不爲也 比不能也

indices.fielddata.cache.expireeditThe experimental feature indices.fielddata.cache.expire has been removed. For indices that have this setting configured, this config will be ignored.실험적인 기능인 indices.fielddata.cache.expire 가 제거되었다. 이 설정을 가진 index의 경우 설정은 무시된다.Forbid changing of thread pool typeseditPreviously, thread pool types could be dynamically adjusted. The thread pool type effectively cont..

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 ..

With the development of per-segment search, the delay between indexing a document and making it visible to search dropped dramatically. New documents could be made searchable within minutes, but that still isn’t fast enough.segment별 검색의 개발로, document의 색인과 검색 시에 보이도록 하는 것 사이에 지연이 크게 발생한다. 새로운 document는 수분 내에 검색이 가능하지만, 아직 충분히 빠르지는 않다.The bottleneck is the disk. Commiting a new segment to disk req..

Without an fsync to flush data in the filesystem cache to disk, we cannot be sure that the data will still be there after a power failure, or even after exiting the application normally. For Elasticsearch to be reliable, it needs to ensure that changes are persisted to disk.fsync 없이, filesystem cache에서 디스크로, 데이터를 flush하면, 정전 후에 또는 응용프로그램이 정상적으로 종료된 후에, 데이터가 거기 있다고 확신할 수 없다. 신뢰할 수 있는 Elasticsearc..

When working with exact values, you will be working with non-scoring, filtering queries. Filters are important because they are very fast. They do not calculate relevance (avoiding the entire scoring phase) and are easily cached. We’ll talk about the performance benefits of filters later in All About Caching, but for now, just keep in mind that you should use filtering queries as often as you ca..

Earlier in this chapter (Internal Filter Operation), we briefly discussed how non-scoring queries are calculated. At their heart is a bitset representing which documents match the filter. When Elasticsearch determines a bitset is likely to be reused in the future, it will be cached directly in memory for later use. Once cached, these bitsets can be reused wherever the same query is used, without..

While all queries perform some sort of relevance calculation, not all queries have an analysis phase.Besides specialized queries like the bool or function_score queries, which don’t operate on text at all, textual queries can be broken down into two families:모든 query가 relevance 연산의 일종을 수행하지만, 모든 query가 analysis 절을 가지지는 않는다. 텍스트를 전혀 다루지 않는, bool 이나 function-score query 같은 특별한 query 이외에, 텍스트를 다루는 ..

Once analyzed strings have been loaded into fielddata, they will sit there until evicted (or your node crashes). For that reason it is important to keep an eye on this memory usage, understand how and when it loads, and how you can limit the impact on your cluster.일단 analyzed string이 fielddata에 로드되면, 그것들은 추출될 (또는 node가 crash)) 때까지 그 곳에 있을 것이다. 이런 이유로 이런 메모리 사용을 관찰하고, 그것이 언제 어떻게 로드되는지를 이해하고, clus..

The default behavior of Elasticsearch is to load in-memory fielddata lazily. The first time Elasticsearch encounters a query that needs fielddata for a particular field, it will load that entire field into memory for each segment in the index.Elasticsearch의 기본 동작은 in-memory fielddata를 지연시켜(lazily) 로드 하는 것이다. Elasticsearch가 특정 field에 대해, fielddata가 필요한, query를 처음으로 만나면, index에 있는 각 segment에 대해, 전..