重生之我们在ES顶端相遇第10 章- 分分分词器的基本使用

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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：

1. 0个或多个 character filters
2. 有且仅 1 个 tokenizer
3. 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 asciifolding token 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}]
}

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