I'm looking into changing from Solr to ES. One of the things I can't find info about is whether ES lets me define exclusion filters when faceting.
For example consider producttype
with values: A,B,C
which I want to facet on (i.e: show counts for). Also consider that the query is constrained to producttype: A
.
In this case Solr allows me to specify that I want to exclude the contraint producttype: A
from impacting faceting on producttype
. IOW, it displays counts on producttype
as if the constraint producttype: A
has not been applied.
How to do this in Solr see: http://wiki.apache.org/solr/SimpleFacetParameters > Tagging and excluding Filters
Is there any way to do this in ElasticSearch?
Yes you can.
While you can use filters within the query DSL, the search API also accepts a top-level filter
parameter, which is used for filtering the search results AFTER the facets have been calculated.
For example:
1) First, create your index, and because you want product_type
to be treated as an enum, set it to be not_analyzed
:
curl -XPUT 'http://127.0.0.1:9200/my_index/?pretty=1' -d '
{
"mappings" : {
"product" : {
"properties" : {
"product_type" : {
"index" : "not_analyzed",
"type" : "string"
},
"product_name" : {
"type" : "string"
}
}
}
}
}
'
2) Index some docs (note, doc 3 has a different product_name
):
curl -XPUT 'http://127.0.0.1:9200/my_index/product/1?pretty=1' -d '
{
"product_type" : "A",
"product_name" : "foo bar"
}
'
curl -XPUT 'http://127.0.0.1:9200/my_index/product/2?pretty=1' -d '
{
"product_type" : "B",
"product_name" : "foo bar"
}
'
curl -XPUT 'http://127.0.0.1:9200/my_index/product/3?pretty=1' -d '
{
"product_type" : "C",
"product_name" : "bar"
}
'
3) Perform a search for products whose name contains foo
(which excludes doc 3 and thus product_type
C
), calculate facets for product_type
for all docs which have foo
in the product_name
, then filter the search results by product_type
== A
:
curl -XGET 'http://127.0.0.1:9200/my_index/product/_search?pretty=1' -d '
{
"query" : {
"text" : {
"product_name" : "foo"
}
},
"filter" : {
"term" : {
"product_type" : "A"
}
},
"facets" : {
"product_type" : {
"terms" : {
"field" : "product_type"
}
}
}
}
'
# {
# "hits" : {
# "hits" : [
# {
# "_source" : {
# "product_type" : "A",
# "product_name" : "foo bar"
# },
# "_score" : 0.19178301,
# "_index" : "my_index",
# "_id" : "1",
# "_type" : "product"
# }
# ],
# "max_score" : 0.19178301,
# "total" : 1
# },
# "timed_out" : false,
# "_shards" : {
# "failed" : 0,
# "successful" : 5,
# "total" : 5
# },
# "facets" : {
# "product_type" : {
# "other" : 0,
# "terms" : [
# {
# "count" : 1,
# "term" : "B"
# },
# {
# "count" : 1,
# "term" : "A"
# }
# ],
# "missing" : 0,
# "_type" : "terms",
# "total" : 2
# }
# },
# "took" : 3
# }
4) Perform a search for foo
in the product_name
, but calculate facets for all products in the index, by specifying the global
parameter:
# [Wed Jan 18 17:15:09 2012] Protocol: http, Server: 192.168.5.10:9200
curl -XGET 'http://127.0.0.1:9200/my_index/product/_search?pretty=1' -d '
{
"query" : {
"text" : {
"product_name" : "foo"
}
},
"filter" : {
"term" : {
"product_type" : "A"
}
},
"facets" : {
"product_type" : {
"global" : 1,
"terms" : {
"field" : "product_type"
}
}
}
}
'
# [Wed Jan 18 17:15:09 2012] Response:
# {
# "hits" : {
# "hits" : [
# {
# "_source" : {
# "product_type" : "A",
# "product_name" : "foo bar"
# },
# "_score" : 0.19178301,
# "_index" : "my_index",
# "_id" : "1",
# "_type" : "product"
# }
# ],
# "max_score" : 0.19178301,
# "total" : 1
# },
# "timed_out" : false,
# "_shards" : {
# "failed" : 0,
# "successful" : 5,
# "total" : 5
# },
# "facets" : {
# "product_type" : {
# "other" : 0,
# "terms" : [
# {
# "count" : 1,
# "term" : "C"
# },
# {
# "count" : 1,
# "term" : "B"
# },
# {
# "count" : 1,
# "term" : "A"
# }
# ],
# "missing" : 0,
# "_type" : "terms",
# "total" : 3
# }
# },
# "took" : 4
# }
UPDATE TO ANSWER THE EXPANDED QUESTION FROM THE OP:
You can also apply filters directly to each facet - these are called facet_filters
.
Similar example to before:
1) Create the index:
curl -XPUT 'http://127.0.0.1:9200/my_index/?pretty=1' -d '
{
"mappings" : {
"product" : {
"properties" : {
"color" : {
"index" : "not_analyzed",
"type" : "string"
},
"name" : {
"type" : "string"
},
"type" : {
"index" : "not_analyzed",
"type" : "string"
}
}
}
}
}
'
2) Index some data:
curl -XPUT 'http://127.0.0.1:9200/my_index/product/1?pretty=1' -d '
{
"color" : "red",
"name" : "foo bar",
"type" : "A"
}
'
curl -XPUT 'http://127.0.0.1:9200/my_index/product/2?pretty=1' -d '
{
"color" : [
"red",
"blue"
],
"name" : "foo bar",
"type" : "B"
}
'
curl -XPUT 'http://127.0.0.1:9200/my_index/product/3?pretty=1' -d '
{
"color" : [
"green",
"blue"
],
"name" : "bar",
"type" : "C"
}
'
3) Search, filtering on products that have both type
==A
and color
== blue
, then run facets on each attribute excluding, the "other" filter:
curl -XGET 'http://127.0.0.1:9200/my_index/product/_search?pretty=1' -d '
{
"filter" : {
"and" : [
{
"term" : {
"color" : "blue"
}
},
{
"term" : {
"type" : "A"
}
}
]
},
"facets" : {
"color" : {
"terms" : {
"field" : "color"
},
"facet_filter" : {
"term" : {
"type" : "A"
}
}
},
"type" : {
"terms" : {
"field" : "type"
},
"facet_filter" : {
"term" : {
"color" : "blue"
}
}
}
}
}
'
# [Wed Jan 18 19:58:25 2012] Response:
# {
# "hits" : {
# "hits" : [],
# "max_score" : null,
# "total" : 0
# },
# "timed_out" : false,
# "_shards" : {
# "failed" : 0,
# "successful" : 5,
# "total" : 5
# },
# "facets" : {
# "color" : {
# "other" : 0,
# "terms" : [
# {
# "count" : 1,
# "term" : "red"
# }
# ],
# "missing" : 0,
# "_type" : "terms",
# "total" : 1
# },
# "type" : {
# "other" : 0,
# "terms" : [
# {
# "count" : 1,
# "term" : "C"
# },
# {
# "count" : 1,
# "term" : "B"
# }
# ],
# "missing" : 0,
# "_type" : "terms",
# "total" : 2
# }
# },
# "took" : 3
# }
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