使用 Spark (1.6) 从 Scala 中 Dataframe 的数组列中删除空值

Question

我有一个数据框，其中包含一个键列和一个包含结构数组的列。架构如下所示。

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

数组"desc" 可以有任意数量的空值。我想使用 spark 1.6 创建一个具有空值 none 的数组的最终数据框：

例如：

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

我想要最终数据框为：

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

我尝试用 UDF 和 case class 这样做，但得到

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，我更愿意在不转换为 RDD 的情况下这样做。我也是 spark 和 scala 的新手，所以在此先感谢！！！

Answer 1

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)

定义一个 udf 函数 以从数组中删除空值应该 对你有效

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)

你应该得到

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

Answer 2

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]]|
+----+-----------------------------------------------+-----------------------------------+

使用 Spark (1.6) 从 Scala 中 Dataframe 的数组列中删除空值

Remove Null from Array Columns in Dataframe in Scala with Spark (1.6)

scala

apache-spark

spark-dataframe