Christian Zichichi - 5 months ago 31

Scala Question

I have an RDD of many Rows (namely,

`RDDmacReturns`

`case class macReturns (macAddress: String,`

hourReturns: Long,

threeHoursReturns: Long,

sixHoursReturns: Long,

halfDailyReturns: Long,

dailyReturns: Long,

threeDailyReturns: Long,

weeklyReturns: Long,

biWeeklyReturns: Long,

threeWeeklyReturns: Long,

monthlyReturns: Long)

so, for example, a row of that RDD would be like:

`macReturns(a2:b2:c3:d3:f4:c5,3,4,1,0,3,4,3,5,1,7)`

macAddresses have already been grouped so they are all distinct.

Now, I have to create a new RDD with a single row, doing transformations/actions on

`RDDmacReturns`

`macReturns(00:00:00:00:00:00,`

averageHourReturns,

averageThreeHoursReturns,

averageSixHoursReturns,

averageHalfDailyReturns,

averageDailyReturns,

averageThreeDailyReturns,

averageWeeklyReturns,

averageBiWeeklyReturns,

averageThreeWeeklyReturns,

averageMonthlyReturns)

To sum up, I need a function that applied to RDDmacReturns, returns RDDaverageReturns, that contains a single row (described just above)

Thanks for the help

Answer

You could use `colStats()`

which returns an instance of `MultivariateStatisticalSummary`

, containing among other things the column-wise `mean`

. Here's a reproducible example similar to your problem:

```
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
val rdd = sc.parallelize(Seq(
("id1",1,2,3,4),
("id2",3,5,1,5),
("id3",3,0,9,8),
("id4",4,4,1,2)))
// First we convert to RDD of dense vectors
val rdd_dense = rdd.map(x => Vectors.dense(x._2, x._3, x._4, x._5))
// Attain colStats and grab the mean
val summary: MultivariateStatisticalSummary = Statistics.colStats(rdd_dense)
println(summary.mean)
[2.75,2.75,3.5000000000000004,4.75]
```