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Java中ShardingSphere 数据分片的实现

︶ㄣSDDLL学院 メ
SDDLL学院V管理员 / 阅读
0719
其实很多人对分库分表多少都有点恐惧，我们今天用ShardingSphere 给大家演示数据分片，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

其实很多人对分库分表多少都有点恐惧，我们今天用ShardingSphere 给大家演示数据分片，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

 

前言

其实很多人对分库分表多少都有点恐惧，其实我也是，总觉得这玩意是运维干的、数据量上来了或者sql过于复杂、一些数据分片的中间件支持的也不是很友好、配置繁琐等多种问题。

我们今天用ShardingSphere 给大家演示数据分片，包括分库分表、只分表不分库进行说明。

下一节有时间的话在讲讲读写分离吧。

github地址：https://github.com/362460453/boot-sharding-JDBC

 

ShardingSphere介绍

ShardingSphere是一套开源的分布式数据库中间件解决方案组成的生态圈，它由Sharding-JDBC、Sharding-Proxy和Sharding-Sidecar（计划中）这3款相互独立的产品组成。 他们均提供标准化的数据分片、分布式事务和数据库治理功能，可适用于如Java同构、异构语言、容器、云原生等各种多样化的应用场景。

ShardingSphere的功能能帮助我们做什么

  • 数据分片
  • 读写分离
  • 编排治理
  • 分布式事务

2016年初Sharding-JDBC被开源，这个产品是当当的，加入了Apache 后改名为 ShardingSphere 。他是我们应用和数据库之间的中间层，虽代码入侵性很强，但不会对现有业务逻辑进行改变。

更多文档请点击官网：https://shardingsphere.apache.org/document/current/en/overview/

 

为什么不用mycat

大家如果去查相关资料会知道，mycat和ShardingSphere是同类型的中间件，主要的功能，数据分片和读写分离两个都能去做，但是姿势却有很大的差别， 从字面意义上看Sharding 含义是分片、碎片的意思，所以不难理解ShardingSphere 对数据分片有很强对能力，对于99%对sql都是支持的，官网也有sql支持的相关内容，大家详细阅读，只有 类似sum 这种函数不支持，而且对 ORM框架和常用数据库基本都兼容，所以个人建议如果你们做数据分片，也就是是分库分表对话，强烈建议选择ShardingSphere，因为我私下也和一些朋友交流过，mycat 的数据分片对多表查询不是很友好，而且用 mycat 要有很强的运维来做，还有一点就是mycat 都是靠xml配置的，没有代码入侵，所以这也算是他的优点吧。如果你们只做读写分离对话，那么我建议用mycat，是没问题的。

 

实践前的准备工作

启动你的mysql，创建两个数据库，分别叫 sharding_master 和 sharding_salve分别在这两个数据库执行如下sql

CREATE TABLE IF NOT EXISTS `t_order_0` (
  `order_id` INT NOT NULL,
  `user_id`  INT NOT NULL,
  PRIMARY KEY (`order_id`)
);
CREATE TABLE IF NOT EXISTS `t_order_1` (
  `order_id` INT NOT NULL,
  `user_id`  INT NOT NULL,
  PRIMARY KEY (`order_id`)
);

做完以上两步结果如下

Java中ShardingSphere 数据分片的实现

 

代码案例

环境

工具 版本
jdk

1.8.0_144
springboot 2.0.4.RELEASE
sharding 1.3.1
mysql 5.7

创建一个springboot工程，我们使用 JdbcTemplate 框架，如果用mybatis也是无影响的。

pom引用依赖如下

<parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.0.4.RELEASE</version>
    </parent>
 
    <properties>
        <java.version>1.8</java.version>
        <druid.version>1.0.26</druid.version>
        <sharding.jdbc.core.version>1.3.3</sharding.jdbc.core.version>
    </properties>
 
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-jdbc</artifactId>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
        </dependency>
        <dependency>
            <groupId>com.dangdang</groupId>
            <artifactId>sharding-jdbc-core</artifactId>
            <version>${sharding.jdbc.core.version}</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>druid</artifactId>
            <version>${druid.version}</version>
        </dependency>
    </dependencies>

application.yml 配置如下

server:
  port: 8050
sharding:
  jdbc: 
    driverClassName: com.mysql.jdbc.Driver
    url: jdbc:mysql://localhost:3306/sharding_master?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&failOverReadOnly=false
    username: root
    password: 123456
    filters: stat
    maxActive: 100
    initialSize: 1
    maxWait: 15000
    minIdle: 1
    timeBetweenEvictionRunsMillis: 30000
    minEvictableIdleTimeMillis: 180000
    validationQuery: SELECT "x"
    testWhileIdle: true
    testOnBorrow: false
    testOnReturn: false
    poolPreparedStatements: false
    maxPoolPreparedStatementPerConnectionSize: 20
    removeAbandoned: true
    removeAbandonedTimeout: 600
    logAbandoned: false
    connectionInitSqls: 
    
    url0: jdbc:mysql://localhost:3306/sharding_master?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&failOverReadOnly=false
    username0: root
    password0: 123456
    
    url1: jdbc:mysql://localhost:3306/sharding_salve?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&failOverReadOnly=false
    username1: root
    password1: 123456

yml映射成Bean

@Data
@ConfigurationProperties(prefix="sharding.jdbc")
public class ShardDataSourceProperties {
	
	private String driverClassName;
	
	private String url;
	
	private String username;
	
	private String password;
	
	private String url0;
	
	private String username0;
	
	private String password0;
	
	private String url1;
	
	private String username1;
	
	private String password1;
	
	private String filters;
	
	private int maxActive;
	
	private int initialSize;
	
	private int maxWait;
	
	private int minIdle;
	
	private int timeBetweenEvictionRunsMillis;
	
	private int minEvictableIdleTimeMillis;
	
	private String validationQuery;
	
	private boolean testWhileIdle;
	
	private boolean testOnBorrow;
	
	private boolean testOnReturn;
	
	private boolean poolPreparedStatements;
	
	private int maxPoolPreparedStatementPerConnectionSize;
	
	private boolean removeAbandoned;
 
	private int removeAbandonedTimeout;
	
	private boolean logAbandoned;
	
	private List<String> connectionInitSqls;
//省略geter setter

分库策略

//通过实现SingleKeyDatabaseShardingAlgorithm接口实现分库
public class ModuloDatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm<Integer> {
 
	@Override
	public String doEqualSharding(Collection<String> availableTargetNames, ShardingValue<Integer> shardingValue) {
		for (String each : availableTargetNames) {
            if (each.endsWith(shardingValue.getValue() % 2 + "")) {
                return each;
            }
        }
        throw new IllegalArgumentException();
	}
 
	@Override
	public Collection<String> doInSharding(Collection<String> availableTargetNames,
			ShardingValue<Integer> shardingValue) {
		Collection<String> result = new LinkedHashSet<>(availableTargetNames.size());
        for (Integer value : shardingValue.getValues()) {
            for (String targetName : availableTargetNames) {
                if (targetName.endsWith(value % 2 + "")) {
                    result.add(targetName);
                }
            }
        }
        return result;
	}
 
	@Override
	public Collection<String> doBetweenSharding(Collection<String> availableTargetNames,
			ShardingValue<Integer> shardingValue) {
		Collection<String> result = new LinkedHashSet<>(availableTargetNames.size());
        Range<Integer> range = (Range<Integer>) shardingValue.getValueRange();
        for (Integer i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            for (String each : availableTargetNames) {
                if (each.endsWith(i % 2 + "")) {
                    result.add(each);
                }
            }
        }
        return result;
	}
}

分表策略

public class ModuloTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Integer> {
 
    /**
     * 对于分片字段的等值操作 都走这个方法。(包括 插入 更新)
     * 如：
     * <p>
     * select * from t_order from t_order where order_id = 11
     * └── SELECT *  FROM t_order_1 WHERE order_id = 11
     * select * from t_order from t_order where order_id = 44
     * └── SELECT *  FROM t_order_0 WHERE order_id = 44
     * </P>
     */
	@Override
    public String doEqualSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {
        for (String each : tableNames) {
            if (each.endsWith(shardingValue.getValue() % 2 + "")) {
                return each;
            }
        }
        throw new IllegalArgumentException();
    }
    
    /**
     * 对于分片字段的in操作，都走这个方法。
    *  select * from t_order from t_order where order_id in (11,44)  
    *          ├── SELECT *  FROM t_order_0 WHERE order_id IN (11,44) 
    *          └── SELECT *  FROM t_order_1 WHERE order_id IN (11,44) 
    *  select * from t_order from t_order where order_id in (11,13,15)  
    *          └── SELECT *  FROM t_order_1 WHERE order_id IN (11,13,15)  
    *  select * from t_order from t_order where order_id in (22,24,26)  
    *          └──SELECT *  FROM t_order_0 WHERE order_id IN (22,24,26) 
    */
	@Override
    public Collection<String> doInSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {
        Collection<String> result = new LinkedHashSet<>(tableNames.size());
        for (Integer value : shardingValue.getValues()) {
            for (String tableName : tableNames) {
                if (tableName.endsWith(value % 2 + "")) {
                    result.add(tableName);
                }
            }
        }
        return result;
    }
    
    /**
     * 对于分片字段的between操作都走这个方法。
    *  select * from t_order from t_order where order_id between 10 and 20 
    *          ├── SELECT *  FROM t_order_0 WHERE order_id BETWEEN 10 AND 20 
    *          └── SELECT *  FROM t_order_1 WHERE order_id BETWEEN 10 AND 20 
    */
	@Override
    public Collection<String> doBetweenSharding(final Collection<String> tableNames, final ShardingValue<Integer> shardingValue) {
        Collection<String> result = new LinkedHashSet<>(tableNames.size());
        Range<Integer> range = (Range<Integer>) shardingValue.getValueRange();
        for (Integer i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            for (String each : tableNames) {
                if (each.endsWith(i % 2 + "")) {
                    result.add(each);
                }
            }
        }
        return result;
    } 
}

对特定表和库，进行特定的分库分表规则

简单说，就是分库按照了user_id的奇偶区分，分表按照order_id 的奇偶区分，

如果你有多个表进行分片，就写多个TableRule，

配置两个数据源，分别是我在yml里的配置，根据你的需求个性化配置就可以。

@Configuration
@EnableConfigurationProperties(ShardDataSourceProperties.class)
public class ShardDataSourceConfig {
 
	@Autowired
	private ShardDataSourceProperties shardDataSourceProperties;
 
	private DruidDataSource parentDs() throws SQLException {
		DruidDataSource ds = new DruidDataSource();
		ds.setDriverClassName(shardDataSourceProperties.getDriverClassName());
		ds.setUsername(shardDataSourceProperties.getUsername());
		ds.setUrl(shardDataSourceProperties.getUrl());
		ds.setPassword(shardDataSourceProperties.getPassword());
		ds.setFilters(shardDataSourceProperties.getFilters());
		ds.setMaxActive(shardDataSourceProperties.getMaxActive());
		ds.setInitialSize(shardDataSourceProperties.getInitialSize());
		ds.setMaxWait(shardDataSourceProperties.getMaxWait());
		ds.setMinIdle(shardDataSourceProperties.getMinIdle());
		ds.setTimeBetweenEvictionRunsMillis(shardDataSourceProperties.getTimeBetweenEvictionRunsMillis());
		ds.setMinEvictableIdleTimeMillis(shardDataSourceProperties.getMinEvictableIdleTimeMillis());
		ds.setValidationQuery(shardDataSourceProperties.getValidationQuery());
		ds.setTestWhileIdle(shardDataSourceProperties.isTestWhileIdle());
		ds.setTestOnBorrow(shardDataSourceProperties.isTestOnBorrow());
		ds.setTestOnReturn(shardDataSourceProperties.isTestOnReturn());
		ds.setPoolPreparedStatements(shardDataSourceProperties.isPoolPreparedStatements());
		ds.setMaxPoolPreparedStatementPerConnectionSize(
				shardDataSourceProperties.getMaxPoolPreparedStatementPerConnectionSize());
		ds.setRemoveAbandoned(shardDataSourceProperties.isRemoveAbandoned());
		ds.setRemoveAbandonedTimeout(shardDataSourceProperties.getRemoveAbandonedTimeout());
		ds.setLogAbandoned(shardDataSourceProperties.isLogAbandoned());
		ds.setConnectionInitSqls(shardDataSourceProperties.getConnectionInitSqls());
		return ds;
	}
 
	private DataSource ds0() throws SQLException {
		DruidDataSource ds = parentDs();
		ds.setUsername(shardDataSourceProperties.getUsername0());
		ds.setUrl(shardDataSourceProperties.getUrl0());
		ds.setPassword(shardDataSourceProperties.getPassword0());
		return ds;
	}
 
	private DataSource ds1() throws SQLException {
		DruidDataSource ds = parentDs();
		ds.setUsername(shardDataSourceProperties.getUsername1());
		ds.setUrl(shardDataSourceProperties.getUrl1());
		ds.setPassword(shardDataSourceProperties.getPassword1());
		return ds;
	}
 
	private DataSourceRule dataSourceRule() throws SQLException {
		Map<String, DataSource> dataSourceMap = new HashMap<>(2);
		dataSourceMap.put("ds_0", ds0());
		dataSourceMap.put("ds_1", ds1());
		DataSourceRule dataSourceRule = new DataSourceRule(dataSourceMap);
		return dataSourceRule;
	}
//对order对策略
	private TableRule orderTableRule() throws SQLException {
		TableRule orderTableRule = TableRule.builder("t_order").actualTables(Arrays.asList("t_order_0", "t_order_1"))
				.dataSourceRule(dataSourceRule()).build();
		return orderTableRule;
	}
 
//分库分表策略
	private ShardingRule shardingRule() throws SQLException {
		ShardingRule shardingRule = ShardingRule.builder().dataSourceRule(dataSourceRule())
				.tableRules(Arrays.asList(orderTableRule(), orderItemTableRule()))
				.databaseShardingStrategy(
						new DatabaseShardingStrategy("user_id", new ModuloDatabaseShardingAlgorithm()))
				.tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
				.build();
		return shardingRule;
	}
 
	@Bean
	public DataSource dataSource() throws SQLException {
		return ShardingDataSourceFactory.createDataSource(shardingRule());
	}
 
 
    @Bean
    public PlatformTransactionManager transactionManager() throws SQLException {
        return new DataSourceTransactionManager(dataSource());
    }
}

我们需要从controller调用接口进行对数据的增加和查询

下面所有的类都是用来模拟请求进行测试

@RestController
@RequestMapping("/order")
public class OrderController {
    @Autowired
    private OrderDao orderDao;
 
    @RequestMapping(path = "/createOrder/{userId}/{orderId}", method = {RequestMethod.GET})
    public String createOrder(@PathVariable("userId") Integer userId, @PathVariable("orderId") Integer orderId) {
        Order order = new Order();
        order.setOrderId(orderId);
        order.setUserId(userId);
        orderDao.createOrder(order);
        return "success";
    }
 
    @RequestMapping(path = "/{userId}", method = {RequestMethod.GET})
    public List<Order> getOrderListByUserId(@PathVariable("userId") Integer userId) {
        return orderDao.getOrderListByUserId(userId);
    }
}
 
 
---------------------------------------------------
public interface OrderDao {
    List<Order> getOrderListByUserId(Integer userId);
 
    void createOrder(Order order);
}
---------------------------------------------------
@Service
public class OrderDaoImpl implements OrderDao {
    @Autowired
    JdbcTemplate jdbcTemplate;
 
 
    @Override
    public List<Order> getOrderListByUserId(Integer userId) {
 
        StringBuilder sqlBuilder = new StringBuilder();
        sqlBuilder
                .append("select order_id, user_id from t_order where user_id=? ");
        return jdbcTemplate.query(sqlBuilder.toString(), new Object[]{userId},
                new int[]{Types.INTEGER}, new BeanPropertyRowMapper<Order>(
                        Order.class));
    }
 
    @Override
    public void createOrder(Order order) {
        StringBuffer sb = new StringBuffer();
        sb.append("insert into t_order(user_id, order_id)");
        sb.append("values(");
        sb.append(order.getUserId()).append(",");
        sb.append(order.getOrderId());
        sb.append(")");
        jdbcTemplate.update(sb.toString());
 
    }
}
 
---------------------------------------------------
public class Order implements Serializable {
 
	private int userId;
 
	private int orderId;
 
---------------------------------------------------
@SpringBootApplication
public class Application {
	
	public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

测试

启动项目，访问：http://localhost:8050/order/createOrder/1/1

更换参数多次访问，可以插入多条记录，观察你的数据库入库情况，已经按照我们制定的分库分表策略进行划分了。

需要注意的是

shareding是不支持jdbctemplate的批量修改操作的。

表名前不要加上库名，原生的情况加库名，不加库名其实是一样的，但使用shareding的表就会报错。

Java中ShardingSphere 数据分片的实现

如果想进行只分表不分库的话

  • 注释掉 ModuloDatabaseShardingAlgorithm 类
  • 还有ShardDataSourceConfig.shardingRule() 中的分库策略那行代码
  • 还有相关数据源配置改成 1 个

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原文链接：https://blog.csdn.net/weixin_38003389/article/details/90518112