简单实例

这是一个简单的分析器，将文本通过空格拆分成各个tokens

POST _analyze
{
  "analyzer": "whitespace",
  "text":     "The quick brown fox."
}

{
  "tokens" : [
    {
      "token" : "The",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "quick",
      "start_offset" : 4,
      "end_offset" : 9,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 10,
      "end_offset" : 15,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "fox.",
      "start_offset" : 16,
      "end_offset" : 20,
      "type" : "word",
      "position" : 3
    }
  ]
}
带完整解析的文本分析 
POST _analyze
{
  "char_filter": [
    "html_strip"
  ],
  "tokenizer": "standard",
  "filter":  [ "lowercase", "asciifolding" ],
  "text": "Is this déja vu  <b>test</b> ?"
}

其中对应的是

  • char_filter , character filter 文本过滤器，可以配置0到多个
  • tokenizer，令牌分析器，有且仅有一个
  • filter，token filter，令牌过滤器，可以配置0到多个

经过上面的过滤查询，分别对应

  • 去掉HTML标签
  • 默认分析器，可以删除大部分标点符号
  • 拼写转小写，将不在基本拉丁Unicode块中的字母，数字和符号字符（前127个ASCII字符）转换为等效的ASCII（如果存在）

得到的结果如下

{
  "tokens" : [
    {
      "token" : "is",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "this",
      "start_offset" : 3,
      "end_offset" : 7,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "deja",
      "start_offset" : 8,
      "end_offset" : 12,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "vu",
      "start_offset" : 13,
      "end_offset" : 15,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "test",
      "start_offset" : 20,
      "end_offset" : 28,
      "type" : "<ALPHANUM>",
      "position" : 4
    }
  ]
}
参考资料
  • https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-custom-analyzer.html
  • https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-overview.html