给定表1，其中一列“ x”为String类型。我想用“ y”列创建表2，该列是“ x”中给出的日期字符串的整数表示。重要的是将null值保留在“ y”列中。表1（数据框df1）：+----------+| x|+----------+|2015-09-12||2015-09-13|| null|| null|+----------+root |-- x: string (nullable = true)表2（资料框df2）：+----------+--------+ | x| y|+----------+--------+| null| null|| null| null||2015-09-12|20150912||2015-09-13|20150913|+----------+--------+root |-- x: string (nullable = true) |-- y: integer (nullable = true)用户定义的函数（udf）将“ x”列的值转换为“ y”列的值是：val extractDateAsInt = udf[Int, String] ( (d:String) => d.substring(0, 10) .filterNot( "-".toSet) .toInt )并且有效，无法处理空值。即使，我可以做类似的事情val extractDateAsIntWithNull = udf[Int, String] ( (d:String) => if (d != null) d.substring(0, 10).filterNot( "-".toSet).toInt else 1 )我发现没有办法null通过udfs “产生” 值（当然，因为Ints不能null）。我当前用于创建df2的解决方案（表2）如下：// holds data of table 1 val df1 = ... // filter entries from df1, that are not nullval dfNotNulls = df1.filter(df1("x") .isNotNull) .withColumn("y", extractDateAsInt(df1("x"))) .withColumnRenamed("x", "right_x")// create df2 via a left join on df1 and dfNotNull having val df2 = df1.join( dfNotNulls, df1("x") === dfNotNulls("right_x"), "leftouter" ).drop("right_x")问题：当前的解决方案似乎很麻烦（并且可能无法有效地提高性能）。有没有更好的办法？@ Spark-developers：是否有NullableInt计划/可用的类型，以便可以使用以下udf（请参见代码摘录）？代码摘录val extractDateAsNullableInt = udf[NullableInt, String] ( (d:String) => if (d != null) d.substring(0, 10).filterNot( "-".toSet).toInt else null )
3 回答
守着星空守着你
TA贡献1799条经验 获得超8个赞
这是Option派上用场的地方：
val extractDateAsOptionInt = udf((d: String) => d match {
case null => None
case s => Some(s.substring(0, 10).filterNot("-".toSet).toInt)
})
或在一般情况下使其更加安全：
import scala.util.Try
val extractDateAsOptionInt = udf((d: String) => Try(
d.substring(0, 10).filterNot("-".toSet).toInt
).toOption)
一切归功于德米特里Selivanov谁已经指出，这种解决方案为（失踪？）编辑这里。
另一种方法是null在UDF之外处理：
import org.apache.spark.sql.functions.{lit, when}
import org.apache.spark.sql.types.IntegerType
val extractDateAsInt = udf(
(d: String) => d.substring(0, 10).filterNot("-".toSet).toInt
)
df.withColumn("y",
when($"x".isNull, lit(null))
.otherwise(extractDateAsInt($"x"))
.cast(IntegerType)
)
噜噜哒
TA贡献1784条经验 获得超7个赞
我创建了以下代码，以使用户定义的函数可用，以处理所述的空值。希望对别人有帮助！
/**
* Set of methods to construct [[org.apache.spark.sql.UserDefinedFunction]]s that
* handle `null` values.
*/
object NullableFunctions {
import org.apache.spark.sql.functions._
import scala.reflect.runtime.universe.{TypeTag}
import org.apache.spark.sql.UserDefinedFunction
/**
* Given a function A1 => RT, create a [[org.apache.spark.sql.UserDefinedFunction]] such that
* * if fnc input is null, None is returned. This will create a null value in the output Spark column.
* * if A1 is non null, Some( f(input) will be returned, thus creating f(input) as value in the output column.
* @param f function from A1 => RT
* @tparam RT return type
* @tparam A1 input parameter type
* @return a [[org.apache.spark.sql.UserDefinedFunction]] with the behaviour describe above
*/
def nullableUdf[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT]): UserDefinedFunction = {
udf[Option[RT],A1]( (i: A1) => i match {
case null => None
case s => Some(f(i))
})
}
/**
* Given a function A1, A2 => RT, create a [[org.apache.spark.sql.UserDefinedFunction]] such that
* * if on of the function input parameters is null, None is returned.
* This will create a null value in the output Spark column.
* * if both input parameters are non null, Some( f(input) will be returned, thus creating f(input1, input2)
* as value in the output column.
* @param f function from A1 => RT
* @tparam RT return type
* @tparam A1 input parameter type
* @tparam A2 input parameter type
* @return a [[org.apache.spark.sql.UserDefinedFunction]] with the behaviour describe above
*/
def nullableUdf[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT]): UserDefinedFunction = {
udf[Option[RT], A1, A2]( (i1: A1, i2: A2) => (i1, i2) match {
case (null, _) => None
case (_, null) => None
case (s1, s2) => Some((f(s1,s2)))
} )
}
}
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