springboot2 shardingjdbc4 yml配置单库分表+根据年月份进行分表

文章目录

1.下面已经实现了根据年份月份进行分表，使用的是shardingJDBC ID 生成策略为 SNOWFLAKE

2.自己传ID进来也可以根据ID进行分表

3.要注意配置文件中的每个参数名的配置层级，我在进行配置的时候，就是因为参数层级位置不对的问题弄了一下午

比如：spring下面是shardingsphere 必须严格按照层级问题，重点注意

需要注意如果shardingJDBC要和druid一起使用，那么druid是spring的jar包不是springboot的jar重点注意

<?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>
 
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.2.3.RELEASE</version>
        <relativePath/> <!-- lookup parent from repository -->
    </parent>
 
    <groupId>com.mayikt</groupId>
    <artifactId>shardingjdbc4</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <sharding-sphere.version>4.1.1</sharding-sphere.version>
    </properties>
 
    <dependencies>
        <!-- SpringBoot整合Web组件 -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
   
        <!-- druid数据源 -->
       <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>druid</artifactId>
            <version>1.2.11</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
        </dependency>
 
        <!-- shardingJDBC-->
        <dependency>
            <groupId>org.apache.shardingsphere</groupId>
            <artifactId>sharding-jdbc-spring-boot-starter</artifactId>
            <version>${sharding-sphere.version}</version>
        </dependency>
    </dependencies>
 
</project>

全部采用YML配置进行数据源的配置
主配置源
application.yml

spring:
  profiles:
    active: sharding
  #日期格式化
  jackson:
    time-zone: GMT+8
    date-format: yyyy-MM-dd HH:mm:ss
  datasource:
    url: jdbc:mysql://127.0.0.1:3306/tg_project?useUnicode=true&characterEncoding=utf-8&useSSL=true&serverTimezone=UTC
    username: root
    password: 123456
    type: com.alibaba.druid.pool.DruidDataSource
    driver-class-name: com.mysql.jdbc.Driver
    druid:
      initial-size: 5 #初始化大小
      min-idle: 5 #最小
      max-active: 100 #最大
      max-wait: 60000 #连接超时时间
      ##配置间隔多久进行一次检测，检测需要关闭的空闲连接，单位毫秒
      time-between-eviction-runs-millis: 60000
      ##指定空闲连接最少空闲多久可以被清除
      min-evictable-idle-time-millis: 30000
      validation-query: select 'X'
      ##当连接空闲时，是否执行连接测试
      test-while-idle: true
      ##当从连接池借用连接时，是否测试该连接
      test-on-borrow: false
      ##当连接归还到连接池时，是否测试该连接
      test-on-return: false
      ##配置监控统计拦截的filters,去掉后监控界面sql无法统计，‘wall’ 用于防火墙
      filters: wall,stat,log4j
      pool-prepared-statements: true
      max-pool-prepared-statement-per-connection-size: 20
      max-open-prepared-statements: 20
      # 通过connectProperties属性来打开mergeSql功能；慢SQL记录
      connectionProperties: druid.stat.slowSqlMillis=200;druid.stat.logSlowSql=true
      # WebStatFilter配置，说明请参考Druid Wiki，配置_配置WebStatFilter
      #是否启用StatFilter默认值true
      web-stat-filter:
        enabled: true
        url-pattern: /*
        exclusions: /druid/*,*.js,*.gif,*.jpg,*.bmp,*.png,*.css,*.ico
        session-stat-enable: true
        session-stat-max-count: 10
      # StatViewServlet配置，说明请参考Druid Wiki，配置_StatViewServlet配置
      #是否启用StatViewServlet默认值true
      stat-view-servlet:
        enabled: true
        url-pattern: /druid/*
        reset-enable: true
        login-username: admin
        login-password: admin
      filter:
        stat:
          db-type: mysql
          log-slow-sql: true
          slow-sql-millis: 5000
        wall:
          enabled: true
          db-type: mysql
          config:
            drop-table-allow: false
    resources:
      static-locations: classpath:/static/**,classpath:/templates/
    thymeleaf:
      #prefix：指定模板所在的目录
      prefix: classpath:templates/
      suffix: .html
      check-template-location: true
      encoding: UTF-8
      content-type: text/html
      mode: HTML5
      cache: false
 
mybatis:
  type-aliases-package: com.taguan.entity
  mapper-locations: classpath:mapper/*.xml
  configuration:
    map-underscore-to-camel-case: true
 
server:
  port: 8080
  tomcat:
    uri-encoding: UTF-8
    max-threads: 1000
    min-spare-threads: 10

application-sharding.yml
shardingJDBC配置，具体配置参数，可以看官网，不做多的介绍，东西太多了

# 数据分片应用于单库分表操作
spring:
  shardingsphere:
    datasource:
      names: db1
      db1: # 数据库
        type: com.alibaba.druid.pool.DruidDataSource
        driver-class-name: com.mysql.jdbc.Driver
        url: jdbc:mysql://127.0.0.1:3306/sys-admin?useUnicode=true&characterEncoding=utf-8&useSSL=true&serverTimezone=UTC
        username: root
        password: root
    sharding:
      tables:
        tg_weld_after_rule:
          # 配置表的分布，表的策略db1.tg_weld_after_rule_$->{2020..2021}0$->{1..9},db1.tg_weld_after_rule_$->{2020..2021}1$->{0..2}
          actual-data-nodes: db1.tg_weld_after_rule_$->{2021..2021}0$->{1..5}
          # 指定tg_weld_after_rule_表 主键id 生成策略为 SNOWFLAKE
          key-generator:
            column: id
            type: SNOWFLAKE
          # 指定分片策略
          table-strategy:
#            inline:
##              约定id值是偶数添加到tg_weld_after_rule_0表，如果id是奇数添加到tg_weld_after_rule_1表
#              sharding-column: id
#              algorithm-expression: tg_weld_after_rule_$->{id%2}
            standard:
              #根据年月份进行分表，分表规则自定义handler
              sharding-column: create_time
              precise-algorithm-class-name: com.taguan.utils.USerTablePreciseShardingAlgorithm
              #range-algorithm-class-name: com.taguan.utils.MyRangeShardingAlgorithm
        tg_weld_after_rule_test:
          # 配置表的分布，表的策略db1.tg_weld_after_rule_$->{2020..2021}0$->{1..9},db1.tg_weld_after_rule_$->{2020..2021}1$->{0..2}
          actual-data-nodes: db1.tg_weld_after_rule_test_$->{2021..2021}0$->{1..5}
          # 指定tg_weld_after_rule_表 主键id 生成策略为 SNOWFLAKE
          key-generator:
            column: id
            type: SNOWFLAKE
          # 指定分片策略
          table-strategy:
            standard:
              #根据年月份进行分表，分表规则自定义handler
              sharding-column: create_time
              precise-algorithm-class-name: com.taguan.utils.USerTablePreciseShardingAlgorithm
              range-algorithm-class-name: com.taguan.utils.MyRangeShardingAlgorithm
    props:
      # 打开sql输出日志
      sql:
        show: true

分片策略采用
精确分片算法
对应PreciseShardingAlgorithm，用于处理使用单一键作为分片键的=与IN进行分片的场景。需要配合StandardShardingStrategy使用。

具体可以看shardingjdbc官网：​​https://shardingsphere.apache.org/document/legacy/3.x/document/cn/features/sharding/concept/sharding/​​ 采用精确分片算法 PreciseShardingAlgorithm 可以不使用 范围分片算法 RangeShardingAlgorithm

采用范围分片算 RangeShardingAlgorithm 就必须使用精确分片算法 PreciseShardingAlgorithm

分表分库策略类：
USerTablePreciseShardingAlgorithm

public class USerTablePreciseShardingAlgorithm implements PreciseShardingAlgorithm<Date> {
 
    private SimpleDateFormat formatter = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
 
    @Override
    public String doSharding(Collection<String> collection, PreciseShardingValue<Date> preciseShardingValue) {
        try {
            String tableName = preciseShardingValue.getLogicTableName();
            log.info("tableName--------------{}",tableName);
            String dataTime = formatter.format(preciseShardingValue.getValue());
            String year = dataTime.substring(0, 4);
            System.out.println("-------------------------"+year);
            String key = dataTime.substring(5, 7);
            System.out.println("-------------------------"+key);
            return tableName.concat("_").concat(year).concat(key);
        }catch (Exception e){
            throw new IllegalArgumentException("没有匹配到库:" + preciseShardingValue.getValue());
        }
 
 
    }
}