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+8date-format: yyyy-MM-dd HH:mm:ssdatasource:url: jdbc:mysql://127.0.0.1:3306/tg_project?useUnicode=true&characterEncoding=utf-8&useSSL=true&serverTimezone=UTCusername: rootpassword: 123456type: com.alibaba.druid.pool.DruidDataSourcedriver-class-name: com.mysql.jdbc.Driverdruid: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: 30000validation-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,log4jpool-prepared-statements: truemax-pool-prepared-statement-per-connection-size: 20max-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trueweb-stat-filter:enabled: trueurl-pattern: /*exclusions: /druid/*,*.js,*.gif,*.jpg,*.bmp,*.png,*.css,*.icosession-stat-enable: truesession-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truestat-view-servlet:enabled: trueurl-pattern: /druid/*reset-enable: truelogin-username: adminlogin-password: adminfilter:stat:db-type: mysqllog-slow-sql: trueslow-sql-millis: 5000wall:enabled: truedb-type: mysqlconfig:drop-table-allow: falseresources:static-locations: classpath:/static/**,classpath:/templates/thymeleaf:#prefix：指定模板所在的目录prefix: classpath:templates/suffix: .htmlcheck-template-location: trueencoding: UTF-8content-type: text/htmlmode: HTML5cache: falsemybatis:type-aliases-package: com.taguan.entitymapper-locations: classpath:mapper/*.xmlconfiguration:map-underscore-to-camel-case: trueserver:port: 8080tomcat:uri-encoding: UTF-8max-threads: 1000min-spare-threads: 10
logging:level:com.taguan: debug

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: db1db1: # 数据库type: com.alibaba.druid.pool.DruidDataSourcedriver-class-name: com.mysql.jdbc.Driverurl: jdbc:mysql://127.0.0.1:3306/sys-admin?useUnicode=true&characterEncoding=utf-8&useSSL=true&serverTimezone=UTCusername: rootpassword: rootsharding: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 SNOWFLAKEkey-generator:column: idtype: 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_$->{id%2}standard:#根据年月份进行分表，分表规则自定义handlersharding-column: create_timeprecise-algorithm-class-name: com.taguan.utils.USerTablePreciseShardingAlgorithm#range-algorithm-class-name: com.taguan.utils.MyRangeShardingAlgorithmtg_weld_after_rule_test:# 配置表的分布，表的策略db1.tg_weld_after_rule_$->{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 SNOWFLAKEkey-generator:column: idtype: 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handlersharding-column: create_timeprecise-algorithm-class-name: com.taguan.utils.USerTablePreciseShardingAlgorithmrange-algorithm-class-name: com.taguan.utils.MyRangeShardingAlgorithmprops:# 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");@Overridepublic 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());}}
}