最近忙於項目已經好久幾天沒寫博客了，前2篇文章我給大家介紹了搭建基礎springMvc+mybatis的maven工程，這個簡單框架已經可以對付一般的小型項目。但是我們實際項目中會碰到很多復雜的場景，比如數據量很大的情況下如何保證性能。今天我就給大家介紹數據庫分庫分表的優化，本文介紹mybatis結合當當網的sharding-jdbc分庫分表技術（原理這里不做介紹）
首先在pom文件中引入需要的依賴
<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>
二、新建一個sharding-jdbc.xml文件，實現分庫分表的配置
<?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"/> <rdb:data-source id="shardingDataSource"> <rdb:sharding-rule data-sources="dataSource"> <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"/> </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>
這里我簡單介紹下一些屬性的含義，
<rdb:strategy id="tableShardingStrategy" sharding-columns="user_id" algorithm-class="com.meiren.member.common.sharding.MemberSingleKeyTableShardingAlgorithm"/> 配置分表規則器 sharding-columns：分表規 則
依賴的名（根據user_id取模分表），algorithm-class:分表規則的實現類
<rdb:sharding-rule data-sources="dataSource"> 這里填寫關聯數據源（多個數據源用逗號隔開）,
<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"/> logic-table：邏輯表名（mybatis中代替的表名）actual-tables：
數據庫實際的表名，這里支持inline表達式，比如：member_index_tbl_${0..2}會解析成member_index_tbl_0，member_index_tbl_1，member_index_tbl_2；member_index_tbl_${[a,b,c]}會被解析成
member_index_tbl_a,member_index_tbl_b和member_index_tbl_c,兩種表達式一起使用的時候，會采取笛卡爾積的方式：member_index_tbl_${[a,b]}${0..2}解析為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:前面定義的分表規則器；
三、配置好改文件后，需要修改之前我們的spring-dataSource的幾個地方，把sqlSessionFactory和transactionManager原來關聯的dataSource統一修改為shardingDataSource（這一步作用就是把數據源全部托管給sharding去管理）
四、實現分表（分庫）邏輯，我們的分表邏輯類需要實現SingleKeyTableShardingAlgorithm接口的三個方法doBetweenSharding、doEqualSharding、doInSharding
/** * 分表邏輯 * @author zhangwentao * */ public class MemberSingleKeyTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Long> { /** * sql between 規則 */ 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 == 規則 */ 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 規則 */ 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; } }
五、以上四步，我們就完成了sharding-jdbc的搭建，我們可以寫一個測試demo來檢查我們的成果
<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>
private static final String SERVICE_PROVIDER_XML = "/spring/member-service.xml";
private static final String BEAN_NAME = "idcacheService";
private ClassPathXmlApplicationContext context = null;
IdcacheServiceImpl bean = null;
IdcacheDao idcacheDao;
@Before
public void before() {
context= new ClassPathXmlApplicationContext(
new String[] {SERVICE_PROVIDER_XML});
idcacheDao=context.getBean("IdcacheDao", IdcacheDao.class);
}
@Test
public void getAllCreditActionTest() {
// int id = bean.insertIdcache();
Long s=100l;
MemberDetailsDO memberDetailsDO=idcacheDao.getDetailsById(s);
System.out.println("QQ---------------------"+memberDetailsDO.getQq());
}
打印sql語句，輸出結果：QQ-------------------------------------100，證明成功！
注意點：這次搭建過程中，我有碰到一個小坑，就是執行的時候會報錯：，官方文檔是有解決方案：引入 <context:property-placeholder location="classpath:/member_service.properties" ignore-unresolvable="true" /> ，引入這行代碼的時候，·必須要要把這邊管理配配置文件的bean刪除，換句話說，即Spring容器僅允許最多定義一個PropertyPlaceholderConfigurer(或<context:property-placeholder/>)，其余的會被Spring忽略掉（當時搞了半天啊）
小結：這次給大家分享了sharding-jdbc的配置是為了解決大數據量進行分庫分表的架構，下一張，我將介紹拆分業務所需的duboo+zookeeper的配置（分布式），歡迎關注！