SpringCloud day12

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Docker案例

关闭防火墙

# 关闭防火墙
systemctl stop firewalld.service
# 禁止防火墙开机启动
systemctl disable firewalld.service

启动或重启docker

只要网络环境变化,防火墙的关闭,ip地址变化等都需要重启docker

# 启动docker
systemctl start docker

# 重启docker
systemctl restart docker

清理掉所有容器:
SpringCloud day12_第6张图片

redis 加载镜像

docker load < redis-docker-image.gz

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redis镜像之前已经导入

启动容器

docker run -d --name redis7000 -p 7000:6379 redis

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docker run -d --name redis7000(容器的名字) -p 7000:6379(映射的端口容器的端口:redis端口) redis

#启动容器后,进入容器访问检测
docker exec -it redis7000(redis7000容器) redis-cli(连接redis客户端)
docker exec -it redis7000 redis-cli

#测试没有问题后加上700017002
docker run -d --name redis7001 -p 7001:6379 redis
docker run -d --name redis7002 -p 7002:6379 redis

在这里插入图片描述

jedis 连接测试

SpringCloud day12_第9张图片
在这里插入图片描述
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<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>cn.tedu</groupId>
    <artifactId>docker</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
    </dependencies>

</project>

编写测试类

package test;

import redis.clients.jedis.JedisPoolConfig;
import redis.clients.jedis.JedisShardInfo;
import redis.clients.jedis.ShardedJedis;
import redis.clients.jedis.ShardedJedisPool;

import java.util.ArrayList;
import java.util.List;

public class Test1 {
     
    public static void main(String[] args) {
     
        //配置对象
        JedisPoolConfig config=new JedisPoolConfig();
       //服务器列表
        List<JedisShardInfo> list=new ArrayList<>();
        list.add(new JedisShardInfo("192.168.64.150",7000));
        list.add(new JedisShardInfo("192.168.64.150",7001));
        list.add(new JedisShardInfo("192.168.64.150",7002));
        //创建ShardJedisPool对象
        ShardedJedisPool pool=new ShardedJedisPool(config,list);
        ShardedJedis j=pool.getResource();
        for (int i=0;i <100;i++) {
     
        j.set("key"+i,"value"+i);
        }
        pool.close();

    }
}

在容器中查看数据
分别进入三个redis容器,执行 keys *查看结果

docker exec -it redis7000 redis-cli
docker exec -it redis7001 redis-cli
docker exec -it redis7002 redis-cli

启动多个 redis 容器
启动三个redis容器,将端口分别映射到7000,7001和7002端口
SpringCloud day12_第11张图片

虚拟网络的部署

web+数据库

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删除容器

docker rm -f redis7000 redis7001 redis7002

删除以前的虚拟网络

docker network rm my-net

准备虚拟网络:

docker network create dockernet --subnet=172.18.0.0/24

docker network ls
docker inspect dockernet

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数据库容器

http://hub.docker.com
数据库数据挂载:
SpringCloud day12_第15张图片
SpringCloud day12_第16张图片
端口的映射
宿主机3306
在这里插入图片描述
root密码的设置
在这里插入图片描述
SpringCloud day12_第17张图片
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设置五个参数:
SpringCloud day12_第19张图片

准备数据卷:

docker volume create mysql-data
docker volume ls
docker inspect mysql-data
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启动mysql容器

docker run -d --name mysql  --restart=always \
-v mysql-data:/var/lib/mysql \   数据卷挂载
-p 3306:3306 \                 端口的连接
-e MYSQL_ROOT_PASSWORD=root \   指定密码
--net dockernet \    连接到网络
--ip 172.18.0.11 \    指定固定ip
mariadb




docker run -d --name mysql  --restart=always \
-v mysql-data:/var/lib/mysql \
-p 3306:3306 \
-e MYSQL_ROOT_PASSWORD=root \
--net dockernet \
--ip 172.18.0.11 \
mariadb

#进入容器登陆mysql
docker exec -it mysql mysql -uroot -p
#或者测试
docker exec -it mysql bash

mysql -uroot -p

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连接数据库,导入数据

从外围连接服务器3306端口,向内部转发到容器的3306
SpringCloud day12_第22张图片
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/*
SQLyog Ultimate v8.32 
MySQL - 5.0.22-community-nt : Database - easymall
*********************************************************************
*/


/*!40101 SET NAMES utf8 */;

/*!40101 SET SQL_MODE=''*/;

/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
CREATE DATABASE /*!32312 IF NOT EXISTS*/`easymall` /*!40100 DEFAULT CHARACTER SET utf8 */;

USE `easymall`;

/*Table structure for table `orderitem` */

DROP TABLE IF EXISTS `orderitem`;

CREATE TABLE `orderitem` (
  `order_id` varchar(100) NOT NULL default '',
  `product_id` varchar(100) NOT NULL default '',
  `buynum` int(11) default NULL,
  PRIMARY KEY  (`order_id`,`product_id`),
  KEY `product_id` (`product_id`),
  CONSTRAINT `orderitem_ibfk_1` FOREIGN KEY (`order_id`) REFERENCES `orders` (`id`),
  CONSTRAINT `orderitem_ibfk_2` FOREIGN KEY (`product_id`) REFERENCES `products` (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

/*Data for the table `orderitem` */

insert  into `orderitem`(`order_id`,`product_id`,`buynum`) values ('1dc4b5c7-2ef3-41ec-b149-6101efb52c2e','a84fc7b3-944d-4b30-b67d-eba5cd2b326a',900),('2789c695-8f45-40fe-bba6-1bd399efddd2','103e5414-0da2-4fba-b92f-0ba876e08939',1),('2789c695-8f45-40fe-bba6-1bd399efddd2','258554f0-2cce-4cd1-9db3-139f6228b318',1),('294918ca-06f3-4209-b0b5-c0e2652f0a37','103e5414-0da2-4fba-b92f-0ba876e08939',1),('294918ca-06f3-4209-b0b5-c0e2652f0a37','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('31bdf06b-ca3e-4398-b966-595a4868a123','103e5414-0da2-4fba-b92f-0ba876e08939',100),('31bdf06b-ca3e-4398-b966-595a4868a123','258554f0-2cce-4cd1-9db3-139f6228b318',100),('3311b6ba-69d9-4563-b239-3bdf944aae3f','103e5414-0da2-4fba-b92f-0ba876e08939',1),('3311b6ba-69d9-4563-b239-3bdf944aae3f','26128d47-423b-4220-8047-544ff899db50',1),('36365b01-1b7d-400f-a778-de5b38aabb32','103e5414-0da2-4fba-b92f-0ba876e08939',1),('3f214bcd-3cb7-4dea-b205-15a72b77a13f','36b9407f-746a-4956-988e-557122bc74d0',1),('3f214bcd-3cb7-4dea-b205-15a72b77a13f','a84fc7b3-944d-4b30-b67d-eba5cd2b326a',50),('3f214bcd-3cb7-4dea-b205-15a72b77a13f','b1f9c947-4f72-4245-b09d-8c5a8c311ae1',1),('43d824e6-3a93-4c1b-948d-b16ad02804d1','09f47493-214d-44bc-927d-6ce0bf89a057',1),('43d824e6-3a93-4c1b-948d-b16ad02804d1','17c3f20e-ef86-4857-9293-f29e52954a95',3),('4745c002-5aa9-4d2b-acc6-2e2926ba82b0','103e5414-0da2-4fba-b92f-0ba876e08939',1),('4745c002-5aa9-4d2b-acc6-2e2926ba82b0','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('49e8dd1b-1ab3-42d3-b79f-788958fd71ef','103e5414-0da2-4fba-b92f-0ba876e08939',1),('49e8dd1b-1ab3-42d3-b79f-788958fd71ef','26128d47-423b-4220-8047-544ff899db50',1),('4cc513f4-c67c-4f95-a175-49858768233b','103e5414-0da2-4fba-b92f-0ba876e08939',1),('4cc513f4-c67c-4f95-a175-49858768233b','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('4d1a0d4e-672d-45b0-94a0-d2ec3ee24daa','103e5414-0da2-4fba-b92f-0ba876e08939',3),('4d1a0d4e-672d-45b0-94a0-d2ec3ee24daa','258554f0-2cce-4cd1-9db3-139f6228b318',20),('4d1a0d4e-672d-45b0-94a0-d2ec3ee24daa','34ff133c-8ff5-433b-86d9-ec86217d203d',30),('548d1455-5113-4e8e-8bb8-34e84d779cde','09f47493-214d-44bc-927d-6ce0bf89a057',1),('548d1455-5113-4e8e-8bb8-34e84d779cde','17c3f20e-ef86-4857-9293-f29e52954a95',23),('66def42f-1391-4ff4-97d7-ac93ed55f87f','103e5414-0da2-4fba-b92f-0ba876e08939',1),('66def42f-1391-4ff4-97d7-ac93ed55f87f','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('66e3231d-102c-4075-a14f-e18e97575e2e','05e20c1a-0401-4c0a-82ab-6fb0f37db397',1),('686b5794-3bd2-4fd6-bef1-7cb65a1dcd22','103e5414-0da2-4fba-b92f-0ba876e08939',1),('70baa50d-e105-4320-9792-5c558235c182','103e5414-0da2-4fba-b92f-0ba876e08939',1),('72b227da-1af0-4360-a1da-562469e51f88','103e5414-0da2-4fba-b92f-0ba876e08939',1),('72b227da-1af0-4360-a1da-562469e51f88','26128d47-423b-4220-8047-544ff899db50',1),('7ccd887f-9c46-4d0b-a7d3-707ea670bf1d','103e5414-0da2-4fba-b92f-0ba876e08939',1),('7ccd887f-9c46-4d0b-a7d3-707ea670bf1d','26128d47-423b-4220-8047-544ff899db50',1),('7f2872b2-7474-42e7-8a69-fcb54b1b316f','103e5414-0da2-4fba-b92f-0ba876e08939',2),('7f2872b2-7474-42e7-8a69-fcb54b1b316f','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('85f51b3c-a851-4be1-b5f2-a0b1acbfd9b8','103e5414-0da2-4fba-b92f-0ba876e08939',1),('85f51b3c-a851-4be1-b5f2-a0b1acbfd9b8','26128d47-423b-4220-8047-544ff899db50',1),('89b7ebb5-28a7-470b-bc31-c5dcd304b953','103e5414-0da2-4fba-b92f-0ba876e08939',1),('89b7ebb5-28a7-470b-bc31-c5dcd304b953','26128d47-423b-4220-8047-544ff899db50',1),('8af7af6c-5690-4857-92c1-d64a5d320131','103e5414-0da2-4fba-b92f-0ba876e08939',1),('8af7af6c-5690-4857-92c1-d64a5d320131','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('92a75c42-5254-4896-9c99-da3f8c5a52c9','103e5414-0da2-4fba-b92f-0ba876e08939',1),('9531164b-f2f0-4adb-9faf-6e96b3679dd0','09f47493-214d-44bc-927d-6ce0bf89a057',1),('96927957-0c71-403a-980f-d73553327aba','103e5414-0da2-4fba-b92f-0ba876e08939',1),('9ab2cca6-8951-48d8-8445-b59b45683245','a84fc7b3-944d-4b30-b67d-eba5cd2b326a',2),('9c35d974-dc8f-4e0f-ac94-d0debf3ed05d','103e5414-0da2-4fba-b92f-0ba876e08939',1),('9c35d974-dc8f-4e0f-ac94-d0debf3ed05d','26128d47-423b-4220-8047-544ff899db50',1),('a063606d-8da9-4ae3-a8f6-f34f4d0948e4','103e5414-0da2-4fba-b92f-0ba876e08939',2),('a063606d-8da9-4ae3-a8f6-f34f4d0948e4','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('ac5defdb-c193-4ff2-b2d8-d8bd8b36742d','103e5414-0da2-4fba-b92f-0ba876e08939',1),('adec3640-87a8-4897-888b-f505b1441746','103e5414-0da2-4fba-b92f-0ba876e08939',1),('adec3640-87a8-4897-888b-f505b1441746','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('b3e0f0f0-afac-464a-b6d4-3a00d7886736','103e5414-0da2-4fba-b92f-0ba876e08939',1),('b58e6909-4eee-442f-ba42-5eb6b50aa1f3','d73ab7ed-9f78-4775-a93b-4d355b2d5fc0',2),('b6454235-bba9-470c-948b-6d02c5d7bc25','103e5414-0da2-4fba-b92f-0ba876e08939',1),('b716f817-fb29-46b3-b5ea-93b519efa293','103e5414-0da2-4fba-b92f-0ba876e08939',1),('ba08aa4b-6335-45e3-a038-a7c0edec5bc1','103e5414-0da2-4fba-b92f-0ba876e08939',1),('ba08aa4b-6335-45e3-a038-a7c0edec5bc1','26128d47-423b-4220-8047-544ff899db50',1),('c2665c92-d6ee-4bda-9cd7-9cc377214d9d','09f47493-214d-44bc-927d-6ce0bf89a057',1),('c2665c92-d6ee-4bda-9cd7-9cc377214d9d','103e5414-0da2-4fba-b92f-0ba876e08939',1),('c32e1f5f-e188-4446-9ea2-4469c5f57492','103e5414-0da2-4fba-b92f-0ba876e08939',50),('c32e1f5f-e188-4446-9ea2-4469c5f57492','17c3f20e-ef86-4857-9293-f29e52954a95',100),('c32e1f5f-e188-4446-9ea2-4469c5f57492','258554f0-2cce-4cd1-9db3-139f6228b318',100),('c41524e9-53c3-4830-9c17-6d5d41c4ba7c','05e20c1a-0401-4c0a-82ab-6fb0f37db397',1),('caffb69e-5d0a-43de-9218-fb8b6d794f7a','103e5414-0da2-4fba-b92f-0ba876e08939',1),('caffb69e-5d0a-43de-9218-fb8b6d794f7a','26128d47-423b-4220-8047-544ff899db50',1),('ccd56972-dedf-4b52-b690-606de6ad1ced','21ec8c05-a91e-4993-9dd0-7a032ec1d824',10),('ccd56972-dedf-4b52-b690-606de6ad1ced','258554f0-2cce-4cd1-9db3-139f6228b318',779),('d1fcfff8-ccb6-4134-abe0-edc6bf1ee569','09f47493-214d-44bc-927d-6ce0bf89a057',1),('d1fcfff8-ccb6-4134-abe0-edc6bf1ee569','103e5414-0da2-4fba-b92f-0ba876e08939',1),('d1fcfff8-ccb6-4134-abe0-edc6bf1ee569','17c3f20e-ef86-4857-9293-f29e52954a95',1),('d4342f85-b046-4cd1-8a11-b841943b196d','09f47493-214d-44bc-927d-6ce0bf89a057',1),('d4342f85-b046-4cd1-8a11-b841943b196d','103e5414-0da2-4fba-b92f-0ba876e08939',1),('d4342f85-b046-4cd1-8a11-b841943b196d','17c3f20e-ef86-4857-9293-f29e52954a95',1),('d4954039-8fa4-4c9f-bd91-c5a919b6aa2d','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('d4a83695-3182-4555-8e61-a8c334027efa','103e5414-0da2-4fba-b92f-0ba876e08939',1),('d4a83695-3182-4555-8e61-a8c334027efa','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('d71dd880-bd78-48a7-8a1e-f158bf92e766','103e5414-0da2-4fba-b92f-0ba876e08939',1),('d71dd880-bd78-48a7-8a1e-f158bf92e766','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('d907990b-0286-45d4-b51e-77e1af6281ca','05e20c1a-0401-4c0a-82ab-6fb0f37db397',5),('dd372acf-f8ce-4185-9ba4-706565db635d','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',23000),('dd6691c4-3872-4bcf-b168-eec9611db4b1','103e5414-0da2-4fba-b92f-0ba876e08939',1),('dd6691c4-3872-4bcf-b168-eec9611db4b1','26128d47-423b-4220-8047-544ff899db50',1),('de5f212e-b614-4061-aa86-c238e56e04ef','103e5414-0da2-4fba-b92f-0ba876e08939',1),('e5773553-fa93-4ab1-b75f-4eefeadc2280','103e5414-0da2-4fba-b92f-0ba876e08939',1),('e952b2f4-bc3e-4cf3-bccf-296cb72d3635','26128d47-423b-4220-8047-544ff899db50',1),('e952b2f4-bc3e-4cf3-bccf-296cb72d3635','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('ed5f7326-065d-41c1-a979-86fbe99932b1','103e5414-0da2-4fba-b92f-0ba876e08939',2),('ed5f7326-065d-41c1-a979-86fbe99932b1','2ad0d041-8c5f-4b70-a0ef-1ca2fd476dba',1),('ee6d591b-8f70-4afa-83df-18e26bc7933e','d8cb845e-37f6-4515-9fc1-dea07719ee06',100000),('fe47e4df-acd2-4e32-93e5-737116602c88','09f47493-214d-44bc-927d-6ce0bf89a057',1),('fe47e4df-acd2-4e32-93e5-737116602c88','103e5414-0da2-4fba-b92f-0ba876e08939',1);

/*Table structure for table `orders` */

DROP TABLE IF EXISTS `orders`;

CREATE TABLE `orders` (
  `id` varchar(100) NOT NULL,
  `money` double default NULL,
  `receiverinfo` varchar(255) default NULL,
  `paystate` int(11) default NULL,
  `ordertime` timestamp NOT NULL default CURRENT_TIMESTAMP on update CURRENT_TIMESTAMP,
  `user_id` int(11) default NULL,
  PRIMARY KEY  (`id`),
  KEY `user_id` (`user_id`),
  CONSTRAINT `orders_ibfk_1` FOREIGN KEY (`user_id`) REFERENCES `user` (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

/*Data for the table `orders` */

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/*Table structure for table `products` */

DROP TABLE IF EXISTS `products`;

CREATE TABLE `products` (
  `id` varchar(100) NOT NULL,
  `name` varchar(255) default NULL,
  `price` double default NULL,
  `category` varchar(255) default NULL,
  `pnum` int(11) default NULL,
  `imgurl` varchar(255) default NULL,
  `description` varchar(255) default NULL,
  PRIMARY KEY  (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

/*Data for the table `products` */

insert  into `products`(`id`,`name`,`price`,`category`,`pnum`,`imgurl`,`description`) values 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/*Table structure for table `user` */

DROP TABLE IF EXISTS `user`;

CREATE TABLE `user` (
  `id` int(11) NOT NULL auto_increment,
  `username` varchar(20) default NULL,
  `password` varchar(40) default NULL,
  `nickname` varchar(60) default NULL,
  `email` varchar(60) default NULL,
  `role` varchar(20) default NULL,
  PRIMARY KEY  (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

/*Data for the table `user` */

insert  into `user`(`id`,`username`,`password`,`nickname`,`email`,`role`) values (16,'tom1','456','tom','[email protected]','user'),(17,'tom2','202cb962ac59075b964b07152d234b70','tom2','[email protected]','user'),(22,'guanyu','202cb962ac59075b964b07152d234b70','guanyu','[email protected]','user'),(23,'boss','ceb8447cc4ab78d2ec34cd9f11e4bed2','boss','[email protected]','user'),(24,'朴乾','202cb962ac59075b964b07152d234b70','朴朴','[email protected]','user'),(25,'guanyu1','202cb962ac59075b964b07152d234b70','guanyui1','[email protected]','user'),(26,'tuhao1','698d51a19d8a121ce581499d7b701668','tuhao','[email protected]','user'),(27,'tuhao2','698d51a19d8a121ce581499d7b701668','tuhao2','[email protected]','user'),(28,'tuhao3','202cb962ac59075b964b07152d234b70','tuhao3','[email protected]','admin'),(33,'jinxf','202cb962ac59075b964b07152d234b70','靳幸福','[email protected]','admin'),(34,'jinxf1','202cb962ac59075b964b07152d234b70','jinxf','[email protected]','admin'),(35,'jinxf898','202cb962ac59075b964b07152d234b70','jinxf898','[email protected]','admin'),(36,'张飞','202cb962ac59075b964b07152d234b70','123','[email protected]','admin'),(37,'qwer','962012d09b8170d912f0669f6d7d9d07','qwer','[email protected]','admin'),(38,'aa	','202cb962ac59075b964b07152d234b70','ASGIUI','[email protected]','admin'),(39,'高嘉星','202cb962ac59075b964b07152d234b70','星星','[email protected]','admin'),(40,'张三','202cb962ac59075b964b07152d234b70','小三','[email protected]','admin'),(41,'123','202cb962ac59075b964b07152d234b70','123','[email protected]','admin'),(42,'宝哥','202cb962ac59075b964b07152d234b70','123','[email protected]','admin'),(43,'dfd','202cb962ac59075b964b07152d234b70','123','[email protected]','admin'),(44,'jlkj','202cb962ac59075b964b07152d234b70','123','[email protected]','admin'),(45,'zm','e10adc3949ba59abbe56e057f20f883e','aaaa','[email protected]','admin'),(46,'lilei','202cb962ac59075b964b07152d234b70','李雷','[email protected]','admin'),(47,'spd','1e59132c5c434e25e01a39e0e1bbe9f3','spd','[email protected]','admin'),(48,'tuhao','202cb962ac59075b964b07152d234b70','tuhao','[email protected]','admin'),(49,' diaosi','123','doapso','[email protected]','admin'),(50,'baifumei','123','baifumei','baifumei.qq.com','admin'),(51,' diaosi01','123','123','123','admin'),(52,'车彬彬','123','123','[email protected]','admin'),(53,'th','th','th','th','admin'),(54,'sg','sg','sg','sg','admin'),(55,'gaoshuo','123','shabi','[email protected]','admin');

/*!40101 SET SQL_MODE=@OLD_SQL_MODE */;
/*!40014 SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS */;
/*!40014 SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS */;
/*!40111 SET SQL_NOTES=@OLD_SQL_NOTES */;

tomcat

SpringCloud day12_第24张图片
准备web应用打包文件

mkdir /opt/easymall
把ROOT.WAR 文件上传到/opt/easymall目录

SpringCloud day12_第25张图片
SpringCloud day12_第26张图片

#启动容器
docker run -d --name easymall --restart=always \
-v /opt/easymall:usr/tomcat/webapps \
-p 80:8080 \
--net dockernet \
--ip 172.18.0.12 \
tomcat:7

SpringCloud day12_第27张图片
访问测试:

http://192.168.64.150

SpringCloud day12_第28张图片

euraka

文件的准备
SpringCloud day12_第29张图片

构建jdk镜像

1.准备文件夹jdk
2.把jdk压缩文件上传到jdk文件夹
SpringCloud day12_第30张图片

3.在jdk文件夹添加到Dockerfile文件夹

SpringCloud day12_第31张图片

cat <<EOF >Dockerfile
FROM centos:7
ADD jdk-8u212-linux-x64.tar.gz /opt/
ENV JAVA_HOME=/OPT/JDK1.8.0_212 \
    PATH=$PATH:/opt/jdk1.8.0_212/bin
ENTRYPOINT bash
EOF

构建jdk镜像
SpringCloud day12_第32张图片
SpringCloud day12_第33张图片

cd jdk
docker build -t jdk:8 .
docker images

SpringCloud day12_第34张图片

构建eureka镜像

1.创建文件夹eureka

cd ~/
mkdir eureka

2.上传eureka的jar文件到eureka目录
SpringCloud day12_第35张图片

3.创建Dockerfile

# 启动命令:
# java -jar /opt/sp05-eureka-0.0.1-SNAPSHOT.jar
# --spring.profiles.active=eureka1 --server.port=2001
​
cd eureka
​
​
cat <<EOF >Dockerfile
FROM jdk:8
COPY sp05-eureka-0.0.1-SNAPSHOT.jar /opt/
ENTRYPOINT ["java", "-jar", "/opt/sp05-eureka-0.0.1-SNAPSHOT.jar"]
CMD ["--spring.profiles.active=eureka1", "--server.port=2001"]
EOF

4.构建eureka镜像

cd eureka
docker build -t eureka:v1 .
​
docker images

SpringCloud day12_第36张图片

运行启动eureka容器

docker run -d --name eureka1 --restart=always \
-p 2001:2001 \
eureka:v1
​
浏览器访问
http://192.168.64.150:2001
​
查看eureka的启动日志
docker logs eureka1

SpringCloud day12_第37张图片

elasticsearch

全文检索服务器
数据在数据库中保存一份,在es中也保存一份,主要用于全文检索服务

Docker 搭建es服务

关闭防火墙
后面我们要使用多个端口,为了避免繁琐的开放端口操作,我们关掉防火墙

# 关闭防火墙
systemctl stop firewalld.service

# 禁用防火墙
systemctl disable firewalld.service

安装Docker
下载 Elastic Search 镜像

docker pull elasticsearch:7.9.3

清空容器
SpringCloud day12_第38张图片
关闭虚拟机
SpringCloud day12_第39张图片
重新分配内存
SpringCloud day12_第40张图片
重新运行此虚拟机
SpringCloud day12_第41张图片
上传文件
SpringCloud day12_第42张图片
SpringCloud day12_第43张图片
导入镜像:

docker load  -i es-img.gz
#导入后检查
docker images

SpringCloud day12_第44张图片

集群部署结构
SpringCloud day12_第45张图片
在一台服务器上,使用Docker部署三个ES容器组成的集群
准备虚拟网络和挂载目录

# 创建虚拟网络
docker network create es-net

# node1 的挂载目录
mkdir -p -m 777 /var/lib/es/node1/plugins
mkdir -p -m 777 /var/lib/es/node1/data

# node2 的挂载目录
mkdir -p -m 777 /var/lib/es/node2/plugins
mkdir -p -m 777 /var/lib/es/node2/data

# node3 的挂载目录
mkdir -p -m 777 /var/lib/es/node3/plugins
mkdir -p -m 777 /var/lib/es/node3/data

设置 max_map_count
必须修改系统参数 max_map_count,否则 Elasticsearch 无法启动:

在 /etc/sysctl.conf 文件中添加 vm.max_map_count=262144

echo 'vm.max_map_count=262144' >>/etc/sysctl.conf

SpringCloud day12_第46张图片
重新启动容器

启动 Elasticsearch 集群
node1

docker run -d \
  --name=node1 \
  --restart=always \
  --net es-net \
  -p 9200:9200 \  #和客户端连接端口
  -p 9300:9300 \   #集群服务器之间的端口
  -v /var/lib/es/node1/plugins:/usr/share/elasticsearch/plugins \
  -v /var/lib/es/node1/data:/usr/share/elasticsearch/data \
  -e node.name=node1 \
  -e node.master=true \
  -e network.host=node1 \  #容器名,也可以写ip
  -e discovery.seed_hosts=node1,node2,node3 \   #集群中所有主机列表
  -e cluster.initial_master_nodes=node1 \       #候选主节点,所有节点都可能被选为主节点
  -e cluster.name=es-cluster \         #集群名
  -e "ES_JAVA_OPTS=-Xms256m -Xmx256m" \  #java虚拟机的内存大小 
  elasticsearch:7.9.3

SpringCloud day12_第47张图片
node2:

docker run -d \
  --name=node2 \
  --restart=always \
  --net es-net \
  -p 9201:9200 \
  -p 9301:9300 \
  -v /var/lib/es/node2/plugins:/usr/share/elasticsearch/plugins \
  -v /var/lib/es/node2/data:/usr/share/elasticsearch/data \
  -e node.name=node2 \
  -e node.master=true \
  -e network.host=node2 \
  -e discovery.seed_hosts=node1,node2,node3 \
  -e cluster.initial_master_nodes=node1 \
  -e cluster.name=es-cluster \
  -e "ES_JAVA_OPTS=-Xms256m -Xmx256m" \
  elasticsearch:7.9.3

node3:

docker run -d \
  --name=node3 \
  --restart=always \
  --net es-net \
  -p 9202:9200 \
  -p 9302:9300 \
  -v /var/lib/es/node3/plugins:/usr/share/elasticsearch/plugins \
  -v /var/lib/es/node3/data:/usr/share/elasticsearch/data \
  -e node.name=node3 \
  -e node.master=true \
  -e network.host=node3 \
  -e discovery.seed_hosts=node1,node2,node3 \
  -e cluster.initial_master_nodes=node1 \
  -e cluster.name=es-cluster \
  -e "ES_JAVA_OPTS=-Xms256m -Xmx256m" \
  elasticsearch:7.9.3

查看启动结果结果
http://192.168.64.181:9200

http://192.168.64.181:9200/_cat/nodes
SpringCloud day12_第48张图片

chrome浏览器插件:elasticsearch-head

elasticsearch-head 项目提供了一个直观的界面,可以很方便地查看集群、分片、数据等等。elasticsearch-head最简单的安装方式是作为 chrome 浏览器插件进行安装。
第一种安装方式:
SpringCloud day12_第49张图片
SpringCloud day12_第50张图片

SpringCloud day12_第51张图片
SpringCloud day12_第52张图片
在这里插入图片描述
SpringCloud day12_第53张图片

第二种方式:
1 在 elasticsearch-head 项目仓库中下载 chrome 浏览器插件
https://github.com/mobz/elasticsearch-head/raw/master/crx/es-head.crx

2 将文件后缀改为 zip

3 解压缩

4 在 chrome 浏览器中选择“更多工具”–“扩展程序”

5 在“扩展程序”中确认开启了“开发者模式”

6 点击“加载已解压的扩展程序”

7 选择前面解压的插件目录

8 在浏览器中点击 elasticsearch-head 插件打开 head 界面,并连接 http://192.168.64.181:9200/
成功连接界面:
SpringCloud day12_第54张图片

Elasticsearch IK中文分词器

安装 ik 分词器

从 ik 分词器项目仓库中下载 ik 分词器安装包,下载的版本需要与 Elasticsearch 版本匹配:
https://github.com/medcl/elasticsearch-analysis-ik

或者可以访问 gitee 镜像仓库:
https://gitee.com/mirrors/elasticsearch-analysis-ik

下载 elasticsearch-analysis-ik-7.9.3.zip 复制到 /root/ 目录下

在三个节点上安装 ik 分词器

cd ~/

# 从宿主机复制 ik 分词器到三个 es 容器
docker cp elasticsearch-analysis-ik-7.9.3.zip node1:/root/
docker cp elasticsearch-analysis-ik-7.9.3.zip node2:/root/
docker cp elasticsearch-analysis-ik-7.9.3.zip node3:/root/

# 在 node1 中安装 ik 分词器
docker exec -it node1 elasticsearch-plugin install file:///root/elasticsearch-analysis-ik-7.9.3.zip

# 在 node2 中安装 ik 分词器
docker exec -it node2 elasticsearch-plugin install file:///root/elasticsearch-analysis-ik-7.9.3.zip

# 在 node2 中安装 ik 分词器
docker exec -it node3 elasticsearch-plugin install file:///root/elasticsearch-analysis-ik-7.9.3.zip

# 重启三个 es 容器
docker restart node1
docker restart node2
docker restart node3

查看安装结果
在浏览器中访问 http://192.168.64.150:9200/_cat/plugins

SpringCloud day12_第55张图片
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ik分词测试

ik分词器提供两种分词器: ik_max_word 和 ik_smart

ik_max_word: 会将文本做最细粒度的拆分,比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”,会穷尽各种可能的组合,适合 Term Query;

ik_smart: 会做最粗粒度的拆分,比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”,适合 Phrase 查询。

ik_max_word 分词测试

使用 head 执行下面测试:
向 http://192.168.64.181:9200/_analyze 路径提交 POST 请求,并在协议体中提交 Json 数据:

{
     
  "analyzer":"ik_max_word",
  "text":"中华人民共和国国歌"
}

SpringCloud day12_第57张图片

ik_smart 分词测试

使用 head 执行下面测试:
向 http://192.168.64.181:9200/_analyze 路径提交 POST 请求,并在协议体中提交 Json 数据:

{
     
  "analyzer":"ik_smart",
  "text":"中华人民共和国国歌"
}

SpringCloud day12_第58张图片

Elasticsearch 使用 Kibana 操作 ES

下载 Kibana 镜像

docker pull kibana:7.9.3

启动 Kibana 容器

docker run \
-d \
--name kibana \
--net es-net \
-p 5601:5601 \
-e ELASTICSEARCH_HOSTS='["http://node1:9200","http://node2:9200","http://node3:9200"]' \
--restart=always \
kibana:7.9.3

启动后,浏览器访问 Kibana,进入 Dev Tools:
http://192.168.64.181:5601/
SpringCloud day12_第59张图片

索引、分片和副本

索引
Elasticsearch索引用来存储我们要搜索的数据,以倒排索引结构进行存储。

例如,要搜索商品数据,可以创建一个商品数据的索引,其中存储着所有商品的数据,供我们进行搜索:
SpringCloud day12_第60张图片
当索引中存储了大量数据时,大量的磁盘io操作会降低整体搜索新能,这时需要对数据进行分片存储。
索引分片
在一个索引中存储大量数据会造成性能下降,这时可以对数据进行分片存储。

每个节点上都创建一个索引分片,把数据分散存放到多个节点的索引分片上,减少每个分片的数据量来提高io性能:
SpringCloud day12_第61张图片
每个分片都是一个独立的索引,数据分散存放在多个分片中,也就是说,每个分片中存储的都是不同的数据。搜索时会同时搜索多个分片,并将搜索结果进行汇总。

如果一个节点宕机分片不可用,则会造成部分数据无法搜索
SpringCloud day12_第62张图片
为了解决这一问题,可以对分片创建多个副本来解决。

索引副本
对分片创建多个副本,那么即使一个节点宕机,其他节点中的副本分片还可以继续工作,不会造成数据不可用:
SpringCloud day12_第63张图片
分片的工作机制:

主分片的数据会复制到副本分片
搜索时,以负载均衡的方式工作,提高处理能力
主分片宕机时,其中一个副本分片会自动提升为主分片

下面我们就以上图的结构来创建 products 索引

创建索引

创建一个名为 products 的索引,用来存储商品数据。

分片和副本参数说明:

------number_of_shards:分片数量,默认值是 5
------number_of_replicas:副本数量,默认值是 1
我们有三个节点,在每个节点上都创建一个分片。每个分片在另两个节点上各创建一个副本。

# 创建索引,命名为 products
PUT /products
{
     
  "settings": {
     
    "number_of_shards": 3, 
    "number_of_replicas": 2
  }
}

用索引名称过滤,查看 products 索引:
SpringCloud day12_第64张图片
粗框为主分片,细框为副本分片

映射(数据结构)

类似于数据库表结构,索引数据也被分为多个数据字段,并且需要设置数据类型和其他属性。

映射,是对索引中字段结构的定义和描述。

字段的数据类型
常用类型:

数字类型:

-----byte、short、integer、long
-----float、double
-----unsigned_long
字符串类型:

-------text : 会进行分词
--------keyword : 不会进行分词,适用于email、主机地址、邮编等
日期和时间类型:

-------date

创建映射

在 products 索引中创建映射。

分词器设置:

------analyzer:在索引中添加文档时,text类型通过指定的分词器分词后,再插入倒排索引
------search_analyzer:使用关键词检索时,使用指定的分词器对关键词进行分词
查询时,关键词优先使用 search_analyzer 设置的分词器,如果 search_analyzer 不存在则使用 analyzer 分词器。

# 定义mapping,数据结构
PUT /products/_mapping
{
     
  "properties": {
     
    "id": {
     
      "type": "long"
    },
    "title": {
     
      "type": "text",
      "analyzer": "ik_max_word",
      "search_analyzer": "ik_smart"
    },
    "category": {
     
      "type": "text",
      "analyzer": "ik_smart",
      "search_analyzer": "ik_smart"
    },
    "price": {
     
      "type": "float"
    },
    "city": {
     
      "type": "text",
      "analyzer": "ik_smart",
      "search_analyzer": "ik_smart"
    },
    "barcode": {
     
      "type": "keyword"
    }
  }
}

查看映射

GET /products/_mapping

添加文档

添加的文档会有一个名为_id的文档id,这个文档id可以自动生成,也可以手动指定,通常可以使用数据的id作为文档id。

# 添加文档
PUT /products/_doc/10033
{
     
  "id":"10033",
  "title":"SONOS PLAY:5(gen2) 新一代PLAY:5无线智能音响系统 WiFi音箱家庭,潮酷数码会场",
  "category":"潮酷数码会场",
  "price":"3980.01",
  "city":"上海",
  "barcode":"527848718459"
}


PUT /products/_doc/10034
{
     
  "id":"10034",
  "title":"天猫魔盒 M13网络电视机顶盒 高清电视盒子wifi 64位硬盘播放器",
  "category":"潮酷数码会场",
  "price":"398.00",
  "city":"浙江杭州",
  "barcode":"522994634119"
}



PUT /products/_doc/10035
{
     
  "id":"10035",
  "title":"BOSE SoundSport耳塞式运动耳机 重低音入耳式防脱降噪音乐耳机",
  "category":"潮酷数码会场",
  "price":"860.00",
  "city":"浙江杭州",
  "barcode":"526558749068"
}



PUT /products/_doc/10036
{
     
  "id":"10036",
  "title":"【送支架】Beats studio Wireless 2.0无线蓝牙录音师头戴式耳机",
  "category":"潮酷数码会场",
  "price":"2889.00",
  "city":"上海",
  "barcode":"37147009748"
}


PUT /products/_doc/10037
{
     
  "id":"10037",
  "title":"SONOS PLAY:1无线智能音响系统 美国原创WiFi连接 家庭桌面音箱",
  "category":"潮酷数码会场",
  "price":"1580.01",
  "city":"上海",
  "barcode":"527783392239"
}

也可以自动生成 _id 值:

POST /products/_doc
{
     
  "id":"10027",
  "title":"vivo X9前置双摄全网通4G美颜自拍超薄智能手机大屏vivox9",
  "category":"手机会场",
  "price":"2798.00",
  "city":"广东东莞",
  "barcode":"541396973568"
}

查看文档:

GET /products/_doc/10037

修改文档

底层索引数据无法修改,修改数据实际上是先删除再重新添加。

两种修改方式:

----PUT:对文档进行完整的替换
----POST:可以修改一部分字段
修改价格字段的值:

# 修改文档 - 替换
PUT /products/_doc/10037
{
     
  "id":"10037",
  "title":"SONOS PLAY:1无线智能音响系统 美国原创WiFi连接 家庭桌面音箱",
  "category":"潮酷数码会场",
  "price":"9999.99",
  "city":"上海",
  "barcode":"527783392239"
}

查看文档:

GET /products/_doc/10037

修改价格和城市字段的值:

# 修改文档 - 更新部分字段
POST /products/_update/10037
{
     
  "doc": {
     
    "price":"8888.88",
    "city":"深圳"
  }
}

查看文档

GET /products/_doc/10037

删除文档

DELETE /products/_doc/10037

清空

POST /products/_delete_by_query
{
     
  "query": {
     
    "match_all": {
     }
  }
}

删除索引

# 删除 products 索引
DELETE /products

Elasticsearch 搜索

导入测试数据
为了测试搜索功能,我们首先导入测试数据,3160条商品数据,数据样例如下:

{
      "index": {
     "_index": "pditems", "_id": "536563"}}
{
      "id":"536563","brand":"联想","title":"联想(Lenovo)小新Air13 Pro 13.3英寸14.8mm超轻薄笔记本电脑","sell_point":"清仓!仅北京,武汉仓有货!","price":"6688.0","barcode":"","image":"/images/server/images/portal/air13/little4.jpg","cid":"163","status":"1","created":"2015-03-08 21:33:18","updated":"2015-04-11 20:38:38"}


下载测试数据
https://download.csdn.net/download/weixin_38305440/13129113

将压缩文件中的 pditems.json 上传到服务器

创建映射

PUT /pditems
{
     
  "settings": {
     
    "number_of_shards": 3, 
    "number_of_replicas": 2
  },
  "mappings": {
     
    "properties": {
     
      "id": {
     
        "type": "long"
      },
      "brand": {
     
        "type": "text",
        "analyzer": "ik_smart"
      },
      "title": {
     
        "type": "text",
        "analyzer": "ik_max_word"
      },
      "sell_point": {
     
        "type": "text",
        "analyzer": "ik_max_word",
        "search_analyzer": "ik_smart"
      },
      "price": {
     
        "type": "float"
      },
      "image": {
     
        "type": "keyword"
      },
      "cid": {
     
        "type": "long"
      },
      "status": {
     
        "type": "byte"
      },
      "created": {
     
        "type": "date",
        "format": "yyyy-MM-dd HH:mm:ss"
      },
      "updated": {
     
        "type": "date",
        "format": "yyyy-MM-dd HH:mm:ss"
      }
    } 
  }
}

用 head 查看索引:
SpringCloud day12_第65张图片

导入数据

在服务器上,进入 pditems.json 所在的文件夹,执行批量数据导入:

curl -XPOST 'localhost:9200/pditems/_bulk' \
    -H 'Content-Type:application/json' \
    --data-binary @pditems.json

查看数据

搜索 pditems 索引中全部 3160 条数据:

GET /pditems/_search
{
     
  "query": {
     
    "match_all": {
     }
  },
  "size": 3160
}

搜索文档

搜索所有数据

# 搜索 pditems 索引中全部数据
POST /pditems/_search
{
     
  "query": {
     
    "match_all": {
     }
  }
}

关键词搜索

# 查询 pditems 索引中title中包含"电脑"的商品
POST /pditems/_search
{
     
  "query": {
     
    "match": {
     
      "title": "电脑"
    }
  }
}

搜索结果过滤器

# 价格大于2000,并且title中包含"电脑"的商品
POST /pditems/_search
{
     
  "query": {
     
    "bool": {
     
      "must": [
        {
     
          "match": {
     
            "title": "电脑"
          }
        }
      ],

      "filter": [
        {
     
          "range": {
     
            "price": {
     
              "gte": "2000"
            }
          }
        }
      ]
    }
  }
}

搜索结果高亮显示

POST /pditems/_search
{
     
	"query": {
     
		"multi_match":{
     
			"query": "手机",
			"fields": ["title", "sell_point"]
		}
	},
	"highlight" : {
     
        "pre_tags" : [""],
        "post_tags" : [""],
        "fields" : {
     
            "title" : {
     },
            "sell_point" : {
     
              "pre_tags": "",
              "post_tags": ""
            }
        }
    }
}

Elasticsearch Spring Data Elasticsearch - 增删改查API

Spring Data Elasticsearch
https://docs.spring.io/spring-data/elasticsearch/docs/current/reference/html/#reference

Spring Data Elasticsearch 是 Elasticsearch 搜索引擎开发的解决方案。它提供:

模板对象,用于存储、搜索、排序文档和构建聚合的高级API。

例如,Repository 使开发者能够通过定义具有自定义方法名称的接口来表达查询。

案例说明

在 Elasticsearch 中存储学生数据,并对学生数据进行搜索测试。

数据结构:
在这里插入图片描述
案例测试以下数据操作:

1 创建 students 索引和映射
2 C - 创建学生数据
3 R - 访问学生数据
4 U - 修改学生数据
5 D - 删除学生数据
6 使用 Repository 和 Criteria 搜索学生数据

创建项目

SpringCloud day12_第66张图片
SpringCloud day12_第67张图片
新建 springboot module,添加 spring data elasticsearch 依赖
SpringCloud day12_第68张图片
SpringCloud day12_第69张图片
SpringCloud day12_第70张图片
SpringCloud day12_第71张图片
项目的 pom.xml 文件如下:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<parent>
		<groupId>org.springframework.boot</groupId>
		<artifactId>spring-boot-starter-parent</artifactId>
		<version>2.3.6.RELEASE</version>
		<relativePath/> <!-- lookup parent from repository -->
	</parent>
	<groupId>cn.tedu</groupId>
	<artifactId>es-springboot</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<name>es-springboot</name>
	<description>Demo project for Spring Boot</description>

	<properties>
		<java.version>1.8</java.version>
	</properties>

	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-data-elasticsearch</artifactId>
		</dependency>

		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-test</artifactId>
			<scope>test</scope>
			<exclusions>
				<exclusion>
					<groupId>org.junit.vintage</groupId>
					<artifactId>junit-vintage-engine</artifactId>
				</exclusion>
			</exclusions>
		</dependency>
	</dependencies>

	<build>
		<plugins>
			<plugin>
				<groupId>org.springframework.boot</groupId>
				<artifactId>spring-boot-maven-plugin</artifactId>
			</plugin>
		</plugins>
	</build>

</project>

application.yml 配置
logging.level.tracer=TRACE 作用是在控制台中显示底层的查询日志

spring:
  elasticsearch:
    rest:
      uris: http://192.168.64.181:9200

logging:
  level:
    tracer: TRACE

Student 实体类

package cn.tedu.esspringboot.es;

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.springframework.data.annotation.Id;
import org.springframework.data.elasticsearch.annotations.DateFormat;
import org.springframework.data.elasticsearch.annotations.Document;
import org.springframework.data.elasticsearch.annotations.Field;
import org.springframework.data.elasticsearch.annotations.FieldType;

@Document(indexName = "students",shards = 3,replicas = 2)
@Data
@NoArgsConstructor
@AllArgsConstructor
public class Student {
     
    @Id
    private Long id;

    @Field(analyzer = "ngram",type = FieldType.Text)
    private String name;

    @Field(type = FieldType.Keyword)
    private Character gender;

    @Field(type= FieldType.Date,format = DateFormat.custom,pattern = "yyyy-M-d")
    private String birthDate;
}

@Document 注解
@Documnet注解对索引的参数进行设置。

上面代码中,把 students 索引的分片数设置为3,副本数设置为2。

@Id 注解
在 Elasticsearch 中创建文档时,使用 @Id 注解的字段作为文档的 _id 值

@Field 注解
通过 @Field 注解设置字段的数据类型和其他属性。

文本类型 text 和 keyword
text 类型会进行分词。

keyword 不会分词。

analyzer 指定分词器
通过 analyzer 设置可以指定分词器,例如 ik_smart、ik_max_word 等。

我们这个例子中,对学生姓名字段使用的分词器是 ngram 分词器,其分词效果如下面例子所示:
SpringCloud day12_第72张图片
通过 ElasticsearchRepository 实现 CRUD 操作
Spring Data 的 Repository 接口提供了一种声明式的数据操作规范,无序编写任何代码,只需遵循 Spring Data 的方法定义规范即可完成数据的 CRUD 操作。

ElasticsearchRepository 继承自 Repository,其中已经预定义了基本的 CURD 方法,我们可以通过继承 ElasticsearchRepository,添加自定义的数据操作方法。

Repository 方法命名规范
自定义数据操作方法需要遵循 Repository 规范,示例如下:
SpringCloud day12_第73张图片
SpringCloud day12_第74张图片
SpringCloud day12_第75张图片
SpringCloud day12_第76张图片
SpringCloud day12_第77张图片
StudentRepository

package cn.tedu.esspringboot.es;

import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
import java.util.List;

public interface StudentRepository extends ElasticsearchRepository<Student, Long> {
     
    List<Student> findByName(String name);

    List<Student> findByNameOrBirthDate(String name, String birthDate);
}

业务类 StudentService

package cn.tedu.esspringboot.es;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class StudentService {
     
    @Autowired
    private StudentRepository studentRepo;

    public void save(Student student) {
     
        studentRepo.save(student);
    }

    public void delete(Long id) {
     
        studentRepo.deleteById(id);
    }

    public void update(Student student) {
     
        save(student);
    }

    public List<Student> findByName(String name) {
     
        return studentRepo.findByName(name);
    }

    public List<Student> findByNameOrBirthDate(String name, String birthDate) {
     
        return studentRepo.findByNameOrBirthDate(name, birthDate);
    }
}

在 Elasticsearch 中创建 students 索引
在开始运行测试之前,在 Elasticsearch 中先创建 students 索引:

PUT /students
{
     
  "settings": {
     
    "number_of_shards": 3,
    "number_of_replicas": 2,
    "index.max_ngram_diff":30,
    "analysis": {
     
      "analyzer": {
     
        "ngram_analyzer": {
     
          "tokenizer": "ngram_tokenizer"
        }
      },
      "tokenizer": {
     
        "ngram_tokenizer": {
     
          "type": "ngram",
          "min_gram": 1,
          "max_gram": 30,
          "token_chars": [
            "letter",
            "digit"
          ]
        }
      }
    }
  },
  "mappings": {
     
    "properties": {
     
      "id": {
     
        "type": "long"
      },
      "name": {
     
        "type": "text",
        "analyzer": "ngram_analyzer"
      },
      "gender": {
     
        "type": "keyword"
      },
      "birthDate": {
     
        "type": "date",
        "format": "yyyy-MM-dd"
      }
    }
  }
}

测试学生数据的 CRUD 操作
添加测试类,对学生数据进行 CRUD 测试

package cn.tedu.esspringboot;

import cn.tedu.esspringboot.es.Student;
import cn.tedu.esspringboot.es.StudentService;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;

import java.util.List;

@SpringBootTest
public class Test1 {
     
    @Autowired
    private StudentService studentService;

    @Test
    public void test1() {
     
        studentService.save(new Student(998L,"张三",'男',"2020-12-04"));
    }
    @Test
    public void test2() {
     
        studentService.update(new Student(1L,"李四",'女',"2020-12-04"));
    }
    @Test
    public void test3() {
     
        List<Student> stu = studentService.findByName("四");
        System.out.println(stu);
    }
    @Test
    public void test4() throws Exception {
     
        List<Student> stu;

        stu = studentService.findByNameOrBirthDate("四", "1999-09-09");
        System.out.println(stu);

        stu = studentService.findByNameOrBirthDate("SFSDFS", "2020-12-04");
        System.out.println(stu);
    }
}

依次运行每个测试方法,并使用 head 观察测试结果
SpringCloud day12_第78张图片

使用 Criteria 构建查询

Spring Data Elasticsearch 中,可以使用 SearchOperations 工具执行一些更复杂的查询,这些查询操作接收一个 Query 对象封装的查询操作。

Spring Data Elasticsearch 中的 Query 有三种:

----CriteriaQuery
----StringQuery
----NativeSearchQuery
多数情况下,CriteriaQuery 都可以满足我们的查询求。下面来看两个 Criteria 查询示例:
StudentSearcher

package cn.tedu.esspringboot.es;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.elasticsearch.core.ElasticsearchOperations;
import org.springframework.data.elasticsearch.core.SearchHit;
import org.springframework.data.elasticsearch.core.SearchHits;
import org.springframework.data.elasticsearch.core.query.Criteria;
import org.springframework.data.elasticsearch.core.query.CriteriaQuery;
import org.springframework.stereotype.Component;

import java.util.List;
import java.util.stream.Collectors;

@Component
public class StudentSearcher {
     
    @Autowired
    private ElasticsearchOperations searchOperations;

    public List<Student> searchByBirthDate(String birthDate) {
     
        Criteria c = new Criteria("birthDate").is(birthDate);
        return criteriaSearch(c);
    }

    public List<Student> searchByBirthDate(String ge, String le) {
     
        Criteria c = new Criteria("birthDate").between(ge, le);
        return criteriaSearch(c);
    }

    private List<Student> criteriaSearch(Criteria c) {
     
        CriteriaQuery q = new CriteriaQuery(c);
        SearchHits<Student> hits = searchOperations.search(q, Student.class);
        List<Student> list = hits.stream().map(SearchHit::getContent).collect(Collectors.toList());
        return list;
    }
}

修改 StudentService
在 StudentService 中,调用 StudentSearcher,执行查询:

package cn.tedu.esspringboot.es;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class StudentService {
     
    @Autowired
    private StudentRepository studentRepo;

    @Autowired
    private StudentSearcher studentSearcher;

    public void save(Student student) {
     
        studentRepo.save(student);
    }

    public void delete(Long id) {
     
        studentRepo.deleteById(id);
    }

    public void update(Student student) {
     
        save(student);
    }

    public List<Student> findByName(String name) {
     
        return studentRepo.findByName(name);
    }

    public List<Student> findByNameOrBirthDate(String name, String birthDate) {
     
        return studentRepo.findByNameOrBirthDate(name, birthDate);
    }

    public List<Student> findByBirthDate(String birthDate) {
     
        return studentSearcher.searchByBirthDate(birthDate);
    }

    public List<Student> findByBirthDate(String ge, String le) {
     
        return studentSearcher.searchByBirthDate(ge, le);
    }
}

在测试类中添加测试方法

package cn.tedu.esspringboot;

import cn.tedu.esspringboot.es.Student;
import cn.tedu.esspringboot.es.StudentService;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;

import java.util.List;

@SpringBootTest
public class Test1 {
     
    @Autowired
    private StudentService studentService;

    @Test
    public void test1() {
     
        studentService.save(new Student(998L,"张三",'男',"2020-12-04"));
    }
    @Test
    public void test2() {
     
        studentService.update(new Student(1L,"李四",'女',"2020-12-04"));
    }
    @Test
    public void test3() {
     
        List<Student> stu = studentService.findByName("四");
        System.out.println(stu);
    }
    @Test
    public void test4() throws Exception {
     
        List<Student> stu;

        stu = studentService.findByNameOrBirthDate("四", "1999-09-09");
        System.out.println(stu);

        stu = studentService.findByNameOrBirthDate("SFSDFS", "2020-12-04");
        System.out.println(stu);
    }

    @Test
    public void test5() throws Exception {
     
        List<Student> stu;

        stu = studentService.findByBirthDate("2020-12-04");
        System.out.println(stu);
    }

    @Test
    public void test6() throws Exception {
     
        List<Student> stu;

        stu = studentService.findByBirthDate("2020-12-05", "2020-12-09");
        System.out.println(stu);
    }
}

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