ElasticSearch - How to search for a part of a word with ElasticSearch

Search a part of word with ElasticSearch

来自stackoverflow

https://stackoverflow.com/questions/6467067/how-to-search-for-a-part-of-a-word-with-elasticsearch

场景还原

// 初始化数据

POST /my_idx/my_type/_bulk
{"index": {"_id": "1"}}
{"name": "John Doeman", "function": "Janitor"}
{"index": {"_id": "2"}}
{"name": "Jane Doewoman", "function": "Teacher"}
{"index": {"_id": "3"}}
{"name": "Jimmy Jackal", "function": "Student"}

Question

ElasticSearch中有数据如下:

{
  "_id" : "1",
  "name" : "John Doeman",
  "function" : "Janitor"
}
{
  "_id" : "2",
  "name" : "Jane Doewoman",
  "function" : "Teacher"
}
{
  "_id" : "3",
  "name" : "Jimmy Jackal",
  "function" : "Student"
}

现在期望搜索所有包含Doe的文档

// 并没有返回任何文档

GET /my_idx/my_type/_search?q=Doe
// 返回一个文档

GET /my_idx/my_type/_search?q=Doeman

提问者还更换了分词器,改用请求体的方式,但这也不行:

GET /my_idx/my_type/_search
{
  "query": {
    "term": {
      "name": "Doe"
    }
  }
}

后来使用了nGramtokenizerfilter

{
  "index": {
    "index": "my_idx",
    "type": "my_type",
    "bulk_size": "100",
    "bulk_timeout": "10ms",
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "tokenizer": "my_ngram_tokenizer",
          "filter": [
            "my_ngram_filter"
          ]
        }
      },
      "filter": {
        "my_ngram_filter": {
          "type": "nGram",
          "min_gram": 1,
          "max_gram": 1
        }
      },
      "tokenizer": {
        "my_ngram_tokenizer": {
          "type": "nGram",
          "min_gram": 1,
          "max_gram": 1
        }
      }
    }
  }
}

引入了另外一个问题:任意的查询都可以返回所有文档

Answers

首先这是一个分词引起的问题,索引默认情况下使用standard分词器,对于文档:

{
  "_id" : "1",
  "name" : "John Doeman",
  "function" : "Janitor"
}
{
  "_id" : "2",
  "name" : "Jane Doewoman",
  "function" : "Teacher"
}
{
  "_id" : "3",
  "name" : "Jimmy Jackal",
  "function" : "Student"
}

索引后会得到这样一个映射,这里只考虑了name字段的分词:

segment document id list
john 1
doeman 1
jane 2
doewoman 2
jimmy 3
jackal 3

那么现在考虑我们的搜索

Search 1

GET /my_idx/my_type/_search?q=Doe

standard分词器会将Doe分析为doe,然后到索引表中查找,并不会找到doe这个索引,因此返回空

Search 2

GET /my_idx/my_type/_search?q=Doeman

standard分词器会将Doeman分析为doeman,然后到索引表中找到了该索引,会发现只有doc ID 1包含该索引,所以只返回一个文档

Search 3

GET /my_idx/my_type/_search
{
    "query": {
        "term": {
            "name": "Doe"
        }
    }
}

term查询,Doe还是Doe,不会被分析器分析,但是Doe在索引表中依然是不存在的,所以这个方法也无法返回任何文档。

Search 4

额外说明,题主并没有用这种方式试过

GET /my_idx/my_type/_search
{
    "query": {
        "term": {
            "name": "Doeman"
        }
    }
}

不要以为这样就能找到了,因为term不进行分析,所以直接从索引表中找Doeman也是没有任何文档匹配的,除非把Doeman改为doeman

解决方案

总结了一下stackoverflow上的答案,目前有这么几种可行方案:

  • 正则匹配法
  • 通配符匹配法
  • 前缀匹配法
  • nGram分词器法

正则匹配法

GET my_idx/my_type/_search
{
  "query": {
    "regexp": {
      "name": "doe.*"
    }
  }
}

通配符匹配法

使用query_string配合通配符进行查询,需要注意的是,通配符查找可能使用大量内存且效率低下

后缀匹配(前导通配符)是非常重的操作(e.g. "*ing"),索引中所有的term都会被查找一遍,可以通过allow_leading_wildcard来关闭后缀匹配功能

GET my_idx/my_type/_search
{
  "query": {
    "query_string": {
      "default_field": "name",
      "query": "Doe*"
    }
  }
}

前缀匹配法

原答案说使用prefix,但是prefix并没有对查询进行分析,这里我们使用match_phrase_prefix

GET my_idx/my_type/_search
{
  "query": {
    "match_phrase_prefix": {
      "name": {
        "query": "Doe",
        "max_expansions": 10
      }
    }
  }
}

nGram分词器法

创建索引

PUT my_idx
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "ngram",
          "min_gram": 3,
          "max_gram": 3,
          "token_chars": [
            "letter",
            "digit"
          ]
        }
      }
    }
  }
}

测试一下分词器

POST my_idx/_analyze
{
  "analyzer": "my_analyzer",
  "text": "Doeman"
}

// response

{
  "tokens": [
    {
      "token": "Doe",
      "start_offset": 0,
      "end_offset": 3,
      "type": "word",
      "position": 0
    },
    {
      "token": "oem",
      "start_offset": 1,
      "end_offset": 4,
      "type": "word",
      "position": 1
    },
    {
      "token": "ema",
      "start_offset": 2,
      "end_offset": 5,
      "type": "word",
      "position": 2
    },
    {
      "token": "man",
      "start_offset": 3,
      "end_offset": 6,
      "type": "word",
      "position": 3
    }
  ]
}

再查就可以查到了。而题主虽然使用了ngram,但是min_grammax_gram都配置为1

长度越小,匹配到的文档越多,但匹配的质量会越差
长度越大,检索到的文档越匹配。推荐使用长度为3的tri-gram官方文档对此有详细介绍

posted @ 2019-03-21 11:41  大地的谎言  阅读(...)  评论(...编辑  收藏