We are working on updating this book for the latest version. Some content might be out of date.
We expect a query on structured data like dates and prices to return only documents that match exactly. However, good full-text search shouldn’t have the same restriction. Instead, we can widen the net to include words that may match, but use the relevance score to push the better matches to the top of the result set.
In fact, full-text search that only matches exactly will probably frustrate your users. Wouldn’t you expect a search for “quick brown fox” to match a document containing “fast brown foxes,” “Johnny Walker” to match “Johnnie Walker,” or “Arnold Shcwarzenneger” to match “Arnold Schwarzenegger”?
If documents exist that do contain exactly what the user has queried, they should appear at the top of the result set, but weaker matches can be included further down the list. If no documents match exactly, at least we can show the user potential matches; they may even be what the user originally intended!
We have already looked at diacritic-free matching in Normalizing Tokens, word stemming in Reducing Words to Their Root Form, and synonyms in Synonyms, but all of those approaches presuppose that words are spelled correctly, or that there is only one way to spell each word.
Fuzzy matching allows for query-time matching of misspelled words, while phonetic token filters at index time can be used for sounds-like matching.