elasticsearch - 结合使用精确的前缀/匹配短语前缀查询和Ngram筛选器

Question

我的目标是搜索长度为一或两个字符的查询文本。
这是我的索引设置。

"settings" : {
      "index" : {
        "number_of_shards" : "5",
        "provided_name" : "my_user",
        "analysis" : {
          "filter" : {
            "ngrammed" : {
              "type" : "ngram",
              "min_gram" : "3",
              "max_gram" : "50"
            }
          },
          "analyzer" : {
            "ngrammed_ci" : {
              "filter" : [
                "lowercase",
                "ngrammed"
              ],
              "type" : "custom",
              "tokenizer" : "standard"
            },
            "keyword_ci" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "keyword"
            }
          }
        }
      }
    }

我的一组用户具有以下分析器的显示名称字段。假设我有几个用户，例如Allen，Alec，Kimball，Polly我面临的问题是，当我使用2个字符长度的查询字符串(如al以及Allen和Alec)进行搜索时，它也与Kimball匹配，因为ngram过滤器在反向索引中将Kimball标记为all。我试图避免这种情况。还想知道是否有实现该功能的方法而无需更改事物的Index端的任何东西，而仅在查询端进行更改。

"user_display_name" : {
  "type" : "text",
  "fields" : {
    "ci" : {
    "type" : "text",
    "analyzer" : "keyword_ci"
    }
  "cs" : {
    "type" : "keyword"
    }
  },
  "analyzer" : "ngrammed_ci",
  "search_analyzer" : "standard"
}

Answer 1

在您的情况下，您需要从单词开头开始的ngram。在这种情况下，改为使用edge ngrams更有意义。
添加带有索引映射，索引数据，搜索查询和搜索结果的工作示例。
映射:

{
  "settings": {
    "analysis": {
      "filter": {
        "ngrammed": {
          "type": "edge_ngram",     <<-- note this
          "min_gram": "2",
          "max_gram": "50"
        }
      },
      "analyzer": {
        "ngrammed_ci": {
          "filter": [
            "lowercase",
            "ngrammed"
          ],
          "type": "custom",
          "tokenizer": "standard"
        },
        "keyword_ci": {
          "filter": [
            "lowercase"
          ],
          "type": "custom",
          "tokenizer": "keyword"
        }
      }
    },
    "index.max_ngram_diff": 50
  },
  "mappings": {
    "properties": {
      "user_display_name": {
        "type": "text",
        "fields": {
          "ci": {
            "type": "text",
            "analyzer": "keyword_ci"
          },
          "cs": {
            "type": "keyword"
          }
        },
        "analyzer": "ngrammed_ci",
        "search_analyzer": "standard"
      }
    }
  }
}

将生成以下 token :

GET/_analyze

{
  "analyzer" : "ngrammed_ci",
  "text" : "Allen"
}

"tokens": [
    {
      "token": "al",
      "start_offset": 0,
      "end_offset": 5,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "all",
      "start_offset": 0,
      "end_offset": 5,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "alle",
      "start_offset": 0,
      "end_offset": 5,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "allen",
      "start_offset": 0,
      "end_offset": 5,
      "type": "<ALPHANUM>",
      "position": 0
    }
  ]

索引数据:

{ "user_display_name" : "Allen" }
{ "user_display_name" : "Alec" }
{ "user_display_name" : "Kimball" }
{ "user_display_name" : "Polly" }

搜索查询:

    {
  "query": {
    "query_string": {
      "query": "al",
      "default_field": "user_display_name"
    }
  }
}

搜索结果:

 "hits": [
      {
        "_index": "my-index",
        "_type": "_doc",
        "_id": "1",
        "_score": 1.0087044,
        "_source": {
          "user_display_name": "Allen"
        }
      },
      {
        "_index": "my-index",
        "_type": "_doc",
        "_id": "2",
        "_score": 1.0087044,
        "_source": {
          "user_display_name": "Alec"
        }
      }
    ]