Elasticsearch vs Solr

Elasticsearch and Solr are both built on Apache Lucene but differ in architecture, ecosystem, and operational model. Elasticsearch has captured the majority of the market share, but Solr remains a strong choice for specific use cases.

Architecture Differences

Elasticsearch was designed from the ground up as a distributed system. Clustering, shard allocation, and rebalancing are built into the core. A new node joins the cluster and automatically receives shards. Solr added distributed capabilities later through SolrCloud, which relies on Apache ZooKeeper for coordination.

Both use Lucene under the hood for indexing and search. The difference is in the distributed layer: Elasticsearch's is more automated, while Solr's provides more manual control.

Performance Characteristics

For full-text search, performance is comparable since both use Lucene. The difference emerges in analytics: Elasticsearch's aggregation framework is more powerful and flexible than Solr's faceting, especially for nested aggregations and statistical computations.

Elasticsearch's near real-time search (default 1-second refresh) provides faster search visibility than Solr's default configuration. For search system design, this latency matters.

Trade-offs

Elasticsearch's licensing change from Apache 2.0 to SSPL/Elastic License prompted Amazon to fork OpenSearch. Solr remains fully Apache-licensed. For organizations with strict open-source requirements, this matters.

Elasticsearch's ELK Stack (Elasticsearch, Logstash, Kibana, Beats) provides a complete observability platform. Solr lacks an equivalent integrated ecosystem, requiring more assembly of components.

The OpenSearch Factor

AWS's OpenSearch fork is Apache 2.0 licensed and compatible with older Elasticsearch APIs. For teams wanting Elasticsearch-like capabilities with permissive licensing, OpenSearch is an alternative worth evaluating.

Real-World Usage

Elasticsearch powers search and analytics at Netflix, Uber, and Slack. Solr runs search at Apple (Siri), Netflix (for some use cases), and Bloomberg. Both handle petabyte-scale data.

Learn about search system architecture and inverted indexes. See our system design guide and pricing.