面向字的完成建议器(ElasticSearch 5.x)

ElasticSearch 5.x对Suggester API(文档)引入了一些(重大)更改.最值得注意的变化如下:

完成建议是面向文档的

建议知道他们所属的文件.现在,关联的文档(_source)将作为完成建议的一部分返回.

简而言之,所有完成查询都返回所有匹配的文档而不是匹配的单词.这就是问题所在 - 如果自动填充的单词出现在多个文档中,则会重复这些单词.

假设我们有这个简单的映射:

{
   "my-index": {
      "mappings": {
         "users": {
            "properties": {
               "firstName": {
                  "type": "text"
               },
               "lastName": {
                  "type": "text"
               },
               "suggest": {
                  "type": "completion",
                  "analyzer": "simple"
               }
            }
         }
      }
   }
}

有一些测试文件:

{
   "_index": "my-index",
   "_type": "users",
   "_id": "1",
   "_source": {
      "firstName": "John",
      "lastName": "Doe",
      "suggest": [
         {
            "input": [
               "John",
               "Doe"
            ]
         }
      ]
   }
},
{
   "_index": "my-index",
   "_type": "users",
   "_id": "2",
   "_source": {
      "firstName": "John",
      "lastName": "Smith",
      "suggest": [
         {
            "input": [
               "John",
               "Smith"
            ]
         }
      ]
   }
}

一本书的查询:

POST /my-index/_suggest?pretty
{
    "my-suggest" : {
        "text" : "joh",
        "completion" : {
            "field" : "suggest"
        }
    }
}

这产生以下结果:

{
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "my-suggest": [
      {
         "text": "joh",
         "offset": 0,
         "length": 3,
         "options": [
            {
               "text": "John",
               "_index": "my-index",
               "_type": "users",
               "_id": "1",
               "_score": 1,
               "_source": {
                 "firstName": "John",
                 "lastName": "Doe",
                 "suggest": [
                    {
                       "input": [
                          "John",
                          "Doe"
                       ]
                    }
                 ]
               }
            },
            {
               "text": "John",
               "_index": "my-index",
               "_type": "users",
               "_id": "2",
               "_score": 1,
               "_source": {
                 "firstName": "John",
                 "lastName": "Smith",
                 "suggest": [
                    {
                       "input": [
                          "John",
                          "Smith"
                       ]
                    }
                 ]
               }
            }
         ]
      }
   ]
}

简而言之,对于文本"joh"的完成建议,返回了两(2)个文档 - John和两者都具有相同的text属性值.

但是,我想收到一(1)个字.像这样简单:

{
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "my-suggest": [
      {
         "text": "joh",
         "offset": 0,
         "length": 3,
         "options": [
          "John"
         ]
      }
   ]
}

问题:如何实现基于单词的完成建议器.没有必要返回任何与文档相关的数据,因为此时我不需要它.

"完成建议者"是否适合我的情景？或者我应该使用完全不同的方法？

编辑:正如你们许多人所指出的那样,一个额外的完成指数将是一个可行的解决方案.但是,我可以看到这种方法存在多个问题:

保持新索引同步.

自动完成后续单词可能是全局的,而不是缩小范围.例如,假设您在附加索引中有以下单词:"John", "Doe", "David", "Smith".在查询时"John D",不完整单词的结果应该是,"Doe"而不是"Doe", "David".

要克服第二点,仅索引单个单词是不够的,因为您还需要将所有单词映射到文档,以便正确缩小自动完成后续单词.有了这个,你实际上遇到了与查询原始索引相同的问题.因此,附加索引不再有意义.

正如在评论中暗示的那样,在不获取重复文档的情况下实现此目的的另一种方法是为firstname包含该字段的ngrams的字段创建子字段.首先,您可以像这样定义映射:

PUT my-index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "completion_analyzer": {
          "type": "custom",
          "filter": [
            "lowercase",
            "completion_filter"
          ],
          "tokenizer": "keyword"
        }
      },
      "filter": {
        "completion_filter": {
          "type": "edge_ngram",
          "min_gram": 1,
          "max_gram": 24
        }
      }
    }
  },
  "mappings": {
    "users": {
      "properties": {
        "autocomplete": {
          "type": "text",
          "fields": {
            "raw": {
              "type": "keyword"
            },
            "completion": {
              "type": "text",
              "analyzer": "completion_analyzer",
              "search_analyzer": "standard"
            }
          }
        },
        "firstName": {
          "type": "text"
        },
        "lastName": {
          "type": "text"
        }
      }
    }
  }
}

然后你索引一些文件:

POST my-index/users/_bulk
{"index":{}}
{ "firstName": "John", "lastName": "Doe", "autocomplete": "John Doe"}
{"index":{}}
{ "firstName": "John", "lastName": "Deere", "autocomplete": "John Deere" }
{"index":{}}
{ "firstName": "Johnny", "lastName": "Cash", "autocomplete": "Johnny Cash" }

然后你可以查询joh并获得一个结果John,另一个结果Johnny

{
  "size": 0,
  "query": {
    "term": {
      "autocomplete.completion": "john d"
    }
  },
  "aggs": {
    "suggestions": {
      "terms": {
        "field": "autocomplete.raw"
      }
    }
  }
}

结果:

{
  "aggregations": {
    "suggestions": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "John Doe",
          "doc_count": 1
        },
        {
          "key": "John Deere",
          "doc_count": 1
        }
      ]
    }
  }
}