不爲也 比不能也

I remember a couple years ago I was at a convention talking to folks about Elasticsearch or Lucene and one of them said something like "Everyone knows that if you really want to search fast you need to just use term queries." He was certainly right about term queries being fast but I really wanted to run some numbers and see just how right he was. And that felt like a good time to write a blog p..

Disable the features you do not need By default Elasticsearch indexes and adds doc values to most fields so that they can be searched and aggregated out of the box. For instance if you have a numeric field called foo that you need to run histograms on but that you never need to filter on, you can safely disable indexing on this field in your mappings:기본적으로, Elasticsearch는 대부분의 field가 검색되고 aggreg..

Finding individual words in a field is all well and good, but sometimes you want to match exact sequences of words or phrases. For instance, we could perform a query that will match only employee records that contain both "rock" and "climbing" and that display the words next to each other in the phrase "rock climbing".특정 field에서 개별적인 단어를 찾는 것은 잘 된다. 하지만 가끔씩 정확한 순서의 단어들이나 구(句, phrases) 로 일치하기를 원한..

Standard full-text search with TF/IDF treats documents, or at least each field within a document, as a big bag of words. The match query can tell us whether that bag contains our search terms, but that is only part of the story. It can’t tell us anything about the relationship between words.TF/IDF를 가진 표준 full-text 검색은, document나, 최소한 document내의 각각의 filed를, 단어가 들어 있는 큰 가방(bag of words) 으로 생각한다. 그..

Requiring exact-phrase matches may be too strict a constraint. Perhaps we do want documents that contain "quick brown fox" to be considered a match for the query "quick fox", even though the positions aren’t exactly equivalent.정확한 구문 일치를 요구하는 것은 너무 엄격한 제약사항이 될 수 있다. 아마도 "quick brown fox" 를 포함하는 document는 query "quick fox" 에 일치하는 것으로 간주하길 원할 것이다. 비록 그 위치가 정확히 일치하지 않더라도 말이다.We can introduce a degr..

A curious thing can happen when you try to use phrase matching on multivalue fields. Imagine that you index this document:다중 값(multi-value) field에 구문 일치를 사용하는 경우, 특이한 상황이 발생할 수 있다. 아래 document를 색인한다고 가정해 보자.PUT /my_index/groups/1 { "names": [ "John Abraham", "Lincoln Smith"] }COPY AS CURLVIEW IN SENSE Then run a phrase query for Abraham Lincoln:그 다음에, Abraham Lincoln 에 대한 phrase query를 실행해 보자.GE..

Whereas a phrase query simply excludes documents that don’t contain the exact query phrase, a proximity query—a phrase query where slop is greater than 0—incorporates the proximity of the query terms into the final relevance _score. By setting a high slop value like 50 or 100, you can exclude documents in which the words are really too far apart, but give a higher score to documents in which the..

Phrase and proximity queries are more expensive than simple match queries. Whereas a matchquery just has to look up terms in the inverted index, a match_phrase query has to calculate and compare the positions of multiple possibly repeated terms.phrase와 proximity query는, 단순한 match query에 비해, 더 많은 비용이 든다. match query는 단어를 inverted index에서 찾는 반면에, match_phrase query는 가능한 한 여러 번, 반복해서 단어들의 위치를 계산하고 ..

As useful as phrase and proximity queries can be, they still have a downside. They are overly strict: all terms must be present for a phrase query to match, even when using slop.phrase와 proximity query는 유용하지만, 단점이 있다. 지나치게 엄격하다. phrase query에 일치하기 위해, 심지어 slop 을 사용할 경우에도, 모든 단어가 반드시 존재해야 한다.The flexibility in word ordering that you gain with slop also comes at a price, because you lose the assoc..

About 5% of all queries are phrase queries (see Phrase Matching), but they often account for the majority of slow queries. Phrase queries can perform poorly, especially if the phrase includes very common words; a phrase like "To be, or not to be" could be considered pathological. The reason for this has to do with the amount of data that is necessary to support proximity matching.모든 query의 약 5%는..