数据库拆分5--使用sharding-jdbc来实现水平拆分

有三张表 user log order表，先将user log 和order垂直分库，然后将user表水平拆分

配置文件

spring.shardingsphere.enabled=true

spring.shardingsphere.datasource.names=wim-user,wim-order

spring.shardingsphere.datasource.wim-user.type=com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.wim-user.driver-class-name=com.mysql.cj.jdbc.Driver
spring.shardingsphere.datasource.wim-user.url=jdbc:mysql://127.0.0.1:3306/wim-user?serverTimezone=UTC&useSSL=false
spring.shardingsphere.datasource.wim-user.username=root
spring.shardingsphere.datasource.wim-user.password=123456

spring.shardingsphere.datasource.wim-order.type=com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.wim-order.driver-class-name=com.mysql.cj.jdbc.Driver
spring.shardingsphere.datasource.wim-order.url=jdbc:mysql://127.0.0.1:3306/wim-order?serverTimezone=UTC&useSSL=false
spring.shardingsphere.datasource.wim-order.username=root
spring.shardingsphere.datasource.wim-order.password=123456

spring.shardingsphere.sharding.tables.user_t.actual-data-nodes=wim-user.user_t_$->{0..1}
spring.shardingsphere.sharding.tables.log_t.actual-data-nodes=wim-user.log_t

spring.shardingsphere.sharding.tables.user_t.table-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.tables.user_t.table-strategy.inline.algorithm-expression=user_t_$->{user_id % 2}

spring.shardingsphere.sharding.tables.log_t.table-strategy.inline.sharding-column=id
spring.shardingsphere.sharding.tables.log_t.table-strategy.inline.algorithm-expression=log_t


spring.shardingsphere.sharding.default-data-source-name=wim-order

spring.shardingsphere.props.sql.show=true

拆分的配置方法 直接看一下对应代码和实现类

学习一下各种分片策略：

行表达式分片策略（InlineShardingStrategy）

YamlInlineShardingStrategyConfiguration

public final class YamlInlineShardingStrategyConfiguration implements YamlBaseShardingStrategyConfiguration {
    private String shardingColumn;
    private String algorithmExpression;

    public YamlInlineShardingStrategyConfiguration() {
    }

    @Generated
    public String getShardingColumn() {
        return this.shardingColumn;
    }

    @Generated
    public String getAlgorithmExpression() {
        return this.algorithmExpression;
    }

    @Generated
    public void setShardingColumn(String shardingColumn) {
        this.shardingColumn = shardingColumn;
    }

    @Generated
    public void setAlgorithmExpression(String algorithmExpression) {
        this.algorithmExpression = algorithmExpression;
    }
}

spring.shardingsphere.sharding.tables.user_t.actual-data-nodes=wim-user.user_t_$->{0..1}
spring.shardingsphere.sharding.tables.user_t.table-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.tables.user_t.table-strategy.inline.algorithm-expression=user_t_$->{user_id % 2}

需要配置分片键和分片表达式

有两张user表  user_t_0 user_t_1 根据user_id%2来路由到不同数据库。

1）使用场景：只支持单分片健；支持对 SQL语句中的 = 、IN 等的分片操作，不支持between and。

2）使用方法：适用于做简单的分片算法，在配置中使用 Groovy 表达式，无需自定义分片算法，省去了繁琐的代码开发，是几种分片策略中最为简单的。

标准分片策略（StandardShardingStrategy）

YamlStandardShardingStrategyConfiguration

public final class YamlStandardShardingStrategyConfiguration implements YamlBaseShardingStrategyConfiguration {
    private String shardingColumn;
    private String preciseAlgorithmClassName;
    private String rangeAlgorithmClassName;

SQL 语句中有>，>=, <=，<，=，IN和BETWEEN AND操作符，都可以应用此分片策略。它只支持对单个分片健（字段）为依据的分库分表，并提供了两种分片算法 PreciseShardingAlgorithm（精准分片）和 RangeShardingAlgorithm（范围分片）

精准分库算法：实现 PreciseShardingAlgorithm 接口，并重写 doSharding() 方法。其中 Collection 参数在几种分片策略中使用一致，在分库时值为所有分片库的集合 databaseNames，分表时为对应分片库中所有分片表的集合 tablesNames；PreciseShardingValue 为分片属性，其中 logicTableName 为逻辑表，columnName 分片健（字段），value 为从 SQL 中解析出的分片健的值；

假设log表需要根据id来分片 大于1000的分片0  其他分片1

spring.shardingsphere.sharding.tables.log_t.actual-data-nodes=wim-user.log_t_$->{0..1}
spring.shardingsphere.sharding.tables.log_t.table-strategy.standard.sharding-column=id
spring.shardingsphere.sharding.tables.log_t.table-strategy.standard.precise-algorithm-class-name=com.chen.algorithm.MyPreciseShardingAlgorithm

public class MyPreciseShardingAlgorithm implements PreciseShardingAlgorithm {
	
	@Override
	public String doSharding(Collection collection, PreciseShardingValue preciseShardingValue) {
		Object[] ds = collection.toArray();
		long logId = (long) preciseShardingValue.getValue();
		if (logId > 1000) {
			return (String) ds[0];
		} else {
			return (String) ds[1];
		}
	}
}

在doSharding方法中可以实现更加复杂的判断逻辑

范围分片算法：分片健字段用到 BETWEEN AND操作符会使用到此算法，会根据 SQL中给出的分片健值范围值处理分库、分表逻辑。自定义范围分片算法需实现 RangeShardingAlgorithm 接口，重写 doSharding() 方法，Collection 在分库、分表时分别代表分片库名和表名集合，RangeShardingValue 这里取值方式稍有不同， lowerEndpoint 表示起始值， upperEndpoint 表示截止值

假设log表 当查询时间范围包含2021年时 则从两个数据库查询合并结果返回 否则取第一个数据库数据返回

spring.shardingsphere.sharding.tables.log_t.actual-data-nodes=wim-user.log_t_$->{0..1}
spring.shardingsphere.sharding.tables.log_t.table-strategy.standard.sharding-column=log_date
spring.shardingsphere.sharding.tables.log_t.table-strategy.standard.range-algorithm-class-name=com.chen.algorithm.MyRangeShardingAlgorithm
spring.shardingsphere.sharding.tables.log_t.table-strategy.standard.precise-algorithm-class-name=com.chen.algorithm.MyPreciseShardingAlgorithm

public class MyRangeShardingAlgorithm implements RangeShardingAlgorithm {
	
	@Override
	public Collection doSharding(Collection collection, RangeShardingValue rangeShardingValue) {
		Range range = rangeShardingValue.getValueRange();
		boolean flag = range.contains("2021");
		if (flag) {
			return collection;
		} else {
			return Arrays.asList((String) collection.toArray()[0]);
		}
	}
}

RangeShardingAlgorithm算法需要和PreciseShardingAlgorithm一同实现来使用 否则算法加载会报错 好奇怪的逻辑

 if (null != yamlConfiguration.getStandard()) {
            ++shardingStrategyConfigCount;
            if (null == yamlConfiguration.getStandard().getRangeAlgorithmClassName()) {
                result = new StandardShardingStrategyConfiguration(yamlConfiguration.getStandard().getShardingColumn(), (PreciseShardingAlgorithm)ShardingAlgorithmFactory.newInstance(yamlConfiguration.getStandard().getPreciseAlgorithmClassName(), PreciseShardingAlgorithm.class));
            } else {
                result = new StandardShardingStrategyConfiguration(yamlConfiguration.getStandard().getShardingColumn(), (PreciseShardingAlgorithm)ShardingAlgorithmFactory.newInstance(yamlConfiguration.getStandard().getPreciseAlgorithmClassName(), PreciseShardingAlgorithm.class), (RangeShardingAlgorithm)ShardingAlgorithmFactory.newInstance(yamlConfiguration.getStandard().getRangeAlgorithmClassName(), RangeShardingAlgorithm.class));
            }
        }

复合分片策略

YamlComplexShardingStrategyConfiguration

public final class YamlComplexShardingStrategyConfiguration implements YamlBaseShardingStrategyConfiguration {
    private String shardingColumns;
    private String algorithmClassName;

SQL 语句中有>，>=, <=，<，=，IN 和 BETWEEN AND 等操作符，且复合分片策略支持对多个分片健操作。自定义复合分片策略要实现 ComplexKeysShardingAlgorithm 接口，重新 doSharding()方法

假设log表 user_id log_date  userId=1 log_date包含2022的到表0中查询 其他两张表查询合并

spring.shardingsphere.sharding.tables.log_t.actual-data-nodes=wim-user.log_t_$->{0..1}
spring.shardingsphere.sharding.tables.log_t.table-strategy.complex.sharding-columns=user_id, log_date
spring.shardingsphere.sharding.tables.log_t.table-strategy.complex.algorithm-class-name=com.chen.algorithm.MyComplexKeysShardingAlgorithm

public class MyComplexKeysShardingAlgorithm implements ComplexKeysShardingAlgorithm {
	
	@Override
	public Collection doSharding(Collection collection, ComplexKeysShardingValue complexKeysShardingValue) {
		System.out.println(complexKeysShardingValue);
		List list = (List) complexKeysShardingValue.getColumnNameAndShardingValuesMap().get("user_id");
		Range range = (Range) complexKeysShardingValue.getColumnNameAndRangeValuesMap().get("log_date");
		boolean flag1 = list.contains("1");
		boolean flag2 = range.contains("2022");
		if (flag1 && flag2) {
			return Arrays.asList((String) collection.toArray()[0]);
		}
		return collection;
	}
}

Hint分片策略

YamlHintShardingStrategyConfiguration

public final class YamlHintShardingStrategyConfiguration implements YamlBaseShardingStrategyConfiguration {
    private String algorithmClassName;

这种分片策略无需配置分片健，分片健值也不再从 SQL中解析，而是由外部指定分片信息，让 SQL在指定的分库、分表中执行。ShardingSphere 通过 Hint API实现指定操作，实际上就是把分片规则tablerule 、databaserule由集中配置变成了个性化配置。

实现 HintShardingAlgorithm 接口并重写 doSharding()方法

在请求的上下文中直接指定数据库和表

spring.shardingsphere.sharding.tables.log_t.actual-data-nodes=wim-user.log_t_$->{0..1}
spring.shardingsphere.sharding.tables.log_t.table-strategy.hint.algorithm-class-name=com.chen.algorithm.MyHintShardingAlgorithm

HintManager

@GetMapping("/query/{id}")
	public String query(@PathVariable Long id) {
		HintManager hintManager = HintManager.getInstance();
		hintManager.addTableShardingValue("log_t","1");
		Log logT = logMapper.selectById(id);
		log.info("query log {}", logT);
		if (logT == null) {
			return "cannot find user";
		}
		return logT.toString();
	}

public class MyHintShardingAlgorithm implements HintShardingAlgorithm {
	
	@Override
	public Collection doSharding(Collection collection, HintShardingValue hintShardingValue) {
		Object[] ds = collection.toArray();
		Object[] v = hintShardingValue.getValues().toArray();
		int index = Integer.valueOf((String) v[0]);
		return Arrays.asList((String) ds[index]);
	}
}