不爲也 比不能也

IntroductionIn this blog post, I am going to describe a new feature of Elasticsearch called Adaptive Replica Selection (ARS). Before describing what ARS does, I’ll first describe the problem that it tries to solve. Imagine that you have a cluster with three nodes. And across those three nodes, you have a shard and its replicas.이 게시물에서는 Elasticsearch의 새로운 기능인 Adaptive Replica Selection (ARS)를 설명하..

What If..."What if" questions are fun. "What if you had a time machine: when would you visit?" "What if you had one wish and it would come true: what would it be?" They're the types of hypothetical questions you can ask at a dinner party and get insights about what interests and motives somebody has if barriers were broken down. A few years ago at Elastic, we asked ourselves a "what if" that we ..

Elasticsearch provides failover capabilities by keeping multiple copies of your data in the cluster. In presence of network disruptions or failing nodes, changes might not make it to all the copies. This blog post showcases one of the internal mechanics of Elasticsearch to identify shard copies that are missing changes, providing an in-depth view on how two central components, the consensus modu..

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..

Give memory to the filesystem cache Elasticsearch heavily relies on the filesystem cache in order to make search fast. In general, you should make sure that at least half the available memory goes to the filesystem cache so that Elasticsearch can keep hot regions of the index in physical memory.Elasticsearch는 search 속도를 빠르게 하기 위해, filesystem cache에 많이 의존한다. 일반적으로, Elasticsearch가 index 중 많이 사용하는 ..

Shadow Replicas don’t see much usage and we are planning to remove them.Shadow Replicas는 많이 사용되는 않는 것으로 보여 제거를 계획중이다.

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..

Running a single node means that you have a single point of failure—there is no redundancy.Fortunately, all we need to do to protect ourselves from data loss is to start another node.단일 node를 실행한다는 것은 단일 장애 지점을 가지고 있다(복사본이 없다)는 것을 의미한다. 다행히, 데이터 손실을 피하기 위해, 해야 할 것은, 다른 node를 시작하는 것뿐이다.두 번째 node를 시작하려면To test what happens when you add a second node, you can start a new node in exactly the same wa..

What about scaling as the demand for our application grows? If we start a third node, our cluster reorganizes itself to look like Figure 4, “세 개의 node를 가진 cluster(shard는 부하를 분산하기 위하여 재할당되었다)”.응용프로그램에 대한 수요가 증가함에 따라, 확장하면 어떠할까? 세 번째 node를 시작하면, cluster는 Figure 4, “세 개의 node를 가진 cluster(shard는 부하를 분산하기 위하여 재할당되었다)”처럼 스스로를 인식할 것이다.Figure 4. 세 개의 node를 가진 cluster(shard는 부하를 분산하기 위하여 재할당되었다) One shar..