Spark DataFrame中的条件联接 [英] Conditional Join in Spark DataFrame

问题描述

我正在尝试有条件地加入两个DataFrame.

I am trying to join two DataFrame with condition.

我有两个数据框A和B.

I have two dataframe A and B.

A包含id，m_cd和c_cd列 B包含m_cd，c_cd和记录列

A contains id,m_cd and c_cd columns B contains m_cd,c_cd and record columns

条件是-

• 如果m_cd为null，则将A的c_cd与B连接起来
• 如果m_cd不为null，则将A的m_cd与B连接起来

我们可以在数据框的withcolumn()方法中使用"when"和"otherwise()"，因此对于联接到数据框的情况，有什么方法可以做到这一点.

we can use "when" and "otherwise()" in withcolumn() method of dataframe, so is there any way to do this for the case of join in dataframe.

我已经使用Union完成了此操作.但是想知道是否还有其他可用的选项.

I have already done this using Union.But wanted to know if there any other option available.

推荐答案

您可以在联接条件中使用何时"/否则":

You can use the "when" / "otherwise" in the join condition:

case class Foo(m_cd: Option[Int], c_cd: Option[Int])
val dfA = spark.createDataset(Array(
    Foo(Some(1), Some(2)),
    Foo(Some(2), Some(3)),
    Foo(None: Option[Int], Some(4))
))


val dfB = spark.createDataset(Array(
    Foo(Some(1), Some(5)),
    Foo(Some(2), Some(6)),
    Foo(Some(10), Some(4))
))

val joinCondition = when($"a.m_cd".isNull, $"a.c_cd"===$"b.c_cd")
    .otherwise($"a.m_cd"===$"b.m_cd")

dfA.as('a).join(dfB.as('b), joinCondition).show

不过，使用联合会更易读.

It might still be more readable to use the union, though.

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