Python takeOrdered 降序 Pyspark
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takeOrdered descending Pyspark
提问by arj
i would like to sort K/V pairs by values and then take the biggest five values. I managed to do this with reverting K/V with first map, sort in descending order with FALSE, and then reverse key.value to the original (second map) and then take the first 5 that are the bigget, the code is this:
我想按值对 K/V 对进行排序，然后取最大的五个值。我设法通过使用第一张地图恢复 K/V 来做到这一点，使用 FALSE 按降序排序，然后将 key.value 反转到原始（第二张地图），然后取前 5 个是最大的，代码是这样的：
RDD.map(lambda x:(x[1],x[0])).sortByKey(False).map(lambda x:(x[1],x[0])).take(5)
i know there is a takeOrdered action on pySpark, but i only managed to sort on values (and not on key), i don't know how to get a descending sorting:
我知道在 pySpark 上有一个 takeOrdered 操作，但我只设法对值（而不是键）进行排序，我不知道如何进行降序排序：
RDD.takeOrdered(5,key = lambda x: x[1])
采纳答案by aatishk
Sort by keys (ascending):
按键排序（升序）：
RDD.takeOrdered(5, key = lambda x: x[0])
Sort by keys (descending):
按键排序（降序）：
RDD.takeOrdered(5, key = lambda x: -x[0])
Sort by values (ascending):
按值排序（升序）：
RDD.takeOrdered(5, key = lambda x: x[1])
Sort by values (descending):
按值排序（降序）：
RDD.takeOrdered(5, key = lambda x: -x[1])
