不爲也 比不能也

Mapping types are going away. Elasticsearch 6.0 supports a single mapping type per index only, and it represents the first step on the way to removing types altogether.mapping type이 없어진다. Elasticsearch 6.0은 index 당 하나의 mapping type만 지원하며, 그것은 type을 완전히 제거하는 것의 첫 번째 단계이다.This blog post explains what types are, why we are removing them, how you can migrate your indices to a single type world, and ..

analysisAnalysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases: FOO BAR, Foo-Bar, foo,bar will probably all result in the terms foo and bar. These terms are what is actually stored in the index. A full text query (not a term query) for FoO:bAR will also be analyzed to the terms foo,bar and will thus match the terms stored in the index. It is ..

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..

A document doesn’t consist only of its data. It also has metadata—information about the document.The three required metadata elements are as follows:document가 데이터만으로 구성되어 있지는 않다. document에 대한 정보인 metadata 도 가지고 있다. 3개의 필수 metadata는 아래와 같다._indexWhere the document livesdocument가 존재하는 장소_typeThe class of object that the document representsdocument를 대표하는 오브젝트 class_idThe unique identifier for the d..

How can we be sure, when we index a document, that we are creating an entirely new document and not overwriting an existing one?document를 색인할 때, 기존 document를 덮어쓰는 것이 아니라, 완전히 새로운 것을 생성한다는 것을 어떻게 보장할 수 있을까?Remember that the combination of _index, _type, and _id uniquely identifies a document. So the easiest way to ensure that our document is new is by letting Elasticsearch autogenerate a new uniq..

In order to be able to treat date fields as dates, numeric fields as numbers, and string fields as full-text or exact-value strings, Elasticsearch needs to know what type of data each field contains. This information is contained in the mapping.date field를 날짜로, 숫자 field를 숫자로, string field를 full-text나 exact-value 문자열로 처리하기 위해서는, Elasticsearch는 각 field가 가지고 있는 데이터의 type을 알아야 한다. 이 정보는 mapping에 있다...

Before moving on, we are going to take a detour and talk about how search is executed in a distributed environment. It is a bit more complicated than the basic create-read-update-delete(CRUD) requests that we discussed in Distributed Document Store.더 나아가기에 앞서, 분산 환경에서 검색을 실행하는 방법에 대해 이야기하려 한다. 이것은 Distributed Document Store에서 이야기했던, 기본적인 CRUD(create-read-update-delete) 보다 약간 더 복잡하다.Content Warni..

We have seen how Elasticsearch makes it easy to start developing a new application without requiring any advance planning or setup. However, it doesn’t take long before you start wanting to fine-tune the indexing and search process to better suit your particular use case. Almost all of these customizations relate to the index, and the types that it contains. In this chapter, we introduce the API..

A type in Elasticsearch represents a class of similar documents. A type consists of a name—such as user or blogpost—and a mapping. The mapping, like a database schema, describes the fields or properties that documents of that type may have, the datatype of each field—such as string,integer, or date—and how those fields should be indexed and stored by Lucene. Elasticsearch에서 type 은 유사한 document의 ..

The uppermost level of a mapping is known as the root object. It may contain the following:mapping의 최상위 단계는 root object 로 알려져 있다. 아래와 같은 것을 포함하고 있다.A properties section, which lists the mapping for each field that a document may containdocument가 가지게 될, 각 field의 mapping을 나열하는 properties 부분.Various metadata fields, all of which start with an underscore, such as _type, _id, and _source_type, _id, _..