前言
目前正在出一个Es专题系列教程, 篇幅会较多, 喜欢的话，给个关注❤️ ~
承接上文，本节给大家讲下上节遗留的查询操作以及重点内容分词原理~
为了方便学习, 本节中所有示例沿用上节的索引。本文偏实战一些，好了, 废话不多说直接开整吧~
另外: 之前不少小伙伴私信，说老干讲，没有结合java代码去演示，这里理解大家迫切想要敲代码的心情，希望快速的在项目中用起来。我在学习一个新东西，特别是这种中间件的应用，我会先抛开代码层面,从它根本上去认识它，知道这个过程是怎么样的，因为封装好的sdk，其实也都是对接口进行了封装，方便开发人员去使用, 写代码大家都会写，关键是如何去理解一个东西。就像面试一样，大家都会背，但是不一定都答的好
透露一下，后边会有专门几节讲springboot整合es的实战内容，只要不忙，正常更新教程~
prefix & wildcard & regexp
上节我们讲的查询操作，其最小单位都是基于词条进行查询。有时候，我们需要将查询的最小粒度优化到字符级别。很明显中文是不会基于空格分词的，这个时候我们就需要用到部分匹配查询prefix、wildcard、regexp
其实这个查询我们可以类比成模糊查询
prefix & 前缀查询
POST class_1/_search { "query": { "prefix": { "name": { "value": "i" } } } } 复制代码
返回:
{ "took" : 5, "timed_out" : false, "_shards" : { "total" : 3, "successful" : 3, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 4, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "class_1", "_type" : "_doc", "_id" : "imFt-4UBECmbBdQAnVJg", "_score" : 1.0, "_source" : { "name" : "i", "age" : 10 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "b8fcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi1~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "ccfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi3~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "cMfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so zhen haochi2~", "num" : 1 } } ] } } 复制代码
wildcard 通配查询
意思可以指定通配符,比如*
POST class_1/_search { "query": { "wildcard": { "name": { "value": "*a*" } } } } 复制代码
返回:
{ "took" : 5, "timed_out" : false, "_shards" : { "total" : 3, "successful" : 3, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "class_1", "_type" : "_doc", "_id" : "b8fcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi1~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "ccfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi3~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "cMfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so zhen haochi2~", "num" : 1 } } ] } } 复制代码
regexp & 正则表达式查询
我们可以通过regexp进行正则匹配
POST class_1/_search { "query": { "regexp": { "name": { "value": "[A-Za-z0-9]*" } } } } 复制代码
返回:
{ "took" : 8, "timed_out" : false, "_shards" : { "total" : 3, "successful" : 3, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 10, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "class_1", "_type" : "_doc", "_id" : "h2Fg-4UBECmbBdQA6VLg", "_score" : 1.0, "_source" : { "name" : "b", "num" : 6 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "iGFt-4UBECmbBdQAnVJe", "_score" : 1.0, "_source" : { "name" : "g", "age" : 8 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "iWFt-4UBECmbBdQAnVJg", "_score" : 1.0, "_source" : { "name" : "h", "age" : 9 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "imFt-4UBECmbBdQAnVJg", "_score" : 1.0, "_source" : { "name" : "i", "age" : 10 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "3", "_score" : 1.0, "_source" : { "num" : 9, "name" : "e", "age" : 9, "desc" : [ "hhhh" ] } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "4", "_score" : 1.0, "_source" : { "name" : "f", "age" : 10, "num" : 10 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "b8fcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi1~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "ccfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so haochi3~", "num" : 1 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "1", "_score" : 1.0, "_source" : { "name" : "l", "num" : 6 } }, { "_index" : "class_1", "_type" : "_doc", "_id" : "cMfcCoYB090miyjed7YE", "_score" : 1.0, "_source" : { "name" : "I eat apple so zhen haochi2~", "num" : 1 } } ] } } 复制代码
ES中的分词
什么是分词
对于非结构语言的查询，如果采用全文检索的方式进行查询，需要进行对其进行分词处理, 顾名思义，将一句话或一大段话，通过一定的规则，分割成多个词条的过程
分词器
es中的分词通过分词器来处理,主要由以下组成:
Character Filters：针对原始文本处理，比如去除一些标签，标点符号等。一个分词器中可以包含0至多个Character FiltersTokenizer：按照规则将原始文本切分为词条，比如按照空格切分。一个分词器中有且只有一个TokenizerToken Filters：将切分的单词进行加工，比如大写转小写，删除结语词，增加同义语。一个分词器中可以包含0至多个Token Filters
举个例子,假如有这么一句话I eat a apple 开心,通过上述分词流程大致为:
- 开始分词,
Character Filters开始对原始语句处理,处理完之后的语句:I eat a apple Tokenizer根据规则分词,处理完之后的语句会分为几个词条I,eat,a,appleToken Filters词条过滤处理,i,eat,a,apple
当然，不同分词器可能处理的会更复杂
内置分词器
Standard Analyzer：默认分词器，按词切分，小写处理Simple Analyzer：按照非字母切分（符号被过滤），小写处理Stop Analyzer：小写处理，停用词过滤（the ，a，is）Whitespace Analyzer：按照空格切分，不转小写Keyword Analyzer：不分词，直接将输入当做输出Pattern Analyzer：正则表达式，默认 \W+Language：提供了 30 多种常见语言的分词器CustomerAnalyzer：自定义分词器
elasticsearch-analysis-ik & 中文分词器
通常我们的业务，在大部分应用场景中都是中文，所以比较常用的就是中文分词器,默认是不带的，所以需要大家去安装，这里就不多介绍安装了，文档里有
结束语
本节就到此结束了，下节我们进入ES聚合部分内容，这部分内容也比较重要 ~
