With Elasticsearch and Solr, you can easily customize analysis and scoring. There are several scoring algorithms built into them [1] such as BM25 (the default n

With Elasticsearch and Solr, you can easily customize analysis and scoring. There are several scoring algorithms built into them [1] such as BM25 (the default now) which is considered the state of the art for keyword relevance. For analysis, you can remove stopwords, stem, apply synonyms, etc [2]. Elasticsearch is specifically designed to scale across multiple machines which is necessary for TB datasets. There are also things like "more like this" queries and context-aware spell checking. Some of that you can do with PG, but not all of it. If PG can do it, it is usually harder to set up. 1. https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html https://www.elastic.co/guide/en/elasticsearch/reference/curr... 2. https://www.elastic.co/guide/en/elasticsearch/guide/current/analysis-intro.html https://www.elastic.co/guide/en/elasticsearch/guide/current/...