ShardingSphere-Proxy 5.0 分库分表（一）

一、简述

• 简述
  ShardingSphere-Proxy4.0 已经升级到5.0了，但是两者的配置文件还有一定的差别的，这篇文章讲述的就是ShardingSphere-Proxy 5.0 的落地。概念、分表、分库、分库分表的原理的基本和4.0一样的，需要了解可查看 https://blog.csdn.net/Fu_Shi_rong/article/details/123541413?spm=1001.2014.3001.5501。
• 开发者文档
  https://shardingsphere.apache.org/document/current/cn/dev-manual/

二、ShardingSphere-Proxy5.0 落地

• 环境

  • JAVA JDK下载

    https://pan.baidu.com/s/1A-ksNN0YicT3hXjFscGGwA 
    提取码：r9e0
    

  • JDBC数据连接驱动下载

    https://pan.baidu.com/s/1924iUe7wxGpStAzxxv2K3g 
    提取码：jy7z
    

  • ShardingSphere-Proxy 5.0 下载、

    https://archive.apache.org/dist/shardingsphere/5.0.0/
    

  • Window 11

• 落地

  • 条件

    1. 真实数据库
    2. 逻辑数据库
    3. ShardingSphere-Proxy5.0 配置文件

  • 步骤

    1. 新建真实数据库和表【hmms.user 表为例】

       • 数据库表结构语句

         SET NAMES utf8mb4;
         SET FOREIGN_KEY_CHECKS = 0;
         
         -- ----------------------------
         -- Table structure for user
         -- ----------------------------
         DROP TABLE IF EXISTS `user`;
         CREATE TABLE `user`  (
           `useid` int(11) NOT NULL,
           `usenam` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '登录名',
           `usepwd` varchar(100) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '登录密码',
           `usestate` int(11) NULL DEFAULT 2 COMMENT '-1：删除1：注销 2：正常 3：挂失',
           `usekey` varchar(100) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '用户秘钥',
           `usetel` varchar(11) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '用户手机',
           `createbyid` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '添加人',
           `createbytime` datetime(0) NULL DEFAULT NULL COMMENT '添加时间',
           `modifybyid` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '修改人',
           `modifybytime` datetime(0) NULL DEFAULT NULL COMMENT '修改时间',
           PRIMARY KEY (`useid`) USING BTREE
         ) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
         SET FOREIGN_KEY_CHECKS = 1;
         

       • 运行结果如下：

         

    2. 修改ShardingSphere-Proxy5.0 配置文件，并连接

       config-sharding.yaml

       # 3、创建客户端连接库
       schemaName: hmms
       
       #1、连接mysql
       dataSources:
         hmmsdatasources-0:
           url: jdbc:mysql://localhost:3306/hmms?serverTimezone=UTC&useSSL=false
           username: root
           password: ****
           connectionTimeoutMilliseconds: 30000
           idleTimeoutMilliseconds: 60000
           maxLifetimeMilliseconds: 1800000
           maxPoolSize: 50
           minPoolSize: 1
           
       # 2、分片规则
       rules:
       - !SHARDING
         tables:
           user: #表名
             actualDataNodes: hmmsdatasources-0.user-${0..1}  #分表规则
             tableStrategy:
               standard:
                 shardingColumn: useid   #分片键
                 shardingAlgorithmName: use_MOD  #分表算法
         shardingAlgorithms:  #分表算法
           use_MOD:  #取模算法
             type: MOD
             props:
               sharding-count: 2
       

       server.yaml

       rules:
         - !AUTHORITY
           users:
             - [email protected]%:*****
             - [email protected]:sharding
           provider:
             type: ALL_PRIVILEGES_PERMITTED
       

       运行命令

       #在ShardingSphere-Proxy bin
       start.bat 端口号
       

       运行结果如下：

       

    3. 新建逻辑数据连接，并执行表结构和数据脚本

       逻辑库执行结果如下：

       

       逻辑数据库添加两条数据

       SQL语句

       INSERT INTO `user` VALUES (1, 'admin', '202CB962AC59075B964B07152D234B70', 2, '123', '123123', 'xiaogang', '2021-08-25 20:12:15', 'xiaogang', NULL);
       INSERT INTO `user` VALUES (2, 'admin', '202CB962AC59075B964B07152D234B70', 2, '123', '123123', 'xiaogang', '2021-08-25 20:12:15', 'xiaogang', NULL);
       

       执行结果如下：

       
       

  • 分库分表
    ShardingSphere-Proxy 5.0 配置

    # 3、创建客户端连接库
    schemaName: hmms
    
    #1、连接mysql
    dataSources:
      hmmsdatasources-0:
        url: jdbc:mysql://主机地址:端口号/hmms-0?serverTimezone=UTC&useSSL=false
        username: root
        password: 密码
        connectionTimeoutMilliseconds: 30000
        idleTimeoutMilliseconds: 60000
        maxLifetimeMilliseconds: 1800000
        maxPoolSize: 50
        minPoolSize: 1
    
      hmmsdatasources-1:
        url: jdbc:mysql://主机地址:端口号/hmms-1?serverTimezone=UTC&useSSL=false
        username: root
        password: 密码
        connectionTimeoutMilliseconds: 30000
        idleTimeoutMilliseconds: 60000
        maxLifetimeMilliseconds: 1800000
        maxPoolSize: 50
        minPoolSize: 1
        
    
    # 2、分片规则
    rules:
    - !SHARDING
      tables:
        user:
          actualDataNodes: hmmsdatasources-${0..1}.user-${0..1}   
          tableStrategy:
            standard:
              shardingColumn: useid
              shardingAlgorithmName: use_MOD
          databaseStrategy:  #分库规则
              standard:
                shardingColumn: useid
                shardingAlgorithmName: use_MOD
          keyGenerateStrategy: 
            column: useid
            keyGeneratorName: snowflake
      shardingAlgorithms:
        use_MOD:
          type: MOD
          props:
            sharding-count: 2 
        use_HASH_MOD:
          type: HASH_MOD
          props:
            sharding-count: '2'
      keyGenerators: 
        snowflake: 
           type: SNOWFLAKE
           props: 
             worker-id: 123
    

  • 分片算法

  1. 取模算法

   shardingAlgorithms:  #分表算法
      use_MOD:  #取模算法
        type: MOD
        props:
          sharding-count: 2 #分表数据和分表的表达式必须是一致的
  

2. 范围算法

     shardingAlgorithms:
       use_BOUNDARY_RANGE:
         type: BOUNDARY_RANGE
         props:
           sharding-ranges: 2,100 #多个节点是以逗号分割 [是根据分的表结合配置节点]
    #备注
     #分片键为：useid int类型
     #分表的为3个表
     #sharding-ranges: 2,100 是以2为节点，小于2的数据存到表0，2到99存到表1,100以上存到表3
   

3. 容量算法

   shardingAlgorithms:
   	use_VOLUME_RANGE:
               type: VOLUME_RANGE
               props:
                 range-lower: '20000000'  #最小值
                 range-upper: '40000000'  #最大值
                 # 分片的区间的数据的间隔
                 sharding-volume: '20000000'
    #备注
       最小值为2000万，也就是说表数据量小于等于2000万，最大数量为4000万
       表与表的间隔为2000万
       比如说分了两张表：
       1-2000万       存到表0
       2001万-4000万   存到表1
       是根据分表的数量来定义最大值的
       分了三张表，那最大值为6000万
   

4. HASH-CODE算法 【如果分片键是字符串类型，需要这种算法分表】

     shardingAlgorithms:
       use_HASH_MOD:
         type: HASH_MOD
         props:
           sharding-count: '2'  #分表数量，单引号必须要加
   

5. 根据时间分表算法

   shardingAlgorithms:
       use_AUTO_INTERVAL:
           type: AUTO_INTERVAL
           props:
             datetime-lower: '2020-01-01 23:59:59'
             datetime-upper: '2022-12-31 23:59:59'
             # 以1年度为单位进行划分
             sharding-seconds: '31536000'  
             # 以1个月为单位进行划分
             #sharding-seconds: '2678400'   
             # 以1天为单位进行划分
             #sharding-seconds: '86400'
    #设置的最大值必须和分多少表匹配才行，否者报错，找不到表
   

• 分布式ID

  config-sharding.yaml

  # 3、创建客户端连接库
  schemaName: hmms
  
  #1、连接mysql
  dataSources:
    hmmsdatasources-0:
      url: jdbc:mysql://localhost:3306/hmms?serverTimezone=UTC&useSSL=false
      username: root
      password: 1QAZ2WSX3EDC
      connectionTimeoutMilliseconds: 30000
      idleTimeoutMilliseconds: 60000
      maxLifetimeMilliseconds: 1800000
      maxPoolSize: 50
      minPoolSize: 1
      
  # 2、分片规则
  rules:
  - !SHARDING
    tables:
      user:
        actualDataNodes: hmmsdatasources-0.user-${0..1}
        tableStrategy:
          standard:
            shardingColumn: id
            shardingAlgorithmName: use_HASH_MOD
        keyGenerateStrategy: 
          column: id
          keyGeneratorName: snowflake
    shardingAlgorithms:
      use_MOD:
        type: MOD
        props:
          sharding-count: 2 
      use_HASH_MOD:
        type: HASH_MOD
        props:
          sharding-count: '2'
    keyGenerators: 
      snowflake: 
         type: SNOWFLAKE
         props: 
           worker-id: 123
        
  

  表结构sql语句

  SET NAMES utf8mb4;
  SET FOREIGN_KEY_CHECKS = 0;
  
  -- ----------------------------
  -- Table structure for user
  -- ----------------------------
  DROP TABLE IF EXISTS `user`;
  CREATE TABLE `user`  (
    `id` varchar(100),
    `useid` int(11) NOT NULL,
    `usenam` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '登录名',
    `usepwd` varchar(100) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '登录密码',
    `usestate` int(11) NULL DEFAULT 2 COMMENT '-1：删除1：注销 2：正常 3：挂失',
    `usekey` varchar(100) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '用户秘钥',
    `usetel` varchar(11) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '用户手机',
    `createbyid` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '添加人',
    `createbytime` datetime(0) NULL DEFAULT NULL COMMENT '添加时间',
    `modifybyid` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL COMMENT '修改人',
    `modifybytime` datetime(0) NULL DEFAULT NULL COMMENT '修改时间',
    PRIMARY KEY (`useid`) USING BTREE
  ) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
  SET FOREIGN_KEY_CHECKS = 1;
  

  数据填充语句

  INSERT INTO `user`(useid,usenam,usepwd,usestate,usekey,usetel,createbyid,createbytime,modifybyid,modifybytime) VALUES (1, 'admin', '202CB962AC59075B964B07152D234B70', 2, '123', '123123', 'xiaogang', '2021-08-25 20:12:15', 'xiaogang', NULL);
  

  执行结构如下：