不爲也 比不能也

Let’s make the search a little more complicated. We still want to find all employees with a last name of Smith, but we want only employees who are older than 30. Our query will change a little to accommodate a filter, which allows us to execute structured searches efficiently:약간 더 복잡한 검색을 해 보자. last name이 Smith인 모든 직원을 찾으면서, 나이가 30보다 많은 직원을 찾으려 한다. query는 구조화된 검색을 효율적으로 실행할 수 있는 filter 를 넣기 위해, ..

The searches so far have been simple: single names, filtered by age. Let’s try a more advanced, full-text search—a task that traditional databases would really struggle with.지금까지의 검색은 이름 하나만을 검색하고, 나이를 filtering하는 단순한 검색이었다. 기존의 DB와 진정으로 겨뤄볼 수 있는, 더 고급스러운 full-text 검색을 해 보자.We are going to search for all employees who enjoy rock climbing:rock climbing을 즐겨 하는 모든 직원을 찾아보자.GET /megacorp/employee/_s..

Finding individual words in a field is all well and good, but sometimes you want to match exact sequences of words or phrases. For instance, we could perform a query that will match only employee records that contain both "rock" and "climbing" and that display the words next to each other in the phrase "rock climbing".특정 field에서 개별적인 단어를 찾는 것은 잘 된다. 하지만 가끔씩 정확한 순서의 단어들이나 구(句, phrases) 로 일치하기를 원한..

Many applications like to highlight snippets of text from each search result so the user can see whythe document matched the query. Retrieving highlighted fragments is easy in Elasticsearch.많은 응용프로그램은 왜 document가 query에 일치하는지를, 사용자가 알 수 있도록, 각 검색 결과에 하이라이트(highlight) 표시를 해주기를 원한다. 하이라이트 표시는 Elasticsearch에서 아주 쉽다.Let’s rerun our previous query, but add a new highlight parameter:조금 전의 query에 highl..

Finally, we come to our last business requirement: allow managers to run analytics over the employee directory. Elasticsearch has functionality called aggregations, which allow you to generate sophisticated analytics over your data. It is similar to GROUP BY in SQL, but much more powerful.이제 마지막 요구 사항을 처리해 보자. 요구 사항은 관리자가 전체 직원 임명 사전을 분석할 수 있도록 해 주는 것이다. Elasticsearch는 aggregation 이라는 기능을 가지고 있는..

Hopefully, this little tutorial was a good demonstration about what is possible in Elasticsearch. It is really just scratching the surface, and many features—such as suggestions, geolocation, percolation, fuzzy and partial matching—were omitted to keep the tutorial short. But it did highlight just how easy it is to start building advanced search functionality. No configuration was needed—just ad..

At the beginning of this chapter, we said that Elasticsearch can scale out to hundreds (or even thousands) of servers and handle petabytes of data. While our tutorial gave examples of how to use Elasticsearch, it didn’t touch on the mechanics at all. Elasticsearch is distributed by nature, and it is designed to hide the complexity that comes with being distributed.이 장을 시작하면서, Elasticsearch가 수백 개..

By now you should have a taste of what you can do with Elasticsearch, and how easy it is to get started. Elasticsearch tries hard to work out of the box with minimal knowledge and configuration. The best way to learn Elasticsearch is by jumping in: just start indexing and searching!지금까지 Elasticsearch를 가지고 무엇을 할 수 있는지, 얼마나 쉽게 시작할 수 있는지 맛을 보았다. Elasticsearch는 최소한의 지식과 설정을 가지고, 동작할 수 있도록 노력하고 있다. E..

Supplemental ChapterAs mentioned earlier, this is the first of several supplemental chapters about how Elasticsearch operates in a distributed environment. In this chapter, we explain commonly used terminology like cluster, node, and shard, the mechanics of how Elasticsearch scales out, and how it deals with hardware failure.앞에서 언급했듯이, 이 장은 분산 환경에서 Elasticsearch가 어떻게 동작하는지에 대한 여러 추가 장들 중의 첫 번째이다..

If we start a single node, with no data and no indices, our cluster looks like Figure 1, “비어있는 단일 node로 된 cluster”.데이터도 없고, indices도 없는, 단일 node로 시작해보면, cluster는 Figure 1, “비어있는 단일 node로 된 cluster”처럼 보일 것이다.Figure 1. 비어있는 단일 node로 된 cluster A node is a running instance of Elasticsearch, while a cluster consists of one or more nodes with the same cluster.name that are working together to share th..