Question

我的数据集是RDD[Array[String]]，列数超过140列。如何在不对列号进行硬编码的情况下选择列的子集(.map(x => (x(0),x(3),x(6)...))？

这是我迄今为止尝试过的（成功）：

val peopleTups = people.map(x => x.split(",")).map(i => (i(0),i(1)))

但是，我需要多列，并且希望避免对它们进行硬编码。

这是我迄今为止所尝试过的（我认为会更好，但失败了）：

// Attempt 1
val colIndices = [0,3,6,10,13]
val peopleTups = people.map(x => x.split(",")).map(i => i(colIndices))

// Error output from attempt 1:
<console>:28: error: type mismatch;
 found   : List[Int]
 required: Int
       val peopleTups = people.map(x => x.split(",")).map(i => i(colIndices))

// Attempt 2
colIndices map peopleTups.lift

// Attempt 3
colIndices map peopleTups

// Attempt 4
colIndices.map(index => peopleTups.apply(index))

我发现了这个问题并试了一下，但是因为我正在查看RDD而不是数组，所以它不起作用：How can I select a non-sequential subset elements from an array using Scala and Spark?

Answer 1

You should map over the RDD instead of the indices.

val list = List.fill(2)(Array.range(1, 6))
// List(Array(1, 2, 3, 4, 5), Array(1, 2, 3, 4, 5))

val rdd = sc.parallelize(list) // RDD[Array[Int]]
val indices = Array(0, 2, 3)

val selectedColumns = rdd.map(array => indices.map(array)) // RDD[Array[Int]]

selectedColumns.collect() 
// Array[Array[Int]] = Array(Array(1, 3, 4), Array(1, 3, 4))

Answer 2

What about this?

val data = sc.parallelize(List("a,b,c,d,e", "f,g,h,i,j"))
val indices =  List(0,3,4)
data.map(_.split(",")).map(ss => indices.map(ss(_))).collect

This should give

res1: Array[List[String]] = Array(List(a, d, e), List(f, i, j))

在Spark RDD（Scala）中指定元素的子集

2 个答案: