不爲也 比不能也

UPDATE: This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.이 게시물은 기존에 Found라는 이름으로 제공된 Elasticsearch 서비스에 관한 것이다. Found은 이제 Elasticsearch Cloud로 알려져 있다.The previous article in this series, Elasticsearch from the Bottom Up, covered essential data structures within a single shard. In this article, we will look at t..

UPDATE: This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud.이 게시물은 기존에 Found라는 이름으로 제공된 Elasticsearch 서비스에 관한 것이다. Found은 이제 Elasticsearch Cloud로 알려져 있다.In this article series, we look at Elasticsearch from a new perspective. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradua..

The first order of business is storing employee data. This will take the form of an employee document: a single document represents a single employee. The act of storing data in Elasticsearch is called indexing, but before we can index a document, we need to decide where to store it.첫 번째 요구사항은 직원 데이터를 저장하는 것이다. 이것은 employee document 의 형태를 가질 것이다. 하나의 document는 직원 1명을 나타낸다. Elasticsearch에 데이터를 저장..

Whatever program we write, the intention is the same: to organize data in a way that serves our purposes. But data doesn’t consist of just random bits and bytes. We build relationships between data elements in order to represent entities, or things that exist in the real world. A name and an email address have more meaning if we know that they belong to the same person.어떤 program을 작성하더라도, 그 목표(목..

Elasticsearch uses a structure called an inverted index, which is designed to allow very fast full-text searches. An inverted index consists of a list of all the unique words that appear in any document, and for each word, a list of the documents in which it appears.Elasticsearch는 full-text 검색을 매우 빠르게 할 수 있도록 설계된, inverted index 라는 구조를 사용한다. inverted index는 특정 document에 나타나는 유일한 단어 모두의 목록과, 각각의 ..

Our final topic in this chapter is about an internal aspect of Elasticsearch. While we don’t demonstrate any new techniques here, doc values are an important topic that we will refer to repeatedly, and is something that you should be aware of.이 장의 마지막 주제는 Elasticsearch의 내부에 대한 것이다. 여기에서는 새로운 기술을 보여주지는 않지만, doc values는 반복적으로 언급할 중요한 주제이며, 알아야 한다.When you sort on a field, Elasticsearch needs acces..

The third important index setting is the analysis section, which is used to configure existing analyzers or to create new custom analyzers specific to your index.세 번째로 중요한 index 설정은 analysis 부분이다. 이것은 기존의 analyzer를 설정하거나, index에 지정된 새로운 사용자 정의 analyzer를 생성하는데 사용된다.In Analysis and Analyzers, we introduced some of the built-in analyzers, which are used to convert full-text strings into an inverted..

The first challenge that had to be solved was how to make text searchable. Traditional databases store a single value per field, but this is insufficient for full-text search. Every word in a text field needs to be searchable, which means that the database needs to be able to index multiple values—words, in this case—in a single field.해결해야 할 첫 번째 과제는, 텍스트를 검색 가능하도록 만드는 방법이다. 전통적인 데이터베이스는 field당 ..

The next problem that needed to be solved was how to make an inverted index updatable without losing the benefits of immutability? The answer turned out to be: use more than one index.해결해야 할 다음 문제는, 불변성의 장점을 잃지 않고, inverted index를 업데이트 가능하도록 만드는 방법이다. 답은 하나 이상의 index를 사용하는 것이다.Instead of rewriting the whole inverted index, add new supplementary indices to reflect more-recent changes. Each invert..

The term query is useful for finding a single value, but often you’ll want to search for multiple values.What if you want to find documents that have a price of $20 or $30?term query는 단일 값을 찾는데 유용하다. 그러나, 가끔은 다중 값을 찾을 수도 있다. 가격이 $20 또는 $30인 document를 찾으려면 어떻게 해야 할까?Rather than using multiple term queries, you can instead use a single terms query (note the s at the end). The terms query is simply..