How GitHub Docs’ new search works

This post details the migration of GitHub Docs site search from an in-memory solution to Elasticsearch. It explains the local Elasticsearch setup for developers, the core search implementation strategy of a single, complex query to Elasticsearch, and the detailed ranking logic. The ranking logic involves a matrix of matching techniques (match_phrase, match with AND/OR) applied to different fields (title, headings, content) with distinct analyzers ('explicit' vs. 'regular' with stemming/synonyms). Each combination has a calculated boost, and the post illustrates the boost values and their impact on result ranking. It also mentions the use of pageview metrics for further ranking refinement.