CODEWITHSUNDEEP
scalaapache-sparkrdd
Wangkkkkkk
Wangkkkkkk

Reputation: 15

the operation about rdd and reducebykey

I create a rdd like this:

val rdd = sc.parallelize(Seq(
  ("A:0.740:8, B:0.242:5, C:0.145:2"),
  ("A:0.232:8, C:0.14:7, D:0.164:5, E:0.34:2, F:0.37:1"),
  ("B:0.245:13, H:0.123:4, I:0.673:2")
))

if I want to reduce by the key and sum the last two elements. Then get the results like:

"A:##:##"
"B:##:##"
"C:##:##"
...

What should I code?

Upvotes: 0

Views: 71

Answers (1)

koiralo
koiralo

Reputation: 23119

Here is what you need, First, flatten all the data split with ":" and get the first index as a key and use reduceByKey to calculate sum 

  rdd.flatMap(_.split(",")).map(x => {
    val s = x.split(":")
    (s(0).trim, (s(1).toDouble, s(2).toInt))
  })
    .reduceByKey((x, y) => ((x._1 + y._1), (x._2 + y._2)))
    .map(r => (r._1 + ":" + r._2._1 + ":" + r._2._2))

 .toDF().show()

Or convert to Dataframe and concat it later, which will make it clear to understand and easy

rdd.flatMap(_.split(",")).map(x => {
    val s = x.split(":")
    (s(0).trim, s(1).toDouble, s(2).toInt)
  }).toDF("a", "b", "c")
    .groupBy("a").agg(sum("b").as("sumB"), sum("c").as("sumC"))
    .select(concat_ws(":", Seq($"a", $"sumB", $"sumC"): _*).as("value"))
    .toDF().show()

Output:

+-----------------------+
|value                  |
+-----------------------+
|H:0.246:8              |
|A:0.972:16             |
|I:0.673:2              |
|B:0.487:18             |
|C:0.28500000000000003:9|
|D:0.164:5              |
|E:0.34:2               |
|F:0.37:1               |
+-----------------------+

Hope this helped!

Upvotes: 1

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