内容简介
1.声明端口和maven包依赖
2.生成dao继承es仓库
3.增改查删
1.声明端口和导包
es服务器地址配置:
application.properties
spring.data.elasticsearch.repositories.enabled = true
spring.data.elasticsearch.cluster-nodes =127.0.0.1:9300
相关包依赖:
maven pom.xml
<dependency>
<groupId>org.springframework.data</groupId>
<artifactId>spring-data-commons</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>
2.生成dao继承es仓库
![](https://img.haomeiwen.com/i16380251/dcfa26a5f8d7517c.png)
3.增改查删
增
![](https://img.haomeiwen.com/i16380251/33bc4faa7644347c.png)
改
![](https://img.haomeiwen.com/i16380251/10662ec50da636e1.png)
查询单个
![](https://img.haomeiwen.com/i16380251/bb05addbf1c8c855.png)
查询多个(搜索、分页、排序、多字段同时匹配即成功和单个字段满足匹配即成功)
![](https://img.haomeiwen.com/i16380251/07b1290e02f7308d.png)
自定义通过某字段精准查询
![](https://img.haomeiwen.com/i16380251/f97e92325c95e933.png)
删
![](https://img.haomeiwen.com/i16380251/ebc408a239bb5f9b.png)
小总结
在企业中对用户信息的增删改查可能并不需要es,反而传统数据库例如mysql等更适合,所以es更适合来处理大数据下的数据查询,更实用的例子请参见本人的另一篇文章spring整合es之后的普通查询与聚合查询:https://www.jianshu.com/p/ba21d7aabd4d
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