ES-分析器

分析器

两种常用的英语分析器

1 测试工具

#可以通过这个来测试分析器 实际生产环境中我们肯定是配置在索引中来工作
GET _analyze
{
  "text": "My Mom's Son is an excellent teacher",
  "analyzer": "english"
}

2 实际效果
比如我们有下面这样一句话：My Mom’s Son is an excellent teacher

GET _analyze
{
  "text": "My Mom's Son is an excellent teacher",
  "analyzer": "english"
}

分析器分析以后,大写统一转换为了小写，is 被省了 等，所以经过这个分析器处理以后会得到下面的结果

我们换一个分析器结果就会不一样

GET _analyze
{
  "text": "My Mom's Son is an excellent teacher",
  "analyzer": "standard"
}

结果如下：

char_filter

• html_strip 用来处理html标签

PUT my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
      #这里是申明
        "my_char_filter": {
          "type": "html_strip", #过滤html 标签 
          "escaped_tags": [
            "a" #忽略a标签
          ]
        }
      },
      "analyzer": {
      #这里是使用
        "my_analyzer": {
          "char_filter": [
            "my_char_filter"
          ],
          "tokenizer": "keyword"
        }
      }
    }
  }
}

GET /my_index/_analyze 
{
  "text" : "fdsf",
  "analyzer": "my_analyzer"
}

可以看到html这个表签被替换掉了：

• mapping 用来处理映射

PUT my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
        "my_char_filter": {
          "type": "mapping",
          "mappings": ["S=>*","B=>*"]
        }
      },
      "analyzer": {
        "my_analyzer": {
          "char_filter": [
            "my_char_filter"
          ],
          "tokenizer": "keyword"
        }
      }
    }
  }
}

GET /my_index/_analyze 
{
  "text" : "总是加班真SB",
  "analyzer": "my_analyzer"
}

结果如下：

• pattern_replace

PUT my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
        "my_char_filter": {
          "type": "pattern_replace",
          "pattern":"(\\d{3})\\d(\\d{4})",
          "replacement" : "$1****$2"
        }
      },
      "analyzer": {
        "my_analyzer": {
          "char_filter": [
            "my_char_filter" #这里是可以写多个的
          ],
          "tokenizer": "keyword"
        }
      }
    }
  }
}

GET /my_index/_analyze 
{
  "text" : "1008610086",
  "analyzer": "my_analyzer"
}

Filter

• synonym_graph

PUT my_index
{
  "settings": {
    "analysis": {
      "filter": {
        "my_filter": {
          "type": "synonym_graph",
          "synonyms_path" : "analysis/analysis.txt" #这里修改文件好像是不能直接生效需要重新创建索引
        }
      },
      "analyzer": {
        "my_analyzer": {
          "filter": [
            "my_filter"
          ],
          "tokenizer": "keyword"
        }
      }
    }
  }
}

GET /my_index/_analyze 
{
  "text" : ["liyong","love","baby"],
  "analyzer": "my_analyzer"
}

运行结果如下：

也可以直接写到下面：

PUT my_index
{
  "settings": {
    "analysis": {
      "filter": {
        "my_filter": {
          "type": "synonym_graph",
          "synonyms" : ["liyong,love,baby=>99"] #直接把映射的东西写到这里
        }
      },
      "analyzer": {
        "my_analyzer": {
          "filter": [
            "my_filter"
          ],
          "tokenizer": "keyword"
        }
      }
    }
  }
}

GET /my_index/_analyze 
{
  "text" : ["liyong","love","baby"],
  "analyzer": "my_analyzer"
}

GET my_index/_analyze
{
 "tokenizer": "standard",
 "filter":{
 "type": "condition", #条件也就是根据下面的条件
 "filter":"uppercase", #转换为大写
 "script": {
 "source": "token.getTerm().length()<5" #小于5的字符串替换为大写
 }
 },
 "text":["assas assa sasa dsdsdsdsdsd sdsdsdss"]
}

• stop
  Stopwords⽤于删除不要的介词和词语,以下为简写

PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "standard",
          "stopwords": [
            "me",
            "you"
          ]
        }
      }
    }
  }
}

也可以这样写：

PUT my_index
{
  "settings": {
    "analysis": {
      "filter": {
        "my_filter": {
          "type": "stop",
          "stopwords": [
            "me",
            "you"
          ]
        }
      },
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "standard",
          "filter": [
            "my_filter"
          ]
        }
      }
    }
  }
}

自定义分析器

PUT my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
        "my_char_filter": {
          "type": "mapping",
          "mappings": [
            "&=>and",
            "|=>or"
          ]
        }
      },
      "filter": {
        "my_filter": {
          "type": "stop",
          "stopwords": [
            "is",
            "in",
            "a",
            "at"
          ]
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "pattern",
          "pattern": "[ ,.!]"
        }
      },
      "analyzer": {
        "my_analyzer": {
          "char_filter": [
            "my_char_filter"
          ],
          "filter": [
            "my_filter"
          ],
          "tokenizer": "my_tokenizer",
          "type": "custom" #指定自定义
        }
      }
    }
  }
}

tokenizer 重写了分词方式 比如这个例子就是按照, . !来分割，然后进行后续的过滤处理，在实际生产环境中非常重要。

中文分词器

ik下载
安装到插件下面：

#由于没有对应的版本需要修改这个文件强行改成我们的版本
vim plugin-descriptor.properties

注意ik文件的所属用户和所属组

• 使用

GET /my_index/_analyze
{
  "text": "我是一个兵来自老百姓",
  "analyzer": "ik_smart"
}

• 自定义分词库
  我再config 新建一个目录config/custom.dic 自定义输入
  

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
        <comment>IK Analyzer 扩展配置</comment>
        <!--用户可以在这里配置自己的扩展字典 -->
        <entry key="ext_dict">./custom/custom.dic;./custom/custom1.dic</entry> #如果有多个用;隔开
         <!--用户可以在这里配置自己的扩展停止词字典-->
        <entry key="ext_stopwords"></entry>
        <!--用户可以在这里配置远程扩展字典 --> #这里支持远程网址词典获取这样做的好处是不用重启es 这里就可以写一个controller 来把词典打印到网页上 https://blog.csdn.net/qq_34304427/article/details/123539694?spm=1001.2014.3001.5502 可以参考这篇博客
        <!-- <entry key="remote_ext_dict">words_location</entry> -->
        <!--用户可以在这里配置远程扩展停止词字典-->
        <!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>

GET /my_index/_analyze
{
  "text": "我是一个兵来自老百姓,我真的好想你宝宝",
  "analyzer": "ik_smart"
}