接口兼容性深度体验：金仓数据库如何实现无缝技术迁移

// 传统JDBC批处理示例
Connection conn = DriverManager.getConnection(
    "jdbc:kingbase8://localhost:54321/test", "system", "password");
Statement stmt = conn.createStatement();

stmt.addBatch("INSERT INTO users(name, age) VALUES('张三', 25)");
stmt.addBatch("INSERT INTO users(name, age) VALUES('李四', 30)");
stmt.addBatch("UPDATE users SET age = 26 WHERE name = '张三'");

int[] results = stmt.executeBatch();
conn.commit();

// 启用批处理优化的连接配置
String url = "jdbc:kingbase8://192.168.1.30:54321/performance_test?" +
             "optimizeBatchedDML=true&" +
             "reWriteBatchedInserts=false";

Connection conn = DriverManager.getConnection(url, "system", "password");
PreparedStatement pstmt = conn.prepareStatement(
    "INSERT INTO large_table(col1, col2, col3) VALUES(?, ?, ?)");

// 批量插入10万条数据
for (int i = 0; i < 100000; i++) {
    pstmt.setString(1, "value_" + i);
    pstmt.setInt(2, i);
    pstmt.setTimestamp(3, new Timestamp(System.currentTimeMillis()));
    pstmt.addBatch();
    
    // 每1000条执行一次，避免内存溢出
    if (i % 1000 == 0) {
        pstmt.executeBatch();
        conn.commit();
    }
}

pstmt.executeBatch(); // 执行剩余批次
conn.commit();

// 自动提交模式下启用fetchSize优化
String url = "jdbc:kingbase8://192.168.1.30:54321/large_data?" +
             "defaultRowFetchSize=1000&" +
             "useFetchSizeInAutoCommit=true";

Connection conn = DriverManager.getConnection(url, "system", "password");
Statement stmt = conn.createStatement();
ResultSet rs = stmt.executeQuery("SELECT * FROM massive_data_table");

// 流式处理结果集
while (rs.next()) {
    // 处理每行数据
    processRow(rs);
    
    // 每处理1000行检查内存状态
    if (rs.getRow() % 1000 == 0) {
        System.gc(); // 建议的垃圾回收时机
    }
}

<?xml version="1.0"?>
<!DOCTYPE mycat:schema SYSTEM "schema.dtd">
<mycat:schema xmlns:mycat="http://io.mycat/">

    <schema name="TESTDB" checkSQLschema="true" sqlMaxLimit="100">
        <table name="order_info" primaryKey="order_id" 
               dataNode="dn1,dn2" rule="mod-long">
            <childTable name="order_items" primaryKey="item_id" 
                       joinKey="order_id" parentKey="order_id">
            </childTable>
        </table>
    </schema>

    <dataNode name="dn1" dataHost="host1" database="public" />
    <dataNode name="dn2" dataHost="host2" database="public" />

    <dataHost name="host1" maxCon="1000" minCon="10" 
              dbType="postgresql" dbDriver="jdbc">
        <heartbeat>select 1</heartbeat>
        <writeHost host="hostM1" 
                   url="jdbc:kingbase8://192.168.1.31:54321/db1"
                   user="system" password="123456"/>
    </dataHost>

    <dataHost name="host2" maxCon="1000" minCon="10"
              dbType="postgresql" dbDriver="jdbc">
        <heartbeat>select 1</heartbeat>
        <writeHost host="hostM2" 
                   url="jdbc:kingbase8://192.168.1.32:54321/db2"
                   user="system" password="123456"/>
    </dataHost>
</mycat:schema>

// 分布式事务示例
public void distributedOrderOperation(Order order, List<OrderItem> items) {
    Connection conn = null;
    try {
        conn = DriverManager.getConnection(
            "jdbc:mysql://mycat-server:8066/TESTDB", "root", "123456");
        conn.setAutoCommit(false);
        
        // 插入订单主表（可能路由到dn1）
        String orderSql = "INSERT INTO order_info(order_id, user_id, amount) VALUES(?, ?, ?)";
        PreparedStatement orderStmt = conn.prepareStatement(orderSql);
        orderStmt.setString(1, order.getOrderId());
        orderStmt.setInt(2, order.getUserId());
        orderStmt.setBigDecimal(3, order.getAmount());
        orderStmt.executeUpdate();
        
        // 插入订单明细（可能路由到dn2）
        String itemSql = "INSERT INTO order_items(item_id, order_id, product_id, quantity) VALUES(?, ?, ?, ?)";
        PreparedStatement itemStmt = conn.prepareStatement(itemSql);
        
        for (OrderItem item : items) {
            itemStmt.setString(1, item.getItemId());
            itemStmt.setString(2, item.getOrderId());
            itemStmt.setString(3, item.getProductId());
            itemStmt.setInt(4, item.getQuantity());
            itemStmt.addBatch();
        }
        itemStmt.executeBatch();
        
        conn.commit();
    } catch (SQLException e) {
        if (conn != null) {
            try {
                conn.rollback();
            } catch (SQLException ex) {
                logger.error("回滚失败", ex);
            }
        }
        throw new RuntimeException("分布式事务执行失败", e);
    }
}

# Druid连接池配置
spring.datasource.url=jdbc:kingbase8://localhost:54321/app_db?optimizeBatchedDML=true
spring.datasource.driver-class-name=com.kingbase8.Driver
spring.datasource.type=com.alibaba.druid.pool.DruidDataSource

# 连接池参数
spring.datasource.druid.initial-size=5
spring.datasource.druid.min-idle=5
spring.datasource.druid.max-active=20
spring.datasource.druid.test-on-borrow=true
spring.datasource.druid.validation-query=SELECT 1
spring.datasource.druid.filters=stat,wall

# 针对金仓数据库的优化参数
spring.datasource.druid.connection-init-sqls=SET search_path TO public

// 大数据量查询的内存优化方案
public void processLargeDataset() {
    String url = "jdbc:kingbase8://localhost:54321/large_db?" +
                 "defaultRowFetchSize=5000&" +
                 "useFetchSizeInAutoCommit=true";
    
    try (Connection conn = DriverManager.getConnection(url, "system", "password");
         Statement stmt = conn.createStatement(ResultSet.TYPE_FORWARD_ONLY, 
                                              ResultSet.CONCUR_READ_ONLY)) {
        
        stmt.setFetchSize(5000);
        ResultSet rs = stmt.executeQuery("SELECT * FROM huge_table");
        
        int batchCount = 0;
        while (rs.next()) {
            processRecord(rs);
            batchCount++;
            
            // 定期清理内存
            if (batchCount % 10000 == 0) {
                System.gc();
                logger.info("已处理 {} 条记录", batchCount);
            }
        }
    } catch (SQLException e) {
        logger.error("数据处理失败", e);
    }
}