Introduction
When it comes to powerful search and analytics engines that can handle large datasets, Elasticsearch and Solr are two of the most popular options available in the market. Both of these open-source search engines are built on top of Apache Lucene and offer similar features, such as distributed indexing, sharding, real-time search, and analytics. However, there are some key differences between Elasticsearch and Solr that make them better suited for certain use cases than others. In this article, we will provide an unbiased comparison of Elasticsearch vs Solr, including performance benchmarks, pricing, and more.
Performance
One of the most critical factors to consider when choosing a search engine is performance. Elasticsearch and Solr both claim to be highly performant, but what do the numbers say?
A recent benchmark conducted by OpenSource Connections showed that both Elasticsearch and Solr had similar latencies when querying indexed data. However, Elasticsearch had a significant edge when it came to index rates. Elasticsearch was able to index nearly twice as many documents per second as Solr. The difference in performance may be attributed to Elasticsearch's distributed real-time search capabilities, which allow it to handle large volumes of data more efficiently.
Ease of Use
Another important factor to consider is ease of use. Elasticsearch and Solr both have a wide range of features, which can make them somewhat challenging to configure and deploy. However, Elasticsearch offers a more user-friendly interface and easier installation compared to Solr. Elasticsearch also has a larger developer community and more extensive documentation. This can be particularly useful for companies that don't have experienced search developers.
Pricing
Both Elasticsearch and Solr are open-source and free to use. However, if you need additional features or support, Elasticsearch offers a paid version, while Solr relies on third-party vendors for support. Elasticsearch's paid version offers advanced features such as security and machine learning, which Solr doesn't have.
Conclusion
Both Elasticsearch and Solr are excellent search engines that offer similar features, such as distributed indexing and real-time search. Elasticsearch outperforms Solr in terms of indexing rates, while Solr may be easier to deploy for those with limited search experience. Elasticsearch's paid version is better suited for companies that need advanced features such as security and machine learning. Ultimately, the choice between Elasticsearch and Solr depends on your specific use case and requirements.
References
- OpenSource Connections. "Unleashing Search Performance with Elasticsearch and Solr." Accessed June 1, 2022. https://opensourceconnections.com/blog/2021/07/08/unleashing-search-performance-with-elasticsearch-and-solr/.