Sum in Spark gone bad

问题:

Based on Unbalanced factor of KMeans?, I am trying to compute the Unbalanced Factor, but I fail.

Every element of the RDD r2_10 is a pair, where the key is cluster and the value is a tuple of points. All these are IDs. Below I present what happens:

In [1]: r2_10.collect()
Out[1]: 
[(0, ('438728517', '28138008')),
 (13824, ('4647699097', '6553505321')),
 (9216, ('2575712582', '1776542427')),
 (1, ('8133836578', '4073591194')),
 (9217, ('3112663913', '59443972', '8715330944', '56063461')),
 (4609, ('6812455719',)),
 (13825, ('5245073744', '3361024394')),
 (4610, ('324470279',)),
 (2, ('2412402108',)),
 (3, ('4766885931', '3800674818', '4673186647', '350804823', '73118846'))]

In [2]: pdd = r2_10.map(lambda x: (x[0], 1)).reduceByKey(lambda a, b: a + b)

In [3]: pdd.collect()
Out[3]: 
[(13824, 1),
 (9216, 1),
 (0, 1),
 (13825, 1),
 (1, 1),
 (4609, 1),
 (9217, 1),
 (2, 1),
 (4610, 1),
 (3, 1)]

In [4]: n = pdd.count()

In [5]: n
Out[5]: 10

In [6]: total = pdd.map(lambda x: x[1]).sum() 

In [7]: total
Out[7]: 10

and total is supposed to have the total number of points. However, it's 10...The goal is to be 22!

What am I missing here?

回答1:

The problem is because you missed to count the number of points grouped in each cluster, thus you have to change how pdd was created.

pdd = r2_10.map(lambda x: (x[0], len(x[1]))).reduceByKey(lambda a, b: a + b)

However, You could obtain the same result in a single pass (without computing pdd), by mapping the values of the RDD and then reducing by using sum.

total = r2_10.map(lambda x: len(x[1])).sum()