ElasticSearch规范化器性能影响 [英] ElasticSearch normalizer performance impact

问题描述

我们希望使用规范化器能够执行不区分大小写的查询以保持弹性.

We would like to use normalizers to be able to do case insensitive queries to elastic.

"analysis": {
     "normalizer": {
        "custom_analyzer": {
           "type": "custom",
           "filter": [
              "lowercase"
           ]
        }
     }
  }

在我们的关键字属性中使用规范化器的查询操作的性能如何?

Which is the performance in the queries operations using normalizers in our Keyword properties?

阅读文档说明了在索引构建过程中将规范化程序应用于集合属性(我想大多数工作是在索引构建过程中进行的)，但是文档表明在查询数据时还要执行更多操作.

Reading documentation explains during index building it applies the normalizers to the set properties (I guess most of the work is doing during index building) but documentation says some more operations are done when data is queried.

查询数据时的操作仅仅是为了规范化查询文本，还是要执行更多操作?使用规范化器会对性能产生什么影响?

The operations when data is queried is just to normalize the query text, or is doing something more? Which is the performance impact using normalizers?

谢谢

推荐答案

分析器逻辑将一次应用于在索引时间建立索引的数据字段.此外，每次查询时，当针对使用该特定分析器的字段进行搜索时，分析器逻辑都会应用于您的查询.总而言之，分析器将在索引时间(在您的保存输入中)应用一次，并在查询时间(在您的搜索输入中)应用一次.

The analyzer logic will be applied on the field of the data you are indexing on index time once. Moreover every time you do a query the analyzer logic will be applied on your query when search against the field that uses that particular analyzer. Summarising, an analyzer will be applied once on index time (on your save input) and always on query time (on your search input).

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