不爲也 比不能也

This is the final post in a three-part series about Instant Aggregations. Read how it all started in The Tale of Caching and Why It Matters from Simon Willnauer and Shay Banon and the meaty middle detailed in The Great Query Refactoring: Thou Shalt Only Parse Once. Enjoy the trilogy! 이 게시물은 Instant Aggregations에 대한 시리즈 중 마지막 게시물이다. The Tale of Caching and Why It Matters에서 Simon Willnauer와 Shay B..

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.As much as we love Elasticsearch, at Found we've seen customers crash their clusters in numerous ways. Mostly due to simple misunderstandings and usually the fixe..

Significant terms on numeric fieldseditNumeric fields have been refactored to use a different data structure that performs better for range queries. However, since this data structure does not record document frequencies, numeric fields need to fall back to running queries in order to estimate the number of matching documents in the background set, which may incur a performance degradation.numer..

Rivers have been removededitElasticsearch does not support rivers anymore. While we had first planned to keep them around to ease migration, keeping support for rivers proved to be challenging as it conflicted with other important changes that we wanted to bring to 2.0 like synchronous dynamic mappings updates, so we eventually decided to remove them entirely. See Deprecating Rivers for more bac..

Partial fieldseditPartial fields have been removed in favor of source filtering.source filtering를 위하여 partial field는 제거되었다.search_type=count deprecatededitThe count search type has been deprecated. All benefits from this search type can now be achieved by using the (default) query_then_fetch search type and setting size to 0.count search type은 deprecate되었다. 이 search type으로 얻을 수 있었던 것은 이제 (기본적으로)..

Our preceding empty search told us that 14 documents in the cluster match our (empty) query. But there were only 10 documents in the hits array. How can we see the other documents?이전의 empty search에서, cluster에는 (empty) query에 일치하는 14개의 document가 있다. 그런데 hits 배열에는 10개의 document만 있었다. 다른 document는 어떻게 볼 수 있을까?In the same way as SQL uses the LIMIT keyword to return a single "page" of results, Elasti..

During the initial query phase, the query is broadcast to a shard copy (a primary or replica shard) of every shard in the index. Each shard executes the search locally and builds a priority queue of matching documents.초기 query 절 에서, query는 index에 있는 모든 shard의, shard 복사본(primary 또는 replica shard)에 전달된다. 각 shard는 내부적으로 검색을 실행하고, 일치하는 document의 우선순위(priority) queue 를 생성한다.우선순위(priority) QueueA prio..

The query phase identifies which documents satisfy the search request, but we still need to retrieve the documents themselves. This is the job of the fetch phase, shown in Figure 15, “분산 검색의 fetch 절”.query 절은 어떤 document가 검색 request를 만족하는지를 확인할 뿐이다. 이제 document 자체를 가져와야 한다. 이것이, Figure 15, “분산 검색의 fetch 절”에서 보이는 fetch 절의 역할이다.Figure 15. 분산 검색의 fetch 절 The distributed phase consists of the follow..

A scroll query is used to retrieve large numbers of documents from Elasticsearch efficiently, without paying the penalty of deep pagination.scroll query는 Elasticsearch에서 매우 많은 수의 document를, deep 페이지 계산의 불이익 없이, 효과적으로 가져오기 위해 함께 사용된다.Scrolling allows us to do an initial search and to keep pulling batches of results from Elasticsearch until there are no more results left. It’s a bit like a cursor ..

We could spend the next few pages defining the various aggregations and their syntax, but aggregations are truly best learned by example. Once you learn how to think about aggregations, and how to nest them appropriately, the syntax is fairly trivial.다음 몇 페이지에서, 다양한 aggregation과 그 문법에 대해 이야기할 것이다. 그러나, aggregation은 예제로 배우는 것이 제일 좋다. aggregation에 대해 생각하는 방법과, 적절하게 중첩시키는 방법을 배우기만 하면, 문법은 매우 간단하다.A..