Spark:分解结构的数据框数组并附加ID [英] Spark: Explode a dataframe array of structs and append id

问题描述

我目前有一个带有ID和一个列的数据框，该列是结构数组:

I currently have a dataframe with an id and a column which is an array of structs:

 root
 |-- id: integer (nullable = true)
 |-- lists: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- text: string (nullable = true)
 |    |    |-- amount: double (nullable = true)

这是带有数据的示例表:

Here is an example table with data:

 id | lists
 -----------
 1  | [[a, 1.0], [b, 2.0]]
 2  | [[c, 3.0]]

如何将上面的数据框转换为下面的数据框?我需要分解"数组并同时附加ID.

How do I transform the above dataframe to the one below? I need to "explode" the array and append the id at the same time.

 id | col1  | col2
 -----------------
 1  | a     | 1.0
 1  | b     | 2.0
 2  | c     | 3.0

---

修改后的注释:

请注意，以下两个示例之间存在差异.第一个包含元素结构数组" .而后者仅包含元素数组" .

Note there is a difference between the two examples below. The first one contains "an array of structs of elements". While the later just contains "an array of elements".

 root
 |-- id: integer (nullable = true)
 |-- lists: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- text: string (nullable = true)
 |    |    |-- amount: double (nullable = true)


root
 |-- a: long (nullable = true)
 |-- b: array (nullable = true)
 |    |-- element: long (containsNull = true)

推荐答案

explode正是该功能:

import org.apache.spark.sql.functions._

df.select($"id", explode($"lists")).select($"id", $"col.text", $"col.amount")

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