RDD/Scala Get one column from RDD - scala

Question

I have an RDD[Log] file with various fields (username,content,date,bytes) and I want to find different things for each field/column.
For example, I want to get the min/max and average bytes found in the RDD. When i do:
val q1 = cleanRdd.filter(x => x.bytes != 0)
I get the full lines of the RDD with bytes != 0. But how can I actually sum them, calculate the avg, find the min/max etc? How can I take only one column from my RDD and apply transformations on it?
EDIT: Prasad told me about changing the type to dataframe, he gave no instructions on how to do so though, and I cant find a solid answer on the site. Any help would be great.
EDIT: LOG class:
case class Log (username: String, date: String, status: Int, content: Int)
using a cleanRdd.take(5).foreach(println) gives something like this
Log(199.72.81.55 ,01/Jul/1995:00:00:01 -0400,200,6245)
Log(unicomp6.unicomp.net ,01/Jul/1995:00:00:06 -0400,200,3985)
Log(199.120.110.21 ,01/Jul/1995:00:00:09 -0400,200,4085)
Log(burger.letters.com ,01/Jul/1995:00:00:11 -0400,304,0)
Log(199.120.110.21 ,01/Jul/1995:00:00:11 -0400,200,4179)

Answer 1

Well... you have a lot of questions.
So... you have the following abstraction of a Log
case class Log (username: String, date: String, status: Int, content: Int, byte: Int)
Que - How can I take only one column from my RDD.
Ans - You have a map function with the RDD's. So for an RDD[A], map takes a map/transform function of type A => B to transform it into a RDD[B].
val logRdd: RDD[Log] = ...
val byteRdd = logRdd
.filter(l => l.bytes != 0)
.map(l => l.byte)
Que - how can I actually sum them ?
Ans - You can do it by using reduce / fold / aggregate.
val sum = byteRdd.reduce((acc, b) => acc + b)
val sum = byteRdd.fold(0)((acc, b) => acc + b)
val sum = byteRdd.aggregate(0)(
(acc, b) => acc + b,
(acc1, acc2) => acc1 + acc2
)
Note :: An important thing to notice here is that a sum of Int can grow bigger than what an Int can handle. So in most real life cases we should use at least a Long as our accumulator instead of an Int, which actually removes reduce and fold as options. And we will be left with an aggregate only.
val sum = byteRdd.aggregate(0l)(
(acc, b) => acc + b,
(acc1, acc2) => acc1 + acc2
)
Now if you have to calculate multiple things like min, max, avg then I will suggest that you calculate them in a single aggregate instead of multiple like this,
// (count, sum, min, max)
val accInit = (0, 0, Int.MaxValue, Int.MinValue)
val (count, sum, min, max) = byteRdd.aggregate(accInit)(
{ case ((count, sum, min, max), b) =>
(count + 1, sum + b, Math.min(min, b), Math.max(max, b)) },
{ case ((count1, sum1, min1, max1), (count2, sum2, min2, max2)) =>
(count1 + count2, sum1 + sum2, Math.min(min1, min2), Math.max(max1, max2)) }
})
val avg = sum.toDouble / count

Answer 2

Have a look in DataFrame API. You need to convert your RDD to a DataFrame and then you can use min, max, avg functions like below:
val rdd = cleanRdd.filter(x => x.bytes != 0)
val df = sparkSession.sqlContext.createDataFrame(rdd, classOf[Log])
Assuming you wanted to operations on column bytes then
import org.apache.spark.sql.functions._
df.select(avg("bytes")).show
df.select(min("bytes")).show
df.select(max("bytes")).show
Update:
Tried with the following in spark-shell. check the screenshots for the outcome...
case class Log (username: String, date: String, status: Int, content: Int)
val inputRDD = sc.parallelize(Seq(Log("199.72.81.55","01/Jul/1995:00:00:01 -0400",200,6245), Log("unicomp6.unicomp.net","01/Jul/1995:00:00:06 -0400",200,3985), Log("199.120.110.21","01/Jul/1995:00:00:09 -0400",200,4085), Log("burger.letters.com","01/Jul/1995:00:00:11 -0400",304,0), Log("199.120.110.21","01/Jul/1995:00:00:11 -0400",200,4179)))
val rdd = inputRDD.filter(x => x.content != 0)
val df = rdd.toDF("username", "date", "status", "content")
df.printSchema
import org.apache.spark.sql.functions._
df.select(avg("content")).show
df.select(min("content")).show
df.select(max("content")).show