有一个输入Dataframe df （有10列，col1-col10）其中我添加了一个新列 uuid 通过使用下面的自定义项并转换成另一个Dataframe newdf .
下一个，从 newdf datafame，我正在创建两个独立的Dataframe df1 （uuid，col1-col5）和 df2 （uuid，col6-col10）仅包含上述列。
问题来了，我要 uuid 列对于两个列中的行应相同且唯一 df1 & df2 Dataframe。
因为spark使用惰性评估，所以在我编写时它运行udf df1 以及 df2 因为它给了不同的 uuid 两行中每行的值 df1 以及 df2 Dataframe。
我现在所遵循的解决方案是 newdf 先在temp路径读取Dataframe，然后再读回。但是这种逻辑对于大量的数据是不好的。
下面是一段代码片段：

df.show(false)
+------+------+------+------+------+------+------+------+------+-------+
| col1 | col2 | col3 | col4 | col5 | col6 | col7 | col8 | col9 | col10 |
+------+------+------+------+------+------+------+------+------+-------+
| A1   | A2   | A3   | A4   | A5   | A6   | A7   | A8   | A9   |  A10  |
| B1   | B2   | B3   | B4   | B5   | B6   | B7   | B8   | B9   |  B10  |
| C1   | C2   | C3   | C4   | C5   | C6   | C7   | C8   | C9   |  C10  |
+------+------+------+------+------+-------+------+------+------+------+

val uuid = udf(() => java.util.UUID.randomUUID().toString)
val newdf = df.withColumn("uuid", uuid())

val df1 = newdf.select(uuid, col1, col2, col3, col4, col5)
val df2 = newdf.select(uuid, col6, col7, col8, col9, col10)

df1.write.format("parquet").save("/df1/")
df2.write.format("parquet").save("/df2/")

df1.show()
+-----------------+------+------+------+------+------+
|     uuid        | col1 | col2 | col3 | col4 | col5 |
+-----------------+------+------+------+------+------+
|1abdecf-8303-4a4e| A1   | A2   | A3   | A4   | A5   |
|1dbcecf-1304-4a4e| B1   | B2   | B3   | B4   | B5   |
|1vbdecf-8406-4a4e| C1   | C2   | C3   | C4   | C5   |
+-----------------+------+------+------+------+------+

df2.show()
+-----------------+------+------+------+------+------+
|     uuid        | col6 | col7 | col8 | col9 | col10|
+-----------------+------+------+------+------+------+
|2aodecf-3303-6n5e| A6   | A7   | A8   | A9   | A10  |
|2docecf-6305-6n5e| B6   | B7   | B8   | B9   | B10  |
|2vodecf-1406-6n5e| C6   | C7   | C8   | C9   | C10  |
+-----------------+------+------+------+------+------+

预期输出：跨行的两个Dataframe中的uuid相同

df1.show()
+-----------------+------+------+------+------+------+
|     uuid        | col1 | col2 | col3 | col4 | col5 |
+-----------------+------+------+------+------+------+
|1abdecf-8303-4a4e| A1   | A2   | A3   | A4   | A5   |
|1dbcecf-1304-4a4e| B1   | B2   | B3   | B4   | B5   |
|1vbdecf-8406-4a4e| C1   | C2   | C3   | C4   | C5   |
+-----------------+------+------+------+------+------+

df2.show()
+-----------------+------+------+------+------+------+
|     uuid        | col6 | col7 | col8 | col9 | col10|
+-----------------+------+------+------+------+------+
|1abdecf-8303-4a4e| A6   | A7   | A8   | A9   | A10  |
|1dbcecf-1304-4a4e| B6   | B7   | B8   | B9   | B10  |
|1vbdecf-8406-4a4e| C6   | C7   | C8   | C9   | C10  |
+-----------------+------+------+------+------+------+

请提供克服这个问题的最佳方法。