不爲也 比不能也

This is the second post in a three-part series about Instant Aggregations. Read how it all started in The Tale of Caching and Why It Matters from Simon Willnauer and Shay Banon and how the magnificent end to the story in Instant Aggregations: Rewriting Queries for Fun and Profit. Enjoy the trilogy!이 게시물은 Instant Aggregation에 대한 시리즈 중 두번째 게시물입니다. Simon Willnauer과 Shay Banon의 The Tale of Caching a..

This is the first post in a three-part series about Instant Aggregations. See how the story progresses in The Great Query Refactoring: Thou Shalt Only Parse Once and wraps up in Instant Aggregations: Rewriting Queries for Fun and Profit. Enjoy the trilogy! 이 게시물은 Instant Aggregation에 대한 시리즈 중 첫번째 게시물입니다. The Great Query Refactoring: Thou Shalt Only Parse Once에서 어떻게 진행되는지 확인하고 Instant Aggregation..

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..

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 ..

Finally, we come to our last business requirement: allow managers to run analytics over the employee directory. Elasticsearch has functionality called aggregations, which allow you to generate sophisticated analytics over your data. It is similar to GROUP BY in SQL, but much more powerful.이제 마지막 요구 사항을 처리해 보자. 요구 사항은 관리자가 전체 직원 임명 사전을 분석할 수 있도록 해 주는 것이다. Elasticsearch는 aggregation 이라는 기능을 가지고 있는..

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 ..

In the same way as we need to use the special nested query to gain access to nested objects at search time, the dedicated nested aggregation allows us to aggregate fields in nested objects: 검색 시에, nested object에 액세스하기 위해, 특별히 nested query를 사용해야 하는 것과 동일한 방식으로, nested object에 있는 field를 aggregation하기 위한, 전용 nested aggregation이 있다.GET /my_index/blogpost/_search { "size" : 0, "aggs": { "comments": {..