如何使用ES更有效率的进行多字段模糊匹配-Toy模板网

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背景

有时候需要ES模糊多个多个和中文相关的字段，可以把多个字段合成一个逻辑意义上的字段进行模糊

相关信息

此时需要两个配置:

1、copy_to (将多个字段整合成一个字段)官方文档:https://www.elastic.co/guide/en/elasticsearch/reference/5.5/copy-to.html

2、ngram (分词器在对纯中文或者中英文混合相关等检索的时候很犀利，无脑的将词分隔成成为几个字连接起来)官方文档:https://www.elastic.co/guide/en/elasticsearch/reference/5.5/analysis-ngram-tokenizer.html

如下示配置看，将first_name和last_name字段copy到一个字段full_name中

注意⚠️:(这个会不会产生一个情况?用户输入的前半段和后半段，命中了字段合在一起的中间部分，如A字段值:abc,B字段值为:def,把A、B字段合并后进行模糊分词匹配的时候会不会匹配中:cd这个值？)如果是使用copyto，不用担心这个。如果你使用的是binlog同步使用字符串拼接的方式产生的字段，会产生括号中所属的情况

PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "first_name": {
          "type": "text",
          "copy_to": "full_name" 
        },
        "last_name": {
          "type": "text",
          "copy_to": "full_name" 
        },
        "full_name": {
          "type": "text"
        }
      }
    }
  }
}

PUT my_index/my_type/1
{
  "first_name": "John",
  "last_name": "Smith"
}

GET my_index/_search
{
  "query": {
    "match": {
      "full_name": { 
        "query": "John Smith",
        "operator": "and"
      }
    }
  }
}

试一下没有进行额外配置ngrem默认的分词效果:会为字符创建一个offset，更加方便命中数据

GET _analyze
{
  "tokenizer": "ngram",
  "text": "我的测试"
}

#产生的效果
{
  "tokens": [
    {
      "token": "我",
      "start_offset": 0,
      "end_offset": 1,
      "type": "word",
      "position": 0
    },
    {
      "token": "我的",
      "start_offset": 0,
      "end_offset": 2,
      "type": "word",
      "position": 1
    },
    {
      "token": "的",
      "start_offset": 1,
      "end_offset": 2,
      "type": "word",
      "position": 2
    },
    {
      "token": "的测",
      "start_offset": 1,
      "end_offset": 3,
      "type": "word",
      "position": 3
    },
    {
      "token": "测",
      "start_offset": 2,
      "end_offset": 3,
      "type": "word",
      "position": 4
    },
    {
      "token": "测试",
      "start_offset": 2,
      "end_offset": 4,
      "type": "word",
      "position": 5
    },
    {
      "token": "试",
      "start_offset": 3,
      "end_offset": 4,
      "type": "word",
      "position": 6
    }
  ]
}

进行ngrem配置

min_gram和max_gram 有助于分词的效率，类似一个窗口（如下配置像一个长度1～3单位的窗口，可以在字段字符上来回滑动分词），窗口长度拉的越长，匹配的更加具体，长度越小，匹配质量越低

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

POST my_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": "2 Quick Foxes."
}

后续java示例伪代码文章来源地址https://www.toymoban.com/news/detail-502395.html


        int from = (pageNum - 1) * pageSize;
        SearchSourceBuilder builder = new SearchSourceBuilder();
 		BoolQueryBuilder rootQuery = QueryBuilders.boolQuery();
		BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
		boolQueryBuilder.must(QueryBuilders.matchQuery("full_name", "模糊匹配的内容").operator(Operator.AND));
        rootQuery.filter(boolQueryBuilder);
        builder.setQuery(rootQuery);
        builder.from(from);
        builder.size(pageSize);
        SearchRequest searchRequest = new SearchRequest("indexName");
        searchRequest.source(builder);
        try {
            SearchResponse response = client.search(searchRequest);
            return response;
        } catch (IOException e) {
            throw new BaseException("es查询连接出错：" + e.getMessage());
        }catch (ElasticsearchException e){
            throw new BaseException("es查询出错：" + e.getMessage());
        }