不爲也 比不能也

Now we come to a common pattern: cross-fields entity search. With entities like person, product, or address, the identifying information is spread across several fields. We may have a person indexed as follows:이제 일반적인 패턴(여러 field에 대한 항목 검색 - cross fields entity search)을 시작해 보자. 사람(person), 제품(product), 주소(address) 같은 항목의 식별 정보는, 여러 field에 걸쳐 저장한다. 특정인의 정보는 아래처럼 색인될 것이다.{ "firstname": "Peter", "l..

All three of the preceding problems stem from most_fields being field-centric rather than term-centric: it looks for the most matching fields, when really what we’re interested in is the most matching terms.위의 세 가지 문제점 모두는, most_fields 가 단어 중심(term-centric) 이라기 보다는, field 중심(field-centric) 이라는 사실에서 기인한다. 실제로 관심을 가지는 것은 대부분이 일치하는 단어(terms) 인데, 대부분이 일치하는 fields 를 검색한다.The best_fields type is also ..

The custom _all approach is a good solution, as long as you thought about setting it up before you indexed your documents. However, Elasticsearch also provides a search-time solution to the problem: the multi_match query with type cross_fields. The cross_fields type takes a term-centric approach, quite different from the field-centric approach taken by best_fields and most_fields. It treats all ..

The match query supports fuzzy matching out of the box:match query는 기본적으로 퍼지 일치를 지원한다.GET /my_index/my_type/_search { "query": { "match": { "text": { "query": "SURPRIZE ME!", "fuzziness": "AUTO", "operator": "and" } } } }The query string is first analyzed, to produce the terms [surprize, me], and then each term is fuzzified using the specified fuzziness.query string은, 단어 [surprise, me] 를 만들어내기 위..