Filter search results
editFilter search results
editYou can use two methods to filter search results:
-
Use a boolean query with a
filter
clause. Search requests apply boolean filters to both search hits and aggregations. -
Use the search API’s
post_filter
parameter. Search requests apply post filters only to search hits, not aggregations. You can use a post filter to calculate aggregations based on a broader result set, and then further narrow the results.You can also rescore hits after the post filter to improve relevance and reorder results.
Post filter
editWhen you use the post_filter
parameter to filter search results, the search
hits are filtered after the aggregations are calculated. A post filter has no
impact on the aggregation results.
For example, you are selling shirts that have the following properties:
resp = client.indices.create( index="shirts", mappings={ "properties": { "brand": { "type": "keyword" }, "color": { "type": "keyword" }, "model": { "type": "keyword" } } }, ) print(resp) resp1 = client.index( index="shirts", id="1", refresh=True, document={ "brand": "gucci", "color": "red", "model": "slim" }, ) print(resp1)
response = client.indices.create( index: 'shirts', body: { mappings: { properties: { brand: { type: 'keyword' }, color: { type: 'keyword' }, model: { type: 'keyword' } } } } ) puts response response = client.index( index: 'shirts', id: 1, refresh: true, body: { brand: 'gucci', color: 'red', model: 'slim' } ) puts response
const response = await client.indices.create({ index: "shirts", mappings: { properties: { brand: { type: "keyword", }, color: { type: "keyword", }, model: { type: "keyword", }, }, }, }); console.log(response); const response1 = await client.index({ index: "shirts", id: 1, refresh: "true", document: { brand: "gucci", color: "red", model: "slim", }, }); console.log(response1);
PUT /shirts { "mappings": { "properties": { "brand": { "type": "keyword"}, "color": { "type": "keyword"}, "model": { "type": "keyword"} } } } PUT /shirts/_doc/1?refresh { "brand": "gucci", "color": "red", "model": "slim" }
Imagine a user has specified two filters:
color:red
and brand:gucci
. You only want to show them red shirts made by
Gucci in the search results. Normally you would do this with a
bool
query:
resp = client.search( index="shirts", query={ "bool": { "filter": [ { "term": { "color": "red" } }, { "term": { "brand": "gucci" } } ] } }, ) print(resp)
response = client.search( index: 'shirts', body: { query: { bool: { filter: [ { term: { color: 'red' } }, { term: { brand: 'gucci' } } ] } } } ) puts response
const response = await client.search({ index: "shirts", query: { bool: { filter: [ { term: { color: "red", }, }, { term: { brand: "gucci", }, }, ], }, }, }); console.log(response);
GET /shirts/_search { "query": { "bool": { "filter": [ { "term": { "color": "red" }}, { "term": { "brand": "gucci" }} ] } } }
However, you would also like to use faceted navigation to display a list of
other options that the user could click on. Perhaps you have a model
field
that would allow the user to limit their search results to red Gucci
t-shirts
or dress-shirts
.
This can be done with a
terms
aggregation:
resp = client.search( index="shirts", query={ "bool": { "filter": [ { "term": { "color": "red" } }, { "term": { "brand": "gucci" } } ] } }, aggs={ "models": { "terms": { "field": "model" } } }, ) print(resp)
response = client.search( index: 'shirts', body: { query: { bool: { filter: [ { term: { color: 'red' } }, { term: { brand: 'gucci' } } ] } }, aggregations: { models: { terms: { field: 'model' } } } } ) puts response
const response = await client.search({ index: "shirts", query: { bool: { filter: [ { term: { color: "red", }, }, { term: { brand: "gucci", }, }, ], }, }, aggs: { models: { terms: { field: "model", }, }, }, }); console.log(response);
GET /shirts/_search { "query": { "bool": { "filter": [ { "term": { "color": "red" }}, { "term": { "brand": "gucci" }} ] } }, "aggs": { "models": { "terms": { "field": "model" } } } }
But perhaps you would also like to tell the user how many Gucci shirts are
available in other colors. If you just add a terms
aggregation on the
color
field, you will only get back the color red
, because your query
returns only red shirts by Gucci.
Instead, you want to include shirts of all colors during aggregation, then
apply the colors
filter only to the search results. This is the purpose of
the post_filter
:
resp = client.search( index="shirts", query={ "bool": { "filter": { "term": { "brand": "gucci" } } } }, aggs={ "colors": { "terms": { "field": "color" } }, "color_red": { "filter": { "term": { "color": "red" } }, "aggs": { "models": { "terms": { "field": "model" } } } } }, post_filter={ "term": { "color": "red" } }, ) print(resp)
response = client.search( index: 'shirts', body: { query: { bool: { filter: { term: { brand: 'gucci' } } } }, aggregations: { colors: { terms: { field: 'color' } }, color_red: { filter: { term: { color: 'red' } }, aggregations: { models: { terms: { field: 'model' } } } } }, post_filter: { term: { color: 'red' } } } ) puts response
const response = await client.search({ index: "shirts", query: { bool: { filter: { term: { brand: "gucci", }, }, }, }, aggs: { colors: { terms: { field: "color", }, }, color_red: { filter: { term: { color: "red", }, }, aggs: { models: { terms: { field: "model", }, }, }, }, }, post_filter: { term: { color: "red", }, }, }); console.log(response);
GET /shirts/_search { "query": { "bool": { "filter": { "term": { "brand": "gucci" } } } }, "aggs": { "colors": { "terms": { "field": "color" } }, "color_red": { "filter": { "term": { "color": "red" } }, "aggs": { "models": { "terms": { "field": "model" } } } } }, "post_filter": { "term": { "color": "red" } } }
The main query now finds all shirts by Gucci, regardless of color. |
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Rescore filtered search results
editRescoring can help to improve precision by reordering just the top (eg
100 - 500) documents returned by the
query
and
post_filter
phases, using a
secondary (usually more costly) algorithm, instead of applying the
costly algorithm to all documents in the index.
A rescore
request is executed on each shard before it returns its
results to be sorted by the node handling the overall search request.
Currently the rescore API has only one implementation: the query rescorer, which uses a query to tweak the scoring. In the future, alternative rescorers may be made available, for example, a pair-wise rescorer.
An error will be thrown if an explicit sort
(other than _score
in descending order) is provided with a rescore
query.
when exposing pagination to your users, you should not change
window_size
as you step through each page (by passing different
from
values) since that can alter the top hits causing results to
confusingly shift as the user steps through pages.
Query rescorer
editThe query rescorer executes a second query only on the Top-K results
returned by the query
and
post_filter
phases. The
number of docs which will be examined on each shard can be controlled by
the window_size
parameter, which defaults to 10.
By default the scores from the original query and the rescore query are
combined linearly to produce the final _score
for each document. The
relative importance of the original query and of the rescore query can
be controlled with the query_weight
and rescore_query_weight
respectively. Both default to 1
.
For example:
resp = client.search( query={ "match": { "message": { "operator": "or", "query": "the quick brown" } } }, rescore={ "window_size": 50, "query": { "rescore_query": { "match_phrase": { "message": { "query": "the quick brown", "slop": 2 } } }, "query_weight": 0.7, "rescore_query_weight": 1.2 } }, ) print(resp)
response = client.search( body: { query: { match: { message: { operator: 'or', query: 'the quick brown' } } }, rescore: { window_size: 50, query: { rescore_query: { match_phrase: { message: { query: 'the quick brown', slop: 2 } } }, query_weight: 0.7, rescore_query_weight: 1.2 } } } ) puts response
const response = await client.search({ query: { match: { message: { operator: "or", query: "the quick brown", }, }, }, rescore: { window_size: 50, query: { rescore_query: { match_phrase: { message: { query: "the quick brown", slop: 2, }, }, }, query_weight: 0.7, rescore_query_weight: 1.2, }, }, }); console.log(response);
POST /_search { "query" : { "match" : { "message" : { "operator" : "or", "query" : "the quick brown" } } }, "rescore" : { "window_size" : 50, "query" : { "rescore_query" : { "match_phrase" : { "message" : { "query" : "the quick brown", "slop" : 2 } } }, "query_weight" : 0.7, "rescore_query_weight" : 1.2 } } }
The way the scores are combined can be controlled with the score_mode
:
Score Mode | Description |
---|---|
|
Add the original score and the rescore query score. The default. |
|
Multiply the original score by the rescore query score. Useful
for |
|
Average the original score and the rescore query score. |
|
Take the max of original score and the rescore query score. |
|
Take the min of the original score and the rescore query score. |
Multiple rescores
editIt is also possible to execute multiple rescores in sequence:
resp = client.search( query={ "match": { "message": { "operator": "or", "query": "the quick brown" } } }, rescore=[ { "window_size": 100, "query": { "rescore_query": { "match_phrase": { "message": { "query": "the quick brown", "slop": 2 } } }, "query_weight": 0.7, "rescore_query_weight": 1.2 } }, { "window_size": 10, "query": { "score_mode": "multiply", "rescore_query": { "function_score": { "script_score": { "script": { "source": "Math.log10(doc.count.value + 2)" } } } } } } ], ) print(resp)
response = client.search( body: { query: { match: { message: { operator: 'or', query: 'the quick brown' } } }, rescore: [ { window_size: 100, query: { rescore_query: { match_phrase: { message: { query: 'the quick brown', slop: 2 } } }, query_weight: 0.7, rescore_query_weight: 1.2 } }, { window_size: 10, query: { score_mode: 'multiply', rescore_query: { function_score: { script_score: { script: { source: 'Math.log10(doc.count.value + 2)' } } } } } } ] } ) puts response
const response = await client.search({ query: { match: { message: { operator: "or", query: "the quick brown", }, }, }, rescore: [ { window_size: 100, query: { rescore_query: { match_phrase: { message: { query: "the quick brown", slop: 2, }, }, }, query_weight: 0.7, rescore_query_weight: 1.2, }, }, { window_size: 10, query: { score_mode: "multiply", rescore_query: { function_score: { script_score: { script: { source: "Math.log10(doc.count.value + 2)", }, }, }, }, }, }, ], }); console.log(response);
POST /_search { "query" : { "match" : { "message" : { "operator" : "or", "query" : "the quick brown" } } }, "rescore" : [ { "window_size" : 100, "query" : { "rescore_query" : { "match_phrase" : { "message" : { "query" : "the quick brown", "slop" : 2 } } }, "query_weight" : 0.7, "rescore_query_weight" : 1.2 } }, { "window_size" : 10, "query" : { "score_mode": "multiply", "rescore_query" : { "function_score" : { "script_score": { "script": { "source": "Math.log10(doc.count.value + 2)" } } } } } } ] }
The first one gets the results of the query then the second one gets the results of the first, etc. The second rescore will "see" the sorting done by the first rescore so it is possible to use a large window on the first rescore to pull documents into a smaller window for the second rescore.