How to optimize search results ranking on Elasticsearch and OpenSearch - from the very first steps to the more advanced features it offers.
Elasticsearch (ES) has been dominating the backend of text search apps that are built for heavy usage and top performance. It’s a text-based search engine database that allows you to index, search, and analyze vast volumes of documents within seconds. The near real-time response is achievable by leveraging a unique indexing algorithm that in addition allows distribution across multiple machines and clusters, to support search at scale.
However, performance in terms of query speed and latency is not the only thing you want to have optimized when working with search engine software like Elasticsearch. Retrieving and correctly ranking the relevant documents is a challenge by itself, one that's often viewed as a tradeoff between how fast the results are returned and what’s their relevancy. ES has multiple techniques to improve search relevance ranking, including built in ML features. In this blog post, I’ll demonstrate how you can set up a cluster and apply some methods to control the relevance of the results.
Read more here: https://bigdataboutique.com/blog/optimizing-elasticsearch-relevance-a-detailed-guide-c9efd3
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