This built in capability makes Elasticsearch easier to start in a clustered environment vs. Įlasticsearch takes a different approach, by including a built-in feature called Zen, that directly manages cluster states. Solr adds complexity by requiring the ZooKeeper app, but it is more adept in avoiding inconsistencies that often arise from the split-brain issue that is common in Elasticsearch clusters. SolrCloud can provide a high-availability, fault-tolerant environments that distribute indexed content and manage queries across an array of servers. When running Solr in a clustered architecture, it has an optional distributed SolrCloud deployment configuration that is similar to Elasticsearch, but is dependent on an entirely separate application-Apache ZooKeeper. One area that Elasticsearch and Solr differ is in how they (or you) manage them in large clustered environments. Naturally, a search engine should be modular, scale well, and have facilities for replication-to permit easy clustering and accommodate a distributed architecture. Search engines must have the ability to integrate with large applications and manage huge collections that might contain millions or tens of millions of documents. If you’re running JSON apps, Elasticsearch may be a better option since it has a JSON-based config while Solr allows you to more easily modify and write comments in the config files, allowing for more fine grained control. One of the reasons Solr typically takes longer is that its highly configurable, so if you want better tuning options, Solr gives that to you.ĭepending on your type of configuration, you may lean more towards Elasticsearch vs. In addition, for a basic configuration, you can install and run Elasticsearch within a few minutes while Solr takes much longer to install. As reference, version 5.5 of Elastisearch is only 32MB while the most recent version of Solr is 140 MB. Setup and DeploymentĮlasticsearch is fairly easy to setup, and it also has a considerably smaller footprint than Solr. But Solr is by no means out of the game, since it also continues on with steady product releases, enjoys a sizable worldwide community, and open source publisher support. ![]() Based on Google trends, Elasticsearch appears to enjoy considerably more popularity than Solr. Though many core features are similar in each of these search engines, there are many significant differences-especially in regard to scalability, searches, and deployment. Each is built upon the Apache Lucene platform, which itself is also available as open source software. There are two prominent open source search engines available to address this challenge: Elasticsearch and Solr. As mountains of data continue to accumulate, it becomes more challenging to perform efficient searches. In the cloud era, many software applications now generate hundreds of terabytes or even petabytes of data-while continually increasing demand for higher performance. ![]() A comparison between Elasticsearch and Solr, the two most popular open source search engines
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