文章目录
- 思维导图
- 0. 前言
- 1. 光速上手
- 1.1 指定分词器
- 1.2 测试分词器
- 2. 分词流程(重要)
- 2.1 基本介绍
- 2.2 深入如何测试分词器
- 3. 自定义一个简单的分词器
思维导图
0. 前言
分词器在 ES 搜索使用中非常关键，一个好的分词器能够提高搜索的质量，让用户搜索到其想要的内容。
下面我将带大家从整体了解分词器。
1. 光速上手
1.1 指定分词器
DELETE test8
PUT test8
{"mappings": {"properties": {"title": {"type": "text","analyzer": "whitespace"}}}
}
test8 索引的 title 字段，分词器为 whitespace
1.2 测试分词器
POST _analyze
{"analyzer": "whitespace","text": "hello world"
}
2. 分词流程(重要)
2.1 基本介绍
ES 分词可以包含：
- 0个或多个 character filters
- 有且仅 1 个 tokenizer
- 0个或多个 token filters
工作流程如下：
- character filters: 对输入进行预处理，比如删除 html 元素，比如将表情符号映射为文本
- tokenizer: 分词器，上面说到的
standard,whitespace都属于分词器 - token filters: 对分词后的结果进行处理。例如输入
Is this déja vu, 如果按照空格分词的话，会被分为Is,this,déja,vu。我们可以设置asciifoldingtoken filters, 将déja, 转换为deja。
2.2 深入如何测试分词器
POST _analyze
{"char_filter": ["html_strip", {"type": "mapping","mappings": ["😂 => happy"]}],"tokenizer": "standard","filter": ["lowercase", "asciifolding"],"text": "Is this déja vu? 😂 <b>Important</b>"
}
- html_strip 用于去掉 html 元素
- mapping 则是将表情转换为文本
- standard 用于分词
- lowercase 用于将所有的大写转换为小写
- asciifolding 用于将 Unicode 字符转换为 ASCII 字符
{"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" : "happy","start_offset" : 17,"end_offset" : 19,"type" : "<ALPHANUM>","position" : 4}]
}
3. 自定义一个简单的分词器
DELETE test8
PUT test8
{"mappings": {"properties": {"name": {"type": "text","analyzer": "my_custom_analyzer"}}},"settings": {"analysis": {"char_filter": {"cf_happy": {"type": "mapping","mappings": ["😂 => happy"]}},"analyzer": {"my_custom_analyzer": {"type": "custom","tokenizer": "standard","char_filter": ["html_strip", "cf_happy"],"filter": ["lowercase", "asciifolding"]}}}}
}
测试分词器
POST test8/_analyze
{"analyzer": "my_custom_analyzer","text": "😂 I Like Elasticsearch"
}
输出结果
{"tokens" : [{"token" : "happy","start_offset" : 0,"end_offset" : 2,"type" : "<ALPHANUM>","position" : 0},{"token" : "i","start_offset" : 3,"end_offset" : 4,"type" : "<ALPHANUM>","position" : 1},{"token" : "like","start_offset" : 5,"end_offset" : 9,"type" : "<ALPHANUM>","position" : 2},{"token" : "elasticsearch","start_offset" : 10,"end_offset" : 23,"type" : "<ALPHANUM>","position" : 3}]
}
