Searching natural language is imprecise because computers can’t comprehend entire natural language. Fuzzy Query can find words that need at most a certain number of character modifications to match. In this tutorial, we’re gonna look at way to use Elasticsearch Fuzzy Query that uses similarity based on Levenshtein edit distance.
In this tutorial, we’re gonna look at way to use Elasticsearch Type Query and Ids Query.
regexp query help us to use regular expression term queries. Elasticsearch will apply the
regexp to the terms produced by the tokenizer for that field (not to the original text).
In this tutorial, we’re gonna look at how to use Elasticsearch Prefix Query & Wildcard Query.
Range Query will match documents with fields that have terms within a certain range.
We had known that Full text queries will analyse query string before executing. In this tutorial, we’re gonna look at term-level queries (Term & Terms Query) that operate on the exact terms which are stored in the inverted index.
These queries are usually used for structured data (numbers, dates, enums…), rather than full text fields.