shardingjdbc批量插入例子

以下是使用ShardingJDBC进行批量插入的示例代码：

import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.List;

import org.apache.shardingsphere.api.sharding.standard.PreciseShardingAlgorithm;
import org.apache.shardingsphere.api.sharding.standard.RangeShardingAlgorithm;
import org.apache.shardingsphere.api.sharding.standard.StandardShardingStrategyConfiguration;
import org.apache.shardingsphere.driver.api.ShardingDataSourceFactory;
import org.apache.shardingsphere.shardingjdbc.api.ShardingDataSource;
import org.apache.shardingsphere.shardingjdbc.jdbc.core.datasource.ShardingDataSourceWrapper;
import org.apache.shardingsphere.shardingjdbc.jdbc.core.statement.ShardingPreparedStatement;
import org.apache.shardingsphere.shardingjdbc.jdbc.core.statement.ShardingStatement;

import com.alibaba.druid.pool.DruidDataSource;

public class ShardingJDBCBatchInsertExample {

    public static void main(String[] args) throws SQLException {

        // 配置分库
        List<DruidDataSource> dataSourceList = new ArrayList<>();
        for (int i = 0; i < 2; i++) {
            DruidDataSource dataSource = new DruidDataSource();
            dataSource.setDriverClassName("com.mysql.jdbc.Driver");
            dataSource.setUrl("jdbc:mysql://localhost:3306/db" + i + "?serverTimezone=UTC&useSSL=false");
            dataSource.setUsername("root");
            dataSource.setPassword("root");
            dataSourceList.add(dataSource);
        }

        // 配置分表
        PreciseShardingAlgorithm<Long> preciseShardingAlgorithm = (databaseNames, shardingValue) -> {
            for (String each : databaseNames) {
                if (each.endsWith(shardingValue.getValue() % 2 + "")) {
                    return each;
                }
            }
            throw new UnsupportedOperationException();
        };

        RangeShardingAlgorithm<Long> rangeShardingAlgorithm = (databaseNames, shardingValue) -> {
            List<String> result = new ArrayList<>();
            Long lowerValue = shardingValue.getValueRange().lowerEndpoint();
            Long upperValue = shardingValue.getValueRange().upperEndpoint();
            for (String each : databaseNames) {
                String value = each.substring(each.length() - 1);
                Long intValue = Long.parseLong(value);
                if (intValue >= lowerValue && intValue <= upperValue) {
                    result.add(each);
                }
            }
            return result;
        };

        StandardShardingStrategyConfiguration databaseShardingStrategyConfiguration = new StandardShardingStrategyConfiguration("id", preciseShardingAlgorithm, rangeShardingAlgorithm);
        ShardingDataSource shardingDataSource = ShardingDataSourceFactory.createDataSource(dataSourceList, Collections.singleton(databaseShardingStrategyConfiguration), new Properties());

        // 执行批量插入
        Connection conn = shardingDataSource.getConnection();
        String sql = "insert into t_order (id, user_id, status) values (?, ?, ?)";
        ShardingPreparedStatement pstmt = (ShardingPreparedStatement) conn.prepareStatement(sql);
        for (int i = 1; i <= 100; i++) {
            pstmt.setLong(1, i);
            pstmt.setLong(2, i % 10);
            pstmt.setString(3, "INIT");
            pstmt.addBatch();
        }
        pstmt.executeBatch();

        conn.close();
    }
}

该示例代码中，我们首先配置了两个Druid数据源，分别对应两个数据库db0和db1。然后，我们使用PreciseShardingAlgorithm和RangeShardingAlgorithm两种算法配置分库分表策略。具体而言，我们根据订单ID（id）的值来判断该订单属于哪个数据库和表，其中id % 2的值为0时，订单会被分配到db0数据库，否则分配到db1数据库；对于每个数据库，我们又根据id值的范围来判断该订单属于哪个表。

在执行批量插入时，我们首先通过shardingDataSource.getConnection()方法获取一个连接。然后，我们构造一个PreparedStatement对象，设置好参数，调用addBatch()方法将数据加入批处理队列中，最后调用executeBatch()方法执行批处理操作。由于使用了ShardingJDBC，ShardingPreparedStatement对象会在内部根据id值的分片规则自动将订单插入到对应的数据库和表中。