How to groupby and aggregate multiple fields using

I am new to Apache Spark as well as Scala, currently learning this framework and programming language for big data. I have a sample file I am trying to find out for a given field total number of another field and its count and list of values from another field. I tried on my own and seems that i am not writing in better approach in spark rdd (as starting).

Please find the below sample data (Customerid: Int, Orderid: Int, Amount: Float):

44,8602,37.19
35,5368,65.89
2,3391,40.64
47,6694,14.98
29,680,13.08
91,8900,24.59
70,3959,68.68
85,1733,28.53
53,9900,83.55
14,1505,4.32
51,3378,19.80
42,6926,57.77
2,4424,55.77
79,9291,33.17
50,3901,23.57
20,6633,6.49
15,6148,65.53
44,8331,99.19
5,3505,64.18
48,5539,32.42

My current code:

((sc.textFile("file://../customer-orders.csv").map(x => x.split(",")).map(x => (x(0).toInt,x(1).toInt)).map{case(x,y) => (x, List(y))}.reduceByKey(_ ++ _).sortBy(_._1,true)).
fullOuterJoin(sc.textFile("file://../customer-orders.csv").map(x =>x.split(",")).map(x => (x(0).toInt,x(2).toFloat)).reduceByKey((x,y) => (x + y)).sortBy(_._1,true))).
fullOuterJoin(sc.textFile("file://../customer-orders.csv").map(x =>x.split(",")).map(x => (x(0).toInt)).map(x => (x,1)).reduceByKey((x,y) => (x + y)).sortBy(_._1,true)).sortBy(_._1,true).take(50).foreach(println)

Got a result like this:

(49,(Some((Some(List(8558, 6986, 686....)),Some(4394.5996))),Some(96)))

Expecting result like:

customerid, (orderids,..,..,....), totalamount, number of orderids

Is there any better approach? I just tried combineByKey with the below code but the println inside are not printing.

scala> val reduced = inputrdd.combineByKey(
 | (mark) => {
 | println(s"Create combiner -> ${mark}")
 | (mark, 1)
 | },
 | (acc: (Int, Int), v) => {
 | println(s"""Merge value : (${acc._1} + ${v}, ${acc._2} + 1)""")
 | (acc._1 + v, acc._2 + 1)
 | },
 | (acc1: (Int, Int), acc2: (Int, Int)) => {
 | println(s"""Merge Combiner : (${acc1._1} + ${acc2._1}, ${acc1._2} + ${acc2._2})""")
 | (acc1._1 + acc2._1, acc1._2 + acc2._2)
 | }
 | )
reduced: org.apache.spark.rdd.RDD[(String, (Int, Int))] = ShuffledRDD[27] at combineByKey at <console>:29

scala> reduced.collect()
res5: Array[(String, (Int, Int))] = Array((maths,(110,2)), (physics,(214,3)), (english,(65,1)))

I am using Spark version 2.2.0 , Scala 2.11.8 and Java 1.8 build 101

This is much easier to solve using the newer DataFrame API. First read the csv file and add the column names:

val df = spark.read.csv("file://../customer-orders.csv").toDF("Customerid", "Orderid", "Amount")

Then use groupBy and agg to make the aggregations (here you want collect_list, sum and count):

val df2 = df.groupBy("Customerid").agg(
    collect_list($"Orderid") as "Orderids", 
    sum($"Amount") as "TotalAmount",
    count($"Orderid") as "NumberOfOrderIds"
)

Resulting dataframe using the provided input example:

+----------+------------+-----------+----------------+
|Customerid|    Orderids|TotalAmount|NumberOfOrderIds|
+----------+------------+-----------+----------------+
|        51|      [3378]|       19.8|               1|
|        15|      [6148]|      65.53|               1|
|        29|       [680]|      13.08|               1|
|        42|      [6926]|      57.77|               1|
|        85|      [1733]|      28.53|               1|
|        35|      [5368]|      65.89|               1|
|        47|      [6694]|      14.98|               1|
|         5|      [3505]|      64.18|               1|
|        70|      [3959]|      68.68|               1|
|        44|[8602, 8331]|     136.38|               2|
|        53|      [9900]|      83.55|               1|
|        48|      [5539]|      32.42|               1|
|        79|      [9291]|      33.17|               1|
|        20|      [6633]|       6.49|               1|
|        14|      [1505]|       4.32|               1|
|        91|      [8900]|      24.59|               1|
|         2|[3391, 4424]|      96.41|               2|
|        50|      [3901]|      23.57|               1|
+----------+------------+-----------+----------------+

If you want to work with the data as a RDD after these transformations, you can convert it afterwards:

val rdd = df2.as[(Int, Seq[Int], Float, Int)].rdd

Of course, it is possible to solve using RDDs directly as well. Use aggregateByKey:

val rdd = spark.sparkContext
  .textFile("test.csv")
  .map(x => x.split(","))
  .map(x => (x(0).toInt, (x(1).toInt, x(2).toFloat)))

val res = rdd.aggregateByKey((Seq[Int](), 0.0, 0))(
    (acc, xs) => (acc._1 ++ Seq(xs._1), acc._2 + xs._2, acc._3 + 1), 
    (acc1, acc2) => (acc1._1 ++ acc2._1, acc1._2 + acc2._2, acc1._3 + acc2._3))

This is harder to read but will give the same result as the dataframe approach above.