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..

Elasticsearch is a very versatile platform, that supports a variety of use cases, and provides great flexibility around data organisation and replication strategies. This flexibility can however sometimes make it hard to determine up-front how to best organize your data into indices and shards, especially if you are new to the Elastic Stack. While suboptimal choices will not necessarily cause pr..

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..

Here are some great slides from our Core Elasticsearch: Operations course that help explain the concept. We'd recommend that you take the full course to understand this even better, but I'll provide an overview from our training here:다음은 Core Elasticsearch: Operations 과정(개념을 설명하는)에서 제공하는 몇 개의 멋진 slide이다. 이 내용을 더 잘 이해하기 위해서는 전체 과정을 수강하는 것이 좋겠지만, 여기에서 과정의 개요를 제공한다.Shard allocation is the process o..

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..

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..

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.In this article series, we look at Elasticsearch from a new perspective. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradua..

analysisAnalysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases: FOO BAR, Foo-Bar, foo,bar will probably all result in the terms foo and bar. These terms are what is actually stored in the index. A full text query (not a term query) for FoO:bAR will also be analyzed to the terms foo,bar and will thus match the terms stored in the index. It is ..