不爲也 比不能也

Elasticsearch is a very versatile platform, that supports a variety of use cases, and provides great flexibility around data organisation and replication strategies. This flexibility can however sometimes make it hard to determine up-front how to best organize your data into indices and shards, especially if you are new to the Elastic Stack. While suboptimal choices will not necessarily cause pr..

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..

Running Elasticsearch 2.0? Check out this updated post about performance considerations for Elasticsearch 2.0 indexing. Elasticsearch 2.0을 운영 중이라면? Elasticsearch 2.0 indexing에 대한 성능 고려 사항에서 update된 post를 확인해 보자. Elasticsearch users have delightfully diverse use cases, ranging from appending tiny log-line documents to indexing Web-scale collections of large documents, and maximizing indexing thro..

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..

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 ..

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..

Index aliaseseditFields used in alias filters no longer have to exist in the mapping at alias creation time. Previously, alias filters were parsed at alias creation time and the parsed form was cached in memory. Now, alias filters are parsed at request time and the fields in filters are resolved from the current mapping.alias filter에 사용되는 field는 더 이상 alias 생성 시에 mapping에 존재할 필요가 없다. 이전 버전에서, ali..

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..

To add data to Elasticsearch, we need an index—a place to store related data. In reality, an index is just a logical namespace that points to one or more physical shards.Elasticsearch에 데이터를 추가하기 위해서는, 관련된 데이터를 저장할 수 있는 장소인 index 가 필요하다.실제로, index는 하나 이상의 물리적인 shards 를 가리키는 logical namespace 이다.A shard is a low-level worker unit that holds just a slice of all the data in the index. In Inside a Sh..

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..