先进入kibana的管控台，点击菜单栏中的 dev tools

一、使用默认的分词器

GET _analyze
{
  "text": "中华人名*"
}

分词效果

{
  "tokens" : [
    {
      "token" : "中",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "华",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "人",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "名",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "共",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    },
    {
      "token" : "和",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<IDEOGRAPHIC>",
      "position" : 5
    },
    {
      "token" : "国",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "<IDEOGRAPHIC>",
      "position" : 6
    }
  ]
}

es的默认分词器只会完全的拆分，不管是英文还是中文都会将整个句子拆分成一个一个的字

二、使用 ik分词器

需要elasticsearch 安装了 ik分词器

使用 ik_max_word 方式

GET _analyze
{
  "analyzer":"ik_max_word",
  "text": "中华人名*"
}

分词效果

{
  "tokens" : [
    {
      "token" : "中华",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "CN_WORD",
      "position" : 0
    },
    {
      "token" : "华人",
      "start_offset" : 1,
      "end_offset" : 3,
      "type" : "CN_WORD",
      "position" : 1
    },
    {
      "token" : "人名",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "*",
      "start_offset" : 4,
      "end_offset" : 7,
      "type" : "CN_WORD",
      "position" : 3
    },
    {
      "token" : "共和",
      "start_offset" : 4,
      "end_offset" : 6,
      "type" : "CN_WORD",
      "position" : 4
    },
    {
      "token" : "国",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "CN_CHAR",
      "position" : 5
    }
  ]
}

使用 ik_smart方式

在这里插入代码片GET _analyze
{
  "analyzer":"ik_smart",
  "text": "中华人名*"
}

分词效果

{
  "tokens" : [
    {
      "token" : "中华",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "CN_WORD",
      "position" : 0
    },
    {
      "token" : "人名",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 1
    },
    {
      "token" : "*",
      "start_offset" : 4,
      "end_offset" : 7,
      "type" : "CN_WORD",
      "position" : 2
    }
  ]
}

ik_max_word 和 ik_smart 方式的区别大致就是前者会进行更细粒度的拆分，后者更多的是尽可能的组成词汇短语