相关疑难解决方法(0)

我想与Python解决方案共享这个特定的Apache Spark,因为它的文档很差.

我想用KEY计算K/V对(存储在Pairwise RDD中)的平均值.以下是示例数据的样子:

>>> rdd1.take(10) # Show a small sample.
[(u'2013-10-09', 7.60117302052786),
(u'2013-10-10', 9.322709163346612),
(u'2013-10-10', 28.264462809917358),
(u'2013-10-07', 9.664429530201343),
(u'2013-10-07', 12.461538461538463),
(u'2013-10-09', 20.76923076923077),
(u'2013-10-08', 11.842105263157894),
(u'2013-10-13', 32.32514177693762),
(u'2013-10-13', 26.249999999999996),
(u'2013-10-13', 10.693069306930692)]

现在,下面的代码序列不是最佳的方法,但它确实有效.在我找到更好的解决方案之前,我正在做的事情.这并不可怕但是 - 正如你在答案部分看到的那样 - 有一种更简洁,有效的方式.

>>> import operator
>>> countsByKey = sc.broadcast(rdd1.countByKey()) # SAMPLE OUTPUT of countsByKey.value: {u'2013-09-09': 215, u'2013-09-08': 69, ... snip ...}
>>> rdd1 = rdd1.reduceByKey(operator.add) # Calculate the numerators (i.e. the SUMs).
>>> rdd1 = rdd1.map(lambda x: (x[0], x[1]/countsByKey.value[x[0]])) # Divide each SUM by …