我正在研究从Solr到ES的转变。我找不到有关信息的一件事是，在刻面时ES是否允许我定义排除过滤器。
例如，考虑以下producttype值：A,B,C我要介绍的值（即：显示计数）。还要考虑到查询仅限于producttype: A。
producttype
A,B,C
producttype: A
在这种情况下，Solr允许我指定我要排除约束producttype: A影响其上的构面producttype。IOW，它显示计数，producttype好像producttype: A没有应用约束。
如何在Solr中执行此操作，请参见：http : //wiki.apache.org/solr/SimpleFacetParameters
标记和排除过滤器
在ElasticSearch中有什么方法可以做到这一点？
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。
filter
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：
product_type
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）：
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_type C），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：
foo
C
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：
global
# [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 # }
更新以回答OP中的扩展问题：
您还可以将过滤器直接应用于每个方面-这些都称为facet_filters。
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：
type
color
blue
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 # }
