不爲也 比不能也

In Time Series Data and MongoDB: Part 1 – An Introduction we reviewed the key questions you need to ask to understand query access patterns to your database. In Time Series Data and MongoDB: Part 2 – Schema Design Best Practices we explored various schema design options for time-series data and how they affect MongoDB resources. In this blog post we will cover how to query, analyze and present t..

In the previous blog post, “Time Series Data and MongoDB: Part 1 – An Introduction,” we introduced the concept of time-series data followed by some discovery questions you can use to help gather requirements for your time-series application. Answers to these questions help guide the schema and MongoDB database configuration needed to support a high-volume production application deployment. In th..

Time-series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time-series data can enable organizations to better detect and respond to events ahead of their competitors, or to improve operational efficiency to reduce cost and risk. Working..

In Elasticsearch 6.0, we released a new feature called index sorting. Read up more on this on the linked blog, but in short, what this does is to take the documents at index time and sort them by a key or set of keys in an order of your choosing. This has a few advantages that we’ve talked about:Elasticsearch 6.0 에서 index sorting이라는 새로운 기능이 출시되었다. link된 게시물에서 이에 대해 자세히 읽어 보자. 간단히 말하자면, index시에 d..

The Elastic Stack is used for a lot of different use cases. One of the most common is to store and analyze different types of event or time-series based data, e.g. security events, logs, and metrics. These events often consist of data linked to a specific timestamp representing when the event occurred or was collected and there is often no natural key available to uniquely identify the event. El..

The Ranking Evaluation API that's been added to Elasticsearch is a new, experimental REST API that lets you quickly evaluate the quality of search results for a typical query set. This can be useful either while developing new search queries, incremental improvements of the query templates of an an existing system, or as a basic monitoring tool to detect changes in the search quality of a system..

This blog post is written for engineers that are always looking for ways to improve the result sets of their search application built on Elasticsearch. The goal of this post is to raise awareness of why you should care about relevance, what components are involved and how you can control them.이 게시물은 Elasticsearch에 구축된 search application의 결과 집합을 개선하는 방법을 모색하고 있는 엔지니어를 위해 작성되었다. 이 게시물의 목표는 relevan..

이 글은 한국어, 중국어, 일본어 검색에 대한 3부작 중 마지막 글입니다. 만약 아직 1부를 읽지 않았다면 1부: 형태소 분석기부터 읽으시기 바랍니다. 엘라스틱서치 6.2를 이용한 한국어, 중국어, 일본어 검색 - 2부: Multi-fields에서는 멀티필드를 이용해서 다국어로 작성된 문서를 인덱싱하고 검색하는 방법을 살펴봤습니다. 하지만 하나의 텍스트를 언어에 상관 없이 4개의 필드에 저장하고 4개의 분석기로 분석하는 것은 시간과 저장공간의 낭비입니다. 이제 마지막으로 language detector를 이용해서 최적화해 보겠습니다. 텍스트는 역시 https://www.pyeongchang2018.com/ko/about-the-games에서 발췌했습니다. Language detectorlanguage d..

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 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..