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spring boot使用sharding jdbc的配置方式

 更新時(shí)間：2017年12月28日 10:03:15   作者：J猿  
這篇文章主要介紹了spring boot使用sharding jdbc的配置方式，小編覺得挺不錯(cuò)的，現(xiàn)在分享給大家，也給大家做個(gè)參考。一起跟隨小編過(guò)來(lái)看看吧

本文介紹了spring boot使用sharding jdbc的配置方式，分享給大家，具體如下：

說(shuō)明

要排除DataSourceAutoConfiguration，否則多數(shù)據(jù)源無(wú)法配置

@SpringBootApplication
@EnableAutoConfiguration(exclude={DataSourceAutoConfiguration.class})
public class Application {

  public static void main(String[] args) {
   SpringApplication.run(Application.class, args);
  }
 
}

配置的多個(gè)數(shù)據(jù)源交給sharding-jdbc管理，sharding-jdbc創(chuàng)建一個(gè)DataSource數(shù)據(jù)源提供給mybatis使用

官方文檔：http://shardingjdbc.io/index_zh.html

步驟

配置多個(gè)數(shù)據(jù)源，數(shù)據(jù)源的名稱最好要有一定的規(guī)則，方便配置分庫(kù)的計(jì)算規(guī)則

@Bean(initMethod="init", destroyMethod="close", name="dataSource0")
@ConfigurationProperties(prefix = "spring.datasource")
public DataSource dataSource0(){
  return new DruidDataSource();
}

@Bean(initMethod="init", destroyMethod="close", name="dataSource1")
@ConfigurationProperties(prefix = "spring.datasource2")
public DataSource dataSource1(){
  return new DruidDataSource();
}

配置數(shù)據(jù)源規(guī)則，即將多個(gè)數(shù)據(jù)源交給sharding-jdbc管理，并且可以設(shè)置默認(rèn)的數(shù)據(jù)源，當(dāng)表沒有配置分庫(kù)規(guī)則時(shí)會(huì)使用默認(rèn)的數(shù)據(jù)源

@Bean
public DataSourceRule dataSourceRule(@Qualifier("dataSource0") DataSource dataSource0, 
    @Qualifier("dataSource1") DataSource dataSource1){
  Map<String, DataSource> dataSourceMap = new HashMap<>();
  dataSourceMap.put("dataSource0", dataSource0);
  dataSourceMap.put("dataSource1", dataSource1);
  return new DataSourceRule(dataSourceMap, "dataSource0");
}

配置數(shù)據(jù)源策略和表策略，具體策略需要自己實(shí)現(xiàn)

@Bean
public ShardingRule shardingRule(DataSourceRule dataSourceRule){
  //表策略
  TableRule orderTableRule = TableRule.builder("t_order")
      .actualTables(Arrays.asList("t_order_0", "t_order_1"))
      .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
      .dataSourceRule(dataSourceRule)
      .build();
  TableRule orderItemTableRule = TableRule.builder("t_order_item")
      .actualTables(Arrays.asList("t_order_item_0", "t_order_item_1"))
      .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
      .dataSourceRule(dataSourceRule)
      .build();
  //綁定表策略，在查詢時(shí)會(huì)使用主表策略計(jì)算路由的數(shù)據(jù)源，因此需要約定綁定表策略的表的規(guī)則需要一致，可以一定程度提高效率
  List<BindingTableRule> bindingTableRules = new ArrayList<BindingTableRule>();
  bindingTableRules.add(new BindingTableRule(Arrays.asList(orderTableRule, orderItemTableRule)));
  return ShardingRule.builder()
      .dataSourceRule(dataSourceRule)
      .tableRules(Arrays.asList(orderTableRule, orderItemTableRule))
      .bindingTableRules(bindingTableRules)
      .databaseShardingStrategy(new DatabaseShardingStrategy("user_id", new ModuloDatabaseShardingAlgorithm()))
      .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
      .build();
}

創(chuàng)建sharding-jdbc的數(shù)據(jù)源DataSource，MybatisAutoConfiguration會(huì)使用此數(shù)據(jù)源

@Bean("dataSource")
public DataSource shardingDataSource(ShardingRule shardingRule){
  return ShardingDataSourceFactory.createDataSource(shardingRule);
}

需要手動(dòng)配置事務(wù)管理器（原因未知）

//需要手動(dòng)聲明配置事務(wù)
@Bean
public DataSourceTransactionManager transactitonManager(@Qualifier("dataSource") DataSource dataSource){
  return new DataSourceTransactionManager(dataSource);
}

分庫(kù)策略的簡(jiǎn)單實(shí)現(xiàn)，接口：DatabaseShardingAlgorithm

import java.util.Collection;
import java.util.LinkedHashSet;

import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.database.SingleKeyDatabaseShardingAlgorithm;
import com.google.common.collect.Range;

/**
 * Created by fuwei.deng on 2017年5月11日.
 */
public class ModuloDatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm<Long> {

  @Override
  public String doEqualSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
   for (String each : databaseNames) {
      if (each.endsWith(shardingValue.getValue() % 2 + "")) {
        return each;
      }
    }
    throw new IllegalArgumentException();
  }
  
  @Override
  public Collection<String> doInSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
   Collection<String> result = new LinkedHashSet<>(databaseNames.size());
    for (Long value : shardingValue.getValues()) {
      for (String tableName : databaseNames) {
        if (tableName.endsWith(value % 2 + "")) {
          result.add(tableName);
        }
      }
    }
    return result;
  }
  
  @Override
  public Collection<String> doBetweenSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
   Collection<String> result = new LinkedHashSet<>(databaseNames.size());
    Range<Long> range = (Range<Long>) shardingValue.getValueRange();
    for (Long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
      for (String each : databaseNames) {
        if (each.endsWith(i % 2 + "")) {
          result.add(each);
        }
      }
    }
    return result;
  }

}

分表策略的基本實(shí)現(xiàn)，接口：TableShardingAlgorithm

import java.util.Collection;
import java.util.LinkedHashSet;

import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.table.SingleKeyTableShardingAlgorithm;
import com.google.common.collect.Range;

/**
 * Created by fuwei.deng on 2017年5月11日.
 */
public class ModuloTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Long> {

  @Override
  public String doEqualSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
   for (String each : tableNames) {
      if (each.endsWith(shardingValue.getValue() % 2 + "")) {
        return each;
      }
    }
    throw new IllegalArgumentException();
  }
  
  @Override
  public Collection<String> doInSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
   Collection<String> result = new LinkedHashSet<>(tableNames.size());
    for (Long value : shardingValue.getValues()) {
      for (String tableName : tableNames) {
        if (tableName.endsWith(value % 2 + "")) {
          result.add(tableName);
        }
      }
    }
    return result;
  }
  
  @Override
  public Collection<String> doBetweenSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
   Collection<String> result = new LinkedHashSet<>(tableNames.size());
    Range<Long> range = (Range<Long>) shardingValue.getValueRange();
    for (Long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
      for (String each : tableNames) {
        if (each.endsWith(i % 2 + "")) {
          result.add(each);
        }
      }
    }
    return result;
  }

}

至此，分庫(kù)分表的功能已經(jīng)實(shí)現(xiàn)

讀寫分離

讀寫分離需在創(chuàng)建DataSourceRule之前加一層主從數(shù)據(jù)源的創(chuàng)建

// 構(gòu)建讀寫分離數(shù)據(jù)源, 讀寫分離數(shù)據(jù)源實(shí)現(xiàn)了DataSource接口, 可直接當(dāng)做數(shù)據(jù)源處理. 
// masterDataSource0, slaveDataSource00, slaveDataSource01等為使用DBCP等連接池配置的真實(shí)數(shù)據(jù)源
DataSource masterSlaveDs0 = MasterSlaveDataSourceFactory.createDataSource("ms_0", 
          masterDataSource0, slaveDataSource00, slaveDataSource01);
DataSource masterSlaveDs1 = MasterSlaveDataSourceFactory.createDataSource("ms_1", 
          masterDataSource1, slaveDataSource11, slaveDataSource11);

// 構(gòu)建分庫(kù)分表數(shù)據(jù)源
Map<String, DataSource> dataSourceMap = new HashMap<>(2);
dataSourceMap.put("ms_0", masterSlaveDs0);
dataSourceMap.put("ms_1", masterSlaveDs1);

// 通過(guò)ShardingDataSourceFactory繼續(xù)創(chuàng)建ShardingDataSource

強(qiáng)制使用主庫(kù)時(shí)

HintManager hintManager = HintManager.getInstance();
hintManager.setMasterRouteOnly();
// 繼續(xù)JDBC操作

強(qiáng)制路由

  1. 使用ThreadLocal機(jī)制實(shí)現(xiàn)，在執(zhí)行數(shù)據(jù)庫(kù)操作之前通過(guò)HintManager改變用于計(jì)算路由的值
  2. 設(shè)置HintManager的時(shí)候分庫(kù)和分表的策略必須同時(shí)設(shè)置，并且設(shè)置后需要路由的表都需要設(shè)置用于計(jì)算路由的值。比如強(qiáng)制路由后需要操作t_order和t_order_item兩個(gè)表，那么兩個(gè)表的分庫(kù)和分表的策略都需要設(shè)置
HintManager hintManager = HintManager.getInstance();
hintManager.addDatabaseShardingValue("t_order", "user_id", 1L);
hintManager.addTableShardingValue("t_order", "order_id", order.getOrderId());
hintManager.addDatabaseShardingValue("t_order_item", "user_id", 1L);
hintManager.addTableShardingValue("t_order_item", "order_id", order.getOrderId());

事務(wù)

  1. sharding-jdbc-transaction實(shí)現(xiàn)柔性事務(wù)（默認(rèn)提供了基于內(nèi)存的事務(wù)日志存儲(chǔ)器和內(nèi)嵌異步作業(yè)），可結(jié)合elastic-job（sharding-jdbc-transaction-async-job）實(shí)現(xiàn)異步柔性事務(wù)
  2. 沒有與spring結(jié)合使用的方式，需要自己封裝

以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。

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