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 ..

Suggest stats metrics deprecatededitThe suggest stats were previously folded into the search on the indices stats API. As such, the suggest metric on the indices stats API has been a synonym for the search metric. In 6.3.0, the suggest metric is deprecated in favor of using search.이전에 suggest stats은 indices stats API의 search에 포함되어 있었다. 따라서, indices stats API의 suggest metric은 search metric의 동의어(s..

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 ..

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..

Often, you’ll find yourself wanting to sort based on a metric’s calculated value. For our car sales analytics dashboard, we may want to build a bar chart of sales by car color, but order the bars by the average price, ascending.metric의 계산된 값을 기준으로 정렬해야 하는 경우가 있다. 자동차 판매 분석 대시보드에서, 자동차 색상으로, 판매 bar chart를 만든다고 가정해 보자. 그런데, bar의 순서는 평균가의 오름차순이다.We can do this by adding a metric to our bucket, and ..

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 ..