Question

我有一个带有键列的数据框和一个包含struct数组的列。 Schema如下所示。

root
 |-- id: string (nullable = true)
 |-- desc: array (nullable = false)
 |    |-- element: struct (containsNull = true)
 |    |    |-- name: string (nullable = true)
 |    |    |-- age: long (nullable = false)

阵列＆＃34; desc＆＃34;可以有任意数量的空值。我想使用spark 1.6：

创建一个最终数据帧，该数组没有任何空值

一个例子是：

Key  .   Value
1010 .   [[George,21],null,[MARIE,13],null]
1023 .   [null,[Watson,11],[John,35],null,[Kyle,33]]

我希望最终的数据框为：

Key  .   Value
1010 .   [[George,21],[MARIE,13]]
1023 .   [[Watson,11],[John,35],[Kyle,33]]

我试过用UDF和case类做这个但是得到了

java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to....

非常感谢任何帮助，如果需要，我更愿意不转换为RDD。此外，我是新来的火花和斯卡拉，所以提前感谢!!!

Answer 1

这是另一个版本：

case class Person(name: String, age: Int)

root
 |-- id: string (nullable = true)
 |-- desc: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- name: string (nullable = true)
 |    |    |-- age: integer (nullable = false)

+----+-----------------------------------------------+
|id  |desc                                           |
+----+-----------------------------------------------+
|1010|[[George,21], null, [MARIE,13], null]          |
|1023|[[Watson,11], null, [John,35], null, [Kyle,33]]|
+----+-----------------------------------------------+


val filterOutNull = udf((xs: Seq[Row]) => {
  xs.flatMap {
    case null => Nil
    // convert the Row back to your specific struct:
    case Row(s: String,i: Int) => List(Person(s, i))
  }
})

val result = df.withColumn("filteredListDesc", filterOutNull($"desc"))

+----+-----------------------------------------------+-----------------------------------+
|id  |desc                                           |filteredListDesc                   |
+----+-----------------------------------------------+-----------------------------------+
|1010|[[George,21], null, [MARIE,13], null]          |[[George,21], [MARIE,13]]          |
|1023|[[Watson,11], null, [John,35], null, [Kyle,33]]|[[Watson,11], [John,35], [Kyle,33]]|
+----+-----------------------------------------------+-----------------------------------+

Answer 2

鉴于原始数据框具有以下架构

root
 |-- id: string (nullable = true)
 |-- desc: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- name: string (nullable = true)
 |    |    |-- age: long (nullable = false)

定义udf函数以从数组中删除空值应

import org.apache.spark.sql.functions._
def removeNull = udf((array: Seq[Row])=> array.filterNot(_ == null).map(x => element(x.getAs[String]("name"), x.getAs[Long]("age"))))

df.withColumn("desc", removeNull(col("desc")))

其中element是case class

case class element(name: String, age: Long)

你应该

+----+-----------------------------------+
|id  |desc                               |
+----+-----------------------------------+
|1010|[[George,21], [MARIE,13]]          |
|1010|[[Watson,11], [John,35], [Kyle,33]]|
+----+-----------------------------------+

使用Spark（1.6）{Scra（1.6）删除Scala中Dataframe中数组列的Null

2 个答案: