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..

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..

Anybody who uses Elasticsearch for indexing time-based data such as log events is accustomed to the index-per-day pattern: use an index name derived from the timestamp of the logging event rounded to the nearest day, and new indices pop into existence as soon as they are required. The definition of the new index can be controlled ahead of time using index templates.log event처럼 시간 기반의 data를 index..

Disable the features you do not need By default Elasticsearch indexes and adds doc values to most fields so that they can be searched and aggregated out of the box. For instance if you have a numeric field called foo that you need to run histograms on but that you never need to filter on, you can safely disable indexing on this field in your mappings:기본적으로, Elasticsearch는 대부분의 field가 검색되고 aggreg..