Solr vs Elastic search. Quick comparison

 

Both search engines are based on Lucene, therefore they have a very solid base and in some respects similar functionality. Lucene is responsible for managing what is called an inverted index, so that search terms defined by the user can be found in certain documents of an unstructured nature, usually texts.

 

However, they have certain differences that we detail below.

Solr Elasticsearch
Data visualization
Better data visualization thanks to Kibana
Popularity
More popular
Management
Easier
Customization
More customizable
Export capabilities
More export formats
Processing large amounts of data
Greater ability to process large amounts of data
Scalability
More scalable
Search type
More text-oriented More oriented to analytical searches and data grouping
Documentation
Better and more detailed documentation
Community
Wide "open source" community The company, elastic, controls the direction of the project

Visualization and User Interface of Solr vs Elasticsearch

Elasticsearch, thanks to the ability that Kibana brings to it, has more ability to generate analytical reports and data exploration since Solr has traditionally focused on text searches. So if data visualization is critical, elasticsearch provides more capacity. However, it should be noted that Solr has been incorporating plugins that have increased its functionality in terms of viewing reports.

Solr vs Elasticsearch scalability

This is one of the main reasons why Elasticsearch was developed, so that you can better manage growth without losing quality. However, Solr has managed to improve its scalability thanks to the creation of SolrCloud and integration with Zookeeper.

Solr vs Elasticsearch customization

Solr is more customizable, and can be adjusted to suit the user's needs. The ability to customize Elasticsearch to suit the user's needs is more limited.