WARNING: Version 5.1 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
boost
editboost
editIndividual fields can be boosted automatically — count more towards the relevance score — at query time, with the boost
parameter as follows:
PUT my_index { "mappings": { "my_type": { "properties": { "title": { "type": "text", "boost": 2 }, "content": { "type": "text" } } } } }
Matches on the |
The boost is applied only for term queries (prefix, range and fuzzy queries are not boosted).
You can achieve the same effect by using the boost parameter directly in the query, for instance the following query (with field time boost):
POST _search { "query": { "match" : { "title": { "query": "quick brown fox" } } } }
is equivalent to:
POST _search { "query": { "match" : { "title": { "query": "quick brown fox", "boost": 2 } } } }
The boost is also applied when it is copied with the
value in the _all
field. This means that, when
querying the _all
field, words that originated from the title
field will
have a higher score than words that originated in the content
field.
This functionality comes at a cost: queries on the _all
field are slower
when field boosting is used.
Deprecated in 5.0.0.
index time boost is deprecated. Instead, the field mapping boost is applied at query time. For indices created before 5.0.0 the boost will still be applied at index time.
Why index time boosting is a bad idea
We advise against using index time boosting for the following reasons:
-
You cannot change index-time
boost
values without reindexing all of your documents. -
Every query supports query-time boosting which achieves the same effect. The
difference is that you can tweak the
boost
value without having to reindex. -
Index-time boosts are stored as part of the
norm
, which is only one byte. This reduces the resolution of the field length normalization factor which can lead to lower quality relevance calculations.