Spark和Python的协方差和相关性失败

我是Spark的新手，我试图计算cov和cor。 但当我尝试求和时，apache向我显示了一个错误

我的代码：

from pyspark.mllib.stat import Statistics
rddX = sc.parallelize([1,2,3,4,5,6,7,8,9,10])
rddY = sc.parallelize([7,6,5,4,5,6,7,8,9,10])
XY= rddX.zip(rddY)


Statistics.corr(XY)
Meanx = rddX.sum()/rddX.count()
Meany = rddY.sum()/rddY.count()
print(Meanx,Meany)
cov = XY.map(lambda x,y: (x-Meanx)*(y-Meany)).sum()

我的错误：

opt/ibm/spark/python/pyspark/rdd.py in fold(self, zeroValue, op)
    913         # zeroValue provided to each partition is unique from the one provided
    914         # to the final reduce call
--> 915         vals = self.mapPartitions(func).collect()
    916         return reduce(op, vals, zeroValue)

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如果sum返回RRDD数组，为什么显示此错误？

尝试分离RDD的操作：

sumX = rddX.sum()
countX = rddX.count()
meanX = sumX / countX