不爲也 比不能也

Welcome Back! Last time on Intro to Aggregations, we explored the basic concepts of aggregations and how to start using them. We toyed with simple bucket and metric aggregations, which gave us simple analytics.지난 번에 Intro to Aggregations에서, aggregation의 기본 개념과 사용방법을 살펴보았다. 간단한 bucket과 metric을 다루었는데, 이는 간단한 analytic을 제공했다.Today, we are going to learn about sub-aggregations. The most powerful feat..

Many people are familiar with Elasticsearch for its search functionality. And while it is excellent at search, many organizations are using Elasticsearch for an entirely different purpose: analytics.많은 이들이 search 기능에 대해서는 Elasticsearch에 익숙하다. search에는 탁월하지만, 많은 조직에서는 전혀 다른 목적인 analytic(분석)을 위해 Elasticsearch를 사용하고 있다.Beneath the surface of Elasticsearch is a powerful analytics engine, waiting to ..

Like the query DSL, aggregations have a composable syntax: independent units of functionality can be mixed and matched to provide the custom behavior that you need. This means that there are only a few basic concepts to learn, but nearly limitless combinations of those basic components.query DSL과 마찬가지로, aggregation은 구성이 가능한 문법을 가지고 있다. 필요한 사용자 정의 동작을 제공하기 위해, 독립적인 기능 단위를 혼합하고 일치될 수 있다. 즉, 배워야 할 ..

A bucket is simply a collection of documents that meet certain criteria:bucket 은 어떤 기준을 만족시키는 document의 단순한 집합이다An employee would land in either the male or female bucket.종업원은 남성(male) 이나 여성(female) bucket에 포함된다.The city of Albany would land in the New York state bucket.올버니(Albany)는 뉴욕(New York) 주 bucket에 포함된다.The date 2014-10-28 would land within the October bucket."2014-10-28" 이란 날짜는 10월(Octob..

Buckets allow us to partition documents into useful subsets, but ultimately what we want is some kind of metric calculated on those documents in each bucket. Bucketing is the means to an end: it provides a way to group documents in a way that you can calculate interesting metrics.bucket은 document를 유용한 부분집합으로 분류한다. 그러나, 궁극적인 목표는, 각 bucket에 있는 해당 document에서 계산된, 어떤 종류의 metric이다. bucket으로 분류하는 것은 어..

An aggregation is a combination of buckets and metrics. An aggregation may have a single bucket, or a single metric, or one of each. It may even have multiple buckets nested inside other buckets. For example, we can partition documents by which country they belong to (a bucket), and then calculate the average salary per country (a metric).aggregation 는 bucket과 metric의 조합이다. aggregation은 단일 bucke..

Just as we have seen a dozen times already, buckets can be nested in buckets for more-sophisticated behavior. For illustration, we will build an aggregation that shows the total sum of prices for all makes, listed by quarter. Let us also calculate the sum of prices per individual make per quarter, so we can see which car type is bringing in the most money to our business:이미 여러 번 본 것과 마찬가지로, 더 복잡..

Multivalue buckets—the terms, histogram, and date_histogram—dynamically produce many buckets. How does Elasticsearch decide the order that these buckets are presented to the user?다중 값 bucket(terms, histogram, date_histogram)은 동적으로 많은 bucket을 생성한다. 이런 bucket이 사용자에게 표시되는 순서를 Elasticsearch는 어떻게 결정할까?By default, buckets are ordered by doc_count in descending order. This is a good default because oft..

These sort modes are intrinsic to the bucket: they operate on data that bucket generates, such as doc_count. They share the same syntax but differ slightly depending on the bucket being used.이 정렬 방식은 bucket의 기본 이다. doc_count 처럼, bucket이 생성한 데이터에 따라 동작한다. 동일한 문법을 공유하지만, 사용된 bucket에 따라 약간 다르다.Let’s perform a terms aggregation but sort by doc_count, in ascending order:terms aggregation을 해보자. 그러나 do..

In the prior examples, the metric was a direct child of the bucket. An average price was calculated for each term. It is possible to sort on deeper metrics, which are grandchildren or great-grandchildren of the bucket—with some limitations.이전의 예제에서, metric은 bucket의 직접적인 자식이었다. 평균 가격은 각 단어에 대해 계산된 값이었다. 더 아래의(deeper) metric으로 정렬하는 것이 가능하다. 약간의 제한이 있지만, bucket의 손자, 증손자도 가능하다.You can define a path ..