What is a good use case of the omit_norms
option in elasticsearch? I could not find adequate explanation in es website.
相关问题
- JCR-SQL - contains function doesn't escape spe
- not found value index error on elastic4s
- Merge dataframes that have indices that one contai
- Issues when replicating from couchbase bucket to e
- Postgresql: query on jsonb column - index doesn
相关文章
- es 单字段多分词器时,textField.keyword无法高亮
- 关于asp.net core 中使用Ueditor获取数据的问题
- .net core3.1路由配置伪链接该怎么配置
- Use savefig in Python with string and iterative in
- ElasticSearch: How to search for a value in any fi
- Accessing an array element when returning from a f
- What are the disadvantages of ElasticSearch Doc Va
- Solr - _version_ field must exist in schema and be
Norms are a value stored in the index alongside a field, and used for scoring. With the default scoring algorithm, this combines a
lengthNorm
(which serves to weigh short fields more heavily than long ones), and any field-level boosts. You can see more on the details of it in the Lucene documentation.The LengthNorm aspect is mostly helpful on proper full-text fields. On more structured fields in which you don't need a field boost, you can safely omit them.
Norms are enabled by default for all analyzed fields as they're used for scoring. Put in simple words, they make shorter fields get higher score than longer ones. Whenever one doesn't want that or don't want a field to contribute to
_score
, one could disable norms.From Elastic 2.0 onwards, you could add
"norms": { "enabled": false }
while defining the field in the mapping.More details can be found in this article in elastic guide