match - Elasticsearch :有什么方法可以将逗号分隔列表中以空格分隔的单词视为一个术语？

Question

我不知道是否可行，但是我尝试使用“精确搜索”选项按位置进行搜索。有几个要搜索的字段，最重要的是“location_raw”字段:

"match": {
    "location.location_raw": {
        "type": "boolean",
        "operator": "AND",
        "query": "[location query]",
        "analyzer": "standard"
     }
}

location_raw字段是一个位置字符串，每个位置之间都有一个逗号，例如“Sudbury，Middlesex，Massachusetts”或“Leamington，Warwickshire，England”。如果有人搜索“Middlesex萨德伯里”，则将其作为

"query": "Sudbury Middlesex" 

并且这两个术语都必须存在于location_raw字段中。这部分有效。

问题在于，当location_raw字段包含多字位置时，例如New York或Saint George，当有人搜索“York”或“George”时，这些位置将返回。如果我精确搜索“乔治”，则不想获得“圣乔治”的结果。有什么方法可以使Elastic在字符串“Saint George，Stamford，Lincoln，England”中将“Saint George”视为一个术语？

Answer 1

这是一种方法，但是您也必须在csv中查询，或使用terms filter。

我使用了一个简单的pattern analyzer模式:", "。我用一个文档设置了一个简单的索引:

PUT /test_index
{
   "settings": {
      "number_of_shards": 1,
      "analysis": {
         "analyzer": {
            "csv": {
               "type": "pattern",
               "pattern": ", ",
               "lowercase": false
            }
         }
      }
   },
   "mappings": {
      "doc": {
         "properties": {
            "location": {
               "type": "string",
               "index_analyzer": "csv",
               "search_analyzer": "standard",
               "fields": {
                  "raw": {
                     "type": "string",
                     "index": "not_analyzed"
                  }
               }
            }
         }
      }
   }
}

POST /test_index/_bulk
{"index":{"_index":"test_index","_type":"doc","_id":1}}
{"location":"Saint George, Stamford, Lincoln, England"}

我可以看到使用简单的 terms aggregation生成的术语:

POST /test_index/_search?search_type=count
{
   "aggs": {
      "location_terms": {
         "terms": {
            "field": "location"
         }
      }
   }
}
...
{
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 1,
      "max_score": 0,
      "hits": []
   },
   "aggregations": {
      "location_terms": {
         "buckets": [
            {
               "key": "England",
               "doc_count": 1
            },
            {
               "key": "Lincoln",
               "doc_count": 1
            },
            {
               "key": "Saint George",
               "doc_count": 1
            },
            {
               "key": "Stamford",
               "doc_count": 1
            }
         ]
      }
   }
}

然后，如果我使用相同的csv语法进行查询，则不会返回“英国乔治市”的文档:

POST /test_index/_search
{
   "query": {
      "match": {
         "location": {
            "type": "boolean",
            "operator": "AND",
            "query": "George, England",
            "analyzer": "csv"
         }
      }
   }
}
...
{
   "took": 0,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 0,
      "max_score": null,
      "hits": []
   }
}

但适用于“英格兰圣乔治”:

POST /test_index/_search
{
   "query": {
      "match": {
         "location": {
            "type": "boolean",
            "operator": "AND",
            "query": "Saint George, England",
            "analyzer": "csv"
         }
      }
   }
}
...
{
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 1,
      "max_score": 0.2169777,
      "hits": [
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "1",
            "_score": 0.2169777,
            "_source": {
               "location": "Saint George, Stamford, Lincoln, England"
            }
         }
      ]
   }
}

该查询是等效的，并且性能可能更高:

POST /test_index/_search
{
   "query": {
      "filtered": {
         "query": {
            "match_all": {}
         },
         "filter": {
            "terms": {
               "location": [
                  "Saint George",
                  "England"
               ],
               "execution": "and"
            }
         }
      }
   }
}

这是我用来测试的代码:

http://sense.qbox.io/gist/234ea93accb7b20ad8fd33e62fe92f1d450a51ab