Distributed search

Find the documents that best match a free-text query, ranked by relevance, fast, across more data than one machine holds. Getting it wrong means either slow LIKE '%term%' scans that melt the primary database, or a search box that returns the wrong results and erodes user trust — both are silent until traffic or corpus size exposes them.

When to reach for this

Users type words and expect ranked, relevant matches — not exact-key lookups. The corpus is text-heavy (documents, products, logs, messages), queries are ad-hoc (any term, any combination), and results need ranking, highlighting, facets, or typeahead. Reach for it when a WHERE col LIKE or full-table scan is already the read bottleneck, or when you need fuzzy/partial matching a B-tree index cannot serve.