tokenizers, token filters, analyzers, dynamic fields, and copy fields. For detailed documentation on how the conversion works and how to extend the tool for custom analyzers, see the Schema Migration from Solr to OpenSearch guide.

AWS Migration Resources

For organizations looking to migrate to Amazon OpenSearch Service, the AWS managed solution - AWS provides comprehensive prescriptive guidance for Solr migrations, including detailed documentation on schema migration workflows. While there isn't a single automated tool that handles the entire migration, AWS's Migrating from Apache Solr to Amazon OpenSearch Service guide walks through each step with examples and best practices.

For organizations with more complex requirements, our team specializes in Solr, OpenSearch and search migrations and will be happy to help in any way needed. You can reach out to us here: https://bigdataboutique.com/solutions/migrate-solr-to-opensearch.

Step 2: Configuration Migration

Solr's solrconfig.xml contains numerous settings that need translation to OpenSearch equivalents.

Key Configuration Mappings

Commit and Refresh Settings: Solr's autoCommit and autoSoftCommit map to OpenSearch's refresh_interval. The default of 1 second works for most use cases; increase to 30-60 seconds during bulk indexing for better throughput.

Circuit Breakers: Configure at the cluster level in OpenSearch rather than per-index.

Slow Query Logging: OpenSearch supports slow logs with separate thresholds for query and fetch phases:

PUT /my-index/_settings
{
  "index.search.slowlog.threshold.query.warn": "10s",
  "index.search.slowlog.threshold.query.info": "5s",
  "index.search.slowlog.threshold.fetch.warn": "1s"
}

Request Handlers: OpenSearch doesn't use explicit request handlers. All searches use the _search or _msearch endpoint. For default query parameters, use search templates.

Compression: Both platforms default to LZ4 (BEST_SPEED). OpenSearch also offers BEST_COMPRESSION, zstd, and zstd_no_dict.

Step 3: Query Migration

Query translation is often the most time-consuming part of migration. Solr and OpenSearch process queries differently:

Solr flow: Client request → request handler → query parser → query execution → response

OpenSearch flow: Client request → REST API → Query DSL parser → query execution → JSON response

Query Translation Examples

Basic Field Query

Solr:

q=title:OpenSearch

OpenSearch:

{
  "query": {
    "match": {
      "title": "OpenSearch"
    }
  }
}

Boolean Query with Filters

Solr:

q=*:*&fq=category:programming&fq=year:[2020 TO *]

OpenSearch:

{
  "query": {
    "bool": {
      "must": { "match_all": {} },
      "filter": [
        { "term": { "category": "programming" } },
        { "range": { "year": { "gte": 2020 } } }
      ]
    }
  }
}

Fuzzy Search

Solr:

q=title:OpenSerch~2

OpenSearch:

{
  "query": {
    "fuzzy": {
      "title": {
        "value": "OpenSerch",
        "fuzziness": 2
      }
    }
  }
}

Phrase Query with Slop

Solr:

q="open source search"~3

OpenSearch:

{
  "query": {
    "match_phrase": {
      "content": {
        "query": "open source search",
        "slop": 3
      }
    }
  }
}

Important Query Differences

Be aware that simple_query_string in OpenSearch, while syntactically similar to Solr queries, behaves differently in some cases—particularly with term-specific boosting in multi-word queries. Complex queries with individual term boosts may require the full bool query syntax.

Step 4: Data Migration

With schema, configuration, and queries prepared, migrate your actual data:

  1. Export from Solr: Use Solr's export handler or query with deep pagination
  2. Transform if needed: Adjust document structure for OpenSearch
  3. Bulk import to OpenSearch: Use the _bulk API for efficient ingestion

Step 5: Verification

After migration, verify everything works correctly:

  • Document counts: Ensure all documents migrated successfully
  • Sample queries: Test representative queries and compare results with Solr
  • Performance benchmarks: Measure latency and throughput against baselines
  • Review reports: Migration tools typically generate reports detailing field mappings, any conversion issues, and statistics

Post-Migration Optimization and Support

Migration is just the beginning. To get the most from OpenSearch, consider these optimization steps:

Performance Tuning

  • Review shard sizes and counts based on actual data volumes
  • Tune JVM heap settings appropriately
  • Configure appropriate refresh intervals for your use case
  • Implement query caching for frequently-run searches

Operational Excellence

  • Set up CloudWatch alarms for key metrics
  • Configure automated snapshots for backup and disaster recovery
  • Establish runbooks for common operational tasks
  • Plan for capacity growth

Leveraging Advanced Features

Now that you're on OpenSearch, explore capabilities that weren't available in Solr:

  • Vector search for semantic similarity
  • ML integrations for anomaly detection and insights
  • OpenSearch Dashboards for visualization and monitoring
  • Security Analytics for threat detection

Getting Expert Help

For organizations wanting to ensure a smooth migration and optimal long-term performance, consider onboarding to Pulse for on-going professional support. Pulse provides automated cluster maintenance and optimization, with proactive expert OpenSearch support services available as well

Whether you need hands-on migration assistance, architecture review, or ongoing operational support, working with experienced professionals can accelerate your journey and help you fully realize the benefits of moving to OpenSearch.

Conclusion

Migrating from Apache Solr to OpenSearch is a significant undertaking, but one that positions your organization for the future of search. With OpenSearch's active development, modern AI capabilities, and managed service benefits, teams can focus on building great search experiences rather than maintaining infrastructure.

The key to success is thorough planning, methodical execution, and proper validation at each step. Take time to understand the differences between platforms, test thoroughly in a proof of concept, and don't hesitate to seek expert assistance when needed.

The search landscape has evolved, and OpenSearch represents the path forward. Start your migration journey today.