Solr vs ElasticSearch: Part 4 – Faceting

[Note: for those of you who don’t have the time or inclination to go through all the technical details, here’s a high-level, up-to-date (2015) Solr vs. Elasticsearch overview]

Solr 4 (aka SolrCloud) has just been released, so it’s the perfect time to continue our ElasticSearch vs. Solr series. In the last three parts of the ElasticSearch vs. Solr series we gave a general overview of the two search engines, about data handling, and about their full text search capabilities. In this part we  look at how these two engines handle faceting.

• Solr vs. ElasticSearch: Part 1 – Overview
• Solr vs. ElasticSearch: Part 2 – Indexing and Language Handling
• Solr vs. ElasticSearch: Part 3 – Searching
• Solr vs. ElasticSearch: Part 4 – Faceting
• Solr vs. ElasticSearch: Part 5 – Management API Capabilities
• Solr vs. ElasticSearch: Part 6 – User & Dev Communities Compared

Solr vs ElasticSearch: Part 3 – Searching

[Note: for those of you who don’t have the time or inclination to go through all the technical details, here’s a high-level, up-to-date (2015) Solr vs. Elasticsearch overview]

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In the last two parts of the series we looked at the general architecture and how data can be handled in both Apache Solr 4 (aka SolrCloud) and ElasticSearch and what the language handling capabilities of both enterprise search engines are like. In today’s post we will discuss one of the key parts of any search engine – the ability to match queries to documents and retrieve them.

• Solr vs. ElasticSearch: Part 1 – Overview
• Solr vs. ElasticSearch: Part 2 – Indexing and Language Handling
• Solr vs. ElasticSearch: Part 3 – Searching
• Solr vs. ElasticSearch: Part 4 – Faceting
• Solr vs. ElasticSearch: Part 5 – Management API Capabilities
• Solr vs. ElasticSearch: Part 6 – User & Dev Communities Compared