elasticsearch之分析过程

前言

现在，我们已经了解了如何建立索引和搜索数据了。
那么，是时候来探索背后的故事了！当数据传递到elasticsearch后，到底发生了什么？

一、分析过程

当数据被发送到elasticsearch后并加入到倒排索引之前，elasticsearch会对该文档的进行一系列的处理步骤：

• 字符过滤：使用字符过滤器转变字符。
• 文本切分为分词：将文本（档）分为单个或多个分词。
• 分词过滤：使用分词过滤器转变每个分词。
• 分词索引：最终将分词存储在Lucene倒排索引中。

整体流程如下图所示：

接下来，我们简要的介绍elasticsearch中的分析器、分词器和分词过滤器。它们配置简单，灵活好用，我们可以通过不同的组合来获取我们想要的分词！

是的，无论多么复杂的分析过程，都是为了获取更加人性化的分词！
接下来，我们来看看其中，在整个分析过程的各个组件吧。

二、分析器

在elasticsearch中，一个分析器可以包括：

• 可选的字符过滤器
• 一个分词器
• 0个或多个分词过滤器

接下来简要的介绍各内置分词的大致情况。在介绍之前，为了方便演示。如果你已经按照之前的教程安装了ik analysis，现在请暂时将该插件移出plugins目录。

标准分析器：standard analyzer

标准分析器（standard analyzer）：是elasticsearch的默认分析器，该分析器综合了大多数欧洲语言来说合理的默认模块，包括标准分词器、标准分词过滤器、小写转换分词过滤器和停用词分词过滤器。

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POST _analyze
{
  "analyzer": "standard",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

分词结果如下：

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{
  "tokens" : [
    {
      "token" : "to",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "that",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "莎",
      "start_offset" : 45,
      "end_offset" : 46,
      "type" : "<IDEOGRAPHIC>",
      "position" : 10
    },
    {
      "token" : "士",
      "start_offset" : 46,
      "end_offset" : 47,
      "type" : "<IDEOGRAPHIC>",
      "position" : 11
    },
    {
      "token" : "比",
      "start_offset" : 47,
      "end_offset" : 48,
      "type" : "<IDEOGRAPHIC>",
      "position" : 12
    },
    {
      "token" : "亚",
      "start_offset" : 48,
      "end_offset" : 49,
      "type" : "<IDEOGRAPHIC>",
      "position" : 13
    }
  ]
}

简单分析器：simple analyzer

简单分析器（simple analyzer）：简单分析器仅使用了小写转换分词，这意味着在非字母处进行分词，并将分词自动转换为小写。这个分词器对于亚种语言来说效果不佳，因为亚洲语言不是根据空白来分词的，所以一般用于欧洲言中。

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POST _analyze
{
  "analyzer": "simple",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

分词结果如下：

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{
  "tokens" : [
    {
      "token" : "to",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "that",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 10
    }
  ]
}

空白分析器：whitespace analyzer

空白（格）分析器（whitespace analyzer）：这玩意儿只是根据空白将文本切分为若干分词，真是有够偷懒！

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POST _analyze
{
  "analyzer": "whitespace",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

分词结果如下：

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{
  "tokens" : [
    {
      "token" : "To",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be,",
      "start_offset" : 16,
      "end_offset" : 19,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "That",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "————",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 11
    }
  ]
}

停用词分析器：stop analyzer

停用词分析（stop analyzer）和简单分析器的行为很像，只是在分词流中额外的过滤了停用词。

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POST _analyze
{
  "analyzer": "stop",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果也很简单：

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{
  "tokens" : [
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 10
    }
  ]
}

关键词分析器：keyword analyzer

关键词分析器（keyword analyzer）将整个字段当做单独的分词，如无必要，我们不在映射中使用关键词分析器。

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POST _analyze
{
  "analyzer": "keyword",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

Copy

{
  "tokens" : [
    {
      "token" : "To be or not to be,  That is a question ———— 莎士比亚",
      "start_offset" : 0,
      "end_offset" : 49,
      "type" : "word",
      "position" : 0
    }
  ]
}

说的一点没错，分析结果是将整段当做单独的分词。

模式分析器：pattern analyzer

模式分析器（pattern analyzer）允许我们指定一个分词切分模式。但是通常更佳的方案是使用定制的分析器，组合现有的模式分词器和所需要的分词过滤器更加合适。

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POST _analyze
{
  "analyzer": "pattern",
  "explain": false, 
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "to",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "that",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    }
  ]
}

我们来自定制一个模式分析器，比如我们写匹配邮箱的正则。

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PUT pattern_test
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_email_analyzer":{
          "type":"pattern",
          "pattern":"\W|_",
          "lowercase":true
        }
      }
    }
  }
}

上例中，我们在创建一条索引的时候，配置分析器为自定义的分析器。

需要注意的是，在json字符串中，正则的斜杠需要转义。

我们使用自定义的分析器来查询。

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POST pattern_test/_analyze
{
  "analyzer": "my_email_analyzer",
  "text": "John_Smith@foo-bar.com"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "john",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "smith",
      "start_offset" : 5,
      "end_offset" : 10,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "foo",
      "start_offset" : 11,
      "end_offset" : 14,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "bar",
      "start_offset" : 15,
      "end_offset" : 18,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "com",
      "start_offset" : 19,
      "end_offset" : 22,
      "type" : "word",
      "position" : 4
    }
  ]
}

语言和多语言分析器：chinese

elasticsearch为很多世界流行语言提供良好的、简单的、开箱即用的语言分析器集合：阿拉伯语、亚美尼亚语、巴斯克语、巴西语、保加利亚语、加泰罗尼亚语、中文、捷克语、丹麦、荷兰语、英语、芬兰语、法语、加里西亚语、德语、希腊语、北印度语、匈牙利语、印度尼西亚、爱尔兰语、意大利语、日语、韩国语、库尔德语、挪威语、波斯语、葡萄牙语、罗马尼亚语、俄语、西班牙语、瑞典语、土耳其语和泰语。

我们可以指定其中之一的语言来指定特定的语言分析器，但必须是小写的名字！如果你要分析的语言不在上述集合中，可能还需要搭配相应的插件支持。

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POST _analyze
{
  "analyzer": "chinese",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "莎",
      "start_offset" : 45,
      "end_offset" : 46,
      "type" : "<IDEOGRAPHIC>",
      "position" : 10
    },
    {
      "token" : "士",
      "start_offset" : 46,
      "end_offset" : 47,
      "type" : "<IDEOGRAPHIC>",
      "position" : 11
    },
    {
      "token" : "比",
      "start_offset" : 47,
      "end_offset" : 48,
      "type" : "<IDEOGRAPHIC>",
      "position" : 12
    },
    {
      "token" : "亚",
      "start_offset" : 48,
      "end_offset" : 49,
      "type" : "<IDEOGRAPHIC>",
      "position" : 13
    }
  ]
}

也可以是别语言：

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POST _analyze
{
  "analyzer": "french",
  "text":"Je suis ton père"
}
POST _analyze
{
  "analyzer": "german",
  "text":"Ich bin dein vater"
}

雪球分析器：snowball analyzer

雪球分析器（snowball analyzer）除了使用标准的分词和分词过滤器（和标准分析器一样）也是用了小写分词过滤器和停用词过滤器，除此之外，它还是用了雪球词干器对文本进行词干提取。

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POST _analyze
{
  "analyzer": "snowball",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "莎",
      "start_offset" : 45,
      "end_offset" : 46,
      "type" : "<IDEOGRAPHIC>",
      "position" : 10
    },
    {
      "token" : "士",
      "start_offset" : 46,
      "end_offset" : 47,
      "type" : "<IDEOGRAPHIC>",
      "position" : 11
    },
    {
      "token" : "比",
      "start_offset" : 47,
      "end_offset" : 48,
      "type" : "<IDEOGRAPHIC>",
      "position" : 12
    },
    {
      "token" : "亚",
      "start_offset" : 48,
      "end_offset" : 49,
      "type" : "<IDEOGRAPHIC>",
      "position" : 13
    }
  ]
}

三、字符过滤器

字符过滤器在<charFilter>属性中定义，它是对字符流进行处理。字符过滤器种类不多。elasticearch只提供了三种字符过滤器：

• HTML字符过滤器（HTML Strip Char Filter）
• 映射字符过滤器（Mapping Char Filter）
• 模式替换过滤器（Pattern Replace Char Filter）

我们来分别看看都是怎么玩的吧！

HTML字符过滤器

HTML字符过滤器（HTML Strip Char Filter）从文本中去除HTML元素。

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

结果如下：

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{
  "tokens" : [
    {
      "token" : """

I'm so happy!

""",
      "start_offset" : 0,
      "end_offset" : 32,
      "type" : "word",
      "position" : 0
    }
  ]
}

映射字符过滤器

映射字符过滤器（Mapping Char Filter）接收键值的映射，每当遇到与键相同的字符串时，它就用该键关联的值替换它们。

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PUT pattern_test4
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "tokenizer":"keyword",
          "char_filter":["my_char_filter"]
        }
      },
      "char_filter":{
          "my_char_filter":{
            "type":"mapping",
            "mappings":["苍井空 => 666","武藤兰 => 888"]
          }
        }
    }
  }
}

上例中，我们自定义了一个分析器，其内的分词器使用关键字分词器，字符过滤器则是自定制的，将字符中的苍井空替换为666，武藤兰替换为888。

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POST pattern_test4/_analyze
{
  "analyzer": "my_analyzer",
  "text": "苍井空热爱武藤兰，可惜后来苍井空结婚了"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "666热爱888，可惜后来666结婚了",
      "start_offset" : 0,
      "end_offset" : 19,
      "type" : "word",
      "position" : 0
    }
  ]
}

模式替换过滤器

模式替换过滤器（Pattern Replace Char Filter）使用正则表达式匹配并替换字符串中的字符。但要小心你写的抠脚的正则表达式。因为这可能导致性能变慢！

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PUT pattern_test5
{
  "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_"
        }
      }
    }
  }
}

上例中，我们自定义了一个正则规则。

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POST pattern_test5/_analyze
{
  "analyzer": "my_analyzer",
  "text": "My credit card is 123-456-789"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "My",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "credit",
      "start_offset" : 3,
      "end_offset" : 9,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "card",
      "start_offset" : 10,
      "end_offset" : 14,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "is",
      "start_offset" : 15,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "123_456_789",
      "start_offset" : 18,
      "end_offset" : 29,
      "type" : "<NUM>",
      "position" : 4
    }
  ]
}

我们大致的了解elasticsearch分析处理数据的流程。但可以看到的是，我们极少地在例子中演示中文处理。因为elasticsearch内置的分析器处理起来中文不是很好。所以，后续会介绍一个重量级的插件就是elasticsearch analysis ik（一般习惯称呼为ik分词器）。

四、分词器

由于elasticsearch内置了分析器，它同样也包含了分词器。分词器，顾名思义，主要的操作是将文本字符串分解为小块，而这些小块这被称为分词token。

标准分词器：standard tokenizer

标准分词器（standard tokenizer）是一个基于语法的分词器，对于大多数欧洲语言来说还是不错的，它同时还处理了Unicode文本的分词，但分词默认的最大长度是255字节，它也移除了逗号和句号这样的标点符号。

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POST _analyze
{
  "tokenizer": "standard",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "To",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "That",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "莎",
      "start_offset" : 45,
      "end_offset" : 46,
      "type" : "<IDEOGRAPHIC>",
      "position" : 10
    },
    {
      "token" : "士",
      "start_offset" : 46,
      "end_offset" : 47,
      "type" : "<IDEOGRAPHIC>",
      "position" : 11
    },
    {
      "token" : "比",
      "start_offset" : 47,
      "end_offset" : 48,
      "type" : "<IDEOGRAPHIC>",
      "position" : 12
    },
    {
      "token" : "亚",
      "start_offset" : 48,
      "end_offset" : 49,
      "type" : "<IDEOGRAPHIC>",
      "position" : 13
    }
  ]
}

关键词分词器：keyword tokenizer

关键词分词器（keyword tokenizer）是一种简单的分词器，将整个文本作为单个的分词，提供给分词过滤器，当你只想用分词过滤器，而不做分词操作时，它是不错的选择。

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POST _analyze
{
  "tokenizer": "keyword",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "To be or not to be,  That is a question ———— 莎士比亚",
      "start_offset" : 0,
      "end_offset" : 49,
      "type" : "word",
      "position" : 0
    }
  ]
}

字母分词器：letter tokenizer

字母分词器（letter tokenizer）根据非字母的符号，将文本切分成分词。

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POST _analyze
{
  "tokenizer": "letter",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "To",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "That",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 10
    }
  ]
}

小写分词器：lowercase tokenizer

小写分词器（lowercase tokenizer）结合了常规的字母分词器和小写分词过滤器（跟你想的一样，就是将所有的分词转化为小写）的行为。通过一个单独的分词器来实现的主要原因是，一次进行两项操作会获得更好的性能。

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POST _analyze
{
  "tokenizer": "lowercase",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "to",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be",
      "start_offset" : 16,
      "end_offset" : 18,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "that",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 10
    }
  ]
}

空白分词器：whitespace tokenizer

空白分词器（whitespace tokenizer）通过空白来分隔不同的分词，空白包括空格、制表符、换行等。但是，我们需要注意的是，空白分词器不会删除任何标点符号。

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POST _analyze
{
  "tokenizer": "whitespace",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

Copy

{
  "tokens" : [
    {
      "token" : "To",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "be",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "or",
      "start_offset" : 6,
      "end_offset" : 8,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "not",
      "start_offset" : 9,
      "end_offset" : 12,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "to",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "be,",
      "start_offset" : 16,
      "end_offset" : 19,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "That",
      "start_offset" : 21,
      "end_offset" : 25,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "is",
      "start_offset" : 26,
      "end_offset" : 28,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "a",
      "start_offset" : 29,
      "end_offset" : 30,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "question",
      "start_offset" : 31,
      "end_offset" : 39,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "————",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "莎士比亚",
      "start_offset" : 45,
      "end_offset" : 49,
      "type" : "word",
      "position" : 11
    }
  ]
}

模式分词器：pattern tokenizer

模式分词器（pattern tokenizer）允许指定一个任意的模式，将文本切分为分词。

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POST _analyze
{
  "tokenizer": "pattern",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

现在让我们手动定制一个以逗号分隔的分词器。

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PUT pattern_test2
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer":{
          "tokenizer":"my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer":{
          "type":"pattern",
          "pattern":","
        }
      }
    }
  }
}

上例中，在settings下的自定义分析器my_analyzer中，自定义的模式分词器名叫my_tokenizer；在与自定义分析器同级，为新建的自定义模式分词器设置一些属性，比如以逗号分隔。

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POST pattern_test2/_analyze
{
  "tokenizer": "my_tokenizer",
  "text":"To be or not to be,  That is a question ———— 莎士比亚"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "To be or not to be",
      "start_offset" : 0,
      "end_offset" : 18,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "  That is a question ———— 莎士比亚",
      "start_offset" : 19,
      "end_offset" : 49,
      "type" : "word",
      "position" : 1
    }
  ]
}

根据结果可以看到，文档被逗号分割为两部分。

UAX URL电子邮件分词器：UAX RUL email tokenizer

在处理单个的英文单词的情况下，标准分词器是个非常好的选择，但是现在很多的网站以网址或电子邮件作为结尾，比如我们现在有这样的一个文本：

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作者：张开
来源：未知 
原文：https://www.cnblogs.com/Neeo/articles/10402742.html
邮箱：xxxxxxx@xx.com
版权声明：本文为博主原创文章，转载请附上博文链接！

现在让我们使用标准分词器查看一下：

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POST _analyze
{
  "tokenizer": "standard",
  "text":"作者：张开来源：未知原文：https://www.cnblogs.com/Neeo/articles/10402742.html邮箱：xxxxxxx@xx.com版权声明：本文为博主原创文章，转载请附上博文链接！"
}

结果很长：

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{
  "tokens" : [
    {
      "token" : "作",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "者",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "张",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "开",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "来",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    },
    {
      "token" : "源",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 5
    },
    {
      "token" : "未",
      "start_offset" : 8,
      "end_offset" : 9,
      "type" : "<IDEOGRAPHIC>",
      "position" : 6
    },
    {
      "token" : "知",
      "start_offset" : 9,
      "end_offset" : 10,
      "type" : "<IDEOGRAPHIC>",
      "position" : 7
    },
    {
      "token" : "原",
      "start_offset" : 10,
      "end_offset" : 11,
      "type" : "<IDEOGRAPHIC>",
      "position" : 8
    },
    {
      "token" : "文",
      "start_offset" : 11,
      "end_offset" : 12,
      "type" : "<IDEOGRAPHIC>",
      "position" : 9
    },
    {
      "token" : "https",
      "start_offset" : 13,
      "end_offset" : 18,
      "type" : "<ALPHANUM>",
      "position" : 10
    },
    {
      "token" : "www.cnblogs.com",
      "start_offset" : 21,
      "end_offset" : 36,
      "type" : "<ALPHANUM>",
      "position" : 11
    },
    {
      "token" : "Neeo",
      "start_offset" : 37,
      "end_offset" : 41,
      "type" : "<ALPHANUM>",
      "position" : 12
    },
    {
      "token" : "articles",
      "start_offset" : 42,
      "end_offset" : 50,
      "type" : "<ALPHANUM>",
      "position" : 13
    },
    {
      "token" : "10402742",
      "start_offset" : 51,
      "end_offset" : 59,
      "type" : "<NUM>",
      "position" : 14
    },
    {
      "token" : "html",
      "start_offset" : 60,
      "end_offset" : 64,
      "type" : "<ALPHANUM>",
      "position" : 15
    },
    {
      "token" : "邮",
      "start_offset" : 64,
      "end_offset" : 65,
      "type" : "<IDEOGRAPHIC>",
      "position" : 16
    },
    {
      "token" : "箱",
      "start_offset" : 65,
      "end_offset" : 66,
      "type" : "<IDEOGRAPHIC>",
      "position" : 17
    },
    {
      "token" : "xxxxxxx",
      "start_offset" : 67,
      "end_offset" : 74,
      "type" : "<ALPHANUM>",
      "position" : 18
    },
    {
      "token" : "xx.com",
      "start_offset" : 75,
      "end_offset" : 81,
      "type" : "<ALPHANUM>",
      "position" : 19
    },
    {
      "token" : "版",
      "start_offset" : 81,
      "end_offset" : 82,
      "type" : "<IDEOGRAPHIC>",
      "position" : 20
    },
    {
      "token" : "权",
      "start_offset" : 82,
      "end_offset" : 83,
      "type" : "<IDEOGRAPHIC>",
      "position" : 21
    },
    {
      "token" : "声",
      "start_offset" : 83,
      "end_offset" : 84,
      "type" : "<IDEOGRAPHIC>",
      "position" : 22
    },
    {
      "token" : "明",
      "start_offset" : 84,
      "end_offset" : 85,
      "type" : "<IDEOGRAPHIC>",
      "position" : 23
    },
    {
      "token" : "本",
      "start_offset" : 86,
      "end_offset" : 87,
      "type" : "<IDEOGRAPHIC>",
      "position" : 24
    },
    {
      "token" : "文",
      "start_offset" : 87,
      "end_offset" : 88,
      "type" : "<IDEOGRAPHIC>",
      "position" : 25
    },
    {
      "token" : "为",
      "start_offset" : 88,
      "end_offset" : 89,
      "type" : "<IDEOGRAPHIC>",
      "position" : 26
    },
    {
      "token" : "博",
      "start_offset" : 89,
      "end_offset" : 90,
      "type" : "<IDEOGRAPHIC>",
      "position" : 27
    },
    {
      "token" : "主",
      "start_offset" : 90,
      "end_offset" : 91,
      "type" : "<IDEOGRAPHIC>",
      "position" : 28
    },
    {
      "token" : "原",
      "start_offset" : 91,
      "end_offset" : 92,
      "type" : "<IDEOGRAPHIC>",
      "position" : 29
    },
    {
      "token" : "创",
      "start_offset" : 92,
      "end_offset" : 93,
      "type" : "<IDEOGRAPHIC>",
      "position" : 30
    },
    {
      "token" : "文",
      "start_offset" : 93,
      "end_offset" : 94,
      "type" : "<IDEOGRAPHIC>",
      "position" : 31
    },
    {
      "token" : "章",
      "start_offset" : 94,
      "end_offset" : 95,
      "type" : "<IDEOGRAPHIC>",
      "position" : 32
    },
    {
      "token" : "转",
      "start_offset" : 96,
      "end_offset" : 97,
      "type" : "<IDEOGRAPHIC>",
      "position" : 33
    },
    {
      "token" : "载",
      "start_offset" : 97,
      "end_offset" : 98,
      "type" : "<IDEOGRAPHIC>",
      "position" : 34
    },
    {
      "token" : "请",
      "start_offset" : 98,
      "end_offset" : 99,
      "type" : "<IDEOGRAPHIC>",
      "position" : 35
    },
    {
      "token" : "附",
      "start_offset" : 99,
      "end_offset" : 100,
      "type" : "<IDEOGRAPHIC>",
      "position" : 36
    },
    {
      "token" : "上",
      "start_offset" : 100,
      "end_offset" : 101,
      "type" : "<IDEOGRAPHIC>",
      "position" : 37
    },
    {
      "token" : "博",
      "start_offset" : 101,
      "end_offset" : 102,
      "type" : "<IDEOGRAPHIC>",
      "position" : 38
    },
    {
      "token" : "文",
      "start_offset" : 102,
      "end_offset" : 103,
      "type" : "<IDEOGRAPHIC>",
      "position" : 39
    },
    {
      "token" : "链",
      "start_offset" : 103,
      "end_offset" : 104,
      "type" : "<IDEOGRAPHIC>",
      "position" : 40
    },
    {
      "token" : "接",
      "start_offset" : 104,
      "end_offset" : 105,
      "type" : "<IDEOGRAPHIC>",
      "position" : 41
    }
  ]
}

无论如何，这个结果不符合我们的预期，因为把我们的邮箱和网址分的乱七八糟！那么针对这种情况，我们应该使用UAX URL电子邮件分词器（UAX RUL email tokenizer），该分词器将电子邮件和URL都作为单独的分词进行保留。

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POST _analyze
{
  "tokenizer": "uax_url_email",
  "text":"作者：张开来源：未知原文：https://www.cnblogs.com/Neeo/articles/10402742.html邮箱：xxxxxxx@xx.com版权声明：本文为博主原创文章，转载请附上博文链接！"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "作",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "者",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "张",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "开",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "来",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    },
    {
      "token" : "源",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 5
    },
    {
      "token" : "未",
      "start_offset" : 8,
      "end_offset" : 9,
      "type" : "<IDEOGRAPHIC>",
      "position" : 6
    },
    {
      "token" : "知",
      "start_offset" : 9,
      "end_offset" : 10,
      "type" : "<IDEOGRAPHIC>",
      "position" : 7
    },
    {
      "token" : "原",
      "start_offset" : 10,
      "end_offset" : 11,
      "type" : "<IDEOGRAPHIC>",
      "position" : 8
    },
    {
      "token" : "文",
      "start_offset" : 11,
      "end_offset" : 12,
      "type" : "<IDEOGRAPHIC>",
      "position" : 9
    },
    {
      "token" : "https://www.cnblogs.com/Neeo/articles/10402742.html",
      "start_offset" : 13,
      "end_offset" : 64,
      "type" : "<URL>",
      "position" : 10
    },
    {
      "token" : "邮",
      "start_offset" : 64,
      "end_offset" : 65,
      "type" : "<IDEOGRAPHIC>",
      "position" : 11
    },
    {
      "token" : "箱",
      "start_offset" : 65,
      "end_offset" : 66,
      "type" : "<IDEOGRAPHIC>",
      "position" : 12
    },
    {
      "token" : "xxxxxxx@xx.com",
      "start_offset" : 67,
      "end_offset" : 81,
      "type" : "<EMAIL>",
      "position" : 13
    },
    {
      "token" : "版",
      "start_offset" : 81,
      "end_offset" : 82,
      "type" : "<IDEOGRAPHIC>",
      "position" : 14
    },
    {
      "token" : "权",
      "start_offset" : 82,
      "end_offset" : 83,
      "type" : "<IDEOGRAPHIC>",
      "position" : 15
    },
    {
      "token" : "声",
      "start_offset" : 83,
      "end_offset" : 84,
      "type" : "<IDEOGRAPHIC>",
      "position" : 16
    },
    {
      "token" : "明",
      "start_offset" : 84,
      "end_offset" : 85,
      "type" : "<IDEOGRAPHIC>",
      "position" : 17
    },
    {
      "token" : "本",
      "start_offset" : 86,
      "end_offset" : 87,
      "type" : "<IDEOGRAPHIC>",
      "position" : 18
    },
    {
      "token" : "文",
      "start_offset" : 87,
      "end_offset" : 88,
      "type" : "<IDEOGRAPHIC>",
      "position" : 19
    },
    {
      "token" : "为",
      "start_offset" : 88,
      "end_offset" : 89,
      "type" : "<IDEOGRAPHIC>",
      "position" : 20
    },
    {
      "token" : "博",
      "start_offset" : 89,
      "end_offset" : 90,
      "type" : "<IDEOGRAPHIC>",
      "position" : 21
    },
    {
      "token" : "主",
      "start_offset" : 90,
      "end_offset" : 91,
      "type" : "<IDEOGRAPHIC>",
      "position" : 22
    },
    {
      "token" : "原",
      "start_offset" : 91,
      "end_offset" : 92,
      "type" : "<IDEOGRAPHIC>",
      "position" : 23
    },
    {
      "token" : "创",
      "start_offset" : 92,
      "end_offset" : 93,
      "type" : "<IDEOGRAPHIC>",
      "position" : 24
    },
    {
      "token" : "文",
      "start_offset" : 93,
      "end_offset" : 94,
      "type" : "<IDEOGRAPHIC>",
      "position" : 25
    },
    {
      "token" : "章",
      "start_offset" : 94,
      "end_offset" : 95,
      "type" : "<IDEOGRAPHIC>",
      "position" : 26
    },
    {
      "token" : "转",
      "start_offset" : 96,
      "end_offset" : 97,
      "type" : "<IDEOGRAPHIC>",
      "position" : 27
    },
    {
      "token" : "载",
      "start_offset" : 97,
      "end_offset" : 98,
      "type" : "<IDEOGRAPHIC>",
      "position" : 28
    },
    {
      "token" : "请",
      "start_offset" : 98,
      "end_offset" : 99,
      "type" : "<IDEOGRAPHIC>",
      "position" : 29
    },
    {
      "token" : "附",
      "start_offset" : 99,
      "end_offset" : 100,
      "type" : "<IDEOGRAPHIC>",
      "position" : 30
    },
    {
      "token" : "上",
      "start_offset" : 100,
      "end_offset" : 101,
      "type" : "<IDEOGRAPHIC>",
      "position" : 31
    },
    {
      "token" : "博",
      "start_offset" : 101,
      "end_offset" : 102,
      "type" : "<IDEOGRAPHIC>",
      "position" : 32
    },
    {
      "token" : "文",
      "start_offset" : 102,
      "end_offset" : 103,
      "type" : "<IDEOGRAPHIC>",
      "position" : 33
    },
    {
      "token" : "链",
      "start_offset" : 103,
      "end_offset" : 104,
      "type" : "<IDEOGRAPHIC>",
      "position" : 34
    },
    {
      "token" : "接",
      "start_offset" : 104,
      "end_offset" : 105,
      "type" : "<IDEOGRAPHIC>",
      "position" : 35
    }
  ]
}

路径层次分词器：path hierarchy tokenizer

路径层次分词器（path hierarchy tokenizer）允许以特定的方式索引文件系统的路径，这样在搜索时，共享同样路径的文件将被作为结果返回。

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POST _analyze
{
  "tokenizer": "path_hierarchy",
  "text":"/usr/local/python/python2.7"
}

返回结果如下：

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{
  "tokens" : [
    {
      "token" : "/usr",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "/usr/local",
      "start_offset" : 0,
      "end_offset" : 10,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "/usr/local/python",
      "start_offset" : 0,
      "end_offset" : 17,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "/usr/local/python/python2.7",
      "start_offset" : 0,
      "end_offset" : 27,
      "type" : "word",
      "position" : 0
    }
  ]
}

五、分词过滤器

asticsearch内置很多（真是变态多啊！但一般用不到，美滋滋！！！）的分词过滤器。其中包含分词过滤器和字符过滤器。
常见分词过滤器
这里仅列举几个常见的分词过滤器（token filter）包括：

• 标准分词过滤器（Standard Token Filter）在6.5.0版本弃用。此筛选器已被弃用，将在下一个主要版本中删除。在之前的版本中其实也没干啥，甚至在更老版本的Lucene中，它用于去除单词结尾的s字符，还有不必要的句点字符，但是现在， 连这些小功能都被其他的分词器和分词过滤器顺手干了，真可怜！
• ASCII折叠分词过滤器（ASCII Folding Token Filter）将前127个ASCII字符(基本拉丁语的Unicode块)中不包含的字母、数字和符号Unicode字符转换为对应的ASCII字符(如果存在的话）。
• 扁平图形分词过滤器（Flatten Graph Token Filter）接受任意图形标记流。例如由同义词图形标记过滤器生成的标记流，并将其展平为适合索引的单个线性标记链。这是一个有损的过程，因为单独的侧路径被压扁在彼此之上，但是如果在索引期间使用图形令牌流是必要的，因为Lucene索引当前不能表示图形。 出于这个原因，最好只在搜索时应用图形分析器，因为这样可以保留完整的图形结构，并为邻近查询提供正确的匹配。该功能在Lucene中为实验性功能。
• 长度标记过滤器（Length Token Filter）会移除分词流中太长或者太短的标记，它是可配置的，我们可以在settings中设置。
• 小写分词过滤器（Lowercase Token Filter）将分词规范化为小写，它通过language参数支持希腊语、爱尔兰语和土耳其语小写标记过滤器。
• 大写分词过滤器（Uppercase Token Filter）将分词规范为大写。

其余分词过滤器不一一列举。详情参见官网。

自定义分词过滤器

接下来我们简单的来学习自定义两个分词过滤器。首先是长度分词过滤器。

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PUT pattern_test3
{
  "settings": {
    "analysis": {
      "filter": {
        "my_test_length":{
          "type":"length",
          "max":8,
          "min":2
        }
      }
    }
  }
}

上例中，我们自定义了一个长度过滤器，过滤掉长度大于8和小于2的分词。
需要补充的是，max参数表示最大分词长度。默认为Integer.MAX_VALUE，就是2147483647（231?1），而min则表示最小长度，默认为0。

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POST pattern_test3/_analyze
{
  "tokenizer": "standard",
  "filter": ["my_test_length"],
  "text":"a Small word and a longerword"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "Small",
      "start_offset" : 2,
      "end_offset" : 7,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "word",
      "start_offset" : 8,
      "end_offset" : 12,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "and",
      "start_offset" : 13,
      "end_offset" : 16,
      "type" : "<ALPHANUM>",
      "position" : 3
    }
  ]
}

自定义小写分词过滤器

自定义一个小写分词过滤器，过滤希腊文：

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PUT lowercase_example
{
  "settings": {
    "analysis": {
      "analyzer": {
        "standard_lowercase_example": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase"]
        },
        "greek_lowercase_example": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["greek_lowercase"]
        }
      },
      "filter": {
        "greek_lowercase": {
          "type": "lowercase",
          "language": "greek"
        }
      }
    }
  }
}

过滤内容是：

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POST lowercase_example/_analyze
{
  "tokenizer": "standard",
  "filter": ["greek_lowercase"],
  "text":"?να φ?λτρο διακριτικο? τ?που πεζ? s ομαλοποιε? το κε?μενο διακριτικο? σε χαμηλ?τερη θ?κη"
}

结果如下：

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{
  "tokens" : [
    {
      "token" : "ενα",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "φιλτρο",
      "start_offset" : 4,
      "end_offset" : 10,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "διακριτικου",
      "start_offset" : 11,
      "end_offset" : 22,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "τυπου",
      "start_offset" : 23,
      "end_offset" : 28,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "πεζα",
      "start_offset" : 29,
      "end_offset" : 33,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "s",
      "start_offset" : 34,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 5
    },
    {
      "token" : "ομαλοποιει",
      "start_offset" : 36,
      "end_offset" : 46,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "το",
      "start_offset" : 47,
      "end_offset" : 49,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "κειμενο",
      "start_offset" : 50,
      "end_offset" : 57,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "διακριτικου",
      "start_offset" : 58,
      "end_offset" : 69,
      "type" : "<ALPHANUM>",
      "position" : 9
    },
    {
      "token" : "σε",
      "start_offset" : 70,
      "end_offset" : 72,
      "type" : "<ALPHANUM>",
      "position" : 10
    },
    {
      "token" : "χαμηλοτερη",
      "start_offset" : 73,
      "end_offset" : 83,
      "type" : "<ALPHANUM>",
      "position" : 11
    },
    {
      "token" : "θηκη",
      "start_offset" : 84,
      "end_offset" : 88,
      "type" : "<ALPHANUM>",
      "position" : 12
    }
  ]
}

多个分词过滤器

除此之外，我们可以使用多个分词过滤器。例如我们在使用长度过滤器时，可以同时使用小写分词过滤器或者更多。

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POST _analyze
{
  "tokenizer": "standard",
  "filter": ["length","lowercase"],
  "text":"a Small word and a longerword"
}

上例中，我们用列表来管理多个分词过滤器。
结果如下：

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{
  "tokens" : [
    {
      "token" : "a",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "small",
      "start_offset" : 2,
      "end_offset" : 7,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "word",
      "start_offset" : 8,
      "end_offset" : 12,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "and",
      "start_offset" : 13,
      "end_offset" : 16,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "a",
      "start_offset" : 17,
      "end_offset" : 18,
      "type" : "<ALPHANUM>",
      "position" : 4
    },
    {
      "token" : "longerword",
      "start_offset" : 19,
      "end_offset" : 29,
      "type" : "<ALPHANUM>",
      "position" : 5
    }
  ]
}