是的你可以。
虽然您可以在查询DSL中使用过滤器，但是搜索API也接受一个顶级filter参数，该参数用于在计算构面之后过滤搜索结果。
例如：
1）首先，创建您的索引，并且由于您希望product_type将其视为枚举，因此将其设置为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）为一些文档编制索引（请注意，文档3具有不同的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）执行的产品名称中包含的搜索foo（其不包括文档3，因此product_typeC），计算小面为product_type对于具有所有文档foo中product_name，然后过滤通过搜索结果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），用于进行搜索foo的product_name，但计算小面为索引中的所有产品，通过指定global参数：
# [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
# }
您还可以将过滤器直接应用于每个方面-这些都称为facet_filters。
与之前类似的示例：
1）创建索引：
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）索引一些数据：
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）搜索并过滤同时具有type== A和color==的产品blue，然后对每个属性（不包括“其他”过滤器）运行构面：
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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