将带有 JSON 对象数组的 Spark 数据帧列转换为多行 [英] Convert an Spark dataframe columns with an array of JSON objects to multiple rows

问题描述

我有一个流式 JSON 数据，其结构可以用下面的案例类来描述

case class Hello(A: String, B: Array[Map[String, String]])

相同的样本数据如下

<代码>|一个 |乙 ||-------|------------------------------------------||ABC |[{C:1, D:1}, {C:2, D:4}] ||XYZ |[{C:3, D:6}, {C:9, D:11}, {C:5, D:12}] |

我想把它改成

<代码>|一个 |C |D ||-------|-----|------||ABC |1 |1 ||ABC |2 |4 ||XYZ |3 |6 ||XYZ |9 |11 ||XYZ |5 |12 |

任何帮助将不胜感激.

解决方案

随着问题的演变，我把原来的答案留在那里，这解决了最后一个问题.

<块引用>

重要的一点，现在提到的输入如下:

val df0 = Seq (("ABC", List(Map("C" -> "1", "D" -> "2"), Map("C" -> "3", "D" -> "4"))),("XYZ", List(Map("C" -> "44", "D" -> "55"), Map("C" -> "188", "D" -> "199""), Map("C" -> "88", "D" -> "99")))).toDF("A", "B")

<块引用>

也可以这样，不过接下来需要为此修改脚本，虽然微不足道:

val df0 = Seq (("ABC", List(Map("C" -> "1", "D" -> "2"))),("ABC", List(Map("C" -> "44", "D" -> "55"))),("XYZ", List(Map("C" -> "11", "D" -> "22")))).toDF("A", "B")

<块引用>

从请求的格式开始:

val df1 = df0.select($"A", expand($"B")).toDF("A", "Bn")val df2 = df1.withColumn("SeqNum", monotonically_increasing_id()).toDF("A", "Bn", "SeqNum")val df3 = df2.select($"A", expand($"Bn"), $"SeqNum").toDF("A", "B", "C", "SeqNum")val df4 = df3.withColumn("dummy", concat( $"SeqNum", lit("||"), $"A"))val df5 = df4.select($"dummy", $"B", $"C").groupBy("dummy").pivot("B").agg(first($"C"))val df6 = df5.withColumn("A", substring_index(col("dummy"), "||", -1)).drop("dummy")df6.show(假)

返回:

+---+---+---+|C |D |A |+---+---+---+|3 |4 |ABC||1 |2 |ABC||88 |99 |XYZ||188|199|XYZ||44 |55 |XYZ|+---+---+---+

您可以对列重新排序.

I have a streaming JSON data, whose structure can be described with the case class below

case class Hello(A: String, B: Array[Map[String, String]])

Sample data for the same is as below

|  A    | B                                        |
|-------|------------------------------------------|
|  ABC  |  [{C:1, D:1}, {C:2, D:4}]                | 
|  XYZ  |  [{C:3, D :6}, {C:9, D:11}, {C:5, D:12}] |

I want to transform it to

|   A   |  C  |  D   |
|-------|-----|------|
|  ABC  |  1  |  1   |
|  ABC  |  2  |  4   |
|  XYZ  |  3  |  6   |
|  XYZ  |  9  |  11  |
|  XYZ  |  5  |  12  | 

Any help will be appreciated.

解决方案

As the question went through an evolution I leave the original answer there and this addresses the final question.

Important point, the input mentioned as follows is now catered for:

val df0 = Seq (
            ("ABC", List(Map("C" -> "1", "D" -> "2"), Map("C" -> "3", "D" -> "4"))),
            ("XYZ", List(Map("C" -> "44", "D" -> "55"), Map("C" -> "188", "D" -> "199"), Map("C" -> "88", "D" -> "99")))
              )
             .toDF("A", "B")

Can also be done like this, but then the script needs to be modified for this, although trivial:

val df0 = Seq (
           ("ABC", List(Map("C" -> "1",  "D" -> "2"))), 
           ("ABC", List(Map("C" -> "44", "D" -> "55"))),
           ("XYZ", List(Map("C" -> "11", "D" -> "22")))
              )
            .toDF("A", "B")

Following on from requested format then:

val df1 = df0.select($"A", explode($"B")).toDF("A", "Bn")

val df2 = df1.withColumn("SeqNum", monotonically_increasing_id()).toDF("A", "Bn", "SeqNum") 

val df3 = df2.select($"A", explode($"Bn"), $"SeqNum").toDF("A", "B", "C", "SeqNum")

val df4 = df3.withColumn("dummy", concat( $"SeqNum", lit("||"), $"A"))

val df5 = df4.select($"dummy", $"B", $"C").groupBy("dummy").pivot("B").agg(first($"C")) 

val df6 = df5.withColumn("A", substring_index(col("dummy"), "||", -1)).drop("dummy")

df6.show(false)

returns:

+---+---+---+
|C  |D  |A  |
+---+---+---+
|3  |4  |ABC|
|1  |2  |ABC|
|88 |99 |XYZ|
|188|199|XYZ|
|44 |55 |XYZ|
+---+---+---+

You may re-sequence columns.

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