Elasticsearch07-分词器

分词器作用

1.切词
2.提升召回率：能搜索到的结果的比率

 

分析器

•  

  character filter：分词之前预处理（过滤无用字符、标签等，转换一些&=>and 《Elasticsearch》=> Elasticsearch

  # html标标签解析
  PUT index_my_char_filter_html
  {
    "settings": {
      "analysis": {
        "char_filter": {
          "my_char_filter": {
            "type": "html_strip",
            "escaped_tags": ["a"] # a标签不用解析，escaped_tags：需要保留的html标签
          }
        },
        "analyzer": {
          "my_analyzerr_html": {
            "tokenizer": "keyword",
            "char_filter": ["my_char_filter"]
          }
        }
      }
    }
  }
  
  验证：
  POST index_my_char_filter_html/_analyze
  {
    "analyzer": "my_analyzerr_html",
    "text": "<p>I&apos;m so <a>happy</a>!</p>"
  }
  
  
  
  #filter mapping
  PUT index_my_char_filter_mapping
  {
    "settings": {
      "analysis": {
        "analyzer": {
          "my_analyzer": {
            "tokenizer": "keyword",
            "char_filter": [
              "analyzer_mapping"
            ]
          }
        },
        "char_filter": {
          "analyzer_mapping": {
            "type": "mapping",
            "mappings": [
              "٠ => 0",
              "١ => 1",
              "٢ => 2",
              "٣ => 3",
              "٤ => 4",
              "٥ => 5",
              "٦ => 6",
              "٧ => 7",
              "٨ => 8",
              "٩ => 9"
            ]
          }
        }
      }
    }
  }
  
  
  验证：
  POST index_my_char_filter_mapping/_analyze
  {
    "analyzer": "my_analyzer",
    "text": "My license plate is ٢٥٠١٥"
  }
  
  
  #正则
  PUT my_index
  {
    "settings": {
      "analysis": {
        "analyzer": {
          "my_analyzer": {
            "tokenizer": "standard",
            "char_filter": ["my_char_filter"]
          }
        },
        "char_filter": {
          "my_char_filter": {
            "type": "pattern_replace",
            "pattern": "(\\d+)-(?=\\d)",
            "replacement": "$1_"
          }
        }
      }
    }
  }
  
  验证:
  POST my_index/_analyze
  {
    "analyzer": "my_analyzer",
    "text": "My credit card is 123-456-789"
  }

   

• tokenizer（分词器）：分词

  GET _analyze
  {
    "tokenizer" : "standard", #默认分词器
    "filter" : ["lowercase"],
    "text" : "THE Quick FoX JUMPs"
  }

   

• token filter：停用词、时态转换、大小写转换、同义词转换、语气词处理等。比如：has=>have  him=>he  apples=>apple  the/oh/a=>干掉

  GET /_analyze
  {
    "tokenizer": "standard",
    "filter": [
      {
        "type": "condition",
        "filter": [ "lowercase" ],
        "script": {
          "source": "token.getTerm().length() < 5"
        }
      }
    ],
    "text": "THE QUICK BROWN FOX"
  }
  
  #停用词 stopwords token filter
  PUT /index_stopword
  {
    "settings": {
      "analysis": {
        "analyzer": {
          "index_stopword_analyzer":{
            "type":"standard",
            "stopwords":"_english_"
          }
        }
      }
    }
  }
  
  GET index_stopword/_analyze
  {
    "analyzer": "index_stopword_analyzer",
    "text": "Teacher Ma is in the restroom"
  }

  
  
  

 

分词器：analyzer

• standard：默认分词器，中文支持的不理想，会逐字拆分。

  GET _analyze
  {
    "tokenizer" : "standard",
    "text" : "THE Quick FoX JUMPs"
  }

   

• Pattern Tokenizer：以正则匹配分隔符，把文本拆分成若干词项。

   

• Simple Pattern Tokenizer：以正则匹配词项，速度比Pattern Tokenizer快。

  GET /index_stopword/_analyze
  {
    "text": "江山如此多娇,小姐姐哪里可以撩",
    "analyzer": "simple"
  }

   

• whitespace：以空白符分隔

• 自定义分词器
  

  PUT /index_custom
  {
    "settings": {
      "analysis": {
        "char_filter": {
          "test_char_filter": {
            "type": "mapping",
            "mappings": [
              "& => and",
              "| => or"
            ]
          }
        },
        "filter": {
          "test_stopwords": {
            "type": "stop",
            "stopwords": ["is","in","at","the","a","for"]
          }
        },
        "tokenizer": {
          "punctuation": { 
            "type": "pattern",
            "pattern": "[ .,!?]"
          }
        },
        "analyzer": {
          "index_custom_analyzer": {
            "type": "custom",
            "char_filter": [
              "html_strip",
              "test_char_filter"
            ],
            "tokenizer": "standard",
            "filter": ["lowercase","test_stopwords"]
          }
        }
      }
    }
  }
  
  GET /index_custom/_analyze
  {
    "text": "Teacher ma & zhang also thinks [mother's friends] is good | nice!!!",
    "analyzer": "index_custom_analyzer"
  }

   

 

创建mapping时候指定分词器

#创建mapping时候指定分词器
PUT /index_create_mapping_analuzerc/
{
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "standard"
      }
    }
  }
}


#创建mapping字段时候指定分词器
PUT /index_create_mapping_analuzerb/_mapping
{
  "properties": {
    "column": {
      "type": "text",
     "analyzer": "ik_max_word"
    }
  }
}

 

 

 

 

中文分词器

PUT index_ik_max
{
  "settings": {
    "analysis": {
      "analyzer": {
        "default": {
          "type": "ik_max_word"
        }
      }
    }
  }
}
PUT /index_ik_smart
{
  "mappings": {
      "properties": {
        "text": {
          "type": "text",
          "analyzer": "ik_max_word",
          "search_analyzer": "ik_smart"
        }
    }
  }
}

GET /index_ik_max/_analyze
{
  "text": "中华人民共和国国歌",
  "analyzer": "ik_max_word"
}
GET /index_ik_smart/_analyze
{
  "text": "中华人民共和国国歌",
  "analyzer": "ik_smart"
}

 

安装Ik分词器

1.https://github.com/medcl/elasticsearch-analysis-ik/releases
2.Releases -> Assets -> 下载zip文件
3.es的/plugin创建/ik目录
4.将文件解压到ik目录下
5.将mysql驱动包放到ik目录下
6.重新启动

 

 

热加载ik分词器

1.下载https://github.com/medcl/elasticsearch-analysis-ik
2.打包
3.获取jar包: /target/releases/elasticsearch-analysis-ik-7.6.2.zip
4.es的/plugin创建/ik目录
5.将文件elasticsearch-analysis-ik-7.6.2.zip解压在ik目录下
6.将mysql驱动包放到ik目录下
7.重新启动

备注： 修改代码目录：dic/Dictionary initial方法，这里面有对应的主词库、停用词修改代码。
原理： 
1.es启动的时候启动maven项目
2.maven项目中写个定时器，定时去mysql中查询数据
3.将查询的数据临时加载进内存，也就是es启动时候加载这些主词、停用词文件代码的地方

 

ik分词器注意点

1.es版本、kibana版本、ik分词版本你要一致
2.三个最新版本不同步，一般不要下载最新版本