不爲也 比不能也

The DSL used by Elasticsearch has a single set of components called queries, which can be mixed and matched in endless combinations. This single set of components can be used in two contexts: filtering context and query context.elasticsearch에서 사용되는 DSL은"DSL (Domain Specific Language)"query라 불리는 component의 단일 집합을 가지는데, 이것은 무한한 조합으로 섞이고 일치될 수 있다. 이 단일 component 집합은 두 개의 context, filtering과 query c..

When working with exact values, you will be working with non-scoring, filtering queries. Filters are important because they are very fast. They do not calculate relevance (avoiding the entire scoring phase) and are easily cached. We’ll talk about the performance benefits of filters later in All About Caching, but for now, just keep in mind that you should use filtering queries as often as you ca..

The previous two examples showed a single filter in use. In practice, you will probably need to filter on multiple values or fields. For example, how would you express this SQL in Elasticsearch?앞의 두 가지 예제는 단일 filter의 사용을 보여준다. 실제 상황에서는, 아마도 여러 값 또는 field를 filtering해야 할 것이다. 예를 들면, 아래의 SQL을 Elasticsearch에서는 어떻게 표현할까?SELECT product FROM products WHERE (price = 20 OR productID = "XHDK-A-1293-#fJ3")..

Earlier in this chapter (Internal Filter Operation), we briefly discussed how non-scoring queries are calculated. At their heart is a bitset representing which documents match the filter. When Elasticsearch determines a bitset is likely to be reused in the future, it will be cached directly in memory for later use. Once cached, these bitsets can be reused wherever the same query is used, without..

While all queries perform some sort of relevance calculation, not all queries have an analysis phase.Besides specialized queries like the bool or function_score queries, which don’t operate on text at all, textual queries can be broken down into two families:모든 query가 relevance 연산의 일종을 수행하지만, 모든 query가 analysis 절을 가지지는 않는다. 텍스트를 전혀 다루지 않는, bool 이나 function-score query 같은 특별한 query 이외에, 텍스트를 다루는 ..