ElasticSearch倒排索引&Analysis分词

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2022-03-05 约 3741 字预计阅读 8 分钟

正排索引和倒排索引

书籍的目录也是生活中常见的正排索引。

倒排索引核心组成

其主要包含两个部分：

单词词典（Term Dictionary）

记录所有的单词，记录单词到倒排列表的关联关系。

单词词典一般较大，可通过插入查询性能较高 B+树或哈希拉链法实。

倒排列表（Posting List）

记录单词对应的文档组合，由倒排索引项组成。

其中倒排索引项（Posting）：

文档 ID
词频 TF：记录单词在文档出现的次数，用于相关性评分
位置（Position）：单词在文档中分词的位置，利于语句搜索
偏移（Offset）:单词在文档中开始结束位置，实现高亮显示

Analysis & Analyzer

Analysis 即文本分析，是把全文转换一系列单词（term/token）的过程，也叫分词。而 Analysis 就是通过 Analyzer 实现的。

可使用 Elasticsearch 内置的分析器或者按需定制分析器。

除了在数据写入时转换词条，匹配 Query 语句时也需要用相同的分析器对查询语句进行分析。

Analyzer 的组成

分词器是专门处理分词的组件，Analyzer 由三部分组成：

Character Filters：针对原始文本处理，如去除 html
Tokenizer：按照规则，切分为单词
Token Filter：将切分的单词进行加工，小写，删除 stopwords，增加同义词

一般按照顺序 Character Filters -> Tokenizer -> Token Filter 对语句进行拆分。

使用 _analyzer API

可指定 Analyzer 进行测试
指定索引的字段进行测试
自定义分词进行测试

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GET /_analyze 
{
    "analyzer":"standard",
    "text":"xxx"
}

POST index/_analyze 
{
    "field":"xxx",
    "text":"xxx"
}

POST /_analyze 
{
    "tokenizer":"standard",
    "filter":["lowercase"],
    "text":"xxx"
}

Analyzer 类型

Standard Analyzer

默认分词器
按词切分
小写处理

组成如下：

Tokenizer:Standard
TokenFilters:Standard&LowerCase&Stop(默认关闭)

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#standard
GET _analyze
{
  "analyzer": "standard",
  "text": "2 running Quick brown-foxes leap over lazy dogs in the summer evening."
}


{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<NUM>",
      "position" : 0
    },
    {
      "token" : "running",
      "start_offset" : 2,
      "end_offset" : 9,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 16,
      "end_offset" : 21,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 22,
      "end_offset" : 27,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "leap",
      "start_offset" : 28,
      "end_offset" : 32,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 33,
      "end_offset" : 37,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "lazy",
      "start_offset" : 38,
      "end_offset" : 42,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "dogs",
      "start_offset" : 43,
      "end_offset" : 47,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "in",
      "start_offset" : 48,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "the",
      "start_offset" : 51,
      "end_offset" : 54,
      "type" : "<ALPHANUM>",
      "position" : 10
    },
    {
      "token" : "summer",
      "start_offset" : 55,
      "end_offset" : 61,
      "type" : "<ALPHANUM>",
      "position" : 11
    },
    {
      "token" : "evening",
      "start_offset" : 62,
      "end_offset" : 69,
      "type" : "<ALPHANUM>",
      "position" : 12
    }
  ]
}

Simple Analyzer

按照非字母切分，非字母的都被去除
小写处理

组成如下：

Tokenizer：LowerCase

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GET _analyze
{
  "analyzer": "simple",
  "text": "2 running Quick brown-foxes leap over lazy dogs in the summer evening."
}

{
  "tokens" : [
    {
      "token" : "running",
      "start_offset" : 2,
      "end_offset" : 9,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "quick",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 16,
      "end_offset" : 21,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "foxes",
      "start_offset" : 22,
      "end_offset" : 27,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "leap",
      "start_offset" : 28,
      "end_offset" : 32,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "over",
      "start_offset" : 33,
      "end_offset" : 37,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "lazy",
      "start_offset" : 38,
      "end_offset" : 42,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "dogs",
      "start_offset" : 43,
      "end_offset" : 47,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "in",
      "start_offset" : 48,
      "end_offset" : 50,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "the",
      "start_offset" : 51,
      "end_offset" : 54,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "summer",
      "start_offset" : 55,
      "end_offset" : 61,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "evening",
      "start_offset" : 62,
      "end_offset" : 69,
      "type" : "word",
      "position" : 11
    }
  ]
}

Whitespace Analyzer

按照空格切分

组成如下：

Tokenizer:Whitespace

Stop Analyzer

相比 Simple Analyzer 多了 stop filter
- 会把 the\a\is 等修饰性词语去除

组成如下：

Tokenizer:Lowe Case
TokenFilter:Stop

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#stop
GET _analyze
{
  "analyzer": "whitespace",
  "text": "2 running Quick brown-foxes leap over lazy dogs in the summer evening."
}

{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "running",
      "start_offset" : 2,
      "end_offset" : 9,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "Quick",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "brown-foxes",
      "start_offset" : 16,
      "end_offset" : 27,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "leap",
      "start_offset" : 28,
      "end_offset" : 32,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "over",
      "start_offset" : 33,
      "end_offset" : 37,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "lazy",
      "start_offset" : 38,
      "end_offset" : 42,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "dogs",
      "start_offset" : 43,
      "end_offset" : 47,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "in",
      "start_offset" : 48,
      "end_offset" : 50,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "the",
      "start_offset" : 51,
      "end_offset" : 54,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "summer",
      "start_offset" : 55,
      "end_offset" : 61,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "evening.",
      "start_offset" : 62,
      "end_offset" : 70,
      "type" : "word",
      "position" : 11
    }
  ]
}

Keyword Analyzer

不分词，直接把输入当 term 输出

组成如下：

Tokenizer:Keyword

Pattern Analyzer

通过正则表达式进行分词
默认是 \W+,非字符的符号进行分隔

组成如下：

Tokenizer:Pattern
TokenFilters:LowerCase&Stop

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GET _analyze
{
  "analyzer": "pattern",
  "text": "2 running Quick brown-foxes leap over lazy dogs in the summer evening."
}

{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "running",
      "start_offset" : 2,
      "end_offset" : 9,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 16,
      "end_offset" : 21,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "foxes",
      "start_offset" : 22,
      "end_offset" : 27,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "leap",
      "start_offset" : 28,
      "end_offset" : 32,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 33,
      "end_offset" : 37,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "lazy",
      "start_offset" : 38,
      "end_offset" : 42,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "dogs",
      "start_offset" : 43,
      "end_offset" : 47,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "in",
      "start_offset" : 48,
      "end_offset" : 50,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "the",
      "start_offset" : 51,
      "end_offset" : 54,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "summer",
      "start_offset" : 55,
      "end_offset" : 61,
      "type" : "word",
      "position" : 11
    },
    {
      "token" : "evening",
      "start_offset" : 62,
      "end_offset" : 69,
      "type" : "word",
      "position" : 12
    }
  ]
}

Language Analyzer

支持按不同国家语言进行分词。

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#english
GET _analyze
{
  "analyzer": "english",
  "text": "2 running Quick brown-foxes leap over lazy dogs in the summer evening."
}

{
  "tokens" : [
    {
      "token" : "2",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<NUM>",
      "position" : 0
    },
    {
      "token" : "run",
      "start_offset" : 2,
      "end_offset" : 9,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "quick",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "brown",
      "start_offset" : 16,
      "end_offset" : 21,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "fox",
      "start_offset" : 22,
      "end_offset" : 27,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "leap",
      "start_offset" : 28,
      "end_offset" : 32,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "over",
      "start_offset" : 33,
      "end_offset" : 37,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "lazi",
      "start_offset" : 38,
      "end_offset" : 42,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "dog",
      "start_offset" : 43,
      "end_offset" : 47,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "summer",
      "start_offset" : 55,
      "end_offset" : 61,
      "type" : "<ALPHANUM>",
      "position" : 11
    },
    {
      "token" : "even",
      "start_offset" : 62,
      "end_offset" : 69,
      "type" : "<ALPHANUM>",
      "position" : 12
    }
  ]
}

中文分词

中文分词难点较多，如需要切分成词、没有像英文自然的空格作为分隔、不同上下文分词理解不同等。

中文分词使用较为常见的分词器 ICU Analyzer：

需要安装 plugin
- Elasticsearch-plugin install analysis-icu
提供了 Unicode 的支持，更好的支持亚洲语言

组成如下：

CharacterFilters:Normalization
Tokenizer:ICU Tokenizer
TokenFilters:Normalization&Folding&Collation&Transform

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POST _analyze
{
  "analyzer": "icu_analyzer",
  "text": "他说的确实在理”"
}

{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "说的",
      "start_offset" : 1,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "确实",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "在",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "理",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    }
  ]
}



POST _analyze
{
  "analyzer": "standard",
  "text": "他说的确实在理”"
}


{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "说",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "的",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "确",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "实",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    },
    {
      "token" : "在",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 5
    },
    {
      "token" : "理",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 6
    }
  ]
}


POST _analyze
{
  "analyzer": "icu_analyzer",
  "text": "这个苹果不大好吃"
}
{
  "tokens" : [
    {
      "token" : "这个",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "苹果",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "不大",
      "start_offset" : 4,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "好吃",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    }
  ]
}

更多的中文分词器：

IK
- 支持自定义词库，支持热更新分词字典
- https://github.com/medcl/elasticsearch-analysis-ik
THULAC
- THU Lexucal Analyzer for Chinese，清华大学自然语言处理和社会人文计算实验室的一套中文分词器
- https://github.com/microbun/elasticsearch-thulac-plugin

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POST _analyze
{
  "analyzer": "ik_smart",
  "text": "他说的确实在理”"
}

{
  "tokens" : [
    {
      "token" : "他",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "说",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "的确",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "实",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "CN_CHAR",
      "position" : 3
    },
    {
      "token" : "在理",
      "start_offset" : 5,
      "end_offset" : 7,
      "type" : "CN_WORD",
      "position" : 4
    }
  ]
}


POST _analyze
{
  "analyzer": "ik_smart",
  "text": "这个苹果不大好吃"
}

{
  "tokens" : [
    {
      "token" : "这个",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "CN_WORD",
      "position" : 0
    },
    {
      "token" : "苹果",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 1
    },
    {
      "token" : "不大",
      "start_offset" : 4,
      "end_offset" : 6,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "好吃",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "CN_WORD",
      "position" : 3
    }
  ]
}

参考

https://time.geekbang.org/course/intro/100030501?tab=catalog