0

下面是我的示例架构。

|-- provider: string (nullable = true)
 |-- product: string (nullable = true)
 |-- asset_name: string (nullable = true)
 |-- description: string (nullable = true)
 |-- creation_date: string (nullable = true)
 |-- provider_id: string (nullable = true)
 |-- asset: string (nullable = true)
 |-- asset_clas: string (nullable = true)
 |-- Actors: array (nullable = true)
 |    |-- element: string (containsNull = false)
 |-- Actors_Display: array (nullable = true)
 |    |-- element: string (containsNull = false)
 |-- Audio_Type: array (nullable = true)
 |    |-- element: string (containsNull = false)
 |-- Billing_ID: array (nullable = true)
 |    |-- element: string (containsNull = false)
 |-- Bit_Rate: array (nullable = true)
 |    |-- element: string (containsNull = false)
 |-- CA_Rating: array (nullable = true)
 |    |-- element: string (containsNull = false)

我需要分解所有数组类型的列。我有大约 80 多列，并且列不断变化。我目前正在使用explode(array_zip)

   val df= sourcedf.select($"provider",$"asset_name",$"description",$"creation_date",$"provider_id",$"asset_id",$"asset_class",$"product",$"provider_id",$"eligible_platform",$"actors",$"category",
explode_outer(arrays_zip($"Actors_Display",$"Audio_Type",$"Billing_ID",$"Bit_Rate",$"CA_Rating")


val parsed_output = df.select(col("provider"),("asset_name"),col("description"),col("creation_date"),col("product"),col("provider"),
    col("povider_id"),col("asset_id"),col("asset_class"),
  col("col.Actors_Display"),col("col.Audio_Type"),col("col.Billing_ID"),col("col.Bit_Rate"),col("col.CA_Rating"))

通过使用，上面我能够得到输出。但这仅适用于一个特定文件。就我而言，将经常添加新列。那么，是否有任何功能可以分解多个列以更改架构，并从文件中选择非数组列。有人可以举个例子吗

注意：只有数组列不断变化，其余的将保持不变。

下面是样本数据

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<ADIL>
  <Meta>
    <AMS Asset_Name="asd" Provider="Level" Product="MOTD" Version_Major="1" Version_Minor="0" Description="ZXC" Creation_Date="2009-12-03" Provider_ID="qwer.com" Asset_ID="A12we" Asset_Class="package"/>
    <App_Data App="MOD" Name="Actors" Value="CableLa1.1"/>
    <App_Data App="MOD" Name="Actors_Display" Value="RTY"/>
    <App_Data App="MOD" Name="Audio_Type" Value="FGH"/>
  </Meta>
  <Asset>
    <Meta>
      <AMS Asset_Name="bnm" Provider="Level Film" Product="MOTD" Version_Major="1" Version_Minor="0" Description="bnj7" Creation_Date="2009-12-03" Provider_ID="levelfilm.com" Asset_ID="DDDB0610072533182333" Asset_Class="title"/>
      App_Data App="rt" Name="Billing_ID" Value="2020-12-29T00:00:00"/>
      <App_Data App="MOD" Name="Bit_Rate" Value="2021-12-29T23:59:59"/>
      <App_Data App="MOD" Name="CA_Rating" Value="16.99"/>
      </Meta>
    <Asset>
      <Meta>
       <AMS Asset_Name="atysd" Provider="Level1" Product="MOTD2" Version_Major="1" Version_Minor="0" Description="ZXCY" Creation_Date="2009-12-03" Provider_ID="qweDFtrr.com" Asset_ID="A12FGwe" Asset_Class="review"/>

这是xml数据。最初，解析此数据并将所有名称属性值转换为列名，并将所有“值”属性值转换为列名的值。这个 XML 有重复的标签，所以解析后的最终结果是数组列，我在解析逻辑的末尾使用了 collect_list。

这是解析后的示例输出。

+-------------------+-------------------+-----------------+------------+--------------+
|Actors               |Actors_Display    |Audio_Type       |Billing_ID  |Bit_rate 
+-------------+---------------+-----------------------------------------+------------
|["movie","cinema",] | ["Dolby 5.1"]     | ["High", "low"] | ["GAR15"]|  ["15","14"]         
+-------------+-----+-------------------+-----------------+--------------+----------
4

1 回答 1

1

假设您要分解所有 ArrayType 列（否则，相应地过滤）：

val df = Seq(
  (1, "xx", Seq(10, 20), Seq("a", "b"), Seq("p", "q")),
  (2, "yy", Seq(30, 40), Seq("c", "d"), Seq("r", "s"))
).toDF("c1", "c2", "a1", "a2", "a3")

import org.apache.spark.sql.types.{StructField, ArrayType}

val arrCols = df.schema.fields
  .collect{case StructField(name, _: ArrayType, _, _) => name}
  .map(col)

val otherCols = df.columns.map(col) diff arrCols

df.withColumn("arr_zip", explode_outer(arrays_zip(arrCols: _*)))
  .select(otherCols.toList ::: $"arr_zip.*" :: Nil: _*)
  .show
+---+---+---+---+---+
| c1| c2| a1| a2| a3|
+---+---+---+---+---+
|  1| xx| 10|  a|  p|
|  1| xx| 20|  b|  q|
|  2| yy| 30|  c|  r|
|  2| yy| 40|  d|  s|
+---+---+---+---+---+
于 2021-05-14T23:13:18.450 回答