不爲也 比不能也

Elasticsearch supports a wide range of use-cases across our user base, and more and more of these rely on fast indexing to quickly get large amounts of data into Elasticsearch. Even though Elasticsearch is fast and index performance is continually improved, it is still possible to overwhelm it. At that point you typically see parts of bulk requests getting rejected. In this blog post we will loo..

Engineers can resist anything except giving their processes more resources: bigger, better, faster, more of cycles, cores, RAM, disks and interconnects! When these resources are not a bottleneck, this is wasteful but harmless. For processes like Elasticsearch that run on the JVM, the luring temptation is to turn the heap up; what harm could possibly come from having more heap? Alas, the story is..

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..

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..