文章目录
- 环境准备
- Spring框架
-
- [Sharding-JDBC 4.x版本api实现](#Sharding-JDBC 4.x版本api实现)
- [Sharding-JDBC 5.4.x版本yaml实现](#Sharding-JDBC 5.4.x版本yaml实现)
- Springboot框架
-
- [Sharding-JDBC 5.4.x版本yaml实现](#Sharding-JDBC 5.4.x版本yaml实现)
- 分库、加密、读写分离基于yaml的配置示例
需求：按月分区，按年分表，找不到对应年份表走备份表,动态从linux获取账户密码用于创建数据库链接
备份表：table
已经提前建表语句创建好未来年份的表:table_2026,table_2027
环境准备
maven
此依赖会引入很多有漏洞的包，如果有漏洞扫描的情况下，请替换相关包的其他版本或加白名单
java
<dependency>
<groupId>org.apache.shardingsphere</groupId>
<artifactId>shardingsphere-jdbc-core</artifactId>
<version>${sharding-jdbc.version}</version>
</dependency>Spring框架
Sharding-JDBC 4.x版本api实现
注意：4.x版本，在用api实现，如果想要非分片表走相同的数据源的情况下
需要用setDefaultdatasource赋默认数据源，在5.x版本已经移除了该方法，所以仅支持低版本实现，如果不加的话shading会无法路由其他表，会报表不存在的情况
datasource
java
@Bean(name = "dataSource")
public DataSource shardingDataSource() throws SQLException, HKEException {
DataSource dataSource = YamlShardingSphereDataSourceFactory.createDataSource();
// 1. 配置数据源
Map<String, DataSource> dataSourceMap = new HashMap<>();
dataSourceMap.put("ds", dataSource());
// 2. 配置分片规则
ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
// shardingRuleConfig.setDefaultDatabaseShardingStrategy(new NoneShardingStrategyConfiguration());
// 配置table表的分片规则
ShardingTableRuleConfiguration signInfoRule = new ShardingTableRuleConfiguration(
"table",
"ds.table_2025" // 包含备份表和未来几年的表
);
signInfoRule.setTableShardingStrategy(
new StandardShardingStrategyConfiguration(
"operate_time", // 分片字段
"year-sharding-algorithm" // 自定义分片算法
)
);
// signInfoRule.setKeyGenerateStrategy(
// new KeyGenerateStrategyConfiguration("row_id", "snowflake")
// );
// 添加其他表的规则（tablexxx）
// 添加表规则到分片配置
shardingRuleConfig.getTables().add(signInfoRule);
// 3. 配置分片算法
shardingRuleConfig.getShardingAlgorithms().put(
"year-sharding-algorithm",
new AlgorithmConfiguration("CLASS_BASED", new Properties() {
{
setProperty("strategy", "STANDARD");
setProperty("algorithmClassName", "com.package.YearShardingAlgorithm");
}
})
);
// 4. 配置主键生成器
shardingRuleConfig.getKeyGenerators().put(
"snowflake",
new AlgorithmConfiguration("SNOWFLAKE", new Properties())
);
// 5. 创建ShardingSphere数据源
Properties props = new Properties();
props.setProperty("sql-show", "true"); // 显示SQL日志
return ShardingSphereDataSourceFactory.createDataSource(
dataSourceMap,
Collections.singleton(shardingRuleConfig),
props
);
}分表算法实现
注意：implements 哪个类取决于你需要用那个对应的算法，有stand，complex等
java
package cfca.hke.privatization.sharding;
import cfca.hke.privatization.common.logging.LoggerManager;
import org.apache.shardingsphere.sharding.api.sharding.standard.PreciseShardingValue;
import org.apache.shardingsphere.sharding.api.sharding.standard.RangeShardingValue;
import org.apache.shardingsphere.sharding.api.sharding.standard.StandardShardingAlgorithm;
import java.time.LocalDateTime;
import java.util.Collection;
import java.util.Collections;
public class YearShardingAlgorithm implements StandardShardingAlgorithm<LocalDateTime> {
@Override
public String doSharding(Collection<String> availableTargetNames, PreciseShardingValue<LocalDateTime> shardingValue) {
// 获取逻辑表名
String logicTableName = shardingValue.getLogicTableName();
Object value = shardingValue.getValue();
String year = value.toString();
//年份格式会有多种 所以直接截取这里
if (year.length() > 4) {
year = year.substring(0, 4);
}
//25年的存到备份表logicTableName
if (!"2025".equals(year))
{
logicTableName = logicTableName + "_" + year;
}
// 检查表是否存在
if (availableTargetNames.contains(logicTableName)) {
return logicTableName;
} else {
LoggerManager.exceptionLogger.error("The table does not exist,please check HKEPrivateConfig/sharding.yaml, tableName: {}", logicTableName);
return logicTableName;
}
}
@Override
public Collection<String> doSharding(Collection<String> collection, RangeShardingValue<LocalDateTime> rangeShardingValue) {
return Collections.emptyList();
}
}Sharding-JDBC 5.4.x版本yaml实现
此版本有漏洞，当你配置不分表的表时候，配置的表明会区分大小写，此漏洞在5.4.1版本已经兼容了，所以可以尽量用最新版
注意：ds.*不会影响你加分片规则的表，优先级是如果有分片规则则认为是需分片的表，这里配单表不配ds.*的时候，必须升级到5.4.1版本，或未来出现新版之后，尽量使用高版本
yaml
mode:
#设置为单机模式。此模式下，配置信息存储在本地，适用于开发测试或小型应用，无需额外的协调服务（如 ZooKeeper）
type: Standalone
repository:
#指定使用内嵌的 JDBC 数据库来存储 ShardingSphere 的元数据（如逻辑库、表结构等）
type: JDBC
# 数据库标识 随便写
databaseName: zhongxin
# 数据库配置，连接池配置
dataSources:
ds:
dataSourceClassName: com.zaxxer.hikari.HikariDataSource
driverClassName: com.mysql.cj.jdbc.Driver
url: jdbc:mysql://192.168.xx.xx:3306/zhongxin_new?rewriteBatchedStatements=true&allowMultiQueries=true&useLocalSessionState=true&useUnicode=true&characterEncoding=utf-8&socketTimeout=3000000&connectTimeout=60000
username: root
password:
connectionTimeout: 50000
minimumIdle: 5
maximumPoolSize: 10
idleTimeout: 600000
maxLifetime: 1800000
connectionTestQuery: SELECT 1
# 5.x版本的配法
rules:
#分片规则标签
- !SHARDING
#需要进行分片的逻辑表及其规则
tables:
table:
#分片的表名
actualDataNodes: ds.table,ds.table_$->{2026..2028}
tableStrategy:
standard:
#分片字段
shardingColumn: operate_time
#分片算法
shardingAlgorithmName: infoAlgorithm
table1:
actualDataNodes: ds.table1,ds.table1_$->{2026..2028}
tableStrategy:
standard:
shardingColumn: operate_time
shardingAlgorithmName: infoAlgorithm
table2:
actualDataNodes: ds.table2,ds.table2_$->{2026..2028}
tableStrategy:
standard:
shardingColumn: operate_time
shardingAlgorithmName: infoAlgorithm
shardingAlgorithms:
infoAlgorithm:
#分片算法类型为基于自定义类
type: CLASS_BASED
props:
#指定自定义算法实现的策略类型为 STANDARD（标准）
strategy: STANDARD
#自定义分片算法的完整类名
algorithmClassName: com.package.YearShardingAlgorithm
keyGenerators:
snowflake:
type: SNOWFLAKE
# 不分区的表
- !SINGLE
tables:
- ds.*
props:
#是否在日志中打印 SQL
sql-show: true动态修改账号密码
注意：这里要用sharding提供的解析yaml的方法去解析，如果你是低版本可以自己去写流读取，高版本在读取sharding特殊的配置会报错，在解析完后也要用sharding提供的方法转回string或者bytes
java
@Bean(name = "dataSource")
public DataSource shardingDataSource() throws Exception {
//获取用户名密码
String username = credentials[0];
String password = credentials[1];
File yamlFile = new File("./xxx/sharding.yaml");
YamlJDBCConfiguration rootConfig = YamlEngine.unmarshal(yamlFile, YamlJDBCConfiguration.class);
Map<String, Object> dsMap = rootConfig.getDataSources().get("ds");
dsMap.put("username",username);
dsMap.put("password",password);
String marshal = YamlEngine.marshal(rootConfig);
DataSource dataSource = YamlShardingSphereDataSourceFactory.createDataSource(marshal.getBytes());
printDBInfo(username,password);
return dataSource;
}分表算法实现
注意：implements 哪个类取决于你需要用那个对应的算法，有stand，complex等
java
package cfca.hke.privatization.sharding;
import cfca.hke.privatization.common.logging.LoggerManager;
import org.apache.shardingsphere.sharding.api.sharding.standard.PreciseShardingValue;
import org.apache.shardingsphere.sharding.api.sharding.standard.RangeShardingValue;
import org.apache.shardingsphere.sharding.api.sharding.standard.StandardShardingAlgorithm;
import java.time.LocalDateTime;
import java.util.Collection;
import java.util.Collections;
public class YearShardingAlgorithm implements StandardShardingAlgorithm<LocalDateTime> {
@Override
public String doSharding(Collection<String> availableTargetNames, PreciseShardingValue<LocalDateTime> shardingValue) {
// 获取逻辑表名
String logicTableName = shardingValue.getLogicTableName();
Object value = shardingValue.getValue();
String year = value.toString();
//年份格式会有多种 所以直接截取这里
if (year.length() > 4) {
year = year.substring(0, 4);
}
//25年的存到备份表logicTableName
if (!"2025".equals(year))
{
logicTableName = logicTableName + "_" + year;
}
// 检查表是否存在
if (availableTargetNames.contains(logicTableName)) {
return logicTableName;
} else {
LoggerManager.exceptionLogger.error("The table does not exist,please check HKEPrivateConfig/sharding.yaml, tableName: {}", logicTableName);
return logicTableName;
}
}
@Override
public Collection<String> doSharding(Collection<String> collection, RangeShardingValue<LocalDateTime> rangeShardingValue) {
return Collections.emptyList();
}
}Springboot框架
Sharding-JDBC 5.4.x版本yaml实现
在springboot的application.yml文件里面，直接加入分片的配置即可,springboot会自动解析该yaml文件不需要手动读取
yaml
mode:
type: Standalone
repository:
type: JDBC
# 数据库标识 随便写
databaseName: zhongxin
# 数据库配置，连接池配置
dataSources:
ds:
dataSourceClassName: com.zaxxer.hikari.HikariDataSource
driverClassName: com.mysql.cj.jdbc.Driver
url: jdbc:mysql://192.168.xx.xx:3306/zhongxin_new?rewriteBatchedStatements=true&allowMultiQueries=true&useLocalSessionState=true&useUnicode=true&characterEncoding=utf-8&socketTimeout=3000000&connectTimeout=60000
username: root
password:
connectionTimeout: 50000
minimumIdle: 5
maximumPoolSize: 10
idleTimeout: 600000
maxLifetime: 1800000
connectionTestQuery: SELECT 1
# 5.x版本的配法
rules:
- !SHARDING
tables:
table:
actualDataNodes: ds.table,ds.table_$->{2026..2028}
tableStrategy:
standard:
shardingColumn: operate_time
shardingAlgorithmName: infoAlgorithm
table1:
actualDataNodes: ds.table1,ds.table1_$->{2026..2028}
tableStrategy:
standard:
shardingColumn: operate_time
shardingAlgorithmName: infoAlgorithm
table2:
actualDataNodes: ds.table2,ds.table2_$->{2026..2028}
tableStrategy:
standard:
shardingColumn: operate_time
shardingAlgorithmName: infoAlgorithm
shardingAlgorithms:
infoAlgorithm:
type: CLASS_BASED
props:
strategy: STANDARD
algorithmClassName: com.package.YearShardingAlgorithm
keyGenerators:
snowflake:
type: SNOWFLAKE
# 不分区的表
- !SINGLE
tables:
- ds.*
props:
sql-show: true分库、加密、读写分离基于yaml的配置示例
yaml
mode:
type: Standalone
repository:
type: JDBC
databaseName: demo_db
dataSources:
ds_basic:
dataSourceClassName: com.alibaba.druid.pool.DruidDataSource
driverClassName: com.mysql.jdbc.Driver
url: jdbc:mysql://127.0.0.1:3306/demo_basic?characterEncoding=utf-8&allowPublicKeyRetrieval=true&useSSL=false
username: root
password: Ph9ep971fm14nYaZsLl9LY+MCqX9uJSozYRNgP2VVSj/hbmokn5OC6kpiAA1I0okA9GiDHEo7qHUvRQYYUNZvQ==
initialSize: 1
minIdle: 1
maxActive: 64
maxWait: 20000
validationQuery: SELECT 1 FROM DUAL
validationQueryTimeout: 30000
minEvictableIdleTimeMillis: 300000
maxEvictableIdleTimeMillis: 600000
timeBetweenEvictionRunsMillis: 300000
testOnBorrow: true
testWhileIdle: true
filters: config, stat, wall
connectProperties:
connectTimeout: 5000
socketTimeout: '20000'
config.decrypt: 'true'
config.decrypt.key: MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBALZRYgsnvVKPqZTfMOWmmj6OuupFRSk7+Vtqv70cG3y6T3bm+DcQU3zOC993ozbHpmqeODtuLzURhIuXDMyTKW8CAwEAAQ==
ds0000:
dataSourceClassName: com.alibaba.druid.pool.DruidDataSource
driverClassName: com.mysql.jdbc.Driver
url: jdbc:mysql://127.0.0.1:3306/demo_0000?characterEncoding=utf-8&allowPublicKeyRetrieval=true&useSSL=false
username: root
password: Ph9ep971fm14nYaZsLl9LY+MCqX9uJSozYRNgP2VVSj/hbmokn5OC6kpiAA1I0okA9GiDHEo7qHUvRQYYUNZvQ==
initialSize: 1
minIdle: 1
maxActive: 64
maxWait: 20000
validationQuery: SELECT 1 FROM DUAL
validationQueryTimeout: 30000
minEvictableIdleTimeMillis: 300000
maxEvictableIdleTimeMillis: 600000
timeBetweenEvictionRunsMillis: 300000
testOnBorrow: true
testWhileIdle: true
filters: config, stat, wall
connectProperties:
connectTimeout: 5000
socketTimeout: '20000'
config.decrypt: 'true'
config.decrypt.key: MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBALZRYgsnvVKPqZTfMOWmmj6OuupFRSk7+Vtqv70cG3y6T3bm+DcQU3zOC993ozbHpmqeODtuLzURhIuXDMyTKW8CAwEAAQ==
ds0001:
dataSourceClassName: com.alibaba.druid.pool.DruidDataSource
driverClassName: com.mysql.jdbc.Driver
url: jdbc:mysql://127.0.0.1:3306/demo_0001?characterEncoding=utf-8&allowPublicKeyRetrieval=true&useSSL=false
username: root
password: Ph9ep971fm14nYaZsLl9LY+MCqX9uJSozYRNgP2VVSj/hbmokn5OC6kpiAA1I0okA9GiDHEo7qHUvRQYYUNZvQ==
initialSize: 1
minIdle: 1
maxActive: 64
maxWait: 20000
validationQuery: SELECT 1 FROM DUAL
validationQueryTimeout: 30000
minEvictableIdleTimeMillis: 300000
maxEvictableIdleTimeMillis: 600000
timeBetweenEvictionRunsMillis: 300000
testOnBorrow: true
testWhileIdle: true
filters: config, stat, wall
connectProperties:
connectTimeout: 5000
socketTimeout: '20000'
config.decrypt: 'true'
config.decrypt.key: MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBALZRYgsnvVKPqZTfMOWmmj6OuupFRSk7+Vtqv70cG3y6T3bm+DcQU3zOC993ozbHpmqeODtuLzURhIuXDMyTKW8CAwEAAQ==
ds0000_slave:
dataSourceClassName: com.alibaba.druid.pool.DruidDataSource
driverClassName: com.mysql.jdbc.Driver
url: jdbc:mysql://192.168.1.88:3306/demo_0000?characterEncoding=utf-8&allowPublicKeyRetrieval=true&useSSL=false
username: root
password: Ph9ep971fm14nYaZsLl9LY+MCqX9uJSozYRNgP2VVSj/hbmokn5OC6kpiAA1I0okA9GiDHEo7qHUvRQYYUNZvQ==
initialSize: 1
minIdle: 1
maxActive: 64
maxWait: 20000
validationQuery: SELECT 1 FROM DUAL
validationQueryTimeout: 30000
minEvictableIdleTimeMillis: 300000
maxEvictableIdleTimeMillis: 600000
timeBetweenEvictionRunsMillis: 300000
testOnBorrow: true
testWhileIdle: true
filters: config, stat, wall
connectProperties:
connectTimeout: 5000
socketTimeout: '20000'
config.decrypt: 'true'
config.decrypt.key: MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBALZRYgsnvVKPqZTfMOWmmj6OuupFRSk7+Vtqv70cG3y6T3bm+DcQU3zOC993ozbHpmqeODtuLzURhIuXDMyTKW8CAwEAAQ==
ds0001_slave:
dataSourceClassName: com.alibaba.druid.pool.DruidDataSource
driverClassName: com.mysql.jdbc.Driver
url: jdbc:mysql://192.168.1.88:3306/demo_0001?characterEncoding=utf-8&allowPublicKeyRetrieval=true&useSSL=false
username: root
password: Ph9ep971fm14nYaZsLl9LY+MCqX9uJSozYRNgP2VVSj/hbmokn5OC6kpiAA1I0okA9GiDHEo7qHUvRQYYUNZvQ==
initialSize: 1
minIdle: 1
maxActive: 64
maxWait: 20000
validationQuery: SELECT 1 FROM DUAL
validationQueryTimeout: 30000
minEvictableIdleTimeMillis: 300000
maxEvictableIdleTimeMillis: 600000
timeBetweenEvictionRunsMillis: 300000
testOnBorrow: true
testWhileIdle: true
filters: config, stat, wall
connectProperties:
connectTimeout: 5000
socketTimeout: '20000'
config.decrypt: 'true'
config.decrypt.key: MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBALZRYgsnvVKPqZTfMOWmmj6OuupFRSk7+Vtqv70cG3y6T3bm+DcQU3zOC993ozbHpmqeODtuLzURhIuXDMyTKW8CAwEAAQ==
rules:
# 数据分片
- !SHARDING
tables:
t_claim_case_mdtrt:
actualDataNodes: ds$->{['0000','0001']}.t_claim_case_mdtrt_000$->{0..9}
tableStrategy:
standard:
shardingColumn: transaction_no
shardingAlgorithmName: t_claim_case_mdtrt_inline
keyGenerateStrategy:
column: id
keyGeneratorName: snowflake
t_claim_case_info:
actualDataNodes: ds$->{['0000','0001']}.t_claim_case_info_000$->{0..9}
tableStrategy:
standard:
shardingColumn: transaction_no
shardingAlgorithmName: t_claim_case_info_inline
keyGenerateStrategy:
column: id
keyGeneratorName: snowflake
defaultShardingColumn: transaction_no
bindingTables:
- t_claim_case_mdtrt, t_claim_case_info
defaultDatabaseStrategy:
standard:
shardingColumn: transaction_no
shardingAlgorithmName: database_inline
defaultTableStrategy:
none:
shardingAlgorithms:
database_inline:
type: INLINE
props:
algorithm-expression: ds$->{transaction_no[-8..-5]}
t_claim_case_mdtrt_inline:
type: INLINE
props:
algorithm-expression: t_claim_case_mdtrt_$->{transaction_no[-4..-1]}
t_claim_case_info_inline:
type: INLINE
props:
algorithm-expression: t_claim_case_info_$->{transaction_no[-4..-1]}
keyGenerators:
snowflake:
type: SNOWFLAKE
#数据加密
- !ENCRYPT
tables:
t_claim_case_info:
columns:
appl_mobile:
cipher:
name: appl_mobile
encryptorName: sm4_encryptor
opsnId_no:
cipher:
name: opsnId_no
encryptorName: sm4_encryptor
rpter_id_no:
cipher:
name: rpter_id_no
encryptorName: sm4_encryptor
rpter_mobile:
cipher:
name: rpter_mobile
encryptorName: sm4_encryptor
encryptors:
sm4_encryptor:
type: SM4
props:
sm4-key: 86C63180C2806ED1F43A859DE501215C
sm4-mode: ECB
sm4-padding: PKCS5Padding
# 单表
- !SINGLE
tables:
- ds_basic.*
# 读写分离
- !READWRITE_SPLITTING
dataSources:
ds0000:
writeDataSourceName: ds0000
readDataSourceNames:
- ds0000_slave
transactionalReadQueryStrategy: PRIMARY
loadBalancerName: random
ds0001:
writeDataSourceName: ds0001
readDataSourceNames:
- ds0001_slave
transactionalReadQueryStrategy: PRIMARY
loadBalancerName: random
loadBalancers:
random:
type: RANDOM
props:
sql-show: true
max-connections-size-per-query: 5