scala spark foreachpartition之Task not serializable

我想使用foreachpartition对dataframe进行分区以达到并行往数据库写数据的操作，但是与数据库的连接已经在父类中的这个方法中创建过了，我有什么方法可以使spark程序在运行的时候不会报Task not serializable

override def load(dataFrame: DataFrame, delta: Boolean): DataFrame = {
  ......
    dataFrame.repartition(10).foreachPartition(row => {
    
      loadMainData( dataFrame,false , rbkMainFactTable )
      for(rbkUnionTable<-rbkUnionFactTableList) {
        loadRbkUnionData( dataFrame,false , rbkUnionTable )
      }
      for(rbkFactTable <- rbkFactTableLists){
        loadRbkData(dataFrame, false ,rbkFactTable)
      }
    })
    dataFrame
    //writeToTable(dataFrame: DataFrame, delta: Boolean)
  }

这个问题是因为Spark需要序列化。你得将数据库连接对象声明为transient（即不参与序列化），然后在分区中重新创建连接。

override def load(dataFrame: DataFrame, delta: Boolean): DataFrame = {
  ......
  dataFrame.repartition(10).foreachPartition(rows => {
    // Create a new instance of the database connection object
    val conn = createNewConnection()
    try {
      loadMainData(rows, false, rbkMainFactTable, conn)
      for (rbkUnionTable <- rbkUnionFactTableList) {
        loadRbkUnionData(rows, false, rbkUnionTable, conn)
      }
      for (rbkFactTable <- rbkFactTableLists) {
        loadRbkData(rows, false, rbkFactTable, conn)
      }
    } finally {
      // Close the connection after processing the partition
      conn.close()
    }
  })
  dataFrame
  //writeToTable(dataFrame: DataFrame, delta: Boolean)
}