将数组（行）的 RDD 转换为行的 RDD？

Question

I have such data in a file and I'd like to do some statistics using Spark.

File content:

aaa|bbb|ccc
ddd|eee|fff|ggg

I need to assign each line an id. I read them as rdd and use zipWithIndex() .

Then they should be like:

(0, aaa|bbb|ccc)
(1, ddd|eee|fff|ggg)

I need to make each string associated with the id. I can get the RDD of Array(Row), but can't jump out of the array.

How should I modify my code?

import org.apache.spark.sql.{Row, SparkSession}

val fileRDD = spark.sparkContext.textFile(filePath)
val fileWithIdRDD = fileRDD.zipWithIndex()
// make the line like this: (0, aaa), (0, bbb), (0, ccc)
// each line is a record of Array(Row)
fileWithIdRDD.map(x => {
  val id = x._1
  val str = x._2
  val strArr = str.split("\\|")
  val rowArr = strArr.map(y => {
    Row(id, y)
  }) 
  rowArr 
})

Now it looks like:

[(0, aaa), (0, bbb), (0, ccc)]
[(1, ddd), (1, eee), (1, fff), (1, ggg)]

But finally I want:

(0, aaa)
(0, bbb) 
(0, ccc)
(1, ddd)
(1, eee)
(1, fff)
(1, ggg)

Answer 1

You just need to flatten your RDD

yourRDD.flatMap(array => array)

Considering your code (some errors fixed, inside the inner map and in the assignation of id and str)

fileWithIdRDD.map(x => {
  val id = x._1
  val str = x._2
  val strArr = str.split("\\|")
  val rowArr = strArr.map(y => {
    Row(id, y)
  }) 
  rowArr 
}).flatMap(array => array)

Quick example here:

INPUT

fileWithIdRDD.collect
res30: Array[(Int, String)] = Array((0,aaa|bbb|ccc), (1,ddd|eee|fff|ggg))

EXECUTION

scala> fileWithIdRDD.map(x => {
      val id = x._1
      val str = x._2
      val strArr = str.split("\\|")
        val rowArr = strArr.map(y => {
          Row(id, y)
        })
      rowArr
      }).flatMap(array => array)


res31: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[17] at flatMap at <console>:35

OUTPUT

scala> res31.collect
res32: Array[org.apache.spark.sql.Row] = Array([0,aaa], [0,bbb], [0,ccc], [1,ddd], [1,eee], [1,fff], [1,ggg])