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Distance feature query
editDistance feature query
editBoosts the relevance score of documents closer to a
provided origin
date or point. For example, you can use this query to give
more weight to documents closer to a certain date or location.
You can use the distance_feature
query to find the nearest neighbors to a
location. You can also use the query in a bool
search’s should
filter to add boosted relevance scores to the bool
query’s
scores.
Example request
editIndex setup
editTo use the distance_feature
query, your index must include a date
,
date_nanos
or geo_point
field.
To see how you can set up an index for the distance_feature
query, try the
following example.
-
Create an
items
index with the following field mapping:resp = client.indices.create( index="items", mappings={ "properties": { "name": { "type": "keyword" }, "production_date": { "type": "date" }, "location": { "type": "geo_point" } } }, ) print(resp)
response = client.indices.create( index: 'items', body: { mappings: { properties: { name: { type: 'keyword' }, production_date: { type: 'date' }, location: { type: 'geo_point' } } } } ) puts response
const response = await client.indices.create({ index: "items", mappings: { properties: { name: { type: "keyword", }, production_date: { type: "date", }, location: { type: "geo_point", }, }, }, }); console.log(response);
PUT /items { "mappings": { "properties": { "name": { "type": "keyword" }, "production_date": { "type": "date" }, "location": { "type": "geo_point" } } } }
-
Index several documents to this index.
resp = client.index( index="items", id="1", refresh=True, document={ "name": "chocolate", "production_date": "2018-02-01", "location": [ -71.34, 41.12 ] }, ) print(resp) resp1 = client.index( index="items", id="2", refresh=True, document={ "name": "chocolate", "production_date": "2018-01-01", "location": [ -71.3, 41.15 ] }, ) print(resp1) resp2 = client.index( index="items", id="3", refresh=True, document={ "name": "chocolate", "production_date": "2017-12-01", "location": [ -71.3, 41.12 ] }, ) print(resp2)
response = client.index( index: 'items', id: 1, refresh: true, body: { name: 'chocolate', production_date: '2018-02-01', location: [ -71.34, 41.12 ] } ) puts response response = client.index( index: 'items', id: 2, refresh: true, body: { name: 'chocolate', production_date: '2018-01-01', location: [ -71.3, 41.15 ] } ) puts response response = client.index( index: 'items', id: 3, refresh: true, body: { name: 'chocolate', production_date: '2017-12-01', location: [ -71.3, 41.12 ] } ) puts response
const response = await client.index({ index: "items", id: 1, refresh: "true", document: { name: "chocolate", production_date: "2018-02-01", location: [-71.34, 41.12], }, }); console.log(response); const response1 = await client.index({ index: "items", id: 2, refresh: "true", document: { name: "chocolate", production_date: "2018-01-01", location: [-71.3, 41.15], }, }); console.log(response1); const response2 = await client.index({ index: "items", id: 3, refresh: "true", document: { name: "chocolate", production_date: "2017-12-01", location: [-71.3, 41.12], }, }); console.log(response2);
PUT /items/_doc/1?refresh { "name" : "chocolate", "production_date": "2018-02-01", "location": [-71.34, 41.12] } PUT /items/_doc/2?refresh { "name" : "chocolate", "production_date": "2018-01-01", "location": [-71.3, 41.15] } PUT /items/_doc/3?refresh { "name" : "chocolate", "production_date": "2017-12-01", "location": [-71.3, 41.12] }
Example queries
editBoost documents based on date
editThe following bool
search returns documents with a name
value of
chocolate
. The search also uses the distance_feature
query to increase the
relevance score of documents with a production_date
value closer to now
.
resp = client.search( index="items", query={ "bool": { "must": { "match": { "name": "chocolate" } }, "should": { "distance_feature": { "field": "production_date", "pivot": "7d", "origin": "now" } } } }, ) print(resp)
response = client.search( index: 'items', body: { query: { bool: { must: { match: { name: 'chocolate' } }, should: { distance_feature: { field: 'production_date', pivot: '7d', origin: 'now' } } } } } ) puts response
const response = await client.search({ index: "items", query: { bool: { must: { match: { name: "chocolate", }, }, should: { distance_feature: { field: "production_date", pivot: "7d", origin: "now", }, }, }, }, }); console.log(response);
GET /items/_search { "query": { "bool": { "must": { "match": { "name": "chocolate" } }, "should": { "distance_feature": { "field": "production_date", "pivot": "7d", "origin": "now" } } } } }
Boost documents based on location
editThe following bool
search returns documents with a name
value of
chocolate
. The search also uses the distance_feature
query to increase the
relevance score of documents with a location
value closer to [-71.3, 41.15]
.
resp = client.search( index="items", query={ "bool": { "must": { "match": { "name": "chocolate" } }, "should": { "distance_feature": { "field": "location", "pivot": "1000m", "origin": [ -71.3, 41.15 ] } } } }, ) print(resp)
response = client.search( index: 'items', body: { query: { bool: { must: { match: { name: 'chocolate' } }, should: { distance_feature: { field: 'location', pivot: '1000m', origin: [ -71.3, 41.15 ] } } } } } ) puts response
const response = await client.search({ index: "items", query: { bool: { must: { match: { name: "chocolate", }, }, should: { distance_feature: { field: "location", pivot: "1000m", origin: [-71.3, 41.15], }, }, }, }, }); console.log(response);
GET /items/_search { "query": { "bool": { "must": { "match": { "name": "chocolate" } }, "should": { "distance_feature": { "field": "location", "pivot": "1000m", "origin": [-71.3, 41.15] } } } } }
Top-level parameters for distance_feature
edit-
field
-
(Required, string) Name of the field used to calculate distances. This field must meet the following criteria:
-
Be a
date
,date_nanos
orgeo_point
field -
Have an
index
mapping parameter value oftrue
, which is the default -
Have an
doc_values
mapping parameter value oftrue
, which is the default
-
Be a
-
origin
-
(Required, string) Date or point of origin used to calculate distances.
If the
field
value is adate
ordate_nanos
field, theorigin
value must be a date. Date Math, such asnow-1h
, is supported.If the
field
value is ageo_point
field, theorigin
value must be a geopoint. -
pivot
-
(Required, time unit or distance unit) Distance from the
origin
at which relevance scores receive half of theboost
value.If the
field
value is adate
ordate_nanos
field, thepivot
value must be a time unit, such as1h
or10d
.If the
field
value is ageo_point
field, thepivot
value must be a distance unit, such as1km
or12m
. -
boost
-
(Optional, float) Floating point number used to multiply the relevance score of matching documents. This value cannot be negative. Defaults to
1.0
.
Notes
editHow the distance_feature
query calculates relevance scores
editThe distance_feature
query dynamically calculates the distance between the
origin
value and a document’s field values. It then uses this distance as a
feature to boost the relevance score of closer
documents.
The distance_feature
query calculates a document’s
relevance score as follows:
relevance score = boost * pivot / (pivot + distance)
The distance
is the absolute difference between the origin
value and a
document’s field value.
Skip non-competitive hits
editUnlike the function_score
query or other
ways to change relevance scores, the
distance_feature
query efficiently skips non-competitive hits when the
track_total_hits
parameter is not true
.
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