Relative PopularityĪccording to DB-Engines, which ranks database management systems and search engines according to their popularity, Elasticsearch is ranked number one, and Solr is ranked number three. Some of its primary features include distributed full-text distributed search, high availability, powerful query DSL, multitenancy, Geo Search, and horizontal scaling. This tool is much younger than Solr, but it has gained a lot of popularity because of its feature-rich use cases.
#Apache lucene release history series#
Elasticsearch is completely based on JSON and is suitable for time series and NoSQL data. It extends Lucene’s powerful indexing and search functionalities using RESTful APIs, and it archives the distribution of data on multiple servers using the index and shards concept. About ElasticsearchĮlasticsearch is also an open-source search engine built on top of Apache Lucene, as the rest of the ELK Stack, including Logstash and Kibana. Solr offers powerful features such as distributed full-text search, faceting, near real-time indexing, high availability, NoSQL features, integrations with big data tools such as Hadoop, and the ability to handle rich-text documents such as Word and PDF. It has been around for almost a decade and a half, making it a mature product with a broad user community.
About Apache SolrĪpache Solr is an open-source search server built on top of Lucene that provides all of Lucene’s search capabilities through HTTP requests. However, they differ significantly in terms of deployment, scalability, query language, and many other functionalities.
This blog post will pit Solr vs Elasticsearch, two of the most popular open source search engines whose fortunes over the years have gone in different directions.īoth of them are built on top of Apache Lucene, so the features they support are very similar.
Performing searches on terabytes and petabytes of data can be challenging when speed, performance, and high availability are core requirements. Searches are integral parts of any application.
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