>
> The call to Sorting.quicksort is not working. Perhaps I am calling it the
> wrong way.
allaggregates.toArray allocates and creates a new array separate from
allaggregates which is sorted by Sorting.quickSort; allaggregates. Try:
val sortedAggregates = allaggregates.toArray
Sorting.quickSort(sortedAggregates)
> I would like to use the Spark mllib class MultivariateStatisticalSummary
> to calculate the statistical values.
MultivariateStatisticalSummary is a trait (similar to a Java interface);
you probably want to use MultivariateOnlineSummarizer.
> For that I would need to keep all my intermediate values as RDD so that I
> can directly use the RDD methods to do the job.
Correct; you would do an aggregate using the add and merge functions
provided by MultivariateOnlineSummarizer
> At the end I also need to write the results to HDFS for which there is a
> method provided on the RDD class to do so, which is another reason I would
> like to retain everything as RDD.
You can write the RDD[(device_id, MultivariateOnlineSummarizer)] to HDFS,
or you could unpack the relevant statistics from
MultivariateOnlineSummarizer into an array/tuple using a mapValues first
and then write.
On Mon, Jul 13, 2015 at 10:07 AM, Anupam Bagchi <
anupam_bag...@rocketmail.com> wrote:
> I have to do the following tasks on a dataset using Apache Spark with
> Scala as the programming language:
>
> 1. Read the dataset from HDFS. A few sample lines look like this:
>
>
> deviceid,bytes,eventdate15590657,246620,2015063014066921,1907,2015062114066921,1906,201506266522013,2349,201506266522013,2525,20150613
>
>
> 1. Group the data by device id. Thus we now have a map of deviceid =>
> (bytes,eventdate)
> 2. For each device, sort the set by eventdate. We now have an ordered
> set of bytes based on eventdate for each device.
> 3. Pick the last 30 days of bytes from this ordered set.
> 4. Find the moving average of bytes for the last date using a time
> period of 30.
> 5. Find the standard deviation of the bytes for the final date using a
> time period of 30.
> 6. Return two values in the result (mean - k*stddev) and (mean + k*stddev)
> [Assume k = 3]
>
> I am using Apache Spark 1.3.0. The actual dataset is wider, and it has to
> run on a billion rows finally.
> Here is the data structure for the dataset.
>
> package com.testingcase class DeviceAggregates (
> device_id: Integer,
> bytes: Long,
> eventdate: Integer
> ) extends Ordered[DailyDeviceAggregates] {
> def compare(that: DailyDeviceAggregates): Int = {
> eventdate - that.eventdate
> }}object DeviceAggregates {
> def parseLogLine(logline: String): DailyDeviceAggregates = {
> val c = logline.split(",")
> DailyDeviceAggregates(c(0).toInt, c(1).toLong, c(2).toInt)
> }}
>
> The DeviceAnalyzer class looks like this:
> I have a very crude implementation that does the job, but it is not up to
> the mark. Sorry, I am very new to Scala/Spark, so my questions are quite
> basic. Here is what I have now:
>
> import com.testing.DailyDeviceAggregatesimport
> org.apache.spark.{SparkContext, SparkConf}import
> org.apache.spark.mllib.linalg.Vectorimport
> org.apache.spark.mllib.stat.{MultivariateStatisticalSummary,
> Statistics}import org.apache.spark.mllib.linalg.{Vector, Vectors}
> import scala.util.Sorting
> object DeviceAnalyzer {
> def main(args: Array[String]) {
> val sparkConf = new SparkConf().setAppName("Device Analyzer")
> val sc = new SparkContext(sparkConf)
>
> val logFile = args(0)
>
> val deviceAggregateLogs =
> sc.textFile(logFile).map(DailyDeviceAggregates.parseLogLine).cache()
>
> // Calculate statistics based on bytes
> val deviceIdsMap = deviceAggregateLogs.groupBy(_.device_id)
>
> deviceIdsMap.foreach(a => {
> val device_id = a._1 // This is the device ID
> val allaggregates = a._2 // This is an array of all device-aggregates
> for this device
>
> println(allaggregates)
> Sorting.quickSort(allaggregates.toArray) // Sort the CompactBuffer of
> DailyDeviceAggregates based on eventdate
> println(allaggregates) // This does not work - results are not sorted !!
>
> val byteValues = allaggregates.map(dda => dda.bytes.toDouble).toArray
> val count = byteValues.count(A => true)
> val sum = byteValues.sum
> val xbar = sum / count
> val sum_x_minus_x_bar_square = byteValues.map(x =>
> (x-xbar)*(x-xbar)).sum
> val stddev = math.sqrt(sum_x_minus_x_bar_square / count)
>
> val vector: Vector = Vectors.dense(byteValues)
> println(vector)
> println(device_id + "," + xbar + "," + stddev)
>
> //val vector: Vector = Vectors.dense(byteValues)
> //println(vector)
> //val summary: MultivariateStatisticalSummary =
> Statistics.colStats(vector)
> })
>
> sc.stop()
> }}
>
> I would really appreciate if someone can suggests improvements for the
> following:
>
> 1. The call to Sorting.quicksort is not working. Perhaps I am calling
> it the wrong way.
> 2. I would like to use the Spark mllib class
> MultivariateStatisticalSummary to calculate the statistical values.
> 3. For that I would need to keep all my intermediate values as RDD so
> that I can directly use the RDD methods to do the job.
> 4. At the end I also need to write the results to HDFS for which there
> is a method provided on the RDD class to do so, which is another reason I
> would like to retain everything as RDD.
>
>
> Thanks in advance for your help.
>
> Anupam Bagchi
>
>
