python - pyspark intersection by key and cogroup performance -


i'm new spark , have following problem:

in previous job created 2 large files each line holds pair of ids , according value e.g.

(id1, id2, value)

first thing reading files hdfs , map them tuples like:

((id1, id2), value)

def line_to_tuple(line):     x = eval(line)     return ((x[0], x[1]), x[2])  rdd0 = sc.textfile('hdfs:///user/xxx/file1')          .map(lambda x: line_to_tuple(x)) rdd1 = sc.textfile('hdfs:///user/xxx/file2')          .map(lambda x: line_to_tuple(x))

q1: there more efficient way this? can store data in way such don't have eval each line every time read in?

in next step use cogroup , filter out empty results in post write results hdfs:

pyspark, intersection key

rdd0.cogroup(rdd1).filter(lambda x: x[1][0] , x[1][1])                   .map(lambda x: (x[0], list(x[1][0])[0], list(x[1][1])[0]) )                   .saveastextfile('hdfs:///user/xxx/cogrouped_values')

the results of jobs looks this:

((id1, id2), value_file1, value_file2)

q2: code works eats lot of resources , i've been asking myself if there way further improve given following assumptions:

  1. the files sorted id1 , id2 , id1 smaller id2
  2. rdd0 << rdd1 (one of files smaller other)


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