How does Solr compare to Elasticsearch?

Solr and Elasticsearch are both open-source search engines built on Apache Lucene, but they differ in design priorities, ecosystem integration, and typical use cases. Solr, developed by the Apache Foundation, is often chosen for enterprise search applications with structured data, such as e-commerce product catalogs or content management systems. Elasticsearch, created by Elastic NV, emphasizes scalability and real-time analytics, making it popular for log analysis (via the ELK stack) and monitoring. While both handle full-text search efficiently, Elasticsearch leans toward distributed systems and near-real-time operations, while Solr offers more mature features for complex query precision and document processing.

Architecturally, Elasticsearch simplifies horizontal scaling with its distributed design. It uses a built-in node discovery system for cluster management, reducing dependencies. For example, adding nodes to an Elasticsearch cluster typically requires minimal configuration. Solr traditionally relied on a master-slave architecture but now offers SolrCloud, which uses Apache ZooKeeper for coordination. SolrCloud provides similar scalability but introduces more moving parts. In practice, Elasticsearch clusters are often perceived as easier to scale dynamically for cloud-native environments, while Solr’s ZooKeeper dependency appeals to teams already using it for other services (like Kafka) or needing fine-grained control over cluster state.

Feature-wise, Elasticsearch prioritizes developer experience with a JSON-centric REST API and integrated tools like Kibana for visualization. Its aggregation framework supports complex analytics, such as calculating percentile latency metrics from log data. Solr excels in data preprocessing with features like custom update request processor chains and XML-based configurations, which benefit legacy systems. For example, Solr’s built-in rich document processing (e.g., OCR for PDFs) requires fewer third-party tools. Elasticsearch’s licensing shift to SSPL in 2021 has led some organizations to favor Solr’s Apache 2.0 license for avoiding proprietary dependencies. However, Elasticsearch maintains a larger ecosystem of commercial plugins and enterprise support. The choice often hinges on whether real-time analytics (Elasticsearch) or precise search customization (Solr) aligns better with project requirements.