How to group by multiple keys in spark?

Question

I have a bunch of tuples which are in form of composite keys and values. For example,

tfile.collect() = [((\'id1\',\'pd1\',\'t1\'),5.0), 
     ((\'id2\',\'p         


        

Answer 1

My guess is that you want to transpose the data according to multiple fields.

A simple way is to concatenate the target fields that you will group by, and make it a key in a paired RDD. For example:

lines = sc.parallelize(['id1,pd1,t1,5.0', 'id2,pd2,t2,6.0', 'id1,pd1,t2,7.5', 'id1,pd1,t3,8.1'])
rdd = lines.map(lambda x: x.split(',')).map(lambda x: (x[0] + ', ' + x[1], x[3])).reduceByKey(lambda a, b: a + ', ' + b)
print rdd.collect()

Then you will get the transposed result.

[('id1, pd1', '5.0, 7.5, 8.1'), ('id2, pd2', '6.0')]

Answer 2

I grouped ((id1,t1),((p1,5.0),(p2,6.0)) and so on ... as my map function. Later, I reduce using map_group which creates an array for [p1,p2, . . . ] and fills in values in their respective positions.

def map_group(pgroup):
    x = np.zeros(19)
    x[0] = 1
    value_list = pgroup[1]
    for val in value_list:
        fno = val[0].split('.')[0]
        x[int(fno)-5] = val[1]
    return x

tgbr = tfile.map(lambda d: ((d[0][0],d[0][2]),[(d[0][1],d[1])])) \
                .reduceByKey(lambda p,q:p+q) \
                .map(lambda d: (d[0], map_group(d)))

This does feel like an expensive solution in terms of computation. But works for now.