不爲也 比不能也

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..

In Elasticsearch 6.0 we’re introducing a new feature called Index Sorting. Users can now optimize Elasticsearch indexes to store documents on disk in a specific order. We’re very excited for Index Sorting, as it’s another useful tool in optimizing Elasticsearch performance! Elasticsearch 6.0 에서 Index Sorting 이라 불리는 새로운 기능을 도입하였다. 이제 사용자는 document를 disk에 특정 순서로 저장하여 Elasticsearch index를 최적화할 수 있다..

When using elasticsearch for larger time data analytics use cases, we recommend using time-based indices and a tiered architecture with 3 different types of nodes (Master, Hot-Node and Warm-Node), which we refer to as the "Hot-Warm" architecture. Each node has their own characteristics, which are described below.더 큰 시간 data 분석 사례에 대해 elasticsearch를 사용할 경우, 시간 기반의 index와 3가지 다른 유형의 node(Master, H..

As the project manager of stagemonitor, an open source performance monitoring tool, I've recently been looking for a database to replace the cool-but-aging Graphite Time Series DataBase (TSDB) as the backend. TSDBs are specialised packages for storing (performance) metric data, like the response time of your app or the CPU utilisation of a server. Ultimately, we were looking for a datastore that..

A little over a year ago I described how to maximize indexing throughput with Elasticsearch, but a lot has changed since then so here I explain the changes that affect indexing performance in Elasticsearch 2.0.0.약 1년전, Elasticsearch에서 처리량을 최대화하는 방법에 대해 설명한 적이 있다. 그런데, 그때에 비해 많은 것이 변경되어, 여기에서 Elasticsearch 2.0.0 에서 index 성능에 영향을 미치는 변경 사항에 대해 설명한다.Store throttling is now automaticPrior to 2.0.0, ..

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..

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 ..

With the automatic refresh process creating a new segment every second, it doesn’t take long for the number of segments to explode. Having too many segments is a problem. Each segment consumes file handles, memory, and CPU cycles. More important, every search request has to check every segment in turn; the more segments there are, the slower the search will be.매초마다 자동 refresh 프로세스가 새로운 segment를 ..

If you are in an indexing-heavy environment, such as indexing infrastructure logs, you may be willing to sacrifice some search performance for faster indexing rates. In these scenarios, searches tend to be relatively rare and performed by people internal to your organization. They are willing to wait several seconds for a search, as opposed to a consumer facing a search that must return in milli..