Both built on Lucene, but which is right for you? Expert analysis of features, performance, ease of use, and ecosystem to help you choose.
| Feature | Elasticsearch | Apache Solr |
|---|---|---|
| Core Technology | Both built on Apache Lucene | |
| Initial Release | 2010 | 2004 |
| Configuration | JSON-based, simpler | XML-based, more verbose |
| API Style | RESTful JSON | REST + Traditional HTTP |
| Cluster Management | Automatic, built-in | Requires ZooKeeper |
| Scalability | ||
| Real-time Search | ||
| Analytics & Aggregations | Basic | |
| Community Size | Larger, more active | Smaller, mature |
| Learning Curve | Moderate | Steep |
| Managed Cloud Offering | Elastic Cloud | Limited options |
| License | Elastic License 2.0 | Apache 2.0 (Open Source) |
Designed for distributed systems from the ground up. Uses a masterless architecture where any node can handle requests, with automatic cluster formation and shard allocation. Optimized for JSON documents and real-time analytics.
Started as a standalone search server, later evolved distributed capabilities. Uses ZooKeeper for cluster coordination in SolrCloud mode. Emphasizes configurability and traditional search features like joins and faceting.
Winner: Elasticsearch for developer experience and ease of use
Both engines deliver excellent performance as they share the same Lucene core. Real-world performance differences come from architecture and configuration:
Winner: Tie – Performance depends on use case and configuration
Winner: Elasticsearch has significantly larger, more active community
Migrating between Solr and Elasticsearch requires careful planning due to differences in configuration and APIs. We've successfully migrated dozens of applications:
Both are built on Apache Lucene, but Elasticsearch focuses on distributed search with easier clustering and real-time analytics, while Solr emphasizes traditional search with mature enterprise features and flexible configuration. Elasticsearch has better developer experience and JSON-based APIs, while Solr offers more granular control through XML configuration.
Performance is comparable as both use Lucene. Elasticsearch typically has better performance for distributed queries and real-time indexing due to its architecture. Solr can match or exceed Elasticsearch performance for single-node deployments and batch indexing. Real-world performance depends more on configuration and use case than the platform choice.
Elasticsearch is generally considered easier to use with its RESTful JSON APIs, automatic cluster management, and simpler configuration. Solr has a steeper learning curve with XML-based configuration but offers more fine-grained control. Most developers find Elasticsearch faster to get started with.
Consider migrating if you need: better horizontal scaling, easier cluster management, more active community and ecosystem, better real-time analytics, or modern JSON-based APIs. Stay with Solr if you have: complex XML configurations working well, need Solr-specific features like joins and SubQueries, prefer traditional enterprise support, or have deep Solr expertise in your team.
Elasticsearch has a larger, more active community with more plugins, integrations, and third-party tools. Elasticsearch GitHub has 5x more stars and contributors. However, Solr benefits from Apache Software Foundation governance and has a mature, stable community with extensive documentation accumulated over 15+ years.
Our team has deep expertise in both Elasticsearch and Solr. We'll help you make the right choice based on your specific requirements.
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