使用sharing-jdbc进行分表查询

如何分表

一、垂直分表

将冷热数据分离，将经常使用数据和很是用数据进行分开存储。将不经常使用的大字段进行分表存储。

二、水平分表

将表中不一样的数据行按照必定规律分布到不一样的数据库表中。

能够使用求余的方式，也能够使用hash的方式。

使用sharing-jdbc进行分表后的查询

首先引入依赖

<dependency>
            <groupId>com.dangdang</groupId>
            <artifactId>sharding-jdbc-core</artifactId>
            <version>1.4.2</version>
        </dependency>
        <dependency>
            <groupId>com.dangdang</groupId>
            <artifactId>sharding-jdbc-config-spring</artifactId>
            <version>1.4.0</version>
        </dependency>

新建一个sharding-jdbc.xml文件，实现分库分表的配置

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:context="http://www.springframework.org/schema/context" 
    xmlns:tx="http://www.springframework.org/schema/tx"
    xmlns:rdb="http://www.dangdang.com/schema/ddframe/rdb"
    xsi:schemaLocation="http://www.springframework.org/schema/beans
                        http://www.springframework.org/schema/beans/spring-beans.xsd 
                        http://www.springframework.org/schema/tx 
                        http://www.springframework.org/schema/tx/spring-tx.xsd
                        http://www.springframework.org/schema/context 
                        http://www.springframework.org/schema/context/spring-context.xsd
                        http://www.dangdang.com/schema/ddframe/rdb 
                        http://www.dangdang.com/schema/ddframe/rdb/rdb.xsd">
                
    <rdb:strategy id="tableShardingStrategy" sharding-columns="user_id" algorithm-class="com.meiren.member.common.sharding.MemberSingleKeyTableShardingAlgorithm"/>配置分表规则器  sharding-columns：分表规则依赖的名（根据user_id取模分表），algorithm-class:分表规则的实现类 
    
    <rdb:data-source id="shardingDataSource">
        <rdb:sharding-rule data-sources="dataSource">这里填写关联数据源（多个数据源用逗号隔开）,
            <rdb:table-rules>
                <rdb:table-rule logic-table="member_index" actual-tables="member_index_tbl_${[0,1,2,3,4,5,6,7,8,9]}${0..9}"  table-strategy="tableShardingStrategy"/>
                <rdb:table-rule logic-table="member_details" actual-tables="member_details_tbl_${[0,1,2,3,4,5,6,7,8,9]}${0..9}"  table-strategy="tableShardingStrategy"/> 
logic-table：逻辑表名（mybatis中代替的表名）actual-tables：数据库实际的表名，这里支持inline表达式，好比：member_index_tbl_${0..2}会解析成member_index_tbl_0，member_index_tbl_1，member_index_tbl_2；member_index_tbl_${[a,b,c]}会被解析成member_index_tbl_a,member_index_tbl_b和member_index_tbl_c,两种表达式一块儿使用的时候，会采起笛卡尔积的方式：member_index_tbl_${[a,b]}${0..2}解析为member_index_tbl_a0,member_index_tbl_a1,member_index_tbl_a2,member_index_tbl_b0,member_index_tbl_b1,member_index_tbl_b2;table-strategy:前面定义的分表规则器；
            </rdb:table-rules>
        </rdb:sharding-rule>
    </rdb:data-source>
    
    <bean id="transactionManager" class="org.springframework.jdbc.datasource.DataSourceTransactionManager">
        <property name="dataSource" ref="shardingDataSource" />
    </bean>
</beans>

配置好改文件后，须要修改以前咱们的spring-dataSource的几个地方，把sqlSessionFactory和transactionManager原来关联的dataSource统一修改成shardingDataSource（这一步做用就是把数据源所有托管给sharding去管理）

实现分表（分库）逻辑，咱们的分表逻辑类须要实现SingleKeyTableShardingAlgorithm接口的三个方法doBetweenSharding、doEqualSharding、doInSharding

/**
 * 分表逻辑
 * @author zhangwentao
 *
 */
public class MemberSingleKeyTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Long> {
    
    /**
     * sql between 规则
     */
    public Collection<String> doBetweenSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
        Collection<String> result = new LinkedHashSet<String>(tableNames.size());
        Range<Long> range = (Range<Long>) shardingValue.getValueRange();
        for (long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            Long modValue = i % 100;
            String modStr = modValue < 10 ? "0" + modValue : modValue.toString();
            for (String each : tableNames) {
                if (each.endsWith(modStr)) {
                    result.add(each);
                }
            }
        }
        return result;
    }

    /**
     * sql == 规则
     */
    public String doEqualSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
        Long modValue = shardingValue.getValue() % 100;
        String modStr = modValue < 10 ? "0" + modValue : modValue.toString();
        for (String each : tableNames) {
            if (each.endsWith(modStr)) {
                return each;
            }
        }
        throw new IllegalArgumentException();
    }

    /**
     * sql in 规则
     */
    public Collection<String> doInSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {

        Collection<String> result = new LinkedHashSet<String>(tableNames.size());
        for (long value : shardingValue.getValues()) {
            Long modValue = value % 100;
            String modStr = modValue < 10 ? "0" + modValue : modValue.toString();
            for (String tableName : tableNames) {
                if (tableName.endsWith(modStr)) {
                    result.add(tableName);
                }
            }
        }
        return result;
    }
    
}

以上四步，咱们就完成了sharding-jdbc的搭建，咱们能够写一个测试demo来检查咱们的成果

<select id="getDetailsById" resultType="com.meiren.member.dataobject.MemberDetailsDO"
        parameterType="java.lang.Long">
        select user_id userId ,qq,email from member_details where     user_id =#{userId} limit 1
    </select>