我有一个遵循这种结构的许多行的RDD(即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)
所以,例如,RDD的一行就像:
macReturns(a2:b2:c3:d3:f4:c5,3,4,1,0,3,4,3,5,1,7)
macAddresses已经被分组,因此它们都是截然不同的。
现在,我必须创建一个带有单行的新RDD,在RDDmacReturns
上执行转换/操作,它遵循相同的上述结构(案例类MacReturns)并包含一个固定的选择(伪)macAddress和在RDDmacReturns的元素之间计算的每个字段的平均值,如下所示:
macReturns(00:00:00:00:00:00,
averageHourReturns,
averageThreeHoursReturns,
averageSixHoursReturns,
averageHalfDailyReturns,
averageDailyReturns,
averageThreeDailyReturns,
averageWeeklyReturns,
averageBiWeeklyReturns,
averageThreeWeeklyReturns,
averageMonthlyReturns)
总而言之,我需要一个应用于RDDmacReturns的函数,返回包含单行的RDDaverageReturns(如上所述)
感谢您的帮助
答案 0 :(得分:1)
您可以使用colStats()
返回MultivariateStatisticalSummary
的实例,其中包含列式mean
。这是一个类似于您的问题的可重现示例:
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]