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..

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..

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..

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..

Leaving postcodes behind, let’s take a look at how prefix matching can help with full-text queries.Users have become accustomed to seeing search results before they have finished typing their query—so-called instant search, or search-as-you-type. Not only do users receive their search results in less time, but we can guide them toward results that actually exist in our index.우편번호를 뒤로 하고, prefix ..