修改Spark dataframe中的结构列

Question

我有一个PySpark dataframe，其中包含一个列"student“，如下所示：

"student" : {
   "name" : "kaleem",
   "rollno" : "12"
}

在dataframe中，这方面的架构是：

structType(List(
   name: String, 
   rollno: String))

我需要将该列修改为

"student" : {
   "student_details" : {
         "name" : "kaleem",
         "rollno" : "12"
   }
}

在dataframe中，这方面的架构必须是：

structType(List(
  student_details: 
     structType(List(
         name: String, 
         rollno: String))
))

如何在星火中做到这一点？

Answer 1

使用结构函数来实现这一点

1.将json读入列

val  data =
      """
        | {
        |   "student": {
        |       "name": "kaleem",
        |       "rollno": "12"
        |   }
        |}
      """.stripMargin
    val df = spark.read.json(Seq(data).toDS())
    df.show(false)
    println(df.schema("student"))

输出

+------------+
|student     |
+------------+
|[kaleem, 12]|
+------------+

StructField(student,StructType(StructField(name,StringType,true), StructField(rollno,StringType,true)),true)

named_struct 2.使用更改模式

val processedDf = df.withColumn("student",
      expr("named_struct('student_details', student)")
    )
    processedDf.show(false)
    println(processedDf.schema("student"))

输出

+--------------+
|student       |
+--------------+
|[[kaleem, 12]]|
+--------------+

StructField(student,StructType(StructField(student_details,StructType(StructField(name,StringType,true), StructField(rollno,StringType,true)),true)),false)

对于python，step#2将正常工作，只需删除val即可。

Answer 2

有了一个名为星火帽的库--这个库扩展了Spark，并为嵌套结构和任意嵌套级别的数组中的字段转换提供了帮助。

scala> import za.co.absa.spark.hats.Extensions._

scala> df.printSchema
root
 |-- ID: string (nullable = true)

scala> val df2 = df.nestedMapColumn("ID", "ID", c => struct(c as alfa))

scala> df2.printSchema
root
 |-- ID: struct (nullable = false)
 |    |-- alfa: string (nullable = true)

scala> val df3 = df2.nestedMapColumn("ID.alfa", "ID.alfa", c => struct(c as "beta"))

scala> df3.printSchema
root
 |-- ID: struct (nullable = false)
 |    |-- alfa: struct (nullable = false)
 |    |    |-- beta: string (nullable = true)

您的查询将是

df.nestedMapColumn("student", "student", c => struct(c as "student_details"))

Answer 3

星火3.1+

要修改结构类型列，可以使用withField和dropFields

F.col("Student").withField("student_details", F.col("student"))
F.col("Student").dropFields("name", "rollno")

输入：

from pyspark.sql import functions as F
df = spark.createDataFrame([(("kaleem", "12"),)], "student struct<name:string,rollno:string>")
df.printSchema()
# root
#  |-- student: struct (nullable = true)
#  |    |-- name: string (nullable = true)
#  |    |-- rollno: string (nullable = true)

剧本：

df = df.withColumn("student", F.col("Student")
    .withField("student_details", F.col("student"))
    .dropFields("name", "rollno")
)

结果：

df.printSchema()
# root
#  |-- student: struct (nullable = true)
#  |    |-- student_details: struct (nullable = true)
#  |    |    |-- name: string (nullable = true)
#  |    |    |-- rollno: string (nullable = true)