es - elasticsearch自定义分析器 - 内建分词过滤器 - 10世界上并没有完美的程序，但是我们并不因此而

世界上并没有完美的程序，但是我们并不因此而沮丧，因为写程序就是一个不断追求完美的过程。

自定义分析器 :

Character filters :
1. 作用 : 字符的增、删、改转换
2. 数量限制 : 可以有0个或多个
3. 内建字符过滤器 :
1. HTML Strip Character filter : 去除html标签
2. Mapping Character filter : 映射替换
3. Pattern Replace Character filter : 正则替换
Tokenizer :
1. 作用 :
1. 分词
2. 记录词的顺序和位置（短语查询）
3. 记录词的开头和结尾位置（高亮）
4. 记录词的类型（分类）
2. 数量限制 : 有且只能有一个
3. 分类 :
1. 完整分词 :
1. Standard
2. Letter
3. Lowercase
4. whitespace
5. UAX URL Email
6. Classic
7. Thai
2. 切词 :
1. N-Gram
2. Edge N-Gram
3. 文本 :
1. Keyword
2. Pattern
3. Simple Pattern
4. Char Group
5. Simple Pattern split
6. Path
Token filters :
1. 作用 : 分词的增、删、改转换
2. 数量限制 : 可以有0个或多个
3. 分类 :
1. apostrophe
2. asciifolding
3. cjk bigram
4. cjk width
5. classic
6. common grams
7. conditional
8. decimal digit
9. delimited payload
10. dictionary decompounder
11. edge ngram
12. elision
13. fingerprint
14. flatten_graph
15. hunspell
16. hyphenation decompounder
17. keep types
18. keep words
19. keyword marker
20. keyword repeat
21. kstem
22. length
23. limit token count
24. lowercase
25. min_hash
26. multiplexer
27. ngram
28. normalization
29. pattern_capture
30. pattern replace
31. porter stem
32. predicate script
33. remove duplicates
34. reverse
35. shingle
36. snowball
37. stemmer

今天演示 : 34-37

# reverse token filter
# 作用 : 分词反转

GET /_analyze
{
  "tokenizer" : "whitespace",
  "filter"    : ["reverse"],
  "text"      : ["hello gooding me"]
}

# 结果
{
  "tokens" : [
    {
      "token" : "olleh",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "gnidoog",
      "start_offset" : 6,
      "end_offset" : 13,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "em",
      "start_offset" : 14,
      "end_offset" : 16,
      "type" : "word",
      "position" : 2
    }
  ]
}

# shingle token filter
# 作用   : 分词重复显示,连词
# 配置项 :
#   1. max_shingle_size :
#   2. min_shingle_size :
#   3. output_unigrams  : 是否输出原始值，默认true
#   4. output_unigrams_if_no_shingles : 如果没有shingle则输出原始值，如果有则输出shingle
#   5. token_separator  : shingle的连接符，默认空格
#   6. filter_token     : 占位符，如去除停用词后的位置由filter_token指定的词代替，默认下划线

GET /_analyze
{
  "tokenizer": "whitespace",
  "filter": [{
    "type"      : "stop",
    "stopwords" : ["good"]
  }, {
    "type"            : "shingle",
    "token_separator" : "+"
  }],
  "text": ["hello good me this is a dog"]
}

# 结果
{
  "tokens" : [
    {
      "token" : "hello",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "hello+_",
      "start_offset" : 0,
      "end_offset" : 11,
      "type" : "shingle",
      "position" : 0,
      "positionLength" : 2
    },
    {
      "token" : "_+me",
      "start_offset" : 11,
      "end_offset" : 13,
      "type" : "shingle",
      "position" : 1,
      "positionLength" : 2
    },
    {
      "token" : "me",
      "start_offset" : 11,
      "end_offset" : 13,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "me+this",
      "start_offset" : 11,
      "end_offset" : 18,
      "type" : "shingle",
      "position" : 2,
      "positionLength" : 2
    },
    {
      "token" : "this",
      "start_offset" : 14,
      "end_offset" : 18,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "this+is",
      "start_offset" : 14,
      "end_offset" : 21,
      "type" : "shingle",
      "position" : 3,
      "positionLength" : 2
    },
    {
      "token" : "is",
      "start_offset" : 19,
      "end_offset" : 21,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "is+a",
      "start_offset" : 19,
      "end_offset" : 23,
      "type" : "shingle",
      "position" : 4,
      "positionLength" : 2
    },
    {
      "token" : "a",
      "start_offset" : 22,
      "end_offset" : 23,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "a+dog",
      "start_offset" : 22,
      "end_offset" : 27,
      "type" : "shingle",
      "position" : 5,
      "positionLength" : 2
    },
    {
      "token" : "dog",
      "start_offset" : 24,
      "end_offset" : 27,
      "type" : "word",
      "position" : 6
    }
  ]
}

# snowball token filter
# 作用 : 词干提取

GET /_analyze
{
  "tokenizer" : "whitespace",
  "filter"    : ["snowball"],
  "text"      : ["hello gooding me"]
}

# 结果
{
  "tokens" : [
    {
      "token" : "hello",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "good",
      "start_offset" : 6,
      "end_offset" : 13,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "me",
      "start_offset" : 14,
      "end_offset" : 16,
      "type" : "word",
      "position" : 2
    }
  ]
}

# stemmer token filter
# 作用   : 词干提取
# 配置项 :
#   1. language : 多种语言
#   2. name     : 语言的别名

GET /_analyze
{
  "tokenizer" : "whitespace",
  "filter"    : ["stemmer"],
  "text"      : ["hello gooding me"]
}

# 结果
{
  "tokens" : [
    {
      "token" : "hello",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "good",
      "start_offset" : 6,
      "end_offset" : 13,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "me",
      "start_offset" : 14,
      "end_offset" : 16,
      "type" : "word",
      "position" : 2
    }
  ]
}