I have two servers and one OpenSearch node. The obvious move is to put a node on the second server and call it a cluster.
That works, and it is worth doing. But it does not give you high availability, and the reason is counterintuitive enough that a lot of people find out during their first outage. So this article covers the setup and the limitation together.
Reranking is usually introduced as the next thing you add after hybrid search. Retrieve broadly, then rescore the top candidates with a model that reads query and document together. Relevance improves, everyone is pleased.
The standard criticism of user personas is that they are fiction. Someone runs a workshop, the team invents "Marketing Mary, 34, values efficiency, enjoys yoga," and it goes on a wall. Nobody can say whether Mary is right, because nothing about her is falsifiable. She was assembled from assumptions, and she reflects them back with the authority of a laminated poster.
Here is how relevance tuning usually goes. Someone complains that searching for the company name returns a press release above the About page. You raise the title boost. You try the query. It is fixed. You ship it.
There is a class of search failure you cannot tune your way out of. Someone searches for infrastructure automation; you have an excellent page about configuration management; the words never overlap and BM25 scores it zero. No field boost fixes that, no stemmer, no synonym list you will actually maintain.
Most Drupal search articles stop at "and now it works." That is the easy half. The hard half is the phone call three weeks later where someone points out that searching for the company's own name returns a 2019 press release above the About page, and nobody can explain why.
Every other piece of a search engine operates on data you already have. The crawler is the one component that reaches out and spends somebody else's money — their bandwidth, their CPU, their database connections — without asking first.
So far in this series we have crawled pages, broken them into tokens, and built an index that can tell us which documents contain a term. That leaves the question every search engine actually lives or dies by: in what order do we show them?
In a digital world increasingly dominated by data collection and surveillance, the demand for privacy-first alternatives is on the rise. Among the notable responses to this trend is Qwant, a European search engine that positions itself as a defender of digital privacy and an ethical alternative to mainstream platforms like Google and Bing.
Proximity, in the context of search engines and information retrieval, refers to the closeness or nearness of two or more words or terms within a document or a query. It is often used to determine the relevance of a document to a specific search query by considering the spatial relationships between words or terms.