es的分词器analyzer

analyzer

分词器使用的两个情形：
1，Index time analysis. 创建或者更新文档时，会对文档进行分词
2，Search time analysis. 查询时，对查询语句分词

指定查询时使用哪个分词器的方式有：

　　- 查询时通过analyzer指定分词器

GET test_index/_search
{
  "query": {
    "match": {
      "name": {
        "query": "lin",
        "analyzer": "standard"
      }
    }
  }
}

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　　- 创建index mapping时指定search_analyzer

PUT test_index
{
  "mappings": {
    "doc": {
      "properties": {
        "title":{
          "type": "text",
          "analyzer": "whitespace",
          "search_analyzer": "standard"
        }
      }
    }
  }
}

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索引时分词是通过配置 Index mapping中的每个字段的参数analyzer指定的

# 不指定分词时，会使用默认的standard
PUT test_index
{
  "mappings": {
    "doc": {
      "properties": {
        "title":{
          "type": "text",
          "analyzer": "whitespace"     #指定分词器，es内置有多种analyzer
        }
      }
    }}}

注意：

明确字段是否需要分词，不需要分词的字段将type设置为keyword，可以节省空间和提高写性能。

_analyzer api

GET _analyze
{
  "analyzer": "standard",
  "text": "this is a test"
}
# 可以查看text的内容使用standard分词后的结果

{
  "tokens": [
    {
      "token": "this",
      "start_offset": 0,
      "end_offset": 4,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "is",
      "start_offset": 5,
      "end_offset": 7,
      "type": "<ALPHANUM>",
      "position": 1
    },
    {
      "token": "a",
      "start_offset": 8,
      "end_offset": 9,
      "type": "<ALPHANUM>",
      "position": 2
    },
    {
      "token": "test",
      "start_offset": 10,
      "end_offset": 14,
      "type": "<ALPHANUM>",
      "position": 3
    }
  ]
}

View Code

设置analyzer

PUT test
{
  "settings": {
    "analysis": {    #自定义分词器
      "analyzer": {      # 关键字
        "my_analyzer":{   # 自定义的分词器
          "type":"standard",    #分词器类型standard
          "stopwords":"_english_"   #standard分词器的参数，默认的stopwords是\_none_
        }
      }
    }
  },
  "mappings": {
    "doc":{
      "properties": {
        "my_text":{
          "type": "text",
          "analyzer": "standard",  # my_text字段使用standard分词器
          "fields": {
            "english":{            # my_text.english字段使用上面自定义得my_analyzer分词器
              "type": "text", 
              "analyzer": "my_analyzer"
            }}}}}}}

POST test/_analyze
{
  "field": "my_text",    # my_text字段使用的是standard分词器
  "text": ["The test message."]
}
-------------->[the,test,message]

POST test/_analyze
{
  "field": "my_text.english",     #my_text.english使用的是my_analyzer分词器
  "text": ["The test message."]
}
------------>[test,message]

ES内置了很多种analyzer。比如：

standard 由以下组成

tokenizer：Standard Tokenizer

token filter：Standard Token Filter，Lower Case Token Filter，Stop Token Filter

analyzer API测试 ：
POST _analyze
{
  "analyzer": "standard",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

得到结果：

{
  "tokens": [
    {
      "token": "the",
      "start_offset": 0,
      "end_offset": 3,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "2",
      "start_offset": 4,
      "end_offset": 5,
      "type": "<NUM>",
      "position": 1
    },
    {
      "token": "quick",
      "start_offset": 6,
      "end_offset": 11,
      "type": "<ALPHANUM>",
      "position": 2
    },
    {
      "token": "brown",
      "start_offset": 12,
      "end_offset": 17,
      "type": "<ALPHANUM>",
      "position": 3
    },
    {
      "token": "foxes",
      "start_offset": 18,
      "end_offset": 23,
      "type": "<ALPHANUM>",
      "position": 4
    },
    {
      "token": "jumped",
      "start_offset": 24,
      "end_offset": 30,
      "type": "<ALPHANUM>",
      "position": 5
    },
    {
      "token": "over",
      "start_offset": 31,
      "end_offset": 35,
      "type": "<ALPHANUM>",
      "position": 6
    },
    {
      "token": "the",
      "start_offset": 36,
      "end_offset": 39,
      "type": "<ALPHANUM>",
      "position": 7
    },
    {
      "token": "lazy",
      "start_offset": 40,
      "end_offset": 44,
      "type": "<ALPHANUM>",
      "position": 8
    },
    {
      "token": "dog's",
      "start_offset": 45,
      "end_offset": 50,
      "type": "<ALPHANUM>",
      "position": 9
    },
    {
      "token": "bone",
      "start_offset": 51,
      "end_offset": 55,
      "type": "<ALPHANUM>",
      "position": 10
    }
  ]
}

View Code

whitespace 空格为分隔符

POST _analyze
{
  "analyzer": "whitespace",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
-->  [ The,2,QUICK,Brown-Foxes,jumped,over,the,lazy,dog's,bone. ]

simple

POST _analyze
{
  "analyzer": "simple",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
---> [ the, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]

stop 默认stopwords用_english_

POST _analyze
{
  "analyzer": "stop",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
-->[ quick, brown, foxes, jumped, over, lazy, dog, s, bone ]
可选参数：
# stopwords
# stopwords_path

keyword 不分词的

POST _analyze
{
  "analyzer": "keyword",
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."]
}
得到  "token": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone." 一条完整的语句

第三方analyzer插件---中文分词（ik分词器）

es内置很多分词器，但是对中文分词并不友好，例如使用standard分词器对一句中文话进行分词，会分成一个字一个字的。这时可以使用第三方的Analyzer插件，比如 ik、pinyin等。这里以ik为例

1，首先安装插件，重启es：

# bin/elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.3.0/elasticsearch-analysis-ik-6.3.0.zip
# /etc/init.d/elasticsearch restart

2，使用示例：

GET _analyze
{
  "analyzer": "ik_max_word",
  "text": "你好吗?我有一句话要对你说呀。"
}

{
  "tokens": [
    {
      "token": "你好",
      "start_offset": 0,
      "end_offset": 2,
      "type": "CN_WORD",
      "position": 0
    },
    {
      "token": "好吗",
      "start_offset": 1,
      "end_offset": 3,
      "type": "CN_WORD",
      "position": 1
    },
    {
      "token": "我",
      "start_offset": 4,
      "end_offset": 5,
      "type": "CN_CHAR",
      "position": 2
    },
    {
      "token": "有",
      "start_offset": 5,
      "end_offset": 6,
      "type": "CN_CHAR",
      "position": 3
    },
    {
      "token": "一句话",
      "start_offset": 6,
      "end_offset": 9,
      "type": "CN_WORD",
      "position": 4
    },
    {
      "token": "一句",
      "start_offset": 6,
      "end_offset": 8,
      "type": "CN_WORD",
      "position": 5
    },
    {
      "token": "一",
      "start_offset": 6,
      "end_offset": 7,
      "type": "TYPE_CNUM",
      "position": 6
    },
    {
      "token": "句话",
      "start_offset": 7,
      "end_offset": 9,
      "type": "CN_WORD",
      "position": 7
    },
    {
      "token": "句",
      "start_offset": 7,
      "end_offset": 8,
      "type": "COUNT",
      "position": 8
    },
    {
      "token": "话",
      "start_offset": 8,
      "end_offset": 9,
      "type": "CN_CHAR",
      "position": 9
    },
    {
      "token": "要对",
      "start_offset": 9,
      "end_offset": 11,
      "type": "CN_WORD",
      "position": 10
    },
    {
      "token": "你",
      "start_offset": 11,
      "end_offset": 12,
      "type": "CN_CHAR",
      "position": 11
    },
    {
      "token": "说呀",
      "start_offset": 12,
      "end_offset": 14,
      "type": "CN_WORD",
      "position": 12
    }
  ]
}

分词结果

参考：https://github.com/medcl/elasticsearch-analysis-ik

还可以用内置的 character filter, tokenizer, token filter 组装一个analyzer(custom analyzer)

custom 定制analyzer，由以下几部分组成
- 0个或多个e character filters
- 1个tokenizer
- 0个或多个 token filters

PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "type":"custom",
          "tokenizer":"standard",
          "char_filter":["html_strip"],
          "filter":["lowercase"]
        }
      }
    }
  }
}
POST t_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's <b> bone.</b>"]
}
得到：[the,2,quick,brown,foxes,jumped,over,the,lazy,dog's,bone]

View Code

自定义分词器

自定义分词需要在索引的配置中设定，如下所示：

PUT test_index
{
  "settings": {
    "analysis": {    # 分词设置，可以自定义
      "char_filter": {},   #char_filter  关键字
      "tokenizer": {},    #tokenizer 关键字
      "filter": {},     #filter  关键字
      "analyzer": {}    #analyzer 关键字
    }
  }
}

character filter 在tokenizer之前对原始文本进行处理，比如增加，删除，替换字符等

会影响后续tokenizer解析的position和offset信息

html strip 除去html标签和转换html实体
- 参数：escaped_tags不删除的标签

POST _analyze
{
  "tokenizer": "keyword",
  "char_filter": ["html_strip"],
  "text": ["<p>I&apos;m so <b>happy</b>!</p>"]
}
得到：
      "token": """

I'm so happy!

"""
#配置示例
PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {  #关键字
        "my_analyzer":{   #自定义analyzer
          "tokenizer":"keyword",
          "char_filter":["my_char_filter"]
        }
      },
      "char_filter": {  #关键字
        "my_char_filter":{   #自定义char_filter
          "type":"html_strip",
          "escaped_tags":["b"]  #不从文本中删除的HTML标记数组
        }
      }}}}
POST t_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": ["<p>I&apos;m so <b>happy</b>!</p>"]
}
得到：
      "token": """

I'm so <b>happy</b>!

""",

View Code

mapping 映射类型，以下参数必须二选一
- mappings 指定一组映射，每个映射格式为 key=>value
- mappings_path 绝对路径或者相对于config路径 key=>value

PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {     #关键字
        "my_analyzer":{   #自定义分词器
          "tokenizer":"standard",
          "char_filter":"my_char_filter"  
        }
      },
      "char_filter": {    #关键字
        "my_char_filter":{  #自定义char_filter
          "type":"mapping", 
          "mappings":[       #指明映射关系
            ":)=>happy",
            ":(=>sad"
          ]
        }}}}}
POST t_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": ["i am so :)"]
}
得到 [i,am,so,happy]

pattern replace
- pattern参数正则
- replacement 替换字符串可以使用$1..$9
- flags 正则标志

tokenizer 将原始文档按照一定规则切分为单词

standard
- 参数：max_token_length，最大token长度，默认是255

PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "tokenizer":"my_tokenizer"
        }
      },
      "tokenizer": { 
        "my_tokenizer":{
          "type":"standard",
          "max_token_length":5      
        }}}}}
POST t_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."]
}
得到   [ The, 2, QUICK, Brown, Foxes, jumpe, d, over, the, lazy, dog's, bone ]
# jumped 长度为6  在5这个位置被分割

View Code

letter 非字母时分成多个terms

POST _analyze
{
  "tokenizer": "letter",
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."]
}
得到 [ The, QUICK, Brown, Foxes, jumped, over, the, lazy, dog, s, bone ]

View Code

lowcase 跟letter tokenizer一样，同时将字母转化成小写

POST _analyze
{
  "tokenizer": "lowercase",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
得到  [ the, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]

View Code

whitespace 按照空白字符分成多个terms
- 参数：max_token_length

POST _analyze
{
  "tokenizer": "whitespace",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
得到 [ The, 2, QUICK, Brown-Foxes, jumped, over, the, lazy, dog's, bone. ]

View Code

keyword 空操作，输出完全相同的文本
- 参数：buffer_size，单词一个term读入缓冲区的长度，默认256

POST _analyze
{
  "tokenizer": "keyword",
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."]
}
得到"token": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone." 一个完整的文本

View Code

token filter 针对tokenizer 输出的单词进行增删改等操作

lowercase 将输出的单词转化成小写

POST _analyze
{
  "filter": ["lowercase"],
  "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's  bone"]
}
--->
"token": "the 2 quick brown-foxes jumped over the lazy dog's  bone"

PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "type":"custom", 
          "tokenizer":"standard", 
          "filter":"lowercase"
        }
      }
    }
  }
}
POST t_index/_analyze
{
　　"analyzer": "my_analyzer",
    "text": ["The 2 QUICK Brown-Foxes jumped over the lazy dog's  bone"]
}

View Code

stop 从token流中删除stop words 。

参数有：

# stopwords   要使用的stopwords， 默认_english_
# stopwords_path
# ignore_case   设置为true则为小写，默认false
# remove_trailing

PUT t_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "type":"custom",
          "tokenizer":"standard",
          "filter":"my_filter"
        }
      },
      "filter": {
        "my_filter":{
          "type":"stop",
          "stopwords":["and","or","not"]
        }
      }
    }
  }
}
POST t_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": ["lucky and happy not sad"]
}
-------------->
[lucky,happy,sad]

posted @ 2018-07-20 16:49 卡布爱学习阅读(56675) 评论(0) 编辑收藏举报

卡布爱学习

es的分词器analyzer

analyzer

_analyzer api

设置analyzer

第三方analyzer插件---中文分词（ik分词器）

自定义分词器

character filter 在tokenizer之前对原始文本进行处理，比如增加，删除，替换字符等

tokenizer 将原始文档按照一定规则切分为单词

token filter 针对tokenizer 输出的单词进行增删改等操作

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