如何使用Array[Int]将csv文件加载到Spark DataFrame中

Question

csv文件中的每一行结构如下：

u001, 2013-11, 0, 1, 2, ... , 99

+-------+-------------+-----------------+
|    uid|         date|       dataVector|
+-------+-------------+-----------------+
|   u001|      2013-11|  [0,1,...,98,99]|
|   u002|      2013-11| [1,2,...,99,100]|
+-------+-------------+-----------------+

root
 |-- uid: string (nullable = true)
 |-- date: string (nullable = true)
 |-- dataVecotr: array (nullable = true)
 |    |-- element: integer (containsNull = true)

val attributes = Array("uid", "date", "dataVector)
val schema = StructType(
StructField(attributes(0), StringType, true) ::
StructField(attributes(1), StringType, true) ::
StructField(attributes(2), ArrayType(IntegerType), true) :: 
Nil)

 root
  |-- uid: string (nullable = true)
  |-- date: string (nullable = true)
  |-- dataVector: struct (nullable = true)
  |    |-- _c3: string (containsNull = true)
  |    |-- _c4: string (containsNull = true)
  .
  .
  .
  |    |-- _c101: string (containsNull = true)

Answer 1

加载它而不添加任何内容

val df = spark.read.csv(path)

并选择：

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

// Combine data into array
val dataVector: Column = array(
  df.columns.drop(2).map(col): _*  // Skip first 2 columns
).cast("array<int>")  // Cast to the required type
val cols: Array[Column] = df.columns.take(2).map(col) :+ dataVector

df.select(cols: _*).toDF("uid", "date", "dataVector")